Adaptive Radiotherapy Overview

Principles

Definition

Adaptive radiotherapy is a methodological approach in radiation oncology that involves the dynamic modification of a patient's radiation treatment plan during the course of therapy. This concept utilizes repeated imaging and other data acquisition techniques to monitor changes in tumor geometry, patient anatomy, and biological response, enabling adjustments to optimize dose delivery to the target volume while minimizing irradiation of surrounding healthy tissues, thereby enhancing treatment precision and potentially improving therapeutic outcomes.

Ontological type

Core Techniques

Clinical Indications

Challenges and Limitations

Key developments

Key Figures

Biology-Driven DVH Optimization era

Colin G. Orton [1] was active in the biology-driven radiotherapy field during the era, with affiliations at University of Alabama at Birmingham [3] and Case Western Reserve University [4]. He contributed through A simplification in the use of the NSD concept in practical radiotherapy [6], Models in radiotherapy: Volume effects [7], and Models in radiotherapy: Definition of decision criteria [8], which anchored dose–volume considerations in radiobiological endpoint criteria and provided the basis for biology-driven DVH optimization in adaptive planning. Roger Dale [2], The Open University [5], published The application of the linear-quadratic dose-effect equation to fractionated and protracted radiotherapy [9]. These developments collectively underpinned a quantitative, biology-informed approach to treatment planning in this era, linking fractionation and DVH concepts to radiobiological endpoints and enabling adaptive decision making.

4D Motion-Managed Adaptation era

Marcel van Herk [1] is a leading figure in motion-centric radiotherapy during this era, with ties to the Oncode Institute [3]. His key contributions include the 2006 report The management of respiratory motion in radiation oncology report of AAPM Task Group 76a) [6] and the 2000 paper The probability of correct target dosage: dose-population histograms for deriving treatment margins in radiotherapy [7], which advanced motion-aware margin design and target coverage crucial for 4D planning. Steve Jiang [2], affiliated with Harvard University [4] and Stanford University [5], co-authored the respiratory motion management paper [6], the Synchronized moving aperture radiation therapy (SMART): average tumour trajectory for lung patients [8], and the Validation of a Monte Carlo dose calculation tool for radiotherapy treatment planning [9]. Collectively, the respiratory motion management work [6], SMART [8], and Monte Carlo dose validation [9] advanced closed-loop, motion-aware dose delivery and verification that underpinned 4D motion-managed adaptation.

Online MR-Guided Personalization era

Ina M. Jürgenliemk‐Schulz [1] is a leading figure in online MR-guided adaptive radiotherapy during the Online MR-Guided Personalization era, with activity across institutions such as University of California, Los Angeles [3] and Radboud University Nijmegen [4]. Her contributions span proposing the MR-linac adaptive radiotherapy concept [7], advancing MRI-guided brachytherapy approaches [8], and reporting early clinical proof of concept for high-precision MRI-guided radiotherapy [9], which were crucial for enabling real-time plan adaptation and image-guided decision support in this era. Bas W. Raaymakers [2] is a central innovator in MR-guided radiotherapy with activities anchored at Heidelberg University [5] and Lund University [6]. His key contributions include co-developing the adaptive radiotherapy framework on MR-linac platforms [7], reporting clinical proof-of-concept with first patients treated on a 1.5 T MRI-Linac [9], and evaluating feasibility of stereotactic radiotherapy for pelvic oligometastases [10], which collectively accelerated real-time plan adaptation and dose re-optimization in this era [7][9][10].