Prostate Cancer

Identification of biomarkers for non-invasive diagnosis and risk stratification in prostate cancer using NMR-based metabolomics and machine learning.

The non-invasive diagnosis and risk stratification of prostate cancer (PCa) remain clinically challenging due to the limited specificity of prostate-specific antigen (PSA). In this retrospective study, we applied a comparative machine learning (ML) framework to rank and select biomarkers from serum 1H nuclear magnetic resonance (NMR)-based metabolomics, subsequently developing three sequential met...

Cost-effectiveness analysis of resective epilepsy surgery in drug-resistant patients: an artificial intelligence data modeling.

BACKGROUND: Resective epilepsy surgery has been proven to reduce the number of seizures and improve quality of life in patients with drug-resistant epilepsy (DRE) but implies high direct costs. Cost-effectiveness analyses have shown that surgery is cost-effective. We aimed to evaluate whether we can determine the cost-effectiveness of surgery for DRE using artificial intelligence-based extraction ...

Multicentre Evaluation of an AI-Assisted Urine Test for Clinically Significant Prostate Cancer in Men Undergoing Initial Biopsy.

The Extracellular Vesicles Gene-based Prostate Score (EGPS), powered by DeepSeek, is an artificial intelligence (AI) diagnostic tool that enhances the...

Testosterone-Sparing Treatment Strategies for Biochemically Recurrent Prostate Cancer After Radical Prostatectomy.

PURPOSE OF REVIEW: Biochemical recurrence (BCR) after radical prostatectomy occurs in up to one-third of patients and increases the risk of metastasis...

Concurrent AI-human interaction in prostate cancer MRI interpretation: More hype than help?

OBJECTIVE: We evaluated a commercial artificial intelligence (AI) system as a concurrent decision-support tool for clinically significant prostate can...

Development of Machine Learning Models for Predicting Prostate Cancer in Biopsy Candidates Using Prostate-Specific Antigen, Magnetic Resonance Imaging, and Hematologic Parameters.

INTRODUCTION: Prostate-specific antigen (PSA) alone is insufficient for the diagnosis of prostate cancer (PCa), particularly within the gray zone rang...

Artificial Intelligence and Multiomics Beyond PSA Screening in African and Middle Eastern Prostate Cancer Patients.

Prostate cancer (PCa) remains a major global health burden, with incidence rising as populations age. The molecular, histological, and patient-specifi...

Clinically significant prostate cancer detection with deep learning in a multi-center magnetic resonance imaging study.

Accurate early detection of clinically significant prostate cancer is crucial for improving patient outcomes. However, traditional diagnostic methods ...

AI-discovered cellular morphometric biomarkers in needle biopsy of prostate cancer predict neoadjuvant androgen deprivation therapy response and enable therapeutic targeting of mTOR in androgen deprivation therapy-resistant tumors.

It is imperative to identify patients with prostate cancer (PCa) who will not benefit from androgen receptor signaling inhibitors and to improve their...

Inflammatory protein profiling in immune cells identifies molecular signatures for enhanced diagnostic precision in psoriatic arthritis.

Psoriatic arthritis (PsA) lacks reliable biomarkers to support early diagnosis and disease stratification. This study aimed to identify protein signat...

AI-based modeling of treatment decisions in benign prostatic hyperplasia: a transformer-based comparative study.

Benign Prostatic Hyperplasia (BPH) is a common condition among aging men that often causes significant urinary symptoms, impacting their quality of li...

Unsupervised clustering analysis unravels the role of systemic inflammatory indices in the prognosis of patients with locally advanced cervical cancer treated with chemoradiation.

BACKGROUND AND PURPOSE: This study aimed to predict the treatment outcomes and survival of patients with locally advanced cervical cancer (LACC) recei...

Language-Related Functional Connectivity in Post-Stroke Aphasia: Preliminary Findings from a Graph-Theoretical and Interpretable Machine Learning Study.

PURPOSE: To have an insight into language-related functional connectivity in post-stroke aphasia (PSA) from graph theory measurements when performing ...

PSA-1DCNN: Multimodal Biomarker and Text Integration for Lung Cancer Diagnosis.

Integrating multimodal data, such as unstructured clinical narratives and quantitative blood biomarkers, remains a major challenge in modern healthcar...

The influence of age and the presence of prostate cancer on prostate volume, PSA and PSA density.

OBJECTIVE: To assess prostate volume (PV) changes with age in symptomatic and asymptomatic men with and without clinically significant prostate cancer...

Accelerating Screening of Phase Separation Agents for Carbon Dioxide Capture: A Machine-Learning Framework with Interpretable Quantum Chemical Insights.

Against the backdrop of addressing climate change and reducing carbon emissions, carbon dioxide capture technology has gained increasing significance ...

Analysing DCE-MRI scans using hybrid techniques for early detection of prostate cancer based on fusion features of handcrafted and deep learning features.

Prostate cancer is among the most diagnosed malignancies in men worldwide and a leading cause of cancer-related mortality. Early and accurate diagnosi...

ERBB2 as a Prognostic Biomarker in Prostate Cancer: Integration of Single-Cell Transcriptomics, Deep Learning, and Immunohistochemical Validation.

Biochemical recurrence (BCR) is a critical factor affecting the prognosis of prostate cancer (PCa) patients, while T cell exhaustion and metastatic pr...

Artificial intelligence-derived transition zone PSA density as a triage tool to reduce unnecessary prostate systematic biopsies in MRI-negative men.

OBJECTIVES: The study aimed to assess the predictive performance of transition zone PSA density (TZ-PSAD) compared to conventional PSA density (PSAD) ...

EXPRESS: Convolutional Autoencoder for Automated Pre-Processing of Tumor Cell and Tissue Raman Spectra.

Raman spectroscopy (RS) is a label-free, non-destructive optical modality that provides a detailed profile of the molecular composition of a sample. T...