Abstract

Testicular Germ Cell Tumors (TGCTs) are rare but the most common solid cancer in young men, and distinguishing seminomas from Non Seminomatous Germcell Tumors (NSGCTs) preoperatively is essential because therapies and prognoses diverge. Conventional tools serum markers, scrotal ultrasound, and cross sectional imaging often fail to reliably separate subtypes, leading to diagnostic orchidectomy. Radiomics extracts high-dimensional quantitative features from imaging, offering a “digital biopsy” that, when paired with machine learning algorithms, can differentiate TGCT subtypes more accurately than standard methods. Early ultrasound and MRI based radiomics studies show promising results, though reproducibility, standardization, and external validation remain hurdles. This review outlines the current diagnostic landscape, introduces the radiomics ML pipeline, and summarizes early evidence, while highlighting barriers and future directions such as multi institutional collaboration, multi omics integration, deep learning, explainable AI, and prospective trials. Radiomics and ML promise a non-invasive shift toward precision oncology for TGCTs.

Keywords: Testicular germ cell tumors, Seminoma, Non-seminomatous germ cell tumors, Radiomics, Machine learning, Digital biopsy, Precision oncology