Multimodal data integration using deep learning predicts overall survival of patients with glioma

Autor: Yifan Yuan, Xuan Zhang, Yining Wang, Hongyan Li, Zengxin Qi, Zunguo Du, Ying‐Hua Chu, Danyang Feng, Jie Hu, Qingguo Xie, Jianping Song, Yuqing Liu, Jiajun Cai
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: View, Vol 5, Iss 5, Pp n/a-n/a (2024)
Druh dokumentu: article
ISSN: 2688-268X
2688-3988
DOI: 10.1002/VIW.20240001
Popis: Abstract Gliomas 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.
Databáze: Directory of Open Access Journals
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