ABSTRACT
Background and purpose: Automatic review of breast plan quality for clinical trials is time-consuming and has some unique challenges due to the lack of target contours for some planning techniques. We propose using an auto-contouring model and statistical process control to independently assess planning consistency in retrospective data from a breast radiotherapy clinical trial. Materials and methods: A deep learning auto-contouring model was created and tested quantitatively and qualitatively on 104 post-lumpectomy patients' computed tomography images (nnUNet; train/test: 80/20). The auto-contouring model was then applied to 127 patients enrolled in a clinical trial. Statistical process control was used to assess the consistency of the mean dose to auto-contours between plans and treatment modalities by setting control limits within three standard deviations of the data's mean. Two physicians reviewed plans outside the limits for possible planning inconsistencies. Results: Mean Dice similarity coefficients comparing manual and auto-contours was above 0.7 for breast clinical target volume, supraclavicular and internal mammary nodes. Two radiation oncologists scored 95% of contours as clinically acceptable. The mean dose in the clinical trial plans was more variable for lymph node auto-contours than for breast, with a narrower distribution for volumetric modulated arc therapy than for 3D conformal treatment, requiring distinct control limits. Five plans (5%) were flagged and reviewed by physicians: one required editing, two had clinically acceptable variations in planning, and two had poor auto-contouring. Conclusions: An automated contouring model in a statistical process control framework was appropriate for assessing planning consistency in a breast radiotherapy clinical trial.
ABSTRACT
With recent changes in the recommended annual limit on eye lens exposures to ionizing radiation, there is considerable interest in predictive computational dosimetry models of the human eye and its various ocular structures including the crystalline lens, ciliary body, cornea, retina, optic nerve, and central retinal artery. Computational eye models to date have been constructed as stylized models, high-resolution voxel models, and polygon mesh models. Their common feature, however, is that they are typically constructed of nominal size and of a roughly spherical shape associated with the emmetropic eye. In this study, we present a geometric eye model that is both scalable (allowing for changes in eye size) and deformable (allowing for changes in eye shape), and that is suitable for use in radiation transport studies of ocular exposures and radiation treatments of eye disease. The model allows continuous and variable changes in eye size (axial lengths from 20 to 26 mm) and eye shape (diopters from -12 to +6). As an explanatory example of its use, five models (emmetropic eyes of small, average, and large size, as well as average size eyes of -12D and +6D) were constructed and subjected to normally incident beams of monoenergetic electrons and photons, with resultant energy-dependent dose coefficients presented for both anterior and posterior eye structures. Electron dose coefficients were found to vary with changes to both eye size and shape for the posterior eye structures, while their values for the crystalline lens were found to be sensitive to changes in only eye size. No dependence upon eye size or eye shape was found for photon dose coefficients at energies below 2 MeV. Future applications of the model can include more extensive tabulations of dose coefficients to all ocular structures (not only the lens) as a function of eye size and shape, as well as the assessment of x-ray therapies for ocular disease for patients with non-emmetropic eyes.
