Urology

Incorporation of Urinary Neutrophil Gelatinase-Associated Lipocalin and Computed Tomography Quantification to Predict Acute Kidney Injury and In-Hospital Death in COVID-19 Patients.

BACKGROUND: The prevalence of acute kidney injury (AKI) in COVID-19 patients is high, with poor prognosis. Early identification of COVID-19 patients who are at risk for AKI and may develop critical illness and death is of great importance.

Technical Refinements in Superextended Robot-assisted Radical Prostatectomy for Locally Advanced Prostate Cancer Patients at Multiparametric Magnetic Resonance Imaging.

BACKGROUND: The feasibility and efficacy of robot-assisted radical prostatectomy (RARP) in locally advanced prostate cancer (PCa) patients with iT3 lesion at magnetic resonance imaging (MRI) are currently not explored.

Highly Stable Passive Wireless Sensor for Protease Activity Based on Fatty Acid-Coupled Gelatin Composite Films.

Proteases are often used as biomarkers of many pathologies as well as of microbial contamination and infection. Therefore, extensive efforts are devot...

Application of hierarchical clustering to multi-parametric MR in prostate: Differentiation of tumor and normal tissue with high accuracy.

PURPOSE: Hierarchical clustering (HC), an unsupervised machine learning (ML) technique, was applied to multi-parametric MR (mp-MR) for prostate cancer...

As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI.

BACKGROUND: We focus on the importance of interpreting the quality of the labeling used as the input of predictive models to understand the reliabilit...

Automated detection of cribriform growth patterns in prostate histology images.

Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patte...

What factors affect the operative time of robot-assisted laparoscopic radical prostatectomy?

BACKGROUND: Robot-assisted radical prostatectomy (RARP) has gained prominence since the da Vinci surgical system was introduced in 2000. RARP has now ...

A Deep Learning Framework Identifies Pathogenic Noncoding Somatic Mutations from Personal Prostate Cancer Genomes.

Our understanding of noncoding mutations in cancer genomes has been derived primarily from mutational recurrence analysis by aggregating clinical samp...

Prediction modeling-part 2: using machine learning strategies to improve transplantation outcomes.

Kidney transplant recipients and transplant physicians face important clinical questions where machine learning methods may help improve the decision-...

Automatic IMRT planning via static field fluence prediction (AIP-SFFP): a deep learning algorithm for real-time prostate treatment planning.

The purpose of this work was to develop a deep learning (DL) based algorithm, Automatic intensity-modulated radiotherapy (IMRT) Planning via Static Fi...

Subsphincteric Anastomosis During Laparoscopic Robot-Assisted Radical Prostatectomy and Its Positive Impact on Continence Recovery.

To assess the interest of a new sphincter preserving anastomosis technique for continence recovery after robot-assisted laparoscopic radical prostate...

High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and has clinical potential as a prognostic biomarker...

Effects of canagliflozin on serum potassium in the CANagliflozin cardioVascular Assessment Study (CANVAS) Program.

BACKGROUND: The sodium-glucose cotransporter 2 inhibitor canagliflozin has been shown to reduce the risk of cardiovascular and renal events in patient...

T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology.

OBJECTIVES: To explore the associations between T1 and T2 magnetic resonance fingerprinting (MRF) measurements and corresponding tissue compartment ra...

Cone-beam computed tomography-based radiomics in prostate cancer: a mono-institutional study.

PURPOSE: The purpose of the reported study was to investigate the value of cone-beam computed tomography (CBCT)-based radiomics for risk stratificatio...

Time-range based sequential mining for survival prediction in prostate cancer.

BACKGROUND AND OBJECTIVE: Metastatic prostate cancer has a higher mortality rate than localized cancers. There is a need to investigate the survival o...

Impact of rescanning and normalization on convolutional neural network performance in multi-center, whole-slide classification of prostate cancer.

Algorithms can improve the objectivity and efficiency of histopathologic slide analysis. In this paper, we investigated the impact of scanning systems...

Accurate 3D-dose-based generation of MLC segments for robotic radiotherapy.

Radiotherapy treatment planning requires accurate modeling of the delivered patient dose, including radiation scatter effects, multi-leaf collimator (...

Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.

Radiation oncology for prostate cancer is important as it can decrease the morbidity and mortality associated with this disease. Planning for this mod...

Deep-Learning Generation of Synthetic Intermediate Projections Improves Lu SPECT Images Reconstructed with Sparsely Acquired Projections.

The aims of this study were to decrease the Lu-SPECT acquisition time by reducing the number of projections and to circumvent image degradation by add...