AI Medical Compendium Journal:
Urologic oncology

Showing 1 to 10 of 35 articles

A machine learning-based analysis for the definition of an optimal renal biopsy for kidney cancer.

Urologic oncology
OBJECTIVE: Renal Tumor biopsy (RTB) can assist clinicians in determining the most suitable approach for treatment of renal cancer. However, RTB's limitations in accurately determining histology and grading have hindered its broader adoption and data ...

Navigating the gray zone: Machine learning can differentiate malignancy in PI-RADS 3 lesions.

Urologic oncology
INTRODUCTION: The objective of this study is to predict the probability of prostate cancer in PI-RADS 3 lesions using machine learning methods that incorporate clinical and mpMRI parameters.

Development of a microultrasound-based nomogram to predict extra-prostatic extension in patients with prostate cancer undergoing robot-assisted radical prostatectomy.

Urologic oncology
OBJECTIVES: To develop a microultrasound-based nomogram including clinicopathological parameters and microultrasound findings to predict the presence of extra-prostatic extension and guide the grade of nerve-sparing.

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
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...

AI-powered real-time annotations during urologic surgery: The future of training and quality metrics.

Urologic oncology
INTRODUCTION AND OBJECTIVE: Real-time artificial intelligence (AI) annotation of the surgical field has the potential to automatically extract information from surgical videos, helping to create a robust surgical atlas. This content can be used for s...

Exploring the opportunities and challenges of implementing artificial intelligence in healthcare: A systematic literature review.

Urologic oncology
Recent progress in the realm of artificial intelligence has shown effectiveness in various industries, particularly within the healthcare sector. However, there are limited insights on existing studies regarding ethical, social, privacy, and technolo...

Patient-reported outcome measures compared to clinician reported outcomes regarding incontinence and erectile dysfunction in localized prostate carcinoma after robot assisted radical prostatectomy: Impact on management.

Urologic oncology
PURPOSE/ BACKGROUND: Patient-reported outcome measures (PROMs) are widely used after robot assisted radical prostatectomy (RARP) in order to evaluate the impact/burden of the treatment. The most bothersome side effects of RARP are urine incontinence ...

Robot-assisted partial nephrectomy for complex renal tumors: Analysis of a large multi-institutional database.

Urologic oncology
INTRODUCTION: Highly complex renal masses pose a challenge to urologic surgeons' ability to perform robotic partial nephrectomy (RPN). Given the increased utilization of the robotic approach for small renal masses, we sought to characterize the outco...