• Chen-Lu Liu, Fan Zhang, Qing Cai, Yu-Ying Shen, and Shuang-Qing Chen.
• Department of Radiology, the Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou, China.
• J Am Coll Radiol. 2019 Apr 1; 16 (4 Pt A): 435-445.

PurposeTo establish a predictive model for surgical resection of invasive pulmonary adenocarcinoma (IPA) presenting as ground-glass nodules (GGNs) based on a radiomics nomogram.MethodsThe CT images of 239 patients with GGNs were collected, of which 160 cases were included in the training set to construct the predictive model and 79 cases were included in the validation set to verify the established predictive model. The least absolute shrinkage and selection operator algorithm was used to select the radiomic features and construct the radiomics tagging. The predictive model for the surgical resection of IPA was constructed using the radiomics nomogram.ResultsThe presence of IPA showed significant correlations with seven radiomics features (P < .01), which were the independent predictors. The predictive model constructed using the radiomics features performed well on the training set (area under the curve [AUC] 0.792, 95% confidence interval [CI]: 0.720-0.864) and the validation set (AUC 0.773, 95% CI: 0.668-0.877). The predictive model constructed using the clinical information alone was relatively less effective (AUC 0.711, 95% CI: 0.634-0.787). The predictive model constructed by integrating the radiomics features into the clinical information using the radiomics nomogram showed the best predictive ability and calibration in the training set (AUC 0.831, 95% CI: 0.765-0.897) and the validation set (AUC 0.816, 95% CI: 0.724-0.909). Decision curve analysis showed that radiomics nomogram has a certain clinical value.ConclusionThe predictive model for surgical resection of IPA constructed by integrating the radiomics features and the clinical information based on the radiomics nomogram can help clinicians control the operative node and reduce the occurrence of overtreatment.Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

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