Development and clinical utility analysis of a prostate zonal segmentation model on T2-weighted imaging: a multicenter study.

ABSTRACT:

Objectives

To automatically segment prostate central gland (CG) and peripheral zone (PZ) on T2-weighted imaging using deep learning and assess the model's clinical utility by comparing it with a radiologist annotation and analyzing relevant influencing factors, especially the prostate zonal volume.

Methods

A 3D U-Net-based model was trained with 223 patients from one institution and tested using one internal testing group (n = 93) and two external testing datasets, including one public dataset (ETD_pub, n = 141) and one private dataset from two centers (ETD_pri, n = 59). The Dice similarity coefficients (DSCs), 95th Hausdorff distance (95HD), and average boundary distance (ABD) were calculated to evaluate the model's performance and further compared with

SUBMITTER: Xu L 

PROVIDER: S-EPMC10020392 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature