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.
N. Narimani, M. Atarod, Ehsan Zolfi et al.· African Journal of Urology· 0 citations
Autism spectrum disorder (ASD) is a complex neurodevelopmental condition characterized by persistent impairments in social communication, restricted interests, and repetitive behaviors. This narrative review synthesizes advances in machine learning applications to ASD genomic research through May 2026, spanning gene expression analysis, whole-exome sequencing (WES), non-coding variant interpretation, multi-omics integration, single-cell transcriptomics, epigenetic profiling, and gut microbiome analysis. A purposive, thematic literature synthesis approach was employed, allowing broad coverage of emerging methodological innovations and biological insights. We critically evaluate state-of-the-art deep learning architectures-including the Separate Translated Autism Research Neural Network and SHapley Additive exPlanations-based explainable artificial intelligence frameworks. Reported discrimination across the field varies widely, from receiver operating characteristic-area under the curve (ROC-AUC) values near 0.66 to implausibly perfect values of 1.00; the best-validated specialized genomic architecture achieves only modest discrimination (ROC-AUC≈0.73). We emphasize that interpretability and predictive performance are orthogonal properties: specialized architectures yield biologically interpretable feature attributions despite modest discriminative power; and several extreme AUC values in the literature are, in our assessment, more consistent with overfitting or data leakage than with genuine signal, although the primary reports did not always provide the information needed to definitively attribute them. Key themes include: (1) identification of differentially expressed genes through meta-analysis of transcriptomic data; (2) validation of predictive gene features from large-scale WES; (3) detection of non-coding regulatory mutations affecting synaptic transmission pathways; (4) discovery of gut microbiome signatures associated with ASD classification; and (5) discovery of data-driven subtypes enabling precision medicine stratification. Critical challenges include population bias toward European ancestry, socioeconomic ascertainment bias, modest predictive effect sizes, conflation of association with causation, and gaps between computational prediction and clinical utility. Future directions emphasize multi-modal data integration, diverse cohort expansion, engagement with neurodiversity perspectives, and regulatory science development.
Zahra Sadr, Bita Fallahpour, Alireza Alireza Dastgheib et al.· Medeniyet Medical Journal· 0 citations
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