ReviewTranslational andrology and urology2026
Predicting inguinal lymph node metastasis in penile squamous cell carcinoma, from imaging, molecular biomarkers to multimodal AI: a narrative review.
Review in Translational andrology and urology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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Abstract
Background and Objective: Penile squamous cell carcinoma (PSCC) is a relatively uncommon malignancy that significantly impairs patients' quality of life. Inguinal lymph node metastasis (ILNM) is a key prognostic determinant of survival. Accurate preoperative ILNM prediction remains a major clinical challenge, emphasizing the need for better risk stratification. This review evaluates the conventional predictors and explores the potential of artificial intelligence (AI) models to enhance predictive accuracy for ILNM. Methods: In our narrative review, the literature search was conducted in PubMed/MEDLINE, Web of Science, and Google Scholar from January 2005 to July 2025. We used the search terms: penile cancer, penile squamous cell carcinoma, inguinal lymph node metastasis, predictors, artificial intelligence, and multimodal prediction. Relevant titles and abstracts were screened; eligible full texts were reviewed. Key Content and Findings: Conventional clinicopathological predictors and existing predictive models exhibit limited accuracy in predicting ILNM in PSCC. Current imaging techniques, such as ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) provide complementary information, but each modality has limitations. Molecular and genomic biomarkers offer biological insights, but validation remains inconsistent. Recent AI approaches that integrate diverse data types have demonstrated superior predictive performance compared to unimodal models. Multimodal AI-based techniques have the potential to improve personalized risk stratification and inform management strategies. However, clinical adoption of AI-based frameworks in penile cancer is limited by major challenges, including data scarcity, heterogeneity, and lack of standardization. Addressing these issues through prospective multicenter cohorts with external validation will facilitate AI integration in clinical practice. Conclusions: ILNM is a critical prognostic factor and current predictors are suboptimal. Integrating clinicopathological, molecular, and imaging features through AI-based multimodal frameworks may enhance ILNM prediction and guide surgical decision-making. However, AI applications in penile cancer are in the early stages and require validation through large, multicenter trials.
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