Radiomics for lung adenocarcinoma manifesting as pure ground-glass nodules: invasive prediction

Abstract

Objectives To investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs). Methods This study enrolled 395 pGGNs with histopathology-confirmed benign nodules or adenocarcinoma. A total of 396 radiomic features were extracted from each labeled nodule. A Rad-score was constructed with the least absolute shrinkage and selection operator (LASSO) in the training set. Multivariate logistic regression analysis was conducted to establish the radiographic model and the combined radiographic-radiomics model. The predictive performance was validated by receiver operating characteristic (ROC) curve. Based on the multivariate logistic regression analysis, an individual prediction nomogram was developed and the clinical utility was assessed. Results Five radiomic features and four radiographic features were selected for predicting the invasive lesions. The combined radiographic-radiomics model (AUC 0.77; 95% CI, 0.69-0.86) performed better than the radiographic model (AUC 0.71; 95% CI, 0.62-0.81) and Rad-score (AUC 0.72; 95% CI, 0.63-0.81) in the validation set. The clinical utility of the individualized prediction nomogram developed using the Rad-score, margin, spiculation, and size was confirmed in the validation set. The decision curve analysis (DCA) indicated that using a model with Rad-score to predict the invasive lesion would be more beneficial than that without Rad-score and the clinical model. Conclusions The proposed radiomics-based nomogram that incorporated the Rad-score, margin, spiculation, and size may be utilized as a noninvasive biomarker for the assessment of invasive prediction in patients with pGGNs. Key Points • CT-based radiomics analysis helps invasive prediction manifested as pGGNs. • The combined radiographic-radiomics model may be utilized as a noninvasive biomarker for predicting invasive lesion for pGGNs. • Radiomics-based individual nomogram may serve as a vital decision support tool to identify invasive pGGNs, obviating further workup and blind follow-up.

Radiomic-Based Quantitative CT Analysis of Pure Ground-Glass Nodules to Predict the Invasiveness of Lung Adenocarcinoma

“…Eguchi et al examined 124 cases with pGGNs, and 64 pGGNs (51.6%) showed growth during their 2-year follow-up (7). Several previous research revealed that nearly 50% of pGGNs were invasive lesions (3,(8)(9)(10). In clinical practice, pGGNs are usually prescribed to be followed up but data above demonstrated that more detailed diagnosis and more individualized management should be made for pGGNs.…”

“…According to the new classification, lung adenocarcinoma can be divided into preinvasive lesion, minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IVA), and preinvasive lesion includes atypical adenomatous hyperplasia (AAH) and adenocarcinoma in situ (AIS) ( 1 ). The improvement of medical technology and the generalization of lung cancer screening project have led more attention to pure ground-glass nodules (pGGNs) detected on computed tomography (CT) images ( 3 , 4 ).…”

Use of CT radiomics to differentiate minimally invasive adenocarcinomas and invasive adenocarcinomas presenting as pure ground-glass nodules larger than 10 mm

“…I. P. Hwang et al [18] used texture analysis to distinguish IAs from pre-invasive lesions and MIAs manifesting as persistent pGGNs with a diameter of >5 mm, and the model showed good classification performance with an area under the ROC curve (AUC) of 0.962. Furthermore, some studies [19,20] have demonstrated that a mixed clinical radiomics model provides better discrimination between invasive lesions (MIAs/IAs) and pre-invasive lesions than the standalone clinical model. However, none of these studies reported the specificities of radiomics for identifying MIAs and IAs in pGGNs or the use of a cut-off size of 10 mm.…”

Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact

“…Similarly, for the prediction between AIS/MIA and IAC representing as pGGNs, Xu et al [ 34 ] showed the predictive radiomics models built in study (AUC 0.833;95% CI, 0.733–0.934) which provided a good predictive power. Besides, Sun et al [ 35 ] developed a radiomics-based Rad-score utilized as a biomarker for the invasiveness-predicted evaluation in patients with pGGNs (AUC 0.72; 95% CI, 0.63–0.81). Their study confirmed the advantage of radiomics in the diagnosis of Benign/AAH/AIS from MIA/IAC.…”