CT-based deep learning segmentation of ovarian cancer and the stability of the extracted radiomics features

ABSTRACT:

Background

Radiomics analysis could provide complementary tissue characterization in ovarian cancer (OC). However, OC segmentation required in radiomics analysis is time-consuming and labour-intensive. In this study, we aim to evaluate the performance of deep learning-based segmentation of OC on contrast-enhanced CT images and the stability of radiomics features extracted from the automated segmentation.

Methods

Staging abdominopelvic CT images of 367 patients with OC were retrospectively recruited. The training and cross-validation sets came from center A (n=283), and testing set (n=84) came from centers B and C. The tumours were manually delineated by a board-certified radiologist. Four model architectures provided by no-new-Net (nnU-Net) method were tested in this task

SUBMITTER: Wang Y 

PROVIDER: S-EPMC10423396 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature