Dose prediction with deep learning for prostate cancer radiation therapy: Model adaptation to different treatment planning practices.

ABSTRACT:

Purpose

This work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy (VMAT) for prostate cancer and to adapt the model, via transfer learning with minimal input data, to three different internal treatment planning styles and one external institution planning style.

Methods

We built the source model with planning data from 108 patients previously treated with VMAT for prostate cancer. For the transfer learning, we selected patient cases planned with three different styles, 14-29 cases per style, in the same institution and 20 cases treated in a different institution to adapt the source model to four target models in total. We compared the dose distributions predicted by the source model and the t

SUBMITTER: Kandalan RN 

PROVIDER: S-EPMC7908143 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature