AIMC Topic: Prostatic Neoplasms

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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
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...

Treatment Response Evaluation in Prostate Cancer Using PSMA PET/CT.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
In recent years, there has been a headlong rush into the use of prostate-specific membrane antigen (PSMA)-targeted PET for the staging and restaging of men with prostate cancer (PC). To date, there have been regulatory approvals for PSMA PET for purp...

Comparative analysis of machine learning-derived nomogram and biomarkers in predicting side-specific extraprostatic extension: Preliminary findings.

Clinical imaging
AIM: This study aimed to assess and compare the performance of nomograms and machine learning (ML) techniques using preoperative biomarkers for predicting side-specific extraprostatic extension (EPE) in prostate cancer, which is linked to poor outcom...

Machine learning-based comparison of transperineal vs. transrectal biopsy for prostate cancer diagnosis: evaluating procedural effectiveness.

The Canadian journal of urology
BACKGROUND: Transrectal (TR) and transperineal (TP) biopsies are commonly used methods for diagnosing prostate cancer. However, their comparative effectiveness in conjunction with machine learning (ML) techniques remains underexplored. This study aim...

Applications and Outcomes of Telehealth and Integrated Care in Men's Health Urology.

Journal of medical Internet research
Men's health, particularly in the domain of urology, faces significant challenges in access to care, patient outcomes, and cost efficiency. Despite advances in medical treatment, conditions such as prostate cancer remain a leading cause of cancer-rel...

Artificial Intelligence-Based Digital Histologic Classifier for Prostate Cancer Risk Stratification: Independent Blinded Validation in Patients Treated With Radical Prostatectomy.

JCO clinical cancer informatics
PURPOSE: Artificial intelligence (AI) tools that identify pathologic features from digitized whole-slide images (WSIs) of prostate cancer (CaP) generate data to predict outcomes. The objective of this study was to evaluate the clinical validity of an...

Practical implementation of AI in a non-academic, non-commercial Pathology laboratory: Real world experience and lessons learned.

Histopathology
AIMS: As pathology departments transition towards digital workflows, the integration of artificial intelligence (AI) is anticipated to become increasingly common. This study aimed to describe the real-world implementation and impact of AI integration...

Towards more reliable prostate cancer detection: Incorporating clinical data and uncertainty in MRI deep learning.

Computers in biology and medicine
Prostate cancer (PCa) is one of the most common cancers among men, and artificial intelligence (AI) is emerging as a promising tool to enhance its diagnosis. This work proposes a classification approach for PCa cases using deep learning techniques. W...

Simulating workload reduction with an AI-based prostate cancer detection pathway using a prediction uncertainty metric.

European radiology
OBJECTIVES: This study compared two uncertainty quantification (UQ) metrics to rule out prostate MRI scans with a high-confidence artificial intelligence (AI) prediction and investigated the resulting potential radiologist's workload reduction in a c...

Physics-informed neural networks for denoising high b-value diffusion-weighted images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Diffusion-weighted imaging (DWI) is widely applied in tumor diagnosis by measuring the diffusion of water molecules. To increase the sensitivity to tumor identification, faithful high b-value DWI images are expected by setting a stronger strength of ...