Urology

Clinical use of machine learning-based pathomics signature for diagnosis and survival prediction of bladder cancer.

Traditional histopathology performed by pathologists by the naked eye is insufficient for accurate and efficient diagnosis of bladder cancer (BCa). We collected 643 H&E-stained BCa images from Shanghai General Hospital and The Cancer Genome Atlas (TCGA). We constructed and cross-verified automatic diagnosis and prognosis models by performing a machine learning algorithm based on pathomics data. Ou...

Impact of MRI/US fusion-guided prostate biopsy on biopsy-naïve patients: A single urologist's experience.

OBJECTIVES: To report our experience with imaging-guided targeted prostate biopsy (IGTpBx) for patients undergoing initial prostate biopsy in a clinical setting.

Development and Validation of a Deep Learning Model to Quantify Interstitial Fibrosis and Tubular Atrophy From Kidney Ultrasonography Images.

IMPORTANCE: Interstitial fibrosis and tubular atrophy (IFTA) is a strong indicator of decline in kidney function and is measured using histopathologic...

Upper urinary tract surgery and radical prostatectomy with Senhance robotic system: Single center experience-First 100 cases.

BACKGROUND: The Senhance robotic surgery system is a novel robotic platform used in several European and World centres. We present our experience in u...

Automated assessment of glomerulosclerosis and tubular atrophy using deep learning.

In kidney transplantations, pathologists evaluate the architecture of both glomeruli, interstitium and tubules to assess the nephron status. An accura...

Superselective Ischemia in Robotic Partial Nephrectomy Does Not Provide Better Long-term Renal Function than Renal Artery Clamping in a Randomized Controlled Trial (EMERALD): Should We Take the Risk?

BACKGROUND: Superselective clamping of tumor-targeted arteries aims to eliminate ischemia of the remnant kidney while keeping tumor bed bloodless duri...

Independent real-world application of a clinical-grade automated prostate cancer detection system.

Artificial intelligence (AI)-based systems applied to histopathology whole-slide images have the potential to improve patient care through mitigation ...

A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients.

BACKGROUND: Acute Kidney Injury (AKI), a frequent complication of pateints in the Intensive Care Unit (ICU), is associated with a high mortality rate....

Urine leak after robotic radical prostatectomy: not all urine leaks come from the anastomosis.

Radical prostatectomy is the gold standard in patients that are surgical candidates with localized prostate cancer. While most postoperative urine lea...

Evaluating renal lesions using deep-learning based extension of dual-energy FoV in dual-source CT-A retrospective pilot study.

PURPOSE: Dual-source (DS) CT, dual-energy (DE) field of view (FoV) is limited to the size of the smaller detector array. The purpose was to establish ...

The impact of age on pathological insignificant prostate cancer rates in contemporary robot-assisted prostatectomy patients despite active surveillance eligibility.

BACKGROUND: The aim of this study was to assess insignificant prostate cancer (iPCa) rates after robot-assisted radical prostatectomy (RARP) in contem...

Cytoreductive radical prostatectomy after chemohormonal therapy in patients with primary metastatic prostate cancer.

OBJECTIVE: Cytoreductive radical prostatectomy (cRP) has been proposed as local treatment option in metastatic hormone-sensitive prostate cancer (mHSP...

Machine Learning Models for Predicting Stone-Free Status after Shockwave Lithotripsy: A Systematic Review and Meta-Analysis.

We performed a systematic review and meta-analysis to investigate the use of machine learning techniques for predicting stone-free rates following Sho...

Deep Learning Improves Speed and Accuracy of Prostate Gland Segmentations on Magnetic Resonance Imaging for Targeted Biopsy.

PURPOSE: Targeted biopsy improves prostate cancer diagnosis. Accurate prostate segmentation on magnetic resonance imaging (MRI) is critical for accura...

Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models.

INTRODUCTION: We aimed to assess the power of radiomic features based on computed tomography to predict risk of chronic kidney disease in patients und...

Reducing the Risk of Postoperative Complications After Robot-assisted Radical Prostatectomy in Prostate Cancer Patients: Results of an Audit and Feedback Intervention Following the Implementation of Prospective Data Collection.

BACKGROUND: Prospective data collection for perioperative outcomes might increase awareness of surgical results obtained for patients with prostate ca...

Oncologic Equipoise Between Robotic and Open Radical Cystectomy.

Our objective was to establish the incidence of positive surgical margins, recurrence patterns, and recurrence-free (RFS) and overall survival (OS) i...

Development and placement accuracy evaluation of an MR conditional robot for prostate intervention.

Robot-assisted prostate intervention under magnetic resonance imaging (MRI) guidance is a promising method to improve the clinical performance compare...

A hybrid deep learning approach for gland segmentation in prostate histopathological images.

BACKGROUND: In digital pathology, the morphology and architecture of prostate glands have been routinely adopted by pathologists to evaluate the prese...

Analysis of Tumor Microenvironment Characteristics in Bladder Cancer: Implications for Immune Checkpoint Inhibitor Therapy.

The tumor microenvironment (TME) plays a crucial role in cancer progression and recent evidence has clarified its clinical significance in predicting ...