Artificial intelligence in the early diagnosis of prostate cancer: from multimodal imaging to liquid biopsy
TL;DR
This article comprehensively reviews the application of AI in the early intelligent diagnosis of PCa and looks forward to the development prospects of constructing multimodal fusion models based on federated learning and explainable AI (XAI), aiming to promote the transition of PCa diagnosis and treatment from algorithm development to real clinical decision support.
Abstract
Prostate cancer (PCa) is the most prevalent malignant tumor in the urogenital system among men worldwide. Due to its subtle early symptoms and strong tumor heterogeneity, traditional diagnostic methods relying on a single prostate-specific antigen (PSA) initial screening and subjective imaging evaluations often lead to high false positives, overt biopsies, and missed small lesions. The rapid development of artificial intelligence (AI) provides innovative solutions to overcome these clinical bottlenecks. This article comprehensively reviews the application of AI in the early intelligent diagnosis of PCa. In the fields of ultrasound, magnetic resonance imaging (MRI), and positron emission tomography/computed tomography (PET/CT) imaging, AI significantly enhances the accuracy of target lesion identification. It achieves this by deep decoding high-dimensional quantitative features and effectively reducing subjective bias. In non-invasive liquid biopsy, AI-driven multi-omics networks have successfully addressed challenging screening blind spots, such as the PSA gray zone. In light of current challenges such as limited model generalization capability and the “black box effect” of algorithms, this article looks forward to the development prospects of constructing multimodal fusion models based on federated learning and explainable AI (XAI), aiming to promote the transition of PCa diagnosis and treatment from algorithm development to real clinical decision support.