Qingrong Wu

Purpose: Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a challenging task, especially with simultaneous integrated boost treatment approaches. We propose a deep learning (DL) framework to accurately predict fluence maps from patient anatomy and directly generate intensity modulated radiation therapy plans. Methods and Materials: The framework employs 2 convolutional neural networks (CNNs) to sequentially generate beam dose prediction and fluence map prediction, creating a deliverable 9-beam intensity modulated radiation therapy plan. Within the beam dose prediction CNN, axial slices of combined structure contour masks are used to predict 3-dimensional (3D) beam doses for each beam. Each 3D beam dose is projected along its beam's-eye-view to form a 2D beam dose map, which is subsequently used by the fluence map prediction CNN to predict its fluence map. Finally, the 9 predicted fluence maps are imported into the treatment planning system to finalize the plan by leaf sequencing and dose calculation. One hundred patients receiving pancreas SBRT were retrospectively collected for this study. Benchmark plans with unified simultaneous integrated boost prescription (25/33 Gy) were manually optimized for each case. The data set was split into 80/20 cases for training and testing. We evaluated the proposed DL framework by assessing both the fluence maps and the final predicted plans. Further, clinical acceptability of the plans was evaluated by a physician specializing in gastrointestinal cancer. Results: The DL-based planning was, on average, completed in under 2 minutes. In testing, the predicted plans achieved similar dose distribution compared with the benchmark plans (-1.5% deviation for planning target volume 33 V ), with slightly higher planning target volume maximum (+1.03 Gy) and organ at risk maximum (+0.95 Gy) doses. After renormalization, the physician rated 19 cases clinically acceptable and 1 case requiring minor improvement. Conclusions: The DL framework can effectively plan pancreas SBRT cases within 2 minutes. The predicted plans are clinically deliverable, with plan quality approaching that of manual planning. 33Gy

PURPOSE: To evaluate the accuracy of the dose prediction models constructed with machine learning techniques, Support Vector Machine (SVM) and Artificial Neural Network (ANN) for the prediction of dose volume histogram (DVH) of organs-at-risk (OAR) in IMRT, compared to the model constructed by stepwise multiple regression (MR), and to investigate the number of prior plans required for the models to produce reliable predictions. METHODS: IMRT plans from 102 prostate cases were randomly divided into two datasets for training and testing, respectively. The testing dataset contains a fixed number of 20 cases, while the number of cases in the training dataset varied from 5 to 80. Models were constructed with SVM, ANN, or MR to formulate the dependence of the OAR DVH on patient anatomical features including the Distance to Target Histogram (DTH), PTV and OAR volumes and their overlap, among other volumetric or spatial information. The D50 (Dose value at 50% volume) and the mean square of difference between D50 of clinical and predicted DVH were calculated for each modeling technique at each specific training dataset number. RESULTS: The mean square of difference of D50 between clinical and predicted DVH decreases with the number of cases in the training dataset, and reaches stable beyond 30 for MR. With the 80 case training dataset, for the bladder model, the SVM predicted 70% D50 values within 10% error and the ANN predicted 85%, compared to 85% with multiple regression. For the rectum model, the numbers are SVM 80%, ANN 70%, and MR 85%. CONCLUSION: The machine learning techniques SVM and ANN are comparable to MR for producing OAR DVH prediction of the prostate cancer. The minimal number of training cases is around 30. Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical System.

A substantial barrier to the single- and multi-institutional aggregation of data to supporting clinical trials, practice quality improvement efforts, and development of big data analytics resource systems is the lack of standardized nomenclatures for expressing dosimetric data. To address this issue, the American Association of Physicists in Medicine (AAPM) Task Group 263 was charged with providing nomenclature guidelines and values in radiation oncology for use in clinical trials, data-pooling initiatives, population-based studies, and routine clinical care by standardizing: (1) structure names across image processing and treatment planning system platforms; (2) nomenclature for dosimetric data (eg, dose-volume histogram [DVH]-based metrics); (3) templates for clinical trial groups and users of an initial subset of software platforms to facilitate adoption of the standards; (4) formalism for nomenclature schema, which can accommodate the addition of other structures defined in the future. A multisociety, multidisciplinary, multinational group of 57 members representing stake holders ranging from large academic centers to community clinics and vendors was assembled, including physicists, physicians, dosimetrists, and vendors. The stakeholder groups represented in the membership included the AAPM, American Society for Radiation Oncology (ASTRO), NRG Oncology, European Society for Radiation Oncology (ESTRO), Radiation Therapy Oncology Group (RTOG), Children's Oncology Group (COG), Integrating Healthcare Enterprise in Radiation Oncology (IHE-RO), and Digital Imaging and Communications in Medicine working group (DICOM WG); A nomenclature system for target and organ at risk volumes and DVH nomenclature was developed and piloted to demonstrate viability across a range of clinics and within the framework of clinical trials. The final report was approved by AAPM in October 2017. The approval process included review by 8 AAPM committees, with additional review by ASTRO, European Society for Radiation Oncology (ESTRO), and American Association of Medical Dosimetrists (AAMD). This Executive Summary of the report highlights the key recommendations for clinical practice, research, and trials.