Ultrasound-based deep learning radiomics nomogram for risk stratification of testicular masses: a two-center study.

ABSTRACT:

Objective

To develop an ultrasound-driven clinical deep learning radiomics (CDLR) model for stratifying the risk of testicular masses, aiming to guide individualized treatment and minimize unnecessary procedures.

Methods

We retrospectively analyzed 275 patients with confirmed testicular lesions (January 2018 to April 2023) from two hospitals, split into training (158 cases), validation (68 cases), and external test cohorts (49 cases). Radiomics and deep learning (DL) features were extracted from preoperative ultrasound images. Following feature selection, we utilized logistic regression (LR) to establish a deep learning radiomics (DLR) model and subsequently derived its signature. Clinical data underwent univariate and multivariate LR analyses, forming the "clinic signature.

SUBMITTER: Fang F 

PROVIDER: S-EPMC10798931 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature