AIMC Topic: Prostate-Specific Antigen
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Time-dependent personalized prognostic analysis by machine learning in biochemical recurrence after radical prostatectomy: a retrospective cohort study.
BMC cancer
Nov 25, 2024
BACKGROUND: For biochemical recurrence following radical prostatectomy for prostate cancer, treatments such as radiation therapy and androgen deprivation therapy are administered. To diagnose postoperative recurrence as early as possible and to inter...
Urine Analysed by FTIR, Chemometrics and Machine Learning Methods in Determination Spectroscopy Marker of Prostate Cancer in Urine.
Journal of biophotonics
Nov 21, 2024
Prostate-specific antigen (PSA) is the most commonly used marker of prostate cancer. However, nearly 25% of men with elevated PSA levels do not have cancer and nearly 20% of patients with prostate cancer have normal serum PSA levels. Therefore, in th...
Clinically Significant Prostate Cancer Prediction Using Multimodal Deep Learning with Prostate-Specific Antigen Restriction.
Current oncology (Toronto, Ont.)
Nov 15, 2024
Prostate cancer (PCa) is a clinically heterogeneous disease. Predicting clinically significant PCa with low-intermediate prostate-specific antigen (PSA), which often includes aggressive cancers, is imperative. This study evaluated the predictive accu...
A Novel Machine Learning-based Predictive Model of Clinically Significant Prostate Cancer and Online Risk Calculator.
Urology
Nov 12, 2024
OBJECTIVE: To create a machine-learning predictive model combining prostate imaging-reporting and data system (PI-RADS) score, PSA density, and clinical variables to predict clinically significant prostate cancer (csPCa).
A Multimodal Deep Learning Nomogram for the Identification of Clinically Significant Prostate Cancer in Patients with Gray-Zone PSA Levels: Comparison with Clinical and Radiomics Models.
Academic radiology
Nov 3, 2024
RATIONALE AND OBJECTIVES: To establish a multimodal deep learning nomogram for predicting clinically significant prostate cancer in patients with gray-zone PSA levels.
A Machine Learning Approach for Predicting Biochemical Outcome After PSMA-PET-Guided Salvage Radiotherapy in Recurrent Prostate Cancer After Radical Prostatectomy: Retrospective Study.
JMIR cancer
Sep 20, 2024
BACKGROUND: Salvage radiation therapy (sRT) is often the sole curative option in patients with biochemical recurrence after radical prostatectomy. After sRT, we developed and validated a nomogram to predict freedom from biochemical failure.
Explainable artificial intelligence-driven prostate cancer screening using exosomal multi-marker based dual-gate FET biosensor.
Biosensors & bioelectronics
Sep 10, 2024
Prostate Imaging Reporting and Data System (PI-RADS) score, a reporting system of prostate MRI cases, has become a standard prostate cancer (PCa) screening method due to exceptional diagnosis performance. However, PI-RADS 3 lesions are an unmet medic...
Prostate cancer treatment recommendation study based on machine learning and SHAP interpreter.
Cancer science
Sep 2, 2024
This study utilized data from 140,294 prostate cancer cases from the Surveillance, Epidemiology, and End Results (SEER) database. Here, 10 different machine learning algorithms were applied to develop treatment options for predicting patients with pr...
A Comparison of Systematic, Targeted, and Combined Biopsy Using Machine Learning for Prediction of Prostate Cancer Risk: A Multi-Center Study.
Medical principles and practice : international journal of the Kuwait University, Health Science Centre
Jul 24, 2024
OBJECTIVES: The aims of the study were to construct a new prognostic prediction model for detecting prostate cancer (PCa) patients using machine-learning (ML) techniques and to compare those models across systematic and target biopsy detection techni...
Artificial intelligence for detection of prostate cancer in biopsies during active surveillance.
BJU international
Jul 4, 2024
OBJECTIVES: To evaluate a cancer detecting artificial intelligence (AI) algorithm on serial biopsies in patients with prostate cancer on active surveillance (AS).
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