Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Nov 3, 2025
This study aimed to explore the prognostic value of the artificial intelligence-assisted lesion tracking applied to prostate-specific membrane antigen (PSMA) PET in patients with metastatic castration-resistant prostate cancer (mCRPC) treated with PS...
Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Nov 3, 2025
Prostate-specific membrane antigen (PSMA) PET has the potential to monitor the response to taxane-based chemotherapy in patients with prostate cancer and shows promise for predicting outcomes and improving response evaluation. This retrospective stud...
OBJECTIVE: Percutaneous nephrolithotomy is the gold standard for treating large kidney stones. However, traditional scoring systems and logistic regression-based models have limited predictive power due to their reliance on linear assumptions. This s...
OBJECTIVE: This study investigated the relationship between anatomical and metabolic characteristics of large arterial perivascular adipose tissue (PVAT) and hypertensive clinical outcomes using positron emission tomography-computed tomography (PET/C...
OBJECTIVES: This study aimed to investigate the impact of primary tumor resection (PTR) on survival outcomes for patients with metastatic non-small cell neuroendocrine tumors (mNSCLC-NETs), develop a predictive model to identify which patients may be...
BMC medical informatics and decision making
Oct 31, 2025
BACKGROUND: Labor- and cost-intensive manual chart review of Electronic Health Records (EHRs) remains a major bottleneck in retrospective studies, particularly when rare-disease cohorts require high specificity. Automated Natural Language Processing ...
PURPOSE: Objectives were to develop a machine learning (ML) model based on electronic health record (EHR) data to predict the risk of vomiting within a 96-hour window after admission to the pediatric oncology and hematopoietic cell transplant (HCT) s...
PURPOSE: Predicting postoperative persistent hydrocephalus risk in pediatric medulloblastoma remains challenging using conventional clinical features. We investigated whether deep learning (DL) of pathomic features could improve postoperative hydroce...
BMC medical informatics and decision making
Oct 30, 2025
OBJECTIVES: To investigate the feasibility of a deep learning model, using a transfer learning approach, for recognizing high-altitude pulmonary edema (HAPE) on chest X-ray images and exploring its capability for assessing severity.
PURPOSE: To apply quantitative imaging analysis for noninvasive classification of the most frequent subtypes of Non-Hodgkin Lymphoma (NHL) as a basis for a clinical imaging genomic model to support therapeutic monitoring and clinical decision making.
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.