Artificial intelligence in the early diagnosis of prostate cancer: from multimodal imaging to liquid biopsy

Multimodal artificial intelligence in prostate cancer: integrating multiparametric MRI with clinicopathological, molecular, and functional imaging data.

Artificial intelligence-based prediction of biochemical recurrence of prostate cancer using multiparametric mri: a critical narrative review

Prostate cancer (PCa) is the second most common malignancy diagnosed in men worldwide, with approximately 1.47 million new cases reported in 2022. Biochemical recurrence (BCR), defined as a rising prostate-specific antigen (PSA) after radical treatment, is the first clinical sign of disease relapse and a harbinger of metastasis and cancer-specific mortality. Accurate, non-invasive prediction of BCR is essential for guiding individualized treatment decisions and optimizing long-term outcomes. This narrative review critically evaluates the current evidence on artificial intelligence (AI)-based approaches—encompassing radiomics, machine learning (ML), and deep learning (DL)—applied to multiparametric magnetic resonance imaging (mpMRI) for the prediction of BCR in PCa following radical prostatectomy (RP) or radiation therapy (RT). The review further examines multimodal AI approaches integrating mpMRI with prostate-specific membrane antigen positron emission tomography (PSMA-PET), digital pathology, and genomic data. This manuscript is a narrative review; no systematic protocol was registered. Among the reviewed studies, mpMRI-based radiomics models achieved area under the receiver operating characteristic curve (AUC) values ranging from 0.72 to 0.97 for BCR prediction, though this wide range reflects substantial methodological and population heterogeneity. Deep learning models, particularly those combining mpMRI features with clinical parameters, demonstrated C-index values up to 0.83. Because the area under the receiver operating characteristic curve (a discrimination metric for binary classification) and the C-index (for time-to-event survival analysis) are distinct statistical measures, radiomics AUC and deep-learning C-index values are reported separately here and are not directly comparable or interchangeable. AI-powered mpMRI analysis holds substantial promise for non-invasive, accurate BCR prediction in PCa. Integration of radiomics and DL with clinical and multi-omics data within standardized, multi-center frameworks represents the most promising future direction. Regulatory-compliant, externally validated models with demonstrated calibration are required before routine clinical implementation.

Clinical-imaging fusion model for risk assessment of clinically significant prostate cancer

Background Prostate cancer is the second most commonly diagnosed malignancy among men worldwide. Its biological heterogeneity challenges conventional imaging-based risk assessment and complicates biopsy decision-making. Although advanced artificial intelligence may improve risk stratification, medical imaging applications are often limited by small sample sizes. Vision foundation models, with strong transferability in data-constrained settings, offer a promising approach to improving prostate cancer risk assessment, guiding personalized treatment, and reducing overdiagnosis. Methods A robust transfer learning framework based on prostate magnetic resonance imaging (MRI), termed robust transfer learning model (RTLM), was developed to enable the non-invasive risk assessment of clinically significant prostate cancer (csPCa). RTLM employs a feature-matching transfer strategy to adaptively capture task-relevant abstract knowledge from vision foundation models, thereby enhancing the feature representation capability and robustness of convolutional neural networks. Model performance was evaluated on csPCa MRI data, and a multi-task evaluation was conducted on PD-L1 expression prediction in patients with non-small cell lung cancer (NSCLC) to assess the generalizability of the proposed framework. Subsequently, features extracted by the RTLM were used to construct a deep learning signature (DLS), which was then integrated with key clinical variables, including age, serum total PSA level, PSA density, prostate volume, and PI-RADS score, to build the clinical-imaging fusion model (CIFM) for csPCa risk assessment. Diagnostic performance was evaluated using the area under the curve (AUC), decision curve analysis (DCA), integrated discrimination improvement (IDI), and net reclassification improvement (NRI). Results The CIFM achieved AUCs of 0.918, 0.890, 0.828, and 0.852 in the training cohort (n = 585), test cohort (n = 310), and two external validation cohorts (n = 510 and n = 94), respectively. Compared with the clinical model and the RTLM, CIFM showed significant improvements in both IDI and NRI (all p < 0.05). Decision curve analysis further demonstrated that CIFM provided greater net clinical benefit. Conclusion By effectively leveraging knowledge representations from vision foundation models, RTLM enhances the feature learning capability and robustness of CNNs in small-sample medical imaging tasks. The CIFM, which integrates clinical indicators, demonstrates good accuracy in csPCa risk assessment, suggesting potential generalizability.

Radiomics and Artificial Intelligence in Breast Cancer Imaging: Future Directions and Clinical Applicability

This comprehensive review delves into the fundamental concepts of radiomics and AI, summarises their current applications in BC imaging, and explores their evolving roles in clinical practice, highlighting recent advances and presenting case studies demonstrating the clinical impact.

Artificial intelligence in prostate cancer diagnosis and management: state-of-the-art approaches and emerging innovations.

Artificial intelligence in prostate biopsy: diagnostic applications, risk stratification, and precision oncology

Prostate biopsy remains the cornerstone for the diagnosis, risk stratification, and management of prostate cancer. However, biopsy interpretation is challenged by sampling limitations, tumor heterogeneity, and interobserver variability in Gleason grading. Recent advances in digital pathology and artificial intelligence (AI) have created new opportunities to improve diagnostic accuracy, reproducibility, and clinical decision-making. This narrative mini-review summarizes current evidence regarding AI applications in prostate biopsy evaluation. Relevant studies published between 2015 and 2026 were reviewed, focusing on AI-assisted cancer detection, automated Gleason grading, quantitative pathology, prognostic assessment, and multimodal approaches integrating histopathological, molecular, and clinical data. AI-based systems have demonstrated high accuracy in prostate cancer detection, Gleason pattern classification, and tumor burden assessment, with several studies showing strong concordance with expert genitourinary pathologists. These technologies have the potential to improve diagnostic consistency, enhance workflow efficiency, refine risk stratification, and support treatment planning. Emerging multimodal AI models integrating histopathological, genomic, imaging, and clinical information may further improve prognostic assessment and facilitate precision oncology approaches. However, challenges remain, including limited prospective validation, data heterogeneity, regulatory considerations, model interpretability, and integration into routine clinical workflows. AI is emerging as a valuable adjunct in prostate biopsy evaluation, with the potential to enhance diagnostic precision, grading reproducibility, and personalized patient management. Continued multicenter validation, development of explainable AI frameworks, and effective integration into multidisciplinary prostate cancer care pathways will be essential for successful clinical adoption and improved patient outcomes.