ArticleDiagnostics (Basel, Switzerland)2021
Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- PSA density correlates to pathology T stage and ISUP grade: insights from a cohort of 3568 radical prostatectomy cases.World journal of urology · 2025Article
- Advancing Prostate Cancer Diagnosis: A Deep Learning Approach for Enhanced Detection in MRI Images.Diagnostics (Basel, Switzerland) · 2024Article
- Associations of systemic immune-inflammation index with high risk for prostate cancer in middle-aged and older US males: A population-based study.Immunity, inflammation and disease · 2024Article
- Neutrophil-to-lymphocyte and platelet-to-lymphocyte ratios alone or combined with prostate-specific antigen for the diagnosis of prostate cancer and clinically significant prostate cancer.Asian journal of urology · 2023Article
- Article
- Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review.Journal of clinical medicine · 2022Review
- Combining Magnetic Resonance Diffusion-Weighted Imaging with Prostate-Specific Antigen to Differentiate Between Malignant and Benign Prostate Lesions.Medical science monitor : international medical journal of experimental and clinical research · 2022Article
- Prostate Health Index and Multiparametric MRI: Partners in Crime Fighting Overdiagnosis and Overtreatment in Prostate Cancer.Cancers · 2021Article
- A Promising Approach: Artificial Intelligence Applied to Small Intestinal Bacterial Overgrowth (SIBO) Diagnosis Using Cluster Analysis.Diagnostics (Basel, Switzerland) · 2021Article
- Reply to Jue et al. Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on "Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model.Diagnostics (Basel, Switzerland) · 2021Article
- Value of MRI to Improve Deep Learning Model That Identifies High-Grade Prostate Cancer. Comment on Gentile et al. Optimized Identification of High-Grade Prostate Cancer by Combining Different PSA Molecular Forms and PSA Density in a Deep Learning Model.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
After skin cancer, prostate cancer (PC) is the most common cancer among men. The gold standard for PC diagnosis is based on the PSA (prostate-specific antigen) test. Based on this preliminary screening, the physician decides whether to proceed with further tests, typically prostate biopsy, to confirm cancer and evaluate its aggressiveness. Nevertheless, the specificity of the PSA test is suboptimal and, as a result, about 75% of men who undergo a prostate biopsy do not have cancer even if they have elevated PSA levels. Overdiagnosis leads to unnecessary overtreatment of prostate cancer with undesirable side effects, such as incontinence, erectile dysfunction, infections, and pain. Here, we used artificial neuronal networks to develop models that can diagnose PC efficiently. The model receives as an input a panel of 4 clinical variables (total PSA, free PSA, p2PSA, and PSA density) plus age. The output of the model is an estimate of the Gleason score of the patient. After training on a dataset of 190 samples and optimization of the variables, the model achieved values of sensitivity as high as 86% and 89% specificity. The efficiency of the method can be improved even further by training the model on larger datasets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.