Deep learning based clinical target volumes contouring for prostate cancer: Easy and efficient application.

ABSTRACT:

Background

Radiotherapy has been crucial in prostate cancer treatment. However, manual segmentation is labor intensive and highly variable among radiation oncologists. In this study, a deep learning based automated contouring model is constructed for clinical target volumes (CTVs) of intact and postoperative prostate cancer.

Methods

Computed tomography (CT) data sets of 197 prostate cancer patients were collected. Two auto-delineation models were built for radical radiotherapy and postoperative radiotherapy of prostate cancer respectively, and each model included CTVn for pelvic lymph nodes and CTVp for prostate tumors or prostate tumor beds.

Results

In the radical radiotherapy model, the volumetric dice (VD) coefficient of CTVn calculated by AI, was higher than that

SUBMITTER: Wen F 

PROVIDER: S-EPMC11466469 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature