Program Information

Machine Learning Methods for Knowledge Based Treatment Planning of Prostate Cancer

L Hu

L Hu¹*, L Yuan² , Y Ge³ , F Yin⁴ , Q Wu⁵ , (1) Duke University Medical Center, Durham, NC, (2) Duke University Medical Center, Durham, NC, (3) University of North Carolina at Charlotte, Charlotte, NC, (4) Duke University Medical Center, Durham, NC, (5) Duke University Medical Center, Durham, NC

Presentations

SU-E-T-229 Sunday 3:00PM - 6:00PM Room: Exhibit Hall

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.


Funding Support, Disclosures, and Conflict of Interest: Partially supported by NIH/NCI under grant #R21CA161389 and a master research grant by Varian Medical System.


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