Perineural invasion in solid tumors: biological foundations and the emerging integration of machine learning and artificial intelligence

Perineural invasion (PNI) represents a distinct route of cancer spread in many solid tumors. Its presence correlates with aggressive tumor behavior, local recurrence, neuropathic symptoms, and reduced survival across selected tumor types, including pancreatic, prostate, head and neck, colorectal, and gynecologic malignancies, among others. Despite its prognostic value, PNI remains inconsistently detected and reported, and incompletely integrated into the College of American Pathologists (CAP) cancer protocols and clinical decision-making. Over the last decade, advances in tumor-nerve biology have reframed PNI as an active, bidirectional phenomenon driven by molecular crosstalk between cancer cells, Schwann cells, neurons, and the surrounding tumor microenvironment (TME). Parallel advances in digital pathology, machine learning (ML), and artificial intelligence (AI) have opened new opportunities to standardize PNI detection and quantify its extent. This review provides a synopsis of current knowledge on the biological mechanisms and clinical relevance of PNI in solid tumors, with the emerging integration and application of ML- and AI-assisted approaches in histopathology and molecular profiling to advance detection and potential therapeutic targeting of PNI.