Paratesticular Tumors: A Systematic Review and Proposed Risk-Based Management Algorithm

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Abstract

Objective To comprehensively review the literature on paratesticular tumors and to develop a new management algorithm. Methods A systematic review was conducted from 1980 to 2024 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A search was performed on PUBMED to identify studies investigating paratesticular tumors, limited to cases '16 years of age. Data on histologic type, laterality, age, treatment, stage at presentation, imaging findings, follow-up, and outcomes were extracted. Results Of 1073 articles reviewed, 85 met criteria for analysis. A total of 479 cases were reviewed: 397 malignant and 82 benign. For malignant cases, the most common presentation was painless scrotal mass, while benign cases presented with a painless scrotal mass or were identified incidentally. Radiographic characteristics were compared between malignancy and benign histology. The most common malignant and benign diagnosis was liposarcoma (40%) and adenomatoid (34%), respectively. Radical orchiectomy for malignant tumors and local excision for benign tumors were the most described treatments. Important variables identified in literature were used to develop a risk-based management algorithm. Conclusion This first systematic review of paratesticular tumors describes clinical and radiographic characteristics stratified by malignant or benign histology. The analysis of the literature was leveraged to develop a novel management algorithm to help with clinical decision-making. OSF Registry https://doi.org/10.17605/OSF.IO/4BDUY . After extensive searching, this registry was deemed the most appropriate given that all the articles included in the analysis were non-interventional retrospective studies or case series.

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Jhang, D., Amin, K., Farkouh, A., Eshaghian, E., Alsyouf, M., & Hu, B. (2026, January 1). Paratesticular Tumors: A Systematic Review and Proposed Risk-Based Management Algorithm. Urology. Elsevier Inc. https://doi.org/10.1016/j.urology.2025.08.055

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