Automatic segmentation of bladder cancer on MRI using a convolutional neural network and reproducibility of radiomics features: a two-center study.

ABSTRACT: This study aimed to develop a versatile automatic segmentation model of bladder cancer (BC) on MRI using a convolutional neural network and investigate the robustness of radiomics features automatically extracted from apparent diffusion coefficient (ADC) maps. This two-center retrospective study used multi-vendor MR units and included 170 patients with BC, of whom 140 were assigned to training datasets for the modified U-net model with five-fold cross-validation and 30 to test datasets for assessment of segmentation performance and reproducibility of automatically extracted radiomics features. For model input data, diffusion-weighted images with b = 0 and 1000 s/mm², ADC maps, and multi-sequence images (b0-b1000-ADC maps) were used. Segmentation accuracy was compared between our

SUBMITTER: Moribata Y 

PROVIDER: S-EPMC9837183 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature