Postoperative radiotherapy (RT) has been shown to effectively reduce disease recurrence and mortality in breast cancer (BC) treatment. A critical step in the planning workflow is the accurate delineation of clinical target volumes (CTV) and organs-at-risk (OAR). This literature review evaluates recent advancements in deep-learning (DL) and atlas-based auto-contouring techniques for CTVs and OARs in BC planning-CT images for RT.
View Article and Find Full Text PDFTreatments at ultra-high dose rate (UHDR) have the potential to improve the therapeutic index of radiation therapy (RT) by sparing normal tissues compared to conventional dose rate irradiations. Insufficient and inconsistent reporting in physics and dosimetry of preclinical and translational studies may have contributed to a reproducibility crisis of radiobiological data in the field. Consequently, the development of a common terminology, as well as common recording, reporting, dosimetry, and metrology standards is required.
View Article and Find Full Text PDFPurpose: Radiomics is an emerging field that utilizes quantitative features extracted from medical images to predict clinically meaningful outcomes. Validating findings is crucial to assess radiomics applicability. We aimed to validate previously published magnetic resonance imaging (MRI) radiomics models to predict oncological outcomes in oral tongue squamous cell carcinoma (OTSCC).
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