Radiomics nomogram: A noninvasive tool for preoperative evaluation of the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules

• Published in: Translational oncology 2021-01, Vol.14 (1), p.100936, Article 100936
• Main Authors: Meng, Fanyang, Guo, Yan, Li, Mingyang, Lu, Xiaoqian, Wang, Shuo, Zhang, Lei, Zhang, Huimao
• Format: Article
• Language: English
• Subjects: Computed tomography; Ground-glass nodules; Lung adenocarcinoma; Nomogram; Original Research; Radiomics
• ISSN: 1936-5233; 1936-5233
• DOI: 10.1016/j.tranon.2020.100936

•It is vital to distinguish indolent pulmonary adenocarcinomas from invasive pulmonary adenocarcinomas before surgery.•Radiomics is a cutting-edge technology that mines quantitative features from CT images.•We designed a nomogram, which incorporated clinical and CT morphological characteristics with the radiomics signature.•We applied the radiomics nomogram to preoperatively predict the invasiveness of GGNs. In this study, we aimed to establish a radiomics nomogram that noninvasively evaluates the invasiveness of pulmonary adenocarcinomas manifesting as ground-glass nodules (GGNs). Computed tomography (CT) images of 509 patients manifesting as GGNs were collected: 70% of cases were included in the training cohort and 30% in the validation cohort. The Max-Relevance and Min-Redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct a radiomics signature. Univariate and multivariate logistic regression were used to select the invasiveness-related clinical and CT morphological predictors. Age, smoking history, long diameter, and average CT value were retained as independent predictors of GGN invasiveness. A radiomics nomogram was established by integrating clinical and CT morphological features with the radiomics signature. The radiomics nomogram showed good predictive ability in the training set (area under the curve [AUC], 0.940; 95% confidence interval [CI], 0.916–0.964) and validation set (AUC, 0.946; 95% CI, 0.907–0.986). This radiomics nomogram may serve as a noninvasive and accurate predictive tool to determine the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.