Table of Contents
Overview
Definition and Scope
Neuro-oncology is a field closely related to neuropsychology with a rich history dating back to ancient times. During this period, limited knowledge of the brain led prehistoric humans to practice trephination for treating neurological ailments, a method also used by the ancient Greeks and Chinese.[1.1] The evolution of neuroanatomy and brain function understanding in the 16th century laid the foundation for modern neuro-oncology.[1.1] Recognized as a relatively young subspecialty of neurosurgery, neuro-oncology's development is marked by significant milestones, such as the establishment of the Indian Society of Neuro-oncology in 2008, which celebrated its 10th anniversary in 2018.[2.1] This timeline underscores the field's growth and recognition within medical practice. The historical context of neuro-oncology spans various cultures and eras. Dr. Khalili, former head of the Department of Neurosurgery at Baghdad University, reviews the history of medicine across Mesopotamian, Egyptian, Greek, Islamic, Medieval, and Modern times, including Iraqi practices and research in neuro-oncology.[5.1] He also addresses the practice of informed consent in global medical settings.[5.1] Additionally, Rolando F. Del Maestro's work, originating from an exhibition with the Osler Library, provides insights into neuro-oncology's history, despite critiques of its title.[4.1] This exhibition coincided with the 2006 Canadian Congress, further highlighting the field's evolution.[4.1]Importance in Medical Research
Neuro-oncology plays a crucial role in medical research, particularly in the advancement of surgical techniques, imaging modalities, and biomarker identification, all of which significantly enhance patient outcomes. A central tenet of neurosurgical oncology is maximizing tumor resection while minimizing damage to surrounding healthy brain tissue. For high-grade gliomas (HGG), the extent of resection (EOR) is the most important factor determining patient outcomes, consistently shown to confer a survival benefit.[9.1] Recent advances in image-guided surgical techniques have further enhanced the precision of tumor resections, although there remains a critical need for innovative technologies to improve patient outcomes.[10.1] Advancements in neuro-oncological imaging have significantly transformed patient management, particularly in the early detection of brain tumors. Magnetic Resonance Imaging (MRI) is recognized as the modality of choice for brain tumor detection due to its exceptional soft tissue contrast and spatial resolution, which are essential for supporting timely clinical decisions.[19.1] The incorporation of advanced imaging techniques into conventional MRI protocols has enabled the early detection of low-grade gliomas, thereby improving patient management strategies.[18.1] Furthermore, recent advances in artificial intelligence (AI) and deep learning (DL) are expected to enhance neuroimaging diagnosis for patients with brain tumors and stroke.[40.1] These developments hold promise for improving the accuracy and efficiency of imaging interpretations, ultimately benefiting patient outcomes in neuro-oncology.[40.1] Moreover, the role of biomarkers in neuro-oncology is increasingly recognized for its potential to personalize treatment plans. The presence of specific biomarkers can indicate whether a brain tumor will respond to various therapies, allowing neuro-oncologists to tailor treatments effectively.[44.1] Continuous progress in discovering novel tumor biomarkers has significantly promoted personalized medicine and improved outcomes for cancer patients.[46.1] Ongoing clinical trials aim to validate these biomarkers, which could guide future therapeutic choices and ultimately enhance patient outcomes.[45.1]In this section:
History
Early Developments in Neuro-Oncology
Trephination is an ancient surgical practice that involved creating holes in the human skull to treat various brain disorders, including traumatic brain injuries and psychiatric conditions. Archaeological evidence indicates that this method dates back to the Stone Age, demonstrating early attempts to influence brain function through physical means.[61.1] The practice of trephination reflects the intertwined nature of surgery and religion throughout its evolution, which has led to both stagnation and progress in the understanding of neurosurgery.[63.1] Furthermore, the analysis of trepanned skulls provides insights into the diseases that ancient civilizations sought to cure, enhancing our understanding of neuroanatomy and the historical context of medical practices.[62.1] The evolution of neurosurgery can be traced back to these early practices, where surgery and religious beliefs were often intertwined, influencing both the progress and stagnation of medical advancements.[63.1] The initial understanding of cerebral localization, which pertains to the relationship between specific brain regions and their associated functions, emerged from the study of brain pathology, particularly in cases involving cerebral tumors.[64.1] This foundational knowledge paved the way for future developments in neurology and neuro-oncology. Various ancient cultures contributed to the understanding of brain diseases through their medical practices. For instance, the meticulous methods of Ancient Egypt and the holistic approaches of Ayurveda in ancient India emphasized the interconnectedness of mind, body, and spirit, influencing contemporary medical practices.[84.1] The philosophical and empirical advancements made by ancient Greek physicians, such as Hippocrates and Galen, further enriched the field, laying the groundwork for clinical medicine and the systematic study of symptoms and treatment outcomes.[84.1]Key Figures and Milestones
The field of neuro-oncology has evolved significantly over time, marked by key figures and milestones that have shaped its development. The history of neuro-oncology is intertwined with neuropsychology, tracing back to ancient practices such as trephination used by prehistoric humans, as well as by the ancient Greeks and Chinese, who sought to treat neurological ailments despite limited understanding of brain function.[1.1] Neuro-oncology is a relatively young subspecialty of neurosurgery, having emerged in recent years as a distinct field of study and practice. A significant milestone in the formalization of this discipline was the founding of the Indian Society of Neuro-oncology, which occurred in 2008. By 2018, this organization celebrated its 10th anniversary, marking a decade of advancements and contributions to the field.[2.1] A pivotal development in neuro-oncology is the introduction of a new classification system for central nervous system (CNS) tumors. This classification integrates classical histological classification with grading, immunohistochemical, and molecular-genetic data. This milestone is crucial as it enhances the characterization of tumors and facilitates more effective treatment assignments, ultimately leading to improved patient outcomes.[50.1]Recent Advancements
Imaging Technologies
Recent advancements in imaging technologies have significantly transformed the landscape of neuro-oncology, particularly through the integration of artificial intelligence (AI) and machine learning (ML). AI has been instrumental in enhancing the accuracy and efficiency of brain tumor management, particularly in the realms of diagnosis, prognosis, and treatment planning. By utilizing advanced imaging modalities such as MRI, CT, and PET, AI facilitates the detection and classification of brain tumors, optimizing workflows and providing precise measurements that were previously unattainable by human observers alone.[119.1] The application of AI in neuro-oncology has demonstrated superior performance compared to traditional human evaluations, particularly in discerning molecular characteristics from imaging data. This capability not only reduces the reliance on invasive diagnostic procedures but also accelerates the timeline for obtaining molecular diagnoses, thereby improving patient outcomes.[98.1] Furthermore, AI models have been developed to differentiate between true disease progression and treatment-related changes, a critical challenge in current clinical practice.[99.1] In recent years, the fields of radiomics and radiogenomics have gained significant attention in neuro-oncology imaging, emerging as essential components in the diagnosis and management of brain tumors. Radiomics is defined as a high-throughput computational process that extracts microscale quantitative data from conventional imaging, revealing features that are not visible to the naked eye.[121.1] Numerous studies in radiomics and radiogenomics have demonstrated their potential to differentiate between pseudoprogression and true progression, classify tumor subgroups, and accurately predict recurrence, survival, and mutation status.[120.1] The integration of these advanced imaging techniques with artificial intelligence (AI) has the potential to enhance the predictive capabilities of imaging data, thereby paving the way for more personalized treatment approaches in neuro-oncology.[118.1]Liquid Biopsies and AI Integration
Recent advancements in neuro-oncology have highlighted the integration of artificial intelligence (AI) and liquid biopsies as transformative approaches in the management of brain tumors. AI has emerged as a crucial tool in enhancing the diagnosis, prognosis, and treatment planning of brain tumors, particularly through its ability to analyze extensive medical imaging data and identify patterns that may not be easily discernible to human observers.[109.1] This capability is particularly significant in the context of liquid biopsies, which involve the analysis of biomarkers in bodily fluids, such as blood, to detect tumor presence and characteristics. The integration of artificial intelligence (AI) in neuro-oncology is advancing rapidly, with a significant focus on developing diagnostic models that utilize key genomic markers and predictive models for treatment response.[108.1] This evolution is accompanied by the exploration of multimodal data integration and the application of generative AI, which are promising directions for future research.[107.1] While the emergence of AI holds great potential for enhancing personalized treatment strategies for brain tumors, the integration of these technologies into clinical practice remains challenging.[103.1] The ongoing development and benchmarking of AI tools aim to improve diagnosis, prognostication, and therapy, thereby optimizing clinical outcomes for patients.[108.1] The integration of artificial intelligence (AI) in the medical field shows significant promise for enhancing personalized strategies in the diagnosis and treatment of brain tumors, although challenges remain in its implementation within clinical practice.[105.1] Future research directions emphasize the importance of multimodal data integration, generative AI, and large medical language models, which are expected to contribute to precise tumor delineation and characterization.[107.1] Additionally, adaptive personalized treatment strategies are highlighted as crucial for optimizing clinical outcomes in neuro-oncology.[107.1] This ongoing exploration of AI technologies underscores their potential to address complex challenges in brain tumor management and advance personalized medicine.[105.1]Diagnostic Approaches
Imaging Techniques
Imaging techniques play a crucial role in the diagnosis and management of tumors within the nervous system, particularly in neuro-oncology. The gold standard for imaging in this field is contrast-enhanced magnetic resonance imaging (MRI), which provides highly sensitive anatomical information essential for diagnosing brain and spinal tumors, as well as for pre-surgical planning and post-therapeutic monitoring.[127.1] While conventional imaging methods such as CT and MRI are often the first steps in suggesting the presence of a brain tumor, they are complemented by histological examination through biopsy to ensure accurate diagnosis.[143.1] Recent advancements in imaging technology have introduced innovative methods such as magnetic resonance fingerprinting (MRF), which offers quantitative insights into tissue properties by generating T1 and T2 values through a unique single-sequence approach.[130.1] This technique enhances the ability to differentiate between tumor types and grades, which is vital for treatment planning.[163.1] Additionally, advanced MRI techniques, including perfusion-weighted imaging (PWI), MR spectroscopy (MRS), and diffusion-weighted imaging (DWI), provide significant advantages over conventional imaging by improving the evaluation of tumor extent, predicting tumor grade, and assessing treatment response.[162.1] The integration of artificial intelligence (AI) into neuro-oncologic imaging is also transforming diagnostic approaches. AI techniques, including deep learning and radiomics, are being utilized to enhance image quality, detect metastases, and analyze extensive medical imaging data, thereby improving diagnostic accuracy and prognostic capabilities.[140.1] These advancements are paving the way for a more precise and personalized approach to neuro-oncology, addressing the complexities of tumor diagnosis and management.[138.1] Furthermore, theranostics, which combines diagnostic and therapeutic procedures, is emerging as a valuable strategy in neuro-oncology. This approach is particularly beneficial in overcoming challenges posed by the blood-brain barrier and brain-tumor barriers, facilitating more effective treatment options.[129.1] As the field continues to evolve, the integration of these advanced imaging techniques and AI technologies is expected to significantly enhance the diagnostic landscape of neuro-oncology.Biomarkers and Tumor Markers
Biomarkers and tumor markers play a crucial role in the diagnostic landscape of neuro-oncology, significantly enhancing the accuracy of tumor classification and patient stratification. The integration of molecular markers into routine diagnostic practices has become essential, as they provide valuable information that complements traditional WHO classifications and guide clinical decision-making, particularly in glioma subtypes.[166.1] Advances in molecular pathology, including the use of immunohistochemistry, next-generation sequencing (NGS), and DNA methylation profiling, have markedly improved diagnostic accuracy and patient risk stratification compared to standard histopathological analyses.[168.1] NGS, in particular, has revolutionized personalized oncology care by enabling comprehensive cancer profiling, which allows clinicians to tailor treatment strategies based on the molecular basis of the tumor.[148.1] This technology facilitates the identification of actionable genetic alterations, thereby supporting the development of targeted therapies that can substantially improve clinical outcomes.[149.1] Furthermore, the advent of multigene sequencing technologies has established a foundation for precision oncology, emphasizing the importance of having a molecular compass to guide treatment decisions amidst the complexity of genetic mutations.[149.1] Despite these advancements, the implementation of molecular profiling in clinical settings faces challenges, particularly concerning the performance characteristics of biomarker assays. The future utility of treatment selection biomarkers will depend on the availability of robust assays and effective therapeutic options.[170.1] Additionally, while omics-based patient stratification is gaining relevance, particularly in glioblastoma therapy, the integration of these advanced techniques into routine diagnostics remains a complex endeavor.[158.1] Overall, the evolving landscape of biomarkers and tumor markers in neuro-oncology underscores their critical role in enhancing diagnostic precision and informing personalized treatment strategies.In this section:
Treatment Modalities
Surgical Interventions
Surgical interventions in neuro-oncology primarily focus on achieving maximal safe resection of brain tumors, which is considered the current standard of care. This approach is often followed by adjuvant therapies such as chemotherapy and radiation therapy to enhance treatment outcomes for patients with both primary and metastatic brain tumors.[211.1] The decision-making process for selecting a surgical approach is influenced by various factors, including patient preferences, prognostic indicators such as age, tumor size, anatomic location, and the presence of neurological deficits.[192.1] In recent years, the integration of decision grids has emerged as a valuable tool in the shared decision-making (SDM) process for patients with intracranial tumors, facilitating a more informed choice regarding treatment options.[191.1] However, it has been noted that there is often a lack of emphasis on understanding the patient's everyday life and preferences, which are crucial for effective SDM.[190.1] Neurosurgeons have access to a diverse range of surgical modalities and techniques designed to achieve maximal safe resection of brain tumors while minimizing damage to surrounding healthy tissue. Among these techniques are intraoperative MRI, fluorescence-guided surgery, and minimally invasive approaches such as neuroendoscopic surgery, which is also referred to as keyhole surgery. This minimally invasive technique aims to remove brain tumors with less disruption to surrounding healthy brain tissue compared to traditional open surgery, utilizing specialized instruments and techniques for tumor access and removal.[214.1] Recent technological advancements, including image guidance devices and clinical neurophysiology devices, have further enhanced the accuracy and safety of neurosurgical operations, ultimately improving patient outcomes.[213.1] These innovations allow for better visualization and targeting of tumors, thereby enhancing surgical outcomes and reducing the risk of complications.[213.1] Moreover, the application of artificial intelligence (AI) and other cutting-edge technologies in neurosurgical oncology is becoming increasingly prevalent, further impacting tumor treatment and patient outcomes.[212.1] The combination of these innovative surgical techniques and technologies represents a significant evolution in the field of neuro-oncology, aiming to improve the quality of care for patients undergoing brain tumor surgery.Chemotherapy and Radiotherapy
Chemotherapy and radiotherapy are critical components in the treatment of brain tumors, with various modalities tailored to the specific characteristics of the tumor and the patient's overall health. Chemotherapy for brain tumors includes several approved agents, with Temozolomide (TMZ) being a prominent oral chemotherapy drug frequently prescribed for high-grade gliomas. TMZ is recognized for its efficacy as a radiosensitizer and is a key element in chemoradiation therapy for newly diagnosed glioblastoma patients, although its safety profile presents certain disadvantages.[202.1] Additionally, the therapeutic potential of Temozolomide loaded magnetosomes conjugate has been explored, showing promise in enhancing the accuracy and effectiveness of treatment by precisely targeting tumor cells within the complex brain environment.[201.1] Clinical trials have also assessed the effectiveness of TMZ in treating brain metastases, indicating its potential benefits for patients with secondary brain tumors.[203.1] Radiation therapy plays a critical role in the management of brain tumors, including both primary and metastatic forms. The conventional method for administering radiation is external beam radiation therapy (EBRT), but recent advances have introduced techniques such as intensity-modulated radiotherapy (IMRT), volumetric-modulated arc therapy (VMAT), and stereotactic radiosurgery (SRS).[186.1] These modern techniques enable the delivery of higher radiation doses to the target volume while simultaneously reducing the risk of toxicity to surrounding healthy tissue, which is essential for minimizing neurological complications.[206.1] Treatment planning for radiation therapy involves precise mapping of the tumor location using imaging techniques, such as X-rays, to create a three-dimensional representation of the brain. This process allows radiation oncologists to develop a tailored treatment approach, often utilizing custom-fitted masks to enhance the precision of the therapy.[187.1] Overall, these advancements in radiation techniques contribute significantly to achieving maximal tumor control and improving patient outcomes.[206.1] The treatment of brain tumors is contingent upon various factors, including the type, size, and location of the tumor, as well as the patient's symptoms, general health, and treatment preferences.[173.1] A personalized approach is crucial, as the most effective treatment path can differ significantly from one patient to another.[205.1] Factors influencing recovery rates are diverse, encompassing not only the tumor's characteristics but also the patient's age, overall health, genetic factors, and the chosen treatment options.[204.1] Each of these elements plays a vital role in shaping the prognosis and can be influenced by treatment choices and lifestyle decisions.[204.1]Challenges In Neuro-Oncology
Diagnostic Barriers
Diagnostic barriers in neuro-oncology are multifaceted and significantly impact the management of brain tumors and other central nervous system (CNS) conditions. One of the primary challenges is the complexity of drug delivery into the CNS, which is hindered by the presence of the blood-brain barrier (BBB), blood-tumor barrier (BTB), and blood-cerebrospinal fluid barrier (BCSFB).[216.1] These barriers restrict the effective penetration of therapeutic agents, complicating treatment strategies and limiting the efficacy of systemic therapies.[248.1] Moreover, the emotional and psychological aspects of brain tumors contribute to diagnostic barriers. Patients often experience feelings of isolation and despair due to the stigma associated with their conditions and the necessity of undergoing treatments in isolation, particularly during the COVID-19 pandemic.[217.1] This emotional burden can hinder patients from seeking timely medical attention or adhering to treatment protocols, further complicating the diagnostic process. The field of neuro-oncology is characterized by its emerging nature, necessitating an interdisciplinary approach and collaborative efforts among specialized expert teams to address the challenges faced in both clinical patient care and research activities.[245.1] Effective communication among clinician scientists and medical professionals is crucial to overcoming these challenges. Furthermore, the development of novel therapeutics in neuro-oncology is fraught with significant obstacles, including high costs and low success rates. Despite advancements in molecular biology and genomics, targeted therapies have shown limited effectiveness in improving patient outcomes for brain tumors, particularly gliomas, due to the complex, multigenic nature of these malignancies.[246.1]Treatment Limitations
In neuro-oncology, several treatment limitations significantly impact patient outcomes, particularly in the context of glioblastoma (GBM). One of the primary challenges is the inherent molecular heterogeneity of glioblastoma, which complicates the identification of effective individualized treatment paths. This heterogeneity leads to variable responses to therapies, underscoring the necessity for personalized approaches in neuro-oncologic care.[220.1] Despite advancements in molecular profiling and targeted therapies, no personalized treatment for GBM has been clinically implemented to date, highlighting a critical gap in the translation of research into practice.[243.1] Additionally, the integration of psychological care into oncology and palliative care remains insufficiently prioritized, despite evidence supporting its effectiveness in improving patient outcomes.[240.1] This lack of comprehensive care can hinder the overall treatment experience and recovery of neuro-oncology patients. Clinical trial participation for GBM is notably low, which poses another significant barrier to the development of personalized treatment strategies. This low participation rate limits the ability to detect subtle differences in treatment efficacy and complicates the establishment of appropriate controls and stratification based on prognostic factors.[242.1] Furthermore, the definition of clinical endpoints in trials remains a challenge, further complicating the landscape of personalized treatment for glioblastoma.[242.1] The presence of epigenetic changes in tumor cells adds another layer of complexity, as these changes can modify the existing genetic background and create challenges for detection, characterization, and treatment.[241.1] Consequently, while there are promising avenues for personalized medicine in neuro-oncology, the field continues to grapple with significant limitations that impede the effective treatment of brain tumors.In this section:
Future Directions
Emerging Technologies
Emerging technologies in neuro-oncology are significantly transforming the diagnosis and management of brain tumors, which are among the most challenging malignancies due to their high mortality rates and complex neurological effects.[253.1] Recent advancements in imaging techniques, particularly advanced MRI modalities such as perfusion-weighted imaging (PWI), MR spectroscopy (MRS), diffusion-weighted imaging (DWI), and MR chemical exchange saturation transfer (CEST), have shown considerable advantages over conventional imaging methods in evaluating tumor extent, predicting tumor grade, and assessing treatment responses.[259.1] Additionally, the integration of liquid biopsies and artificial intelligence (AI) applications is addressing current diagnostic challenges, including the phenomenon of pseudoprogression, where post-radiation inflammation can mimic actual tumor progression on imaging.[253.1] These advancements collectively enhance the diagnostic accuracy and management strategies for patients with brain tumors, ultimately aiming to improve patient outcomes in this complex field.[253.1] Artificial intelligence (AI) is playing a pivotal role in transforming neuro-oncology through its applications in diagnosis, prognosis, and treatment planning. AI technologies are being utilized to accelerate and enhance MRI imaging, detect abnormalities, optimize workflows, and analyze extensive medical imaging data.[262.1] These tools are capable of identifying patterns that may not be easily discernible to human observers, thereby improving the accuracy of diagnoses and treatment strategies.[262.1] Furthermore, AI is facilitating the integration of genomic data into clinical practice, supporting molecular pathologists in biomarker identification and treatment response prediction.[261.1] Liquid biopsy technologies are emerging as a promising diagnostic tool in neuro-oncology, although their development has faced significant challenges due to the low quantities of circulating tumor elements found in body fluids.[263.1] The integration of liquid biopsies into routine clinical practice is supported by findings that suggest their combination with traditional diagnostic methods may enhance the overall accuracy and reliability of cancer detection and management.[265.1] However, several key challenges must be addressed for successful implementation, including standardization, analyte validation, and the demonstration of clinical utility, alongside regulatory considerations.[264.1] Overcoming these challenges is essential to ensure that liquid biopsy results can positively impact patient care and facilitate more agile, patient-centered decision-making.[266.1]Personalized Medicine in Neuro-Oncology
Personalized medicine is increasingly recognized as a transformative approach in neuro-oncology, focusing on tailoring treatment strategies to the unique genetic and molecular profiles of individual patients. This paradigm shift is facilitated by advancements in genetic profiling and targeted therapies, which aim to optimize therapeutic outcomes while minimizing adverse effects. By considering a patient's genetic makeup, lifestyle, and environmental factors, personalized medicine offers the potential for more effective and precise interventions compared to traditional one-size-fits-all approaches.[279.1] The integration of multi-omics profiling—including genomics, transcriptomics, proteomics, and emerging epigenomic and metabolomic analyses—enables the identification of precise molecular drivers in individual tumors. This comprehensive approach not only enhances the understanding of tumor biology but also aids in the development of targeted therapies that can significantly improve patient prognosis.[280.1] As genetic testing becomes more prevalent, large-scale databases of genetic information are being established, which will facilitate the identification of new genetic markers associated with various brain tumors. This, in turn, is expected to lead to the development of innovative targeted therapies and preventive strategies.[278.1] Artificial intelligence (AI) is increasingly recognized for its transformative potential in neuro-oncology, particularly in the context of gliomas, which pose significant challenges due to their complexity and high mortality rates.[253.1] Recent advancements in AI applications facilitate the handling of diverse datasets, including mutation data, single-cell information, and RNA sequencing, thereby supporting molecular pathologists in biomarker identification and treatment response prediction.[253.1] The feasibility of implementing AI-based models in medical oncology is contingent upon several factors, including data quality, practical application, ethical considerations, regulatory approval, and cost-effectiveness, all of which are essential for their successful integration into clinical workflows.[254.1] Furthermore, AI models can enhance patient prognoses and customize therapeutic plans by analyzing various clinical data, such as patient history and cancer genomics, ultimately optimizing treatment strategies tailored to individual patient profiles.[254.1] This integration of AI into neuro-oncology not only streamlines the complex molecular analysis process but also aims to improve patient outcomes through more precise and personalized treatment approaches.[253.1]In this section:
References
[1] History of neuro-oncology and neuropsychology - ScienceDirect — The history of neuro-oncology and neuropsychology is intertwined and dates back to ancient times when there was little knowledge about the brain and its function. Prehistoric humans used trephination to treat neurological ailments, as did the ancient Greeks and Chinese. More modern concepts of neuroanatomy and brain function arose in the 16th
[2] (PDF) A History of Neuro-Oncology - ResearchGate — Background: Neuro-oncology is a relatively young subspecialty of neurosurgery. 2018 was the 10th year since the founding of the Indian Society of Neuro-oncology. Objective: To assess patterns in
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[5] History of Neuro-oncology over 5000 years; Informed Consent in ... — SUMMARY: Dr. Khalili, Former Head of the Department of Neurosurgery, Baghdad University, Baghdad Iraq reviews the history of Medicine in Mesopotamian, Egyptian, Greek, Moslem/ Arabic, Medieval, Modern times, and Iraqi practice and research in neuro-oncology; He also discusses the Practice of "Informed Consent" in countries around the world and
[9] Innovations in intraoperative therapies in neurosurgical oncology: a ... — A central tenet of neurosurgical oncology is maximizing tumor resection while minimizing damage to the surrounding healthy brain. For high-grade gliomas (HGG), extent of resection (EOR) remains the most important determining factor in patient outcome, consistently shown to confer a survival benefit .However, development of a new neurologic deficit due to overly aggressive resection is
[10] "Beyond the Knife"-Applying Theranostic Technologies to Enhance ... — Recent advances in image-guided surgical techniques have enhanced the precision of tumor resections, yet there remains a critical need for innovative technologies to further improve patient outcomes. Techniques such as fluorescence image-guided neurosurgery in combination with stereotactic radiosurgery have improved outcomes for patients with
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[40] Diverse Applications of Artificial Intelligence in Neuroradiology — Recent advances in artificial intelligence (AI) and deep learning (DL) hold promise to augment neuroimaging diagnosis for patients with brain tumors and stroke.
[44] How biomarkers are changing brain tumor diagnoses and treatments — There are several different biomarkers present in tumor cells. The presence of certain biomarkers may indicate whether a brain tumor will respond to radiation therapy, chemotherapy or targeted therapy. This information lets neuro-oncologists offer patients effective treatments and withhold ineffective treatments.
[45] Incorporation of Biomarker Assessment in Novel Clinical Trial Designs ... — While the limited patient population with brain tumors limits the designs that may be feasible, we have provided several examples of ongoing neurooncology trials, that if successful, will prospectively validate several biomarkers and as such possibly guide future therapeutic choices and ultimately improve outcome for brain tumor patients
[46] Tumor biomarkers for diagnosis, prognosis and targeted therapy — Over the past decades, continuous progress has been made in exploring and discovering novel, sensitive, specific, and accurate tumor biomarkers, which has significantly promoted personalized medicine and improved the outcomes of cancer patients, especially advances in molecular biology technologies developed for the detection of tumor biomarkers. Moreover, several PCR assays approved by the FDA are used for the diagnosis of KRAS mutation status in formalin-fixed paraffin-embedded tissue, thereby guiding anti-EGFR antibody treatment for metastatic CRC.87 Similarly, qPCR assays are effective in the detection of MRD in leukemia, such as the quantification of BCR-ABL-positive cells post-induction chemotherapy/transplantation in acute lymphoblastic leukemia (ALL).85 PCR technology is also widely used to detect abnormal genes and abnormal mRNA amplification in tumors, such as MYCN amplification in neuroblastoma.88 Ligand-targeted PCR is essential for the detection of folate receptor-positive circulating tumor cells as a potential diagnostic biomarker in pancreatic cancer.89
[50] Milestones of the last 10 years - PMC - National Center for ... — This new classification of CNS tumors integrating classical histological classification, grading, and immunohistochemical and molecular-genetic data is a major milestone in the development of neuro-oncology and will allow a better characterization of tumors as better assignment of treatments, will most probably allow more rapidly informative
[61] Deep brain stimulation for psychiatric disorders: where we are now — Fossil records showing trephination in the Stone Age provide evidence that humans have sought to influence the mind through physical means since before the historical record. Attempts to treat psychiatric disease via neurosurgical means in the 20th century provided some intriguing initial results. H …
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[63] From Mystics to Modern Times: A History of Craniotomy & Religion — Neurosurgical treatment of diseases dates back to prehistoric times and the trephination of skulls for various maladies. Throughout the evolution of trephination, surgery and religion have been intertwined to varying degrees, a relationship that has caused both stagnation and progress. ... understanding of neurology. 14 On describing seizures
[64] Violence, mental illness, and the brain - A brief history of ... — The early concept of cerebral localization (i.e., aphasia, hemiplegia, etc.) was derived first from the study of brain pathology, particularly cerebral tumors and operations for their removal; and second, from the observation of dramatically altered behavior in a celebrated case of traumatic brain injury to the frontal lobe that has come
[84] The evolution of ancient healing practices: From shamanism to ... — Similarly, in ancient India, Ayurveda emerged as a holistic system of medicine, emphasizing the balance of mind, body, and spirit. Ayurvedic texts, such as the Charaka Samhita and the Sushruta Samhita, detailed diagnosis, treatment, and prevention principles, including herbal remedies, dietary guidelines, and yoga practices. The ancient Greeks, particularly during the Classical period, made significant contributions to the development of medical science through the work of physicians like Hippocrates and Galen. Hippocratic medicine, named after the renowned physician Hippocrates, emphasized rational observation, naturalistic explanations for disease, and ethical principles guiding medical practice. The Hippocratic Corpus, a collection of texts attributed to Hippocrates and his followers, laid the foundation for clinical medicine, advocating for the systematic study of symptoms, prognosis, and treatment outcomes. Galen, a prominent physician of the Roman Empire, further expanded upon Hippocratic teachings, contributing to advancements in anatomy, physiology, and pharmacology. The legacy of ancient healing practices extends far beyond historical curiosity, influencing contemporary approaches to healthcare and wellness.
[98] Artificial intelligence in neuro-oncology: advances and challenges in ... — Assessing its influence across all facets of malignant brain tumor management- diagnosis, prognosis, and therapy- AI models outperform human evaluations in terms of accuracy and specificity. Their ability to discern molecular aspects from imaging may reduce reliance on invasive diagnostics and may accelerate the time to molecular diagnoses.
[99] Artificial Intelligence for Response Assessment in Neuro Oncology (AI ... — Artificial Intelligence for Response Assessment in Neuro Oncology (AI-RANO), part 1: review of current advancements - ScienceDirect This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after therapy, and differentiation of true disease progression from treatment-related changes, which is a considerable challenge based on current clinical care in neuro-oncology. To facilitate impactful development and implementation of AI-based biomarkers in clinical trials of CNS tumours, the Response Assessment in Neuro Oncology (RANO) working group—an international effort to develop new standardised response criteria for clinical trials in brain tumours—formulated the AI-RANO subgroup.
[103] Artificial Intelligence and Deep Learning in Revolutionizing Brain ... — The emergence of artificial intelligence (AI) in the medical field holds promise in improving medical management, particularly in personalized strategies for the diagnosis and treatment of brain tumors. However, integrating AI into clinical practice has proven to be a challenge. Deep learning (DL) is very convenient for extracting relevant information from large amounts of data that has
[105] Artificial Intelligence and Deep Learning in Revolutionizing Brain ... — The emergence of artificial intelligence (AI) in the medical field holds promise in improving medical management, particularly in personalized strategies for the diagnosis and treatment of brain tumors. However, integrating AI into clinical practice has proven to be a challenge.
[107] Artificial intelligence in neuro-oncology: advances and challenges in ... — Promising directions for future research include multimodal data integration, generative AI, large medical language models, precise tumor delineation and characterization, and addressing racial and gender disparities. Adaptive personalized treatment strategies are also emphasized for optimizing clinical outcomes.
[108] Artificial Intelligence for Response Assessment in Neuro Oncology (AI ... — The development, application, and benchmarking of artificial intelligence (AI) tools to improve diagnosis, prognostication, and therapy in neuro-oncology are increasing at a rapid pace. This Policy Review provides an overview and critical assessment of the work to date in this field, focusing on diagnostic AI models of key genomic markers, predictive AI models of response before and after
[109] Artificial intelligence in neuro-oncology: advances and challenges in ... — AI has brought transformative innovations to brain tumor management, utilizing imaging, histopathological, and genomic tools for efficient detection, categorization, outcome prediction, and treatment planning. In brain tumor management, AI demonstrates its potential across diagnosis, prognosis, and treatment planning by accelerating and enhancing MRI imaging19, detecting abnormalities, optimizing workflows, providing accurate measurements, analyzing extensive medical imaging data, and identifying patterns not easily discernible to human observers20. Selecting the most appropriate dataset depends on factors like tumor type, imaging modality, data type (MRI, CT, PET, etc.), availability of ground truth annotations, and data size, allowing researchers to align their choice with specific research interests for AI-driven investigations into brain tumor diagnosis and treatment planning. AI shows significant promise in diagnosis, prognosis, and treatment planning by effectively detecting and classifying brain tumors from medical images.
[118] Navigating the artificial intelligence revolution in neuro-oncology: A ... — This comprehensive review presents the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in neuro-oncology, focussing their groundbreaking impact on the field. In recent years, Artificial intelligence (AI) has emerged as an important tool in the field of neuro-oncology, which offers innovative and fancy solutions to some of complex challenges in the brain tumor’s diagnosis, treatment, and management . We utilized the following search terms: "Artificial Intelligence," "AI," "Machine Learning," "Radiomics," "Deep Learning," "Neural Networks," "Trial," "Clinical Trial,"," "Brain Tumour," "Glioma," "Brain Metastases," "Glioblastoma," "GBM," "Astrocytoma," "Meningioma," "Neuro-Oncology," and "Neurooncology." Additionally, we examined the references of identified studies. AI-based classification of three common malignant tumors in neuro-oncology: a multi-institutional comparison of machine learning and deep learning methods
[119] Artificial intelligence in neuro-oncology: advances and challenges in ... — AI has brought transformative innovations to brain tumor management, utilizing imaging, histopathological, and genomic tools for efficient detection, categorization, outcome prediction, and treatment planning. In brain tumor management, AI demonstrates its potential across diagnosis, prognosis, and treatment planning by accelerating and enhancing MRI imaging19, detecting abnormalities, optimizing workflows, providing accurate measurements, analyzing extensive medical imaging data, and identifying patterns not easily discernible to human observers20. Selecting the most appropriate dataset depends on factors like tumor type, imaging modality, data type (MRI, CT, PET, etc.), availability of ground truth annotations, and data size, allowing researchers to align their choice with specific research interests for AI-driven investigations into brain tumor diagnosis and treatment planning. AI shows significant promise in diagnosis, prognosis, and treatment planning by effectively detecting and classifying brain tumors from medical images.
[120] Evolving Role and Translation of Radiomics and Radiogenomics in Adult ... — Multiple radiomics and radiogenomics studies performed on conventional and advanced neuro-oncology image modalities show that they have the potential to differentiate pseudoprogression from true progression, classify tumor subgroups, and predict recurrence, survival, and mutation status with high accuracy.
[121] Evolving Role and Translation of Radiomics and Radiogenomics in Adult ... — Radiomics and radiogenomics are rapidly growing fields in imaging and, since their early inception, have been explored in the field of neuro-oncology. 1 Radiomics is a high-throughput computational process that unlocks microscale quantitative data hidden within conventional imaging, not otherwise visualized by the naked human eye; radiogenomics
[127] Diagnosis and treatment in neuro-oncology: an oncological perspective — The diagnosis of a brain tumour is first suggested after conventional contrast-enhanced CT or MRI. MRI of brain tumours is undoubtedly the gold standard non-invasive technique for the diagnosis, pre-surgical planning and post-therapeutic monitoring of brain and spinal tumours but, it has still not replaced the biopsy for accurate histological
[129] Diagnostic and Theranostic Opportunities in Neuro-Oncology — Theranostics, the interlinking of diagnostic and therapeutic procedures, can be particularly valuable in neuro-oncology, addressing the challenges posed by the blood-brain and brain-tumor barriers. Although it is traditionally associated with nuclear medicine, advances in MR imaging techniques have opened new theranostic frontiers. This review covers the present challenges in neuro-oncology
[130] Advances in Neuro-Oncological Imaging: An Update on Diagnostic Approach ... — Magnetic resonance fingerprinting (MRF) has surfaced as a promising imaging method in the field of neuro-oncology, offering quantitative insights into tissue properties. MRF employs a unique single-sequence, pseudorandomized approach to generate T1 and T2 values, providing rapid quantification and tissue identification potential in neuro-oncology.
[138] Navigating the artificial intelligence revolution in neuro-oncology: A ... — This comprehensive review presents the role of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in neuro-oncology, focussing their groundbreaking impact on the field. In recent years, Artificial intelligence (AI) has emerged as an important tool in the field of neuro-oncology, which offers innovative and fancy solutions to some of complex challenges in the brain tumor’s diagnosis, treatment, and management . We utilized the following search terms: "Artificial Intelligence," "AI," "Machine Learning," "Radiomics," "Deep Learning," "Neural Networks," "Trial," "Clinical Trial,"," "Brain Tumour," "Glioma," "Brain Metastases," "Glioblastoma," "GBM," "Astrocytoma," "Meningioma," "Neuro-Oncology," and "Neurooncology." Additionally, we examined the references of identified studies. AI-based classification of three common malignant tumors in neuro-oncology: a multi-institutional comparison of machine learning and deep learning methods
[140] Artificial intelligence in neuro-oncology: advances and ... - Nature — In brain tumor management, AI demonstrates its potential across diagnosis, prognosis, and treatment planning by accelerating and enhancing MRI imaging19, detecting abnormalities, optimizing workflows, providing accurate measurements, analyzing extensive medical imaging data, and identifying patterns not easily discernible to human observers20. The search was focused on recently published articles, with an emphasis on studies related to AI applications in brain tumor diagnosis, prognosis, and precision treatment. Concentrating on diagnostic, prognostic, and treatment planning within the imaging domain, our paper not only explores the latest advancements in AI tailored to pathology, radiology, and genomics but also addresses the gaps left by previous reviews in fully comprehending the interconnected roles of these disciplines in brain tumor management.
[143] Diagnosis and treatment in neuro-oncology: an oncological perspective — The diagnostic accuracy of conventional imaging even in the best centres is only 80-90%, so surgical biopsy or resection is recommended in almost all cases where further treatment is contemplated to rule out non-neoplastic lesions and to provide histological identification and genotyping.
[148] From Genomic Exploration to Personalized Treatment: Next ... - MDPI — Next-generation sequencing (NGS) has revolutionized personalized oncology care by providing exceptional insights into the complex genomic landscape. NGS offers comprehensive cancer profiling, which enables clinicians and researchers to better understand the molecular basis of cancer and to tailor treatment strategies accordingly. Targeted therapies based on genomic alterations identified
[149] Next-Generation Sequencing in Oncology—A Guiding Compass for ... - MDPI — Multigene sequencing technologies provide a foundation for targeted therapy and precision oncology by identifying actionable alterations and enabling the development of treatments that substantially improve clinical outcomes. This review emphasizes the importance of having a molecular compass guiding treatment decision-making through the multitude of alterations and genetic mutations
[158] A novel patient stratification strategy to enhance the therapeutic ... — Omics-based patient stratification to predict therapy responsiveness is gaining more relevance in glioblastoma therapy and clinical trial design. Recently, a clinical trial showed that RTK II methylation class malignant astrocytoma patients benefit more from inhibition with temozolomide. 33 Glioblastoma subtype status has also been reported to
[162] Advanced imaging techniques for neuro-oncologic tumor diagnosis, with ... — Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors - PMC Advanced MRI techniques including perfusion-weighted imaging (PWI), MR spectroscopy (MRS), diffusion-weighted imaging (DWI), and MR chemical exchange saturation transfer (CEST) offer significant advantages over conventional MR imaging when evaluating tumor extent, predicting grade, and assessing treatment response. Keywords: Brain tumor, Advanced MRI, Amino acid PET, FET, Hybrid PET/MRI, Radiogenomics, Glioma, Glioblastoma, Metastasis, High-grade malignancy, Progression, Pseudoprogression, Radiation necrosis, Pseudoresponse, Treatment-related change, Tumor grading, Perfusion-weighted imaging, Diffusion-weighted imaging, Chemical exchange saturation transfer, MR spectroscopy, Radiomics Comparison of 18F-FET PET and perfusion-weighted MR imaging: a PET/MR imaging hybrid study in patients with brain tumors.
[163] Conventional and Advanced MRI in Neuro-Oncology — Further integration of other advanced MRI techniques into the field of neuro-oncology may pave the way toward reliable non-invasive assessment of tumor type and grade, enhanced visualization of tumor infiltration and intratumoral heterogeneity, and accurate differentiation between true tumor progression and treatment-related effects [7, 8, 9
[166] Molecular neuro-oncology in clinical practice: a new horizon — Nevertheless, molecular markers have become an integral part of tumour assessment in modern neuro-oncology practice because they provide useful information in addition to the WHO classification, and molecular-marker status now guides clinical decision making, at least in subtypes of gliomas. 2 At the same time, several genome-wide or
[168] Precision radiotherapy with molecular-profiling of CNS tumours - Springer — Diagnoses of CNS malignancies in the primary and metastatic setting have significantly advanced in the last decade with the advent of molecular pathology. Using a combination of immunohistochemistry, next-generation sequencing, and methylation profiling integrated with traditional histopathology, patient prognosis and disease characteristics can be understood to a much greater extent. This has
[170] Molecular Profiling in Neuro-Oncology: Where We Are, Where We're ... — Uncertainty around the performance characteristics of the biomarker assay can pose significant challenges in clinical implementation. ... The future of molecular profiling in neuro-oncology, particularly concerning the utility of treatment selection biomarkers, will depend on the availability of robust biomarker assays and effective therapeutic
[173] Brain Tumor Treatment - Johns Hopkins Medicine — The treatment for a brain tumor will depend on many things, including the type, size and location of the tumor, as well as your symptoms, general health and treatment preferences. The main treatment options for a brain tumor include: Surgery; Radiation therapy; Chemotherapy; Targeted drug therapy; Tumor treating fields; Clinical trials
[186] Radiation Therapy for Brain Tumors - Virginia Cancer Institute — Radiation therapy may be used alone or in combination with surgery and/or chemotherapy in the treatment of primary or metastatic brain cancers, which are also called brain tumors. External beam radiation therapy (EBRT) is the conventional technique for administering radiation therapy to the brain, but stereotactic radiosurgery has also become a standard treatment. The most
[187] Brain Tumors: Radiation Therapy - Johns Hopkins Medicine — The Radiation Team Treatment planning for radiation therapy includes mapping to pinpoint the exact location of the brain tumor using X-rays or other images. A radiation oncologist uses these images to create a 3D picture of your brain. For some types of radiation therapy, a custom-fitted mask is created to increase the precision of the treatment.
[190] thematic analysis of shared decision-making in consultations with ... — The decision-making processes seemed to focus primarily on medically informing patients with a presumed brain tumor, in line with informed decision-making. However, less emphasis was placed on learning more about the patient's everyday life and preferences to integrate into the decision-making process, which is key to SDM.
[191] A prospective study of shared decision-making in brain tumor surgery ... — Decision grids are important tools in the SDM process, and we developed them for three different types of intracranial tumors. Methods: This prospective study was conducted in a high-volume neuro-oncological center on all consecutive eligible patients undergoing consideration of treatment for intracranial glioma and metastases.
[192] Foundations of Neuro-Oncology: A Multidisciplinary Approach — Decisions regarding surgical management (either biopsy or maximal safe resection) are largely dependent on patient preference and prognostic factors, including age, tumor size, and anatomic location as well as the presence or absence of neurologic deficits. 39 In a patient at high surgical risk, it may be reasonable to monitor the lesion in
[201] Temozolomide-loaded bacterial magnetosomes improve targeted therapy for ... — The therapeutic role of Temozolomide loaded magnetosomes conjugate in brain tumor therapy has been investigated; the results are promising. The potential for improving the accuracy and effectiveness of therapeutic interventions comes from BMs-TMZ's ability to precisely target tumor cells while navigating the complex environment of the brain.
[202] Temozolomide Efficacy and Metabolism: The Implicit Relevance of ... — 3. Temozolomide (TMZ): Efficacy and Metabolism TMZ is a potent radiosensitizer and a key component of chemoradiation therapy for patients with newly diagnosed glioblastoma. The main advantage of temozolomide is its high effectiveness against malignant glioma of the brain, the main disadvantage is the safety profile.
[203] Temozolomide for treatment of brain metastases: A review of 21 clinical ... — To better understand the efficacy of temozolomide in the treatment of brain metastases, we carried out a review of 21 published clinical trials to determine whether temozolomide would benefit patients with brain metastases from solid tumours.
[204] Brain Tumor Recovery: Factors Influencing Survival Rates — The factors influencing brain tumor recovery rates are many and varied. From the type and location of the tumor to the patient's age and overall health, from genetic factors to treatment options, each element plays a role in shaping the prognosis. ... many of which can be influenced by treatment choices and lifestyle decisions.
[205] Metastatic Brain Cancer Treatment Options: Factors To Consider — The Factors Influencing Your Treatment. The most effective way to treat metastatic brain cancer is to choose the right approach. Your tumor is different from the next person's, which is why your doctor will evaluate multiple factors before deciding which brain tumor treatment path will be the most successful for your condition.
[206] PDF — This precision targeting allows for maximal tumor control while sparing adjacent healthy brain tissue, thereby minimizing the risk of neurological complications. Numerous clinical studies have demonstrated the efficacy of SRT in achieving local tumor control and improving overall survival in patients with single brain metastases.
[211] "Beyond the Knife"-Applying Theranostic Technologies to Enhance ... — The current standard of care for brain tumor management includes maximal safe surgical resection followed by concurrent chemotherapy and radiation therapy. Techniques such as fluorescence image-guided neurosurgery in combination with stereotactic radiosurgery have improved outcomes for patients with brain tumors. In this article for Brain Science's Special Issue Recent Advances in Translational Neuro-Oncology, we review the use of image-guided neurosurgery and stereotactic radiosurgery for the treatment of brain tumors. Keywords: Cyberknife; blood–brain barrier; brain tumors; chemotherapy; fluorescence-guided surgery; gliomas; image-guided surgery; nanotechnology; neuro-oncology; neurosurgery; radiosurgery. Modern-day treatment of primary and metastatic brain tumors includes: (A) maximal safe surgical resection; (B) adjuvant stereotactic radiosurgery and/or radiotherapy to the surrounding tumor bed and remaining tumor burden; and (C) systemic chemotherapy, targeted therapies, or immunotherapies.
[212] Artificial intelligence innovations in neurosurgical oncology: a ... — Purpose Artificial Intelligence (AI) has become increasingly integrated clinically within neurosurgical oncology. This report reviews the cutting-edge technologies impacting tumor treatment and outcomes. Methods A rigorous literature search was performed with the aid of a research librarian to identify key articles referencing AI and related topics (machine learning (ML), computer vision (CV
[213] Doing More with Less: Neurosurgery Strategies and Tricks of the Trade ... — In recent decades, numerous technological advancements, such as image guidance devices, clinical neurophysiology devices, neuromodulation devices, ultrasonic surgical aspirators, fluorescence-guided surgery, indocyanine green video angiography, and operating microscopes and endoscopes, have made neurosurgical operations more accurate and safer, thereby improving patient outcomes.
[214] Minimally Invasive Brain Tumor Surgery / Keyhole Surgery — Minimally invasive brain tumor surgery, also known as keyhole surgery or neuroendoscopic surgery, is a surgical approach aimed at removing brain tumors with less disruption to surrounding healthy brain tissue compared to traditional open surgery. It involves the use of specialized instruments and techniques to access and remove the tumor
[216] Scientific and Clinical Challenges within Neuro-Oncology — In this review, we discuss the unique scientific and clinical challenges associated with treatment of brain tumors. We review existing roadblocks to drug delivery into the central nervous system (CNS), including the blood-brain barrier (BBB), blood-tumor barrier (BTB), and blood-cerebrospinal fluid (CSF) barrier (BCSFB), as well as therapeutic challenges posed by the CNS immune system and
[217] Neuro-Oncology - an overview | ScienceDirect Topics — Neuro-oncology involves the diagnosis and treatment of tumors of the central nervous system, as well as metastatic and nonmetastatic neurological complications of systemic cancers. Brain tumors and CNS metastatic cancer have always been emotionally isolating conditions due to disease sequelae and social stigma but in the COVID-19 world where patients are undergoing brain surgery, chemotherapy, and treatment in isolation from family and friends—feelings of isolation and despair have increased.4, 5 Behavioral health and supportive oncological resources for patients and caregivers need to be escalated and distance-based solutions such as virtual platforms of communication must be utilized. Neuro-oncology involves the diagnosis and treatment of tumors of the central nervous system, as well as metastatic and nonmetastatic neurological complications of systemic cancers.
[220] Personalized Medicine in Brain Tumors - PMC — Glioblastoma was the first tumor to be studied in the NIH-funded The Cancer Genome Atlas Program , unraveling its unthinkable molecular heterogeneity. The wide heterogeneity of glioblastoma implies a variable response to treatments; this highlights the great importance of identifying individualized paths of care in neuro-oncologic patients. In Contribution 4, a retrospective study was conducted to evaluate the efficacy and toxicity of hypofractionated radiotherapy with a simultaneous integrated boost in association with temozolomide in glioblastoma poor prognosis patients. 3.Patel A.P., Tirosh I., Trombetta J.J., Shalek A.K., Gillespie S.M., Wakimoto H., Cahill D.P., Nahed B.V., Curry W.T., Martuza R.L., et al. 4.Verhaak R.G.W., Hoadley K.A., Purdom E., Wang V., Qi Y., Wilkerson M.D., Miller C.R., Ding L., Golub T., Mesirov J.P., et al.
[240] Psychological Interventions for Patients With Advanced Disease ... — The integration of palliative care and oncology is now widely accepted in both fields as an important goal, 8,9 but the integration of psychological care into both of these domains has received less priority. Psychological interventions are still not routinely incorporated into oncology or palliative care, despite evidence for their effectiveness.
[241] Personalized medicine for glioblastoma: current challenges and future ... — In addition, epigenetic changes provide another means of modifying the existing heterogeneous genetic background of tumor cells. These cumulative changes create challenges for the detection, characterization and treatment of glioblastomas, but new opportunities allow the development of advanced diagnostic modalities and individualized therapies.
[242] Current Challenges and Opportunities in Treating Glioblastoma — Because GBM is an orphan disease, clinical trial participation is low, which prevents the detection of subtle differences in treatment with statistical significance. Other challenges include determination of appropriate controls, stratification according to prognostic factors, and definition of clinical endpoint ( Reardon et al., 2011 ).
[243] Personalized Glioblastoma Management | University of Pittsburgh — Despite all efforts; no personalized GBM treatment has been clinically implemented to date. The need for rapid and cost efficient personalized in-vitro GBM therapy trials is apparent. In-vitro humanoid brain organoid cancer models are the most apparent, robust and realistic choice toward a personalized high-throughput brain cancer co-clinical
[245] [Challenges and future perspectives in neuro-oncology] — Neuro-oncology is a young and emerging discipline. An interdisciplinary mindset and concerted actions in specialized expert teams are required to meet the challenges, not only in clinical patient care but also in research activities. A close communication network between clinician scientists, medica …
[246] Serendipity in Neuro-Oncology: The Evolution of Chemotherapeutic ... - MDPI — The development of novel therapeutics in neuro-oncology faces significant challenges, often marked by high costs and low success rates. Despite advances in molecular biology and genomics, targeted therapies have had limited impact on improving patient outcomes in brain tumors, particularly gliomas, due to the complex, multigenic nature of these malignancies. While significant efforts have been
[248] Adult neuro-oncology trials in the United States over 5 decades ... — In neuro-oncology, there are significant biological and clinical challenges inherent to the disease itself. For systemic therapy, the blood-brain barrier significantly affects CNS drug efficacy in both preclinical and clinical studies. 8 Clinical trials have the potential to close the gap between unmet needs and future clinical developments.
[253] Modernizing Neuro-Oncology: The Impact of Imaging, Liquid ... - PubMed — Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment - PubMed Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment Advances in neuro-oncology have transformed the diagnosis and management of brain tumors, which are among the most challenging malignancies due to their high mortality rates and complex neurological effects. This review highlights recent advancements in imaging techniques, liquid biopsies, and artificial intelligence (AI) applications addressing current diagnostic challenges. The main obstacles in assessing brain tumors using MRI post-treatment are presented as follows: (A) Pseudoprogression: Post-radiation inflammation can cause temporary tumor enlargement on imaging, mimicking actual tumor progression.
[254] Artificial intelligence in neuro-oncology: advances and ... - Nature — In brain tumor management, AI demonstrates its potential across diagnosis, prognosis, and treatment planning by accelerating and enhancing MRI imaging19, detecting abnormalities, optimizing workflows, providing accurate measurements, analyzing extensive medical imaging data, and identifying patterns not easily discernible to human observers20. The search was focused on recently published articles, with an emphasis on studies related to AI applications in brain tumor diagnosis, prognosis, and precision treatment. Concentrating on diagnostic, prognostic, and treatment planning within the imaging domain, our paper not only explores the latest advancements in AI tailored to pathology, radiology, and genomics but also addresses the gaps left by previous reviews in fully comprehending the interconnected roles of these disciplines in brain tumor management.
[259] Advanced imaging techniques for neuro-oncologic tumor diagnosis, with ... — Advanced imaging techniques for neuro-oncologic tumor diagnosis, with an emphasis on PET-MRI imaging of malignant brain tumors - PMC Advanced MRI techniques including perfusion-weighted imaging (PWI), MR spectroscopy (MRS), diffusion-weighted imaging (DWI), and MR chemical exchange saturation transfer (CEST) offer significant advantages over conventional MR imaging when evaluating tumor extent, predicting grade, and assessing treatment response. Keywords: Brain tumor, Advanced MRI, Amino acid PET, FET, Hybrid PET/MRI, Radiogenomics, Glioma, Glioblastoma, Metastasis, High-grade malignancy, Progression, Pseudoprogression, Radiation necrosis, Pseudoresponse, Treatment-related change, Tumor grading, Perfusion-weighted imaging, Diffusion-weighted imaging, Chemical exchange saturation transfer, MR spectroscopy, Radiomics Comparison of 18F-FET PET and perfusion-weighted MR imaging: a PET/MR imaging hybrid study in patients with brain tumors.
[261] Artificial intelligence in neuro-oncology: advances and challenges in ... — Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment - PubMed Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment This review delves into the most recent advancements in applying artificial intelligence (AI) within neuro-oncology, specifically emphasizing work on gliomas, a class of brain tumors that represent a significant global health issue. e Handling mutation data, single-cell information, methylation patterns, RNA sequencing, and more, AI empowers molecular pathologists by supporting biomarker identification, pathway identification, treatment response prediction, variant identification, and serving as a diagnosis assistant, streamlining the complex molecular analysis process (Created with BioRender.com).
[262] Artificial intelligence in neuro-oncology: advances and ... - Nature — In brain tumor management, AI demonstrates its potential across diagnosis, prognosis, and treatment planning by accelerating and enhancing MRI imaging19, detecting abnormalities, optimizing workflows, providing accurate measurements, analyzing extensive medical imaging data, and identifying patterns not easily discernible to human observers20. The search was focused on recently published articles, with an emphasis on studies related to AI applications in brain tumor diagnosis, prognosis, and precision treatment. Concentrating on diagnostic, prognostic, and treatment planning within the imaging domain, our paper not only explores the latest advancements in AI tailored to pathology, radiology, and genomics but also addresses the gaps left by previous reviews in fully comprehending the interconnected roles of these disciplines in brain tumor management.
[263] Liquid biopsy in neuro-oncology: are we finally there? — In neuro-oncology, the development of liquid biopsy has been particularly challenging because quantities of circulating tumor elements in body fluids are low.
[264] Liquid biopsy into the clinics: Current evidence and future ... — Challenges for the integration of liquid biopsies into clinical practice include standardization, analyte validation and demonstration of clinical utility as well as regulatory considerations.
[265] Editorial: Liquid biopsy in oncology: opportunity and challenges — The findings support the integration of liquid biopsy into routine clinical practice for personalized cancer management. Combining liquid biopsy with other diagnostic methods may enhance cancer detection and management's overall accuracy and reliability.
[266] The future of brain tumor liquid biopsies in the clinic - PMC — We have considered just three of the key challenges for scientists developing liquid biopsies. In whatever was these challenges are overcome, the objective should be to make decision making more agile and patient-centered, and for the liquid biopsy results to positively impact patients.
[278] Personalized Medicine: Tailoring Diagnosis to Individual Genetic Profiles — Moreover, as more people undergo genetic testing, large-scale databases of genetic information will be created, enabling researchers to identify new genetic markers associated with disease. This, in turn, will lead to the development of new targeted therapies and preventive strategies, further advancing the field of personalized medicine.
[279] PDF — By tailoring healthcare to the unique genetic makeup of each patient, personalized medicine holds the promise of more effective, targeted, and precise interventions, minimizing adverse effects and optimizing therapeutic outcomes. Unlike the traditional one-size-fits-all approach to healthcare, personalized medicine considers a patient’s genetic profile, lifestyle, and environmental factors to create customized treatment plans. For patients with rare genetic disorders, personalized medicine offers new hope for diagnosis and treatment. The integration of personalized medicine into routine clinical practice will likely lead to more precise and effective healthcare, reducing the burden of disease and improving patient outcomes. By tailoring diagnosis and treatment to a patient’s genetic profile, personalized medicine offers the potential for more effective and precise interventions, ultimately improving patient outcomes.
[280] Therapeutic Targets in Glioblastoma: Molecular Pathways, Emerging ... — There is also a growing emphasis on personalized medicine approaches: multi-omics profiling, encompassing genomics, transcriptomics, proteomics, and emerging epigenomic and metabolomic analyses, help pinpoint precise molecular drivers in individual tumors .