Aging · 2020 · 48 citations · 35 references
MedicineNuclear MedicineOncologyDiagnostic ImagingDiagnostic RadiologyOncologic Nuclear MedicineComputed TomographyMalignant NeoplasmsPositron Emission TomographyRadiation TherapyTherapy Response AssessmentTumor ImagingOncologic Pet ImagingRadiomicsRadiomics PrognosisRadiomics-based DetectionTherapy Response Evaluation
BACKGROUND: F-PET/CT features can help predict the survival of HNSCC patients. RESULTS: Three radiomics features were identified as prognostic factors. Radiomics score calculated from these features significantly predicted overall survival (OS) and disease-free disease (DFS). The clinicopathological characteristics combined with pre- or post-treatment nomograms showed better ROC curves and decision curves than the nomogram based only on clinicopathological characteristics. CONCLUSIONS: Combining clinicopathological characteristics with radiomics features of pre-treatment PET/CT or post-treatment PET/CT assessment of primary tumor sites as positive or negative may substantially improve prediction of OS and DFS of HNSCC patients. METHODS: F-PET/CT scans with HNSCC in the Cancer Imaging Achieve (TCIA) were included. Nomograms that combined clinicopathological features with either pre-treatment PET/CT radiomics features or post-treatment assessment of primary tumor sites were constructed using data from 154 HNSCC patients. Receiver operating characteristic (ROC) curves and decision curves were used to compare the predictions of these models with those of a model incorporating only clinicopathological features.
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Rebecca L. Siegel, Kimberly D. Miller, Ahmedin Jemal · CA A Cancer Journal for Clinicians · 2017 · 15K citations · Full text
SEER Cancer Statistics Review, 1975-2003
Lynn A. G. Ries, D. Harkins, Martin Krapcho et al. · ScholarWorks - Georgia State University (Georgia State University) · 2006 · 11.5K citations · Full text
Medicine, Biostatistics, Oncology +12