Lung cancer remains a significant cause of mortality, with non-small cell lung cancer (NSCLC) representing most cases. Currently, clinical data based models fall short in predicting survival while more advanced deep learning based image models requir...
This study aimed to develop a fully automated survival prediction (FASP) system that analyzes whole-liver regions from preoperative contrast-enhanced CT scans for predicting recurrence-free survival (RFS) after curative resection in Hepatocellular ca...
PURPOSE: Tooth-level prognostic systems can be used for treatment planning and help to identify teeth at risk of being lost over time. However, several sets of criteria were proposed to estimate tooth prognosis in the context of periodontitis patient...
OBJECTIVE: To develop and validate a 10-year predictive model for cardiovascular and metabolic disease (CVMD) risk using comprehensive health examination data from nearly 37 701 individuals.
This study aimed to develop a dataset comprising computed tomography (CT) images and clinical data for melioidosis pneumonia and to utilize machine learning for assisting in prognosis prediction of the disease. We retrospectively analyzed multicenter...
The longitudinal course of epilepsy remains largely unpredictable. This study aimed to predict final outcome and classify dynamic longitudinal trajectories using artificial intelligence. A total of 2586 patients who first visited our epilepsy special...
BACKGROUND: Non-small cell lung cancer (NSCLC) is one of the most prevalent malignancies and currently shows a poor clinical prognosis. Type II pneumocyte, as one of the main sources of cancer cells in NSCLC, is important to explore the molecular fun...
BACKGROUND: Colon cancer is a leading cause of cancer-related deaths worldwide, with survival influenced by risk factors, treatment type, and patient characteristics. Traditional statistical models, such as Kaplan-Meier curves, have been widely used ...
BACKGROUND: Breast cancer prognosis remains challenging due to tumor heterogeneity and the limited predictive power of conventional clinical models. Integrating imaging features with molecular data may improve individualized risk stratification and c...
BACKGROUND: The global burden of ischaemic stroke (IS) is high, which is potentially relevant to stress hyperglycemia ratio (SHR) and glycaemic variability (GV). This study aims to evaluate the combined effect of the SHR and GV with predict short-ter...
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