Urology

Evaluation of the prostate cancer and its metastases in the [ 68 Ga]Ga-PSMA PET/CT images: deep learning method vs. conventional PET/CT processing.

PURPOSE: This study demonstrates the feasibility and benefits of using a deep learning-based approach for attenuation correction in [ 68 Ga]Ga-PSMA PET scans.

Prostate cancer treatment recommendation study based on machine learning and SHAP interpreter.

This study utilized data from 140,294 prostate cancer cases from the Surveillance, Epidemiology, and End Results (SEER) database. Here, 10 different machine learning algorithms were applied to develop treatment options for predicting patients with prostate cancer, differentiating between surgical and non-surgical treatments. The performances of the algorithms were measured using the area under the...

A commentary on 'Single-cell and bulk RNA-sequence identified fibroblasts signature and CD8 + T cell - fibroblast subtype predicting prognosis and immune therapeutic response of bladder cancer, based on machine-learning'.

TriFusion enables accurate prediction of miRNA-disease association by a tri-channel fusion neural network.

The identification of miRNA-disease associations is crucial for early disease prevention and treatment. However, it is still a computational challenge...

Deep-learning-based method for the segmentation of ureter and renal pelvis on non-enhanced CT scans.

This study aimed to develop a deep-learning (DL) based method for three-dimensional (3D) segmentation of the upper urinary tract (UUT), including uret...

The path to the G protein-coupled receptor structural landscape: Major milestones and future directions.

G protein-coupled receptors (GPCRs) play a crucial role in cell function by transducing signals from the extracellular environment to the inside of th...

Identification of cancer stem cell-related genes through single cells and machine learning for predicting prostate cancer prognosis and immunotherapy.

BACKGROUND: Cancer stem cells (CSCs) are a subset of cells within tumors that possess the unique ability to self-renew and give rise to diverse tumor ...

Multitask Learning on Graph Convolutional Residual Neural Networks for Screening of Multitarget Anticancer Compounds.

Recently, various modern experimental screening pipelines and assays have been developed to find promising anticancer drug candidates. However, it is ...

Identification and evaluation of deep foundation pit construction risks based on Grey-DEMATEL-Fuzzy comprehensive evaluation method.

In recent years, foundation pit construction has been rapidly developing in the direction of deep and large-scale, leading to the frequent occurrence ...

Explainable machine-learning-based prediction of QCT/FEA-calculated femoral strength under stance loading configuration using radiomics features.

Finite element analysis can provide precise femoral strength assessment. However, its modeling procedures were complex and time-consuming. This study ...

Renal tumor segmentation, visualization, and segmentation confidence using ensembles of neural networks in patients undergoing surgical resection.

OBJECTIVES: To develop an automatic segmentation model for solid renal tumors on contrast-enhanced CTs and to visualize segmentation with associated c...

Interpretable machine learning models based on shear-wave elastography radiomics for predicting cardiovascular disease in diabetic kidney disease patients.

BACKGROUND: The risk of cardiovascular complications is significantly elevated in patients with diabetic kidney disease (DKD). Recognizing the link be...

Multiparametric Ultrasound Imaging of Prostate Cancer Using Deep Neural Networks.

OBJECTIVE: A deep neural network (DNN) was trained to generate a multiparametric ultrasound (mpUS) volume from four input ultrasound-based modalities ...

LC-SRM Combined With Machine Learning Enables Fast Identification and Quantification of Bacterial Pathogens in Urinary Tract Infections.

Urinary tract infections (UTIs) are a worldwide health problem. Fast and accurate detection of bacterial infection is essential to provide appropriate...

Letter to the editor for the article "A machine learning approach using stone volume to predict stone-free status at ureteroscopy".

Multiparametric MRI-Based Deep Learning Radiomics Model for Assessing 5-Year Recurrence Risk in Non-Muscle Invasive Bladder Cancer.

BACKGROUND: Accurately assessing 5-year recurrence rates is crucial for managing non-muscle-invasive bladder carcinoma (NMIBC). However, the European ...

Artificial Intelligence-Based Classification of CT Images Using a Hybrid SpinalZFNet.

The kidney is an abdominal organ in the human body that supports filtering excess water and waste from the blood. Kidney diseases generally occur due ...

Robotic MR-guided high dose rate brachytherapy needle implantation in the prostate (ROBiNSon)-a proof-of-concept study.

A robotic needle implant device for MR-guided high-dose-rate (HDR) prostate brachytherapy was developed. This study aimed to assess the feasibility an...

is a novel marker for bladder cancer prognosis: evidence based on experimental studies, machine learning and single-cell sequencing.

BACKGROUND: Bladder cancer, a highly fatal disease, poses a significant threat to patients. Positioned at 19q13.2-13.3, LIG1, one of the four DNA liga...

Automated Bio-AFM Generation of Large Mechanome Data Set and Their Analysis by Machine Learning to Classify Cancerous Cell Lines.

Mechanobiological measurements have the potential to discriminate healthy cells from pathological cells. However, a technology frequently used to meas...