Prostate Cancer

Combining multi-parametric MRI radiomics features with tumor abnormal protein to construct a machine learning-based predictive model for prostate cancer.

This study aims to investigate the diagnostic value of integrating multi-parametric magnetic resonance imaging (mpMRI) radiomic features with tumor abnormal protein (TAP) and clinical characteristics for diagnosing prostate cancer. A cohort of 109 patients who underwent both mpMRI and TAP assessments prior to prostate biopsy were enrolled. Radiomic features were meticulously extracted from T2-weig...

Non-conventional diagnostic tools for lower urinary tract symptoms and bladder outlet obstruction in men: a perspective review.

Introduction Assessing male lower urinary tract symptoms (LUTS) due to Benign outlet obstruction (BOO) remains a challenge in urology due to the limitations of conventional diagnostic methods, which are often invasive, time-consuming, and inefficient. Objective Given these limitations, this review explores emerging non-conventional diagnostic approaches for evaluating benign male LUTS. Methods: A ...

Sex differences in serum proteomic profiles in psoriatic arthritis.

OBJECTIVES: Sex-related differences exist in the clinical presentation and treatment outcomes of patients with psoriatic arthritis (PsA). The biologic...

Prospective cohort study integrating plasma proteomics and machine learning for early risk prediction of prostate cancer.

BACKGROUND: Early detection of prostate cancer (PCa) remains a clinical challenge. Plasma proteomics provides a non-invasive tool for identifying indi...

PruEV-AI: a Simple Approach Combines Urinary Extracellular Vesicle Isolation with AI-Assisted Analysis for Prostate Cancer Diagnosis.

Urinary extracellular vesicles (uEVs) are a promising source of prostate-derived biomarkers for non-invasive prostate cancer (PCa) diagnosis. However,...

Deep learning-based applicator selection between Syed and T&O in high-dose-rate brachytherapy for locally advanced cervical cancer: a retrospective study.

High-dose-rate (HDR) brachytherapy is integral to the standard-of-care for locally advanced cervical cancer (LACC). Currently, selection of brachyther...

SPCF-YOLO: An Efficient Feature Optimization Model for Real-Time Lung Nodule Detection.

Accurate pulmonary nodule detection in CT imaging remains challenging due to fragmented feature integration in conventional deep learning models. This...

Using AI to triage patients without clinically significant prostate cancer using biparametric MRI and PSA.

OBJECTIVES: To train and evaluate the performance of a machine learning triaging tool that identifies MRI negative for clinically significant prostate...

Incorporating organ deformation in biological modeling and patient outcome study for permanent prostate brachytherapy.

Permanent prostate brachytherapy has inherent intraoperative organ deformation due to the inflatable trans-rectal ultrasound probe cover. Since the ma...

A novel network architecture for post-applicator placement CT auto-contouring in cervical cancer HDR brachytherapy.

BACKGROUND: High-dose-rate brachytherapy (HDR-BT) is an integral part of treatment for locally advanced cervical cancer, requiring accurate segmentati...

Combining Clinical, Genetic and Protein Markers Using Machine Learning Models Discriminates Psoriatic Arthritis Patients From Those With Psoriasis.

BACKGROUND: Psoriatic Arthritis (PsA), an immune mediated inflammatory arthritis, affects a quarter of patients with cutaneous psoriasis, usually afte...

Dual nanozyme sensor enables rapid at-home cancer surveillance.

Early detection of cancer is critical for improving patient survival rates, and current in vitro diagnostic methods show great potential, particularly...

Analysis of gray zone PSA and PSAD correlated with PIRADS v2.1 in the MRI-US fusion prostate biopsy era: a retrospective bi-centre study.

BACKGROUND: Multiple parameters including PSA, PSAD, and PIRADS v2.1 score, are being associated in an effort to increase the overall detection rate o...

Deep learning assisted identification of SCUBE2 and SLC16 A5 combination in RNA-sequencing data as a novel specific potential diagnostic biomarker in prostate cancer.

Despite the extensive use of biomarkers like PSA, AMACR, and PCA3, prostate cancer (PCa) is still a major clinical challenge, demanding the developmen...

GLIO-Select: Machine Learning-Based Feature Selection and Weighting of Tissue and Serum Proteomic and Metabolomic Data Uncovers Sex Differences in Glioblastoma.

Glioblastoma (GBM) is a fatal brain cancer known for its rapid and aggressive growth, with some studies indicating that females may have better surviv...

Effective reduction of unnecessary biopsies through a deep-learning-assisted aggressive prostate cancer detector.

Despite being one of the most prevalent cancers, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection an...

Development of a Low-Profile, Piezoelectric Robot for MR-Guided Abdominal Needle Interventions.

PURPOSE: Minimally invasive needle-based interventions are commonly used in cancer diagnosis and treatment, including procedures, such as biopsy, brac...

Open-source deep-learning models for segmentation of normal structures for prostatic and gynecological high-dose-rate brachytherapy: Comparison of architectures.

BACKGROUND: The use of deep learning-based auto-contouring algorithms in various treatment planning services is increasingly common. There is a notabl...

Clinically applicable semi-supervised learning framework for multiple organs at risk and tumor delineation in lung cancer brachytherapy.

PURPOSE: The generalization ability of deep learning-based automatic segmentation techniques for lung cancer in practical clinical applications remain...

Exploring new drug treatment targets for immune related bone diseases using a multi omics joint analysis strategy.

In the field of treatment and prevention of immune-related bone diseases, significant challenges persist, necessitating the urgent exploration of new ...