Prostate Cancer

Predicting Treatment Outcomes in Patients With Psoriatic Arthritis or Axial Spondyloarthritis: An Artificial Intelligence-Driven Approach.

OBJECTIVE: To develop machine learning (ML) models to predict the probability at baseline of achieving low disease activity (LDA) and high health-related quality of life (HRQOL) in patients with psoriatic arthritis (PsA) or axial spondyloarthritis (axSpA) treated with secukinumab (SEC). METHODS: AQUILA is an ongoing multicenter, prospective, noninterventional study assessing the effectiveness and ...

Slice-prompted HR-CTV interactive segmentation for cervical cancer brachytherapy: A multi-center study.

BACKGROUND: In computed tomography (CT)-guided cervical cancer brachytherapy, the manual contouring for the high-risk clinical target volume (HR-CTV) is a time-consuming and expertise-dependent process. Furthermore, automated approaches struggle with ambiguous boundaries of HR-CTV. PURPOSE: We aimed to develop a clinically efficient interactive segmentation framework integrating deep learning with...

Machine learning model for fast prediction and uncertainty quantification of needle deflection during prostate biopsy.

BACKGROUND: Accurate needle placement is essential for prostate biopsy. Recently, transperineal prostate biopsies are receiving renewed interest due t...

From prostate-specific antigen to precision: The future of prostate cancer diagnosis with artificial intelligence, biomarkers, and imaging.

BACKGROUND: Prostate cancer (PCa) diagnosis has historically relied on prostate-specific antigen (PSA) testing. Although PSA screening significantly r...

Towards robust deep learning-based autosegmentation in MRI-planned gynecological brachytherapy: Importance of scalable development and comprehensive evaluation.

PURPOSE: To present comprehensive development and evaluation methodologies for a generalizable deep learning (DL)-driven autocontouring model of stand...

3D transformer-based dose prediction in HDR brachytherapy for cervical cancer.

An old disease, a new linguistic challenge for large language models: patient education on psoriasis and psoriatic arthritis in an underrepresented medical language.

OBJECTIVE: Large Language Models (LLMs) are increasingly applied to patient education, yet their performance in languages that are relatively underrep...

The role of focal therapy for localized prostate cancer: From diagnosis to ablation.

INTRODUCTION: Focal therapy (FT) has emerged as an intermediate therapeutic strategy between active surveillance (AS) and radical treatments for the m...

Artificial intelligence in diagnostic, prognostic, and predictive genomic biomarkers for prostate cancer: Ready for prime time?

INTRODUCTION: Until recently, the widespread use of genetic markers in prostate cancer (PCa) has been limited by the complexities and cost of genomic ...

Artificial intelligence-driven prostate cancer diagnosis: Enhancing accuracy and personalizing patient care.

Prostate cancer remains a major global burden; diagnostic pathways rely on prostate-specific antigen (PSA), multiparametric magnetic resonance imaging...

Evaluation of AI chatbot responses to brachytherapy frequently asked questions.

PURPOSE: Patients are increasingly using artificial intelligence (AI) chatbots for health information. Evaluating their reliability for specialized to...

Application value of prostate-specific antigen density combined with multiparametric MRI in early diagnosis of prostate cancer.

BACKGROUND: Diagnosis of prostate cancer in the PSA gray zone (4-10 ng/mL) and PI-RADS 3 cases remains challenging. Although multiparametric MRI (mpMR...

Applicator reconstruction in cervical cancer brachytherapy: A systematic review of current methods, challenges, and AI-driven future directions.

PURPOSE: Accurate applicator reconstruction is a critical step in 3D image-guided brachytherapy (3D-IGBT) for cervical cancer, directly influencing tu...

Automated quality assurance of imaging dose and protocol adherence in computed tomography radiotherapy planning using TotalSegmentator-based segmentation.

PURPOSE: Computed tomography (CT) scans are vital for radiotherapy planning, providing essential data for dose calculations. This study retrospectivel...

Feasibility of transperineal prostate biopsy guided by fusion of [18F]F-PSMA-1007 PET/MRI with real-time transrectal ultrasound. Prostate cancer classification using imaging biomarkers and radiomics in PIRADS 3 lesions.

OBJECTIVE: To assess the diagnostic feasibility of transperineal biopsy guided by fusion of PET/MRI with [18F]F-PSMA-1007 and real-time transrectal ul...

Lymph Node Invasion Prediction in Prostate Cancer: A Comparative Machine-Learning Study.

BACKGROUND: Accurate preoperative prediction of lymph node invasion (LNI) in prostate cancer is critical for guiding lymph node dissection. Current no...

Explainable machine learning-based prediction of psoriatic arthritis flares using heterogenous real-world data for personalised patient care.

Psoriatic arthritis (PsA) is a chronic inflammatory disease characterised by unpredictable flare-ups that are difficult to forecast, particularly in p...

Molecular targeted therapies, omics, and AI based theranostics approaches for the treatment of prostate cancer.

Cancer is a disease that begins with genetic and epigenetic alterations occurring in specific cells, some of which can spread and migrate to other tis...

Integrative Machine Learning Model Leveraging DCE-MRI and PSA Values for Advanced Risk Stratification in Prostate Cancer.

PURPOSE: Accurate grading of prostate cancer is critical for treatment strategies and risk stratification. This study aims to develop a machine learni...

Development and Deployment of a Machine Learning Model to Triage the Use of Prostate MRI (ProMT-ML) in Patients With Suspected Prostate Cancer.

BACKGROUND: Access to prostate MRI remains limited due to resource constraints and the need for expert interpretation. PURPOSE: To develop machine lea...