ReviewFrontiers in oncology2023
Research progress on deep learning in magnetic resonance imaging-based diagnosis and treatment of prostate cancer: a review on the current status and perspectives.
Review in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- Explainable artificial intelligence reveals key surgical parameters in robot-assisted and open radical prostatectomy.Scientific reports · 2026Article
- Machine Learning Classification of Prostate Cancer Genomic Sequences Using K-Mer and Sequence-Derived Features.Computational molecular bioscience · 2026Article
- Bladder cancer segmentation using u-net-based deep-learning.Scientific reports · 2026Article
- Prioritising Data Quality Governance for AI in Prostate Cancer: A Methodological Proof-of-Concept Study Using Neural Networks for Risk Stratification.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluating the impact of reader experience on PI-RADS 3 of version 2.1 scoring concordance in multiparametric prostate MRI: a single-center analysis.Abdominal radiology (New York) · 2026Article
- Automated Prostate Cancer Detection on T2-Weighted MRI Using a Dual-Stream Attention Network: A Study on Private Saudi Clinical Data and Public Benchmark Datasets.Journal of clinical medicine · 2026Article
- From imaging to omics: deep learning is bridging MRI and liquid biopsy in bone tumor diagnosis.Journal of bone oncology · 2026Review
- Prostate cancer and benign prostatic hyperplasia lesions segmentation using diffusion kurtosis imaging, T2*, and R2* mapping with U-Net++ algorithm.Radiological physics and technology · 2026Article
- Artificial intelligence in urological malignancy diagnosis and prognosis: current status and future prospects.The Canadian journal of urology · 2026Review
- Current state of the art of new prostate MRI technologies and potential future developments.BJR open · 2026Review
- Advances in prostate cancer treatment with moderate and ultra-hypofractionated radiotherapy.World journal of clinical oncology · 2025Review
- Automated MRI system for clinically significant prostate cancer detection development validation and real-world implementation.Nature communications · 2025Article
- The value of habitat analysis based onBMC medical imaging · 2025Article
- Emerging Role of Multiparametric MRI in the Staging of Bladder Cancer: Insights From the BladderPath Trial.Cureus · 2025Review
- External validation of AI for detecting clinically significant prostate cancer using biparametric MRI.Abdominal radiology (New York) · 2025Article
- Utilization of artificial intelligence in prostate cancer detection: a comprehensive review of innovations in screening and diagnosis.Frontiers in immunology · 2025Review
- Integrating shear wave elastography and multiparametric MRI for accurate prostate cancer diagnosis.American journal of cancer research · 2025Article
- Deep learning and radiomics-driven algorithm for automated identification of May-Thurner syndrome in Iliac CTV imaging.Frontiers in medicine · 2025Article
- Systematic Review of AI-Assisted MRI in Prostate Cancer Diagnosis: Enhancing Accuracy Through Second Opinion Tools.Diagnostics (Basel, Switzerland) · 2024Review
- Omics Studies of Specialized Cells and Stem Cells under Microgravity Conditions.International journal of molecular sciences · 2024Review
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multiparametric magnetic resonance imaging (mpMRI) has emerged as a first-line screening and diagnostic tool for prostate cancer, aiding in treatment selection and noninvasive radiotherapy guidance. However, the manual interpretation of MRI data is challenging and time-consuming, which may impact sensitivity and specificity. With recent technological advances, artificial intelligence (AI) in the form of computer-aided diagnosis (CAD) based on MRI data has been applied to prostate cancer diagnosis and treatment. Among AI techniques, deep learning involving convolutional neural networks contributes to detection, segmentation, scoring, grading, and prognostic evaluation of prostate cancer. CAD systems have automatic operation, rapid processing, and accuracy, incorporating multiple sequences of multiparametric MRI data of the prostate gland into the deep learning model. Thus, they have become a research direction of great interest, especially in smart healthcare. This review highlights the current progress of deep learning technology in MRI-based diagnosis and treatment of prostate cancer. The key elements of deep learning-based MRI image processing in CAD systems and radiotherapy of prostate cancer are briefly described, making it understandable not only for radiologists but also for general physicians without specialized imaging interpretation training. Deep learning technology enables lesion identification, detection, and segmentation, grading and scoring of prostate cancer, and prediction of postoperative recurrence and prognostic outcomes. The diagnostic accuracy of deep learning can be improved by optimizing models and algorithms, expanding medical database resources, and combining multi-omics data and comprehensive analysis of various morphological data. Deep learning has the potential to become the key diagnostic method in prostate cancer diagnosis and treatment in the future.
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