Early and accurate diagnosis of prostate cancer is critical for optimizing patient prognosis. However, traditional transrectal ultrasound-guided systematic biopsy (TRUS-Bx) has a relatively high false-negative rate. This is attributed to limitations such as insufficient anatomical coverage and inadequate assessment of tumor heterogeneity. Multiparametric magnetic resonance imaging (mpMRI), when combined with the Prostate Imaging Reporting and Data System (PI-RADS), has substantially improved the diagnostic specificity of clinically significant prostate cancer (csPCa; Gleason grade ≥ 3 + 4). Nevertheless, its discriminatory ability for PI-RADS 3 lesions remains restricted. In recent years, multimodal image fusion technology has boosted the detection rate of csPCa by 10%-15% via precise lesion localization. Molecular imaging exhibits a sensitivity of up to 95% (range: 90-98%) in the whole-body staging of high-risk patients, particularly for nodal metastases. Artificial intelligence (AI), through deep-learning algorithms, optimizes lesion segmentation and image texture analysis, thereby significantly enhancing the detection rate of csPCa in targeted biopsies. Looking ahead, it is essential to integrate multimodal imaging and genomic data, construct individualized risk-stratification models, and facilitate the clinical translation of low-cost and standardized technologies. This article comprehensively examines the synergistic mechanisms of imaging and AI technologies in the diagnosis and biopsy guidance of prostate cancer, offering a theoretical foundation for precision medicine practice.
Wu et al. (2025) studied this question.