Abstract
AbstractGliomas are highly heterogenous diseases with poor prognosis. Precise survival prediction could benefit further clinical decision‐making, clinical trial incursion, and health economics. Recent research has emphasized the prognostic value of magnetic resonance imaging, pathological specimens, and circulating biomarkers. However, the integrative potential and efficacy of these modalities require to be further validated. After incorporating 218 patients of The Cancer Genome Atlas glioma datasets of and 54 patients of the Huashan cohort with complementary prognostic information, we established a squeeze‐and‐excitation deep learning feature extractor for T1‐contrast enhanced images and histological slides and explored to screen significant circulating 5‐hydroxymethylcytosines (5hmC) profiles for glioma survival by least absolute shrinkage and selection operator‐Cox regression. Therefore, a prognostication predictive model with high efficiency was obtained through survival support vector machine multimodal integration of radiologic imaging, histopathologic imaging features, genome‐wide 5hmC in circulating cell‐free DNA and clinical variables, suggesting a valid strategy (concordance‐index = 0.897; Brier score = 0.118) for improved survival risk stratification of glioma patients.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
