Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning.

ABSTRACT:

Background

Distinguishing between benign and malignant testicular lesions on clinical magnetic resonance imaging (MRI) is crucial for guiding treatment planning. However, conventional MRI-based radiomics to identify testicular cancer requires expert machine learning knowledge. This study aims to investigate the potential of utilizing automatic machine learning (AutoML) based on MRI to diagnose testicular lesions without the need for expert algorithm optimization.

Methods

Retrospective preoperative MRI scans from 115 patients diagnosed with testicular disease through pathology were obtained. A total of 1781 radiomics features were extracted from each lesion on the T2-weighted images. Intraclass and interclass correlation coefficients were used to evaluate the intra-observer a

SUBMITTER: Wang L 

PROVIDER: S-EPMC11951623 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature