Towards automatical tumor segmentation in radiomics: a comparative analysis of various methods and radiologists for both region extraction and downstream diagnosis.

ABSTRACT:

Objective

By discussing the difference, stability and classification ability of tumor contour extracted by artificial intelligence and doctors, can a more stable method of tumor contour extraction be obtained?

Methods

We propose a novel framework for the automatic segmentation of lung tumor contours and the differential diagnosis of downstream tasks. This framework integrates four key modules: tumor segmentation, extraction of radiomic features, feature selection, and the development of diagnostic models for clinical applications. Using this framework, we conducted a study involving a cohort of 1,429 patients suspected of lung cancer. Four automatic segmentation methods (RNN, UNET, WFCM, and SNAKE) were evaluated against manual segmentation performed by three radiologists wi

SUBMITTER: Yu Y 

PROVIDER: S-EPMC11863488 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature