ArticleAmerican journal of translational research2025
A nomogram predicts the new high-grade patterns of pulmonary invasive non-mucinous adenocarcinoma based on the radiomics and clinical features.
Article in American journal of translational research, 2025. 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
objectivesTo develop a nomogram for the prediction of the new high-grade patterns of invasive non-mucinous adenocarcinoma (INMA) based on the radiomics and clinical features to provide accurate individualized treatment for patients.
methodsWe collected patients pathologically diagnosed with INMA at our hospital. The study's endpoint, defined as 'new high-grade', was characterized by the presence of micropapillary patterns at ≥5% or high-grade patterns (including solid, micropapillary, and complex glandular) at ≥20%. Patients were randomly divided into training and validation cohorts in a ratio of 8:2. The region of interest (ROI) of chest plain scan images was sketched using 3D slicer software. The image and clinical features were analyzed by Least Absolute Shrinkage and Selection Operator (LASSO), univariate, and multivariate regression to construct the radiomics signature and nomogram model. The nomogram model was validated using the validation cohort.
resultsA total of 226 patients were divided into training (n = 180) and validation (n = 46) cohorts. From the ROI of these patients, 107 image features were extracted. LASSO regression analysis identified 16 image features that were used to construct the radiomics signature. The area under the curve values for the radiomics signature in the training and validation cohorts were 0.803 and 0.772, respectively. The Harrell's concordance index for the model, with 95% confidence intervals (CI), was 0.815 (CI: 0.806-0.824) for the training cohort and 0.802 (CI: 0.761-0.843) for the validation cohort.
conclusionsThe radiomics prediction model demonstrates strong predictive capabilities and could serve as a valuable tool for guiding personalized surgical treatment strategies for patients with INMA.
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