AIMC Topic: Prostate-Specific Antigen
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Transition-zone PSA-density calculated from MRI deep learning prostate zonal segmentation model for prediction of clinically significant prostate cancer.
Abdominal radiology (New York)
Jun 19, 2024
PURPOSE: To develop a deep learning (DL) zonal segmentation model of prostate MR from T2-weighted images and evaluate TZ-PSAD for prediction of the presence of csPCa (Gleason score of 7 or higher) compared to PSAD.
Effectiveness and Cost-effectiveness of Artificial Intelligence-assisted Pathology for Prostate Cancer Diagnosis in Sweden: A Microsimulation Study.
European urology oncology
May 23, 2024
BACKGROUND AND OBJECTIVE: Image-based artificial intelligence (AI) methods have shown high accuracy in prostate cancer (PCa) detection. Their impact on patient outcomes and cost effectiveness in comparison to human pathologists remains unknown. Our a...
Predictive value of magnetic resonance imaging diffusion parameters using artificial intelligence in low-and intermediate-risk prostate cancer patients treated with stereotactic ablative radiotherapy: A pilot study.
Radiography (London, England : 1995)
Apr 27, 2024
INTRODUCTION: To investigate the predictive value of the pre-treatment diffusion parameters of diffusion-weighted magnetic resonance imaging (DW-MRI) using artificial intelligence (AI) for prostate-specific antigen (PSA) response in patients with low...
Explainable and visualizable machine learning models to predict biochemical recurrence of prostate cancer.
Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
Apr 11, 2024
PURPOSE: Machine learning (ML) models presented an excellent performance in the prognosis prediction. However, the black box characteristic of ML models limited the clinical applications. Here, we aimed to establish explainable and visualizable ML mo...
Development and validation of an imageless machine-learning algorithm for the initial screening of prostate cancer.
The Prostate
Apr 4, 2024
PURPOSE: Prostate specific antigen (PSA) testing is a low-cost screening method for prostate cancer (PCa). However, its accuracy is limited. While progress is being made using medical imaging for PCa screening, PSA testing can still be improved as an...
Outcomes of robot-assisted radical prostatectomy in men after trans-urethral resection of the prostate: a matched-pair analysis.
Journal of robotic surgery
Apr 3, 2024
Prior history of transurethral resection of the prostate (TURP) can complicate Robot-assisted radical prostatectomy (RARP). Very few studies analyse the outcomes of RARP in men with a prior history of TURP. We analysed the oncological and functional ...
Development of risk-score model in patients with negative surgical margin after robot-assisted radical prostatectomy.
Scientific reports
Mar 31, 2024
A total of 739 patients underwent RARP as initial treatment for PCa from November 2011 to October 2018. Data on BCR status, clinical and pathological parameters were collected from the clinical records. After excluding cases with neoadjuvant and/or a...
Prognostic factors among patients with pathological Grade Group 5 prostate cancer based on robot-associated radical prostatectomy specimens from a large Japanese cohort (MSUG94).
World journal of urology
Mar 14, 2024
PURPOSE: There are no definitive prognostic factors for patients with pathological Grade Group 5 (pGG 5) prostate cancer (PCa) undergoing robot-associated radical prostatectomy (RARP). This study aimed to explore the prognostic factors among patients...
Deep learning model for the detection of prostate cancer and classification of clinically significant disease using multiparametric MRI in comparison to PI-RADs score.
Urologic oncology
Feb 22, 2024
BACKGROUND: The Prostate Imaging Reporting and Data System (PI-RADS) is an established reporting scheme for multiparametric magnetic resonance imaging (mpMRI) to distinguish clinically significant prostate cancer (csPCa). Deep learning (DL) holds gre...
Development and Validation of Interpretable Machine Learning Models for Clinically Significant Prostate Cancer Diagnosis in Patients With Lesions of PI-RADS v2.1 Score ≥3.
Journal of magnetic resonance imaging : JMRI
Feb 16, 2024
BACKGROUND: For patients with PI-RADS v2.1 ≥ 3, prostate biopsy is strongly recommended. Due to the unsatisfactory positive rate of biopsy, improvements in clinically significant prostate cancer (csPCa) risk assessments are required.
ROC Curve
Magnetic Resonance Imaging
Prostatic Neoplasms
Prostate-Specific Antigen
Machine Learning
Humans
Male
Prostate
Middle Aged
Retrospective Studies
Biopsy
Risk Assessment
Reproducibility of Results
Image Interpretation, Computer-Assisted
Aged
Diffusion Magnetic Resonance Imaging
Echo-Planar Imaging
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