AIMC Topic: Prostate-Specific Antigen
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Combining multi-parametric MRI radiomics features with tumor abnormal protein to construct a machine learning-based predictive model for prostate cancer.
Scientific reports
Jul 2, 2025
This study aims to investigate the diagnostic value of integrating multi-parametric magnetic resonance imaging (mpMRI) radiomic features with tumor abnormal protein (TAP) and clinical characteristics for diagnosing prostate cancer. A cohort of 109 pa...
Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector.
Scientific reports
Apr 30, 2025
Despite being one of the most prevalent cancers, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Currently, several screening and diagnostic tests are required to be carried out in ord...
Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with Ga-PSMA-617 PET/CT.
European journal of radiology
Mar 19, 2025
OBJECTIVE: Prostate cancer (PCa) is highly heterogeneous, making early detection of adverse pathological features crucial for improving patient outcomes. This study aims to predict PCa aggressiveness and identify radiomic and protein biomarkers assoc...
Male
Middle Aged
Risk Assessment
Positron Emission Tomography Computed Tomography
Aged, 80 and over
Sensitivity and Specificity
Biomarkers, Tumor
Gallium Radioisotopes
Gallium Isotopes
Heterocyclic Compounds, 1-Ring
Prostatic Neoplasms
Prostate-Specific Antigen
Radiopharmaceuticals
Dipeptides
Humans
Machine Learning
Retrospective Studies
Aged
Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study.
Journal of the Egyptian National Cancer Institute
Mar 17, 2025
BACKGROUND: Machine learning (ML) is a significant area of artificial intelligence, which can improve the accuracy of predictive or diagnostic models for differentiating between prostate biopsy outcomes. This study aims to develop a novel decision-su...
Development of a deep learning system for predicting biochemical recurrence in prostate cancer.
BMC cancer
Feb 10, 2025
BACKGROUND: Biochemical recurrence (BCR) occurs in 20%-40% of men with prostate cancer (PCa) who undergo radical prostatectomy. Predicting which patients will experience BCR in advance helps in formulating more targeted prostatectomy procedures. Howe...
Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques.
Scientific reports
Feb 4, 2025
In prostate cancer (PCa), risk calculators have been proposed, relying on clinical parameters and magnetic resonance imaging (MRI) enable early prediction of clinically significant cancer (CsPCa). The prostate imaging-reporting and data system (PI-RA...
Prediction of Prostate Cancer From Routine Laboratory Markers With Automated Machine Learning.
Journal of clinical laboratory analysis
Jan 19, 2025
BACKGROUND: In this study, we attempted to select the optimum cases for a prostate biopsy based on routine laboratory test results in addition to prostate-specific antigen (PSA) blood test using H2O automated machine learning (AutoML) software, which...
Using XBGoost, an interpretable machine learning model, for diagnosing prostate cancer in patients with PSA < 20 ng/ml based on the PSAMR indicator.
Scientific reports
Jan 9, 2025
To create a diagnostic tool before biopsy for patients with prostate-specific antigen (PSA) levels < 20 ng/ml to minimize prostate biopsy-related discomfort and risks. Data from 655 patients who underwent transperineal prostate biopsy at the First Af...
An Automatic Deep-Radiomics Framework for Prostate Cancer Diagnosis and Stratification in Patients with Serum Prostate-Specific Antigen of 4.0-10.0 ng/mL: A Multicenter Retrospective Study.
Academic radiology
Jan 2, 2025
RATIONALE AND OBJECTIVES: To develop an automatic deep-radiomics framework that diagnoses and stratifies prostate cancer in patients with prostate-specific antigen (PSA) levels between 4 and 10 ng/mL.
Multi-branch CNNFormer: a novel framework for predicting prostate cancer response to hormonal therapy.
Biomedical engineering online
Dec 23, 2024
PURPOSE: This study aims to accurately predict the effects of hormonal therapy on prostate cancer (PC) lesions by integrating multi-modality magnetic resonance imaging (MRI) and the clinical marker prostate-specific antigen (PSA). It addresses the li...
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