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Prostatic Neoplasms

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A Comparison of Prostate Cancer Screening Information Quality on Standard and Advanced Versions of ChatGPT, Google Gemini, and Microsoft Copilot: A Cross-Sectional Study.

American journal of health promotion : AJHP
PurposeArtificially Intelligent (AI) chatbots have the potential to produce information to support shared prostate cancer (PrCA) decision-making. Therefore, our purpose was to evaluate and compare the accuracy, completeness, readability, and credibil...

ProCUSNet: Prostate Cancer Detection on B-mode Transrectal Ultrasound Using Artificial Intelligence for Targeting During Prostate Biopsies.

European urology oncology
BACKGROUND AND OBJECTIVE: To assess whether conventional brightness-mode (B-mode) transrectal ultrasound images of the prostate reveal clinically significant cancers with the help of artificial intelligence methods.

An Artificial Intelligence Model Using Diffusion Basis Spectrum Imaging Metrics Accurately Predicts Clinically Significant Prostate Cancer.

The Journal of urology
PURPOSE: Conventional prostate magnetic resonance imaging has limited accuracy for clinically significant prostate cancer (csPCa). We performed diffusion basis spectrum imaging (DBSI) before biopsy and applied artificial intelligence models to these ...

PSMA PET/CT based multimodal deep learning model for accurate prediction of pelvic lymph-node metastases in prostate cancer patients identified as candidates for extended pelvic lymph node dissection by preoperative nomograms.

European journal of nuclear medicine and molecular imaging
PURPOSE: To develop and validate a prostate-specific membrane antigen (PSMA) PET/CT based multimodal deep learning model for predicting pathological lymph node invasion (LNI) in prostate cancer (PCa) patients identified as candidates for extended pel...

An Artificial Intelligence-Digital Pathology Algorithm Predicts Survival After Radical Prostatectomy From the Prostate, Lung, Colorectal, and Ovarian Cancer Trial.

The Journal of urology
PURPOSE: Clinical variables alone have limited ability to determine which patients will have recurrence after radical prostatectomy (RP). We evaluated the ability of locked multimodal artificial intelligence (MMAI) algorithms trained on prostate biop...

Prediction of Prostate Cancer From Routine Laboratory Markers With Automated Machine Learning.

Journal of clinical laboratory analysis
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...

Deep Learning for Predicting Difficulty in Radical Prostatectomy: A Novel Evaluation Scheme.

Urology
OBJECTIVE: To explore new metrics for assessing radical prostatectomy difficulty through a two-stage deep learning method from preoperative magnetic resonance imaging.

BMA-Net: A 3D bidirectional multi-scale feature aggregation network for prostate region segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Accurate segmentation of the prostate region in magnetic resonance imaging (MRI) is crucial for prostate-related diagnoses. Recent studies have incorporated Transformers into prostate region segmentation to better capture lo...

Impact of Scanner Manufacturer, Endorectal Coil Use, and Clinical Variables on Deep Learning-assisted Prostate Cancer Classification Using Multiparametric MRI.

Radiology. Artificial intelligence
Purpose To assess the effect of scanner manufacturer and scanning protocol on the performance of deep learning models to classify aggressiveness of prostate cancer (PCa) at biparametric MRI (bpMRI). Materials and Methods In this retrospective study, ...

Bridging the gap: Evaluating ChatGPT-generated, personalized, patient-centered prostate biopsy reports.

American journal of clinical pathology
OBJECTIVE: The highly specialized language used in prostate biopsy pathology reports coupled with low rates of health literacy leave some patients unable to comprehend their medical information. Patients' use of online search engines can lead to misi...