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The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences
To evaluate whether deep learning models trained on a small number of high-quality plans (e.g., ≤30) can predict dose distributions of comparable quality, and whether the predicted quality improvements are achievable.
This work presents the Radiotherapy Dose Inference and Analysis Toolkit (RADIANT), an open-source, fully configurable framework for 3D radiotherapy dose prediction. Built upon the Medical Imaging Segmentation Toolkit, RADIANT supports a wide range of network...
Radiotherapy plan quality education is limited by case availability, frequent clinical interruptions, and difficulty aligning schedules. These barriers leave residents underprepared to identify and correct suboptimal plans, potentially compromising future out...