A comprehensive scoping review reveals that quantitative radiomic models consistently outperform clinical-only tools in predicting gastrointestinal cancer outcomes, despite widespread ...
A multicenter study of 51,843 emergency patients reveals that extracting structured chest CT findings with a large language model reliably ...
A registry-derived proxy for hyperthermic intraperitoneal chemotherapy demonstrated modest incremental prognostic performance in surgically treated appendiceal epithelial malignancies without confirming ...
An integrated bioinformatics analysis revealed that all ten EXOSC family genes are upregulated in lung squamous cell carcinoma, highlighting their ...
Researchers developed an interpretable machine learning model and web-based tool to forecast postoperative in-hospital mortality in patients with acute aortic ...
An automated artificial intelligence framework evaluated left ventricular global longitudinal strain in under ten seconds and provided incremental prognostic discrimination ...
A reanalysis of non-small cell lung cancer data indicates that CT radiomics and frozen deep learning models provide no stable ...
A retrospective multi-sequence MRI study of 305 soft tissue sarcoma patients found that a 3-millimeter peritumoral margin provided optimal prognostic ...
A multicentre derivation and validation study paired with a prospective randomised trial shows an explainable artificial intelligence ensemble predicts multiple ...
A multi-cohort transcriptomic study identified shared genetic biomarkers and immune mechanisms connecting atherosclerosis and lung adenocarcinoma, providing new models for ...
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