Predicting outcomes in oncogenic HPV infection: are we there yet?

Although most human papillomavirus (HPV) infections are transient, a small fraction persist and eventually progress to invasive cancer. Predicting the outcome of an HPV infection remains a major unresolved challenge. Despite decades of intensive research, no single viral, host, or molecular biomarker has demonstrated sufficient accuracy to reliably identify HPV infections and associated preneoplastic lesions at increased risk of persistence or progression. We argue that this limitation reflects the dynamic and multifactorial nature of HPV-associated carcinogenesis rather than a lack of candidate biomarkers. Recent advances in high-throughput molecular analysis, artificial intelligence, and integrative risk modeling provide new opportunities to capture this complexity. Such approaches may ultimately improve clinical decision-making and reduce reliance on prolonged surveillance as a surrogate for accurate prediction.