OBJECTIVE: To create an automated PET/CT segmentation method and radiomics model to forecast Mismatch repair (MMR) and TP53 gene expression in endometrial cancer patients, and to examine the effect of gene expression variability on image texture feat...
BACKGROUND: The Hospital Readmissions Reduction Program determines Medicare readmission penalties through risk-adjusted excess readmissions ratios. This study uses interpretable machine learning to identify associations with coronary artery bypass gr...
BACKGROUND: Early detection of lung cancer (LC) is crucial for curative treatment, but current screening methods face challenges due to high costs and poor adherence. Artificial intelligence tools, such as the LungFlag model, uses routine clinical da...
Computer methods and programs in biomedicine
Jun 4, 2025
OBJECTIVE: Evaluate the utility of a machine learning-based pathomics model in predicting overall survival (OS) post-surgery for gastric cancer patients.
PURPOSE: To develop and evaluate radiomics-based models using contrast-enhanced T1-weighted imaging (CE-T1WI) for the non-invasive differentiation of primary central nervous system lymphoma (PCNSL) and solitary brain metastasis (SBM), aiming to impro...
OBJECTIVE: This study evaluated the relationship between 18F-fluorodeoxyglucose PET/computed tomography (18F-FDG PET/CT) radiomic features and clinical parameters, including tumor localization, histopathological subtype, lymph node metastasis, mortal...
AIM: To develop and validate a combined model based on magnetic resonance imaging (MRI), and whole-slide imaging (WSI) to predict pathological complete response (pCR) after neoadjuvant chemotherapy (NAC) in breast cancer.
European journal of obstetrics, gynecology, and reproductive biology
Jun 4, 2025
OBJECTIVE: Hysteroscopy is commonly used for diagnosing benign endometrial conditions, but its diagnostic performance in malignancies post-treatment surveillance has not been evaluated. This study evaluated the correlation between hysteroscopic appea...
RATIONALE AND OBJECTIVES: To evaluate the impact of AI-generated apparent diffusion coefficient (ADC) maps on diagnostic performance of a 3D U-Net AI model for prostate cancer (PCa) detection and segmentation at biparametric MRI (bpMRI).
OBJECTIVE: Major lower extremity amputation for advanced vascular disease involves significant perioperative risks. Although outcome prediction tools could aid in clinical decision-making, they remain limited. To address this, we developed machine le...
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