Program Information
Deep-Learning Based Prediction of Achievable Dose for Personalizing Inverse Treatment Planning
m mardani korani*, P Dong , L Xing , Stanford University School of Medicine, Stanford, CA
Presentations
MO-G-201-3 (Monday, August 1, 2016) 5:15 PM - 6:00 PM Room: 201
Purpose: Inverse planning starts with prescribed dose or DVHs. The time/effort spent on obtaining a clinically acceptable plan and, more importantly, the quality of the resultant treatment plan depends critically on the prescription. The present work aims to facilitate the treatment planning workflow by predicting the achievable dose distribution based on a dataset of quality plans designed for past patients.
Methods: A novel learning-empowered approach is developed that predicts achievable 3D dose volume for a new patient based on the geometrical features of the contoured anatomical-scan. Two major steps are involved: (1) Feature extraction: To extract a rich set of features resilient to patient and tumor-shape variations, multi-layer convolutional auto-encoders (CAE) are applied separately to structural contours and datasets of isodose distribution. (2) Learning the relation map: From the historical dose and contour features, a predictive model based on multi-task regression is trained, where dose features are linearly correlated with a subset of contour features selected via sparsity regularization. IMRT plans for 115 prostate patients were used to train the models, and the final method is tested on additional 10 prostate plans. Dose and contour volumes are rearranged to form a 3D polar grid with (r,φ,z) coordinates.
Results: A deep-learning based dose prediction model for VMAT/IMRT treatment is established. Our preliminary results using a two-layer linear auto-encoder achieve more than %70 overlap volume between the actual and predicted isodose surfaces associated with V60%, V75%, and V90. Analysis of dose and contour data reveal nonlinear behaviors which we further deploy enhance prediction accuracy.
Conclusion: The present work puts forth a novel predictive model for dose volume based on anatomical scan. Deep CAEs extract a subset of representative features that are subsequently used to derive a contour-to-dose relation map. The resultant dose map serves as a prior to facilitate treatment planning.
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