ArticleFrontiers in oncology2026
Development and validation of a CT-based radiomics nomogram for predicting overall survival in primary tracheal malignancy.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objectives: Primary tracheal malignancies (PTMs) are rare and histologically diverse, leading to complex and contentious prognostic factors. Consequently, accurately predicting the survival outcomes of these patients is challenging. This study aimed to develop a radiomics-based prognostic model for individual prediction of survival risk in PTM patients. Methods: A total of 115 patients with PTM were reviewed retrospectively and divided into the training cohort (n = 85) and validation cohort (n = 30). Radiomics features associated with overall survival (OS) were selected using the least absolute shrinkage and selection operator (LASSO) method and combined to form the radiomics score (Radscore). Multivariable analyses were used to identify clinical and CT features as independent risk factors for OS. Radscore and identified risk factors were combined to construct a radiomics nomogram. The predictive efficacy and clinical net benefit of the prognostic models were assessed using the C-index and decision curve analysis (DCA). Results: Seven radiomics features were selected by LASSO to form a Radscore. Longitudinal length was identified as an independent prognostic factor for OS. Compared with longitudinal length (C-index: 0.59; 95% confidence interval [CI]: 0.47-0.70), both the Radscore (C-index: 0.75; 95% CI, 0.63-0.87) and nomogram (C-index: 0.79; 95% CI, 0.69-0.88) demonstrated better predictive performance and were confirmed in the validation cohort. In addition, DCA indicated that both the Radscore and nomogram provided favorable clinical net benefits. Conclusions: The CT-based nomogram, which combined Radscore and longitudinal length, could individually predict the survival outcomes of PTM patients, aiding clinical decision-making.
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