Ultrasound-based deep learning radiomics nomogram for risk stratification of testicular masses: a two-center study
Fuxiang Fang,
Yan Sun,
Hualin Huang
et al.
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… Show more
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