Progressive Knowledge Modeling for Pelvic IMRT/VMAT Treatment Planning

Abstract

Intensity Modulated Radiation Therapy (IMRT) and Volumetric Modulated Arc Therapy (VMAT) have become effective tools for treating cancer with radiation. Designing a high quality IMRT/VMAT treatment plan is time consuming. Different kinds of knowledge-based methods are being developed to reduce planning time and improve the plan quality by extracting knowledge from previous expert plans to form knowledge models and applying such models to the new patient cases. Currently, these methods are mostly limited to a particular cancer type and therefore various diseases types require training of multiple knowledge models with a large number of cases.

To investigate the feasibility of knowledge modeling of IMRT/VMAT treatment planning for multiple cancer types, a progressive study is conducted with a treatment planning knowledge model that quantifies correlations between patient pelvic anatomical features and the OAR sparing features. Low risk prostate plans with relatively simpler PTV-OAR geometry, which is the most common geometry type in previous knowledge based studies, are used to train the model as the starting point of the progressive modeling process. Cases with more complex PTV-OAR anatomies (prostate cancer cases with lymph node irradiation, and anal rectal cancer cases) are added to the training dataset one by one until the model prediction accuracies reach plateau. The DVHs predicted by the knowledge model for bladder, femoral heads and rectum are validated by cases from all three types of cases. Dosimetric parameters are extracted from the predicted DVHs and the corresponding actual plan values measure the prediction accuracy of this multi-disease type model. Further, its accuracy was also compared with the models trained by single disease type cases (including low risk prostate cancer, or type 1, high risk prostate cancer with lymph nodes, or type 2 and anal rectal cancer, or type 3).

Prediction accuracy reaches plateau when 6 high risk prostate cancer with lymph nodes irradiation cases and 8 anal rectal cancer cases were added to the training dataset. The determination coefficients R2 for the OARs are: Bladder: 0.90, rectum: 0.64 and femoral heads: 0.82. The prediction accuracies by the multi-disease type model and single-disease type models have no significant differences by F-test (p-value: bladder: 0.58, femoral head: 0.44, rectum: 0.97).

Conclusion:

Progressive knowledge modeling of OAR sparing for multiple cancer types in in the pelvic region is feasible and has comparable accuracy to single-disease type modeling.