ArticleDigital health
MRI-based intratumoral heterogeneity analysis for prognostic evaluation of disease-free survival in locally advanced nasopharyngeal carcinoma.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Objectives: This study aimed to develop a disease-free survival (DFS) prediction model incorporating radiomics and intratumor heterogeneity (ITH) scores for locally advanced nasopharyngeal carcinoma (LANPC), and to establish an anti-epidermal growth factor receptor (EGFR) therapy risk model. Materials and Methods: A retrospective analysis was conducted on 950 pathologically confirmed LANPC patients (training cohort: n = 632, including 81 receiving anti-EGFR therapy; test cohort: n = 318, including 47 receiving anti-EGFR therapy). All patients underwent 1.5 T MRI (T2-Weighted Imaging and contrast-enhanced T1-Weighted Imaging). Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) and support vector machine (SVM) survival algorithms. Subsequently, five predictive models were developed and compared: a Comprehensive Risk Model (CRM) integrating clinical features, ITH score, and radiomics score; an ITH-radiomics model (IRM); a standalone ITH model (ITHM); a standalone radiomics model (RM); and a clinical model (CM). Model performance was evaluated using the area under the curve (AUC) and the concordance index (C-index), and clinical utility was assessed with decision curve analysis. Finally, Kaplan-Meier analysis with the log-rank test compared survival between the model-defined risk groups. Additionally, the DeepSurv deep neural network was employed to simulate personalized treatment recommendations based on the patient's risk profile. Results: With median follow-ups of 73 months (training) and 68.1 months (test), disease progression occurred in 34.2% (216/632) and 36.5% (116/318) of cases, respectively. The CRM achieved the highest C-index value for assessing DFS in patients with LANPC, with values of 0.829 and 0.760 in the training and test cohorts, respectively. Patients who met the DeepSurv treatment recommendations had better DFS. Conclusion: The superior performance of the CRM supports its potential to enhance DFS prediction in LANPC and to inform anti-EGFR therapy selection.
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