OBJECTIVE: Artificial intelligence (AI) and its subsects, such as machine learning (ML) and virtual reality (VR) are subject to great interest from the urological community, and gaining space in the surgical treatment of benign prostatic hyperplasia ...
The rapid advancement of artificial intelligence, particularly large language models like ChatGPT-3.5, presents promising applications in healthcare. This study evaluates ChatGPT-3.5's validity in responding to post-operative patient inquiries follow...
Robotic-assisted radical prostatectomy (RARP) has become the gold standard treatment for localized prostate cancer. However, predicting post-operative outcomes remains challenging. This study aims to develop and validate predictive models for key out...
We read with interest the recent article by Yu et al., "Interpretable machine learning model predicts 1-year inguinal hernia risk after robot-assisted radical prostatectomy" (DOI: 10.1007/s11701-025-02723-5) , which represents an important step in ap...
Inguinal hernia represents a clinically significant yet underreported complication of robot-assisted radical prostatectomy (RARP) for localized prostate cancer, with a notably high incidence within the first postoperative year. Despite its adverse im...
BACKGROUND: To develop and validate a deep learning pipeline using prostate biopsy H&E slides to predict extraprostatic extension (EPE) in prostate cancer (PCa) patients.
Prostate cancer remains a significant public health concern, with a substantial proportion of patients experiencing biochemical recurrence (BCR) after radical prostatectomy (RP). Traditional risk models, such as CAPRA-S, have demonstrated moderate pr...
Cancer imaging : the official publication of the International Cancer Imaging Society
Aug 19, 2025
OBJECTIVE: This study aimed to construct a multimodal imaging deep learning (DL) model integrating mpMRI and F-PSMA-PET/CT for the prediction of extraprostatic extension (EPE) in prostate cancer, and to assess its effectiveness in enhancing the diagn...
OBJECTIVE: To develop and evaluate a intralesional and perilesional radiomics strategy based on different machine learning model to differentiate International Society of Urological Pathology (ISUP) grade > 2 group and ISUP ≤ 2 prostate cancers (PCa)...
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...
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