Abstract
Objective: To explore the diagnostic value of CT radiographic images and radiomics features for invasive classification of lung adenocarcinoma manifesting as ground-glass nodules (GGNs) in computer tomography (CT). Methods: A total of 312 GGNs were enrolled in this retrospective study. All GGNs were randomly divided into training set (n = 219) and test set (n = 93). Univariate and multivariate logistic regressions were used to establish a clinical model, while the minimum redundancy maximum relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithm were used to select the radiomics features and construct the radiomics model. A combined model was finally built by combining these two models. The performance of these models was assessed in both training and test set. A combined nomogram was developed based on the combined model and evaluated with its calibration curves and C-index. Results: Diameter [odds ratio (OR), 1.159; p ＜ 0.001], lobulation (OR, 2.953; p = 0.002), and vascular changes (OR, 3.431; p ＜ 0.001) were retained as independent predictors of the invasive adenocarcinoma (IAC) group. Eleven radiomics features were selected by mRMR and LASSO method to established radiomics model. The clinical model and radiomics mode showed good predictive ability in both training set and test set. When two models were combined, the diagnostic area under the curve (AUC) value was higher than the single clinical or radiomics model (training set: 0.86 vs. 0.83 vs. 0.82; test set: 0.80 vs. 0.78 vs. 0.79). The constructed combined nomogram could effectively quantify the risk degree of 3 image features and Rad score with a C-index of 0.855 (95%: 0.805∼0.905). Conclusion: Radiographic and radiomics features show high accuracy in the invasive diagnosis of GGNs, and their combined analysis can improve the diagnostic efficacy of IAC manifesting as GGNs. The nomogram, serving as a noninvasive and accurate predictive tool, can help judge the invasiveness of GGNs prior to surgery and assist clinicians in creating personalized treatment strategies.
Highlights
Lung cancer is the major cancer leading in cancer-related deaths, and imaging played an important role in diagnosis and treatment
The inclusion criteria were as follows: (1) the nodules showed as ground-glass nodules (GGNs) at lung window setting, image thickness ≤1.25 mm; 2) maximum diameter of nodules measured on lung windows
The univariate analysis showed that multiple clinical parameters were larger in IAC groups (Table 3), including diameter (17 vs. 11 mm, p < 0.001), volume (1,351 vs. 509 mm3, p < 0.001), ratio of consolidation (0.24 vs. 0.04, p < 0.001), mean computed tomography (CT) value (−442 vs. −588 HU, p < 0.001), and mass (775 vs. 199 mg, p < 0.001)
Summary
Lung cancer is the major cancer leading in cancer-related deaths, and imaging played an important role in diagnosis and treatment. With the popularity of computed tomography (CT) and artificial intelligence (AI), the discovery of lung cancer manifesting as ground-glass nodules (GGNs) increased sequentially during the process of CT screening. Follow-up, and timely intervention were of positive significance for GGNs. Early detection, follow-up, and timely intervention were of positive significance for GGNs No doubt, these findings deserved the attention of society, medical professionals, and the general public. GGNs could be divided into pure ground-glass nodules (pGGNs) and mixed ground-glass nodules (mGGNs) according to the presence of the solid composition. GGN was in nonspecific radiologic findings, persistent GGN was more likely to be malignant. Studies had shown that 20% of pGGNs and 40% of mGGNs increase gradually or show a trend of increasing solid composition (Kobayashi et al, 2018). The GGN growth was slow and the process of deterioration may take several years, which was why multiple current guidelines recommend longer follow-up times
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