  In this study, we propose a tailored DL framework for patient-specific
performance that leverages the behavior of a model intentionally overfitted to
a patient-specific training dataset augmented from the prior information
available in an ART workflow - an approach we term Intentional Deep Overfit
Learning (IDOL). Implementing the IDOL framework in any task in radiotherapy
consists of two training stages: 1) training a generalized model with a diverse
training dataset of N patients, just as in the conventional DL approach, and 2)
intentionally overfitting this general model to a small training
dataset-specific the patient of interest (N+1) generated through perturbations
and augmentations of the available task- and patient-specific prior information
to establish a personalized IDOL model. The IDOL framework itself is
task-agnostic and is thus widely applicable to many components of the ART
workflow, three of which we use as a proof of concept here: the auto-contouring
task on re-planning CTs for traditional ART, the MRI super-resolution (SR) task
for MRI-guided ART, and the synthetic CT (sCT) reconstruction task for MRI-only
ART. In the re-planning CT auto-contouring task, the accuracy measured by the
Dice similarity coefficient improves from 0.847 with the general model to 0.935
by adopting the IDOL model. In the case of MRI SR, the mean absolute error
(MAE) is improved by 40% using the IDOL framework over the conventional model.
Finally, in the sCT reconstruction task, the MAE is reduced from 68 to 22 HU by
utilizing the IDOL framework.
