BACKGROUND: The surgeries in drug-resistant ulcerative colitis are determined by complex factors. This study evaluated the predictive performance of radiomics analysis on the basis of whether patients with ulcerative colitis in hospital were in the s...
INTRODUCTION: The aim of this study was to identify the influencing factors for all-cause mortality in elderly patients with intertrochanteric and femoral neck fractures and to construct predictive models.
PURPOSE: This study is aimed to develop and validate a machine learning model, which combined radiomics and clinical characteristics to predicting the definitive chemoradiotherapy (dCRT) treatment response in esophageal squamous cell carcinoma (ESCC)...
BACKGROUND: To develop and validate deep learning (DL) and traditional clinical-metabolic (CM) models based on 18 F-FDG PET/CT images for noninvasively predicting high-grade patterns (HGPs) of invasive lung adenocarcinoma (LUAD).
OBJECTIVE: This study develops and validates a machine learning model using peritoneal cytology to predict distant metastasis in uterine carcinosarcoma, aiding clinical decision-making.
The Journal of international medical research
40279206
ObjectiveOur objective was to investigate a novel cancer-associated fibroblast-related gene signature for predicting clinical outcomes in patients with diffuse large B cell lymphoma.MethodsThe cancer-associated fibroblast-related module genes were id...
The aim of this study was to establish a nomogram based on clinical, radiomics, and deep transfer learning (DTL) features to predict meningioma grade. Three hundred forty meningiomas from one hospital composed the training set, and 102 meningiomas fr...
Radiation pneumonia (RP) is the most common side effect of chest radiotherapy, and can affect patients' quality of life. This study aimed to establish a combined model of radiomics, dosiomics, deep learning (DL) based on simulated location CT and dos...