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Ground-glass nodules of the lung in never-smokers and smokers: clinical and genetic insights.

ABSTRACT: Pulmonary ground-glass nodules (GGNs) are hazy radiological findings on computed tomography (CT). GGNs are detected more often in never-smokers. Retrospective and prospective studies have revealed that approximately 20% of pure GGNs and 40% of part-solid GGNs gradually grow or increase their solid components, whereas others remain stable for years. Most persistent or growing GGNs are lung adenocarcinomas or their preinvasive lesions. To distinguish GGNs with growth from those without growth, GGNs should be followed for at least 5 years. Lesion size and smoking history are predictors of GGN growth. Genetic analyses of resected GGNs have suggested that EGFR mutations are also predictors for growth but a subset of KRAS- or BRAF-mutated GGNs may undergo spontaneous regression because the frequencies of KRAS or BRAF mutations decrease with the advance of pathological invasiveness. Although lobectomy is the standard surgical procedure for lung cancer, limited surgery such as wedge resection or segmentectomy for lung cancers ?2 cm with consolidation/tumor ratio ?0.25 can be a viable alternative based on the recent clinical trial. Further genetic analyses and clinical trials can contribute to elucidation of the biological aspects of preinvasive adenocarcinoma and the development of less invasive management strategies for patients with GGNs.

SUBMITTER: Kobayashi Y

PROVIDER: S-EPMC6131181 | biostudies-other | 2018 Aug

REPOSITORIES: biostudies-other

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Ground-glass nodules of the lung in never-smokers and smokers: clinical and genetic insights.

Kobayashi Yoshihisa Y Ambrogio Chiara C Mitsudomi Tetsuya T

Translational lung cancer research 20180801 4

Pulmonary ground-glass nodules (GGNs) are hazy radiological findings on computed tomography (CT). GGNs are detected more often in never-smokers. Retrospective and prospective studies have revealed that approximately 20% of pure GGNs and 40% of part-solid GGNs gradually grow or increase their solid components, whereas others remain stable for years. Most persistent or growing GGNs are lung adenocarcinomas or their preinvasive lesions. To distinguish GGNs with growth from those without growth, GGN ...[more]

PMID: 30225212

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Project description:ObjectivesTo investigate the value of radiomics based on CT imaging in predicting invasive adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).MethodsThis 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.ResultsFive 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.ConclusionsThe 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.

Project description:BackgroundPatients with consistent lung pure ground-glass nodules (pGGNs) have a high incidence of lung adenocarcinoma that can be classified as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Regular follow-up is recommended for AIS and MIA, while surgical resection should be considered for IAC. This study sought to develop a multi-parameter prediction model to increase the diagnostic accuracy in discriminating between IAC and AIS or MIA.MethodsThe training data set comprised consecutive patients with lung pGGNs who underwent resection from January to December 2017 at the Zhongshan Hospital. Of the 370 resected pGGNs, 344 were pathologically confirmed to be AIS, MIA, or IAC and were included in the study. The 26 benign pGGNs were excluded. We compared differences in the clinical features (e.g., age and gender), the content of serum tumor biomarkers, the computed tomography (CT) parameters (e.g., nodule size and the maximal CT value), and the morphologic characteristics of nodules (e.g., lobulation, spiculation, pleura indentation, vacuole sign, and normal vessel penetration or abnormal vessel) between the pathological subtypes of AIS, MIA, and IAC. An abnormal vessel was defined as "vessel curve" or "vessel enlargement". Statistical analyses were performed using the chi-square test, analysis of variance (ANOVA), and rank test. The IAC prediction model was constructed via a multivariate logistical regression. Our prediction model for lung pGGNs was further validated in a data set comprising consecutive patients from multiple medical centers in China from July to December 2018. In total, 345 resected pGGNs were pathologically diagnosed as lung adenocarcinoma in the validation data set.ResultsIn the training data set, patients with pGGNs ≥10 mm in size had a high incidence (74.5%) of IAC. The maximal CT value of IAC [-416.1±121.2 Hounsfield unit (HU)] was much higher than that of MIA (-507.7±138.0 HU) and AIS (-602.6±93.3 HU) (P<0.001). IAC was more common in pGGNs that displayed any of the following CT manifestations: lobulation, spiculation, pleura indentation, vacuole sign, and vessel abnormality. The IAC prediction model was constructed using the parameters that were assessed as risk factors (i.e., the nodule size, maximal CT value, and CT signs). The receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) of this model for diagnosing IAC was 0.910, which was higher than that of the AUC for nodule size alone (0.891) or the AUC for the maximal CT value alone (0.807) (P<0.05, respectively). A multicenter validation data set was used to validate the performance of our prediction model in diagnosing IAC, and our model was found to have an AUC of 0.883, which was higher than that of the AUC of 0.827 for the module size alone model or the AUC of 0.791 for the maximal CT value alone model (P<0.05, respectively).ConclusionsOur multi-parameter prediction model was more accurate at diagnosing IAC than models that used only nodule size or the maximal CT value alone. Thus, it is an efficient tool for identifying the IAC of malignant pGGNs and deciding if surgery is needed.

Dataset Information

Ground-glass nodules of the lung in never-smokers and smokers: clinical and genetic insights.

Publications

Ground-glass nodules of the lung in never-smokers and smokers: clinical and genetic insights.

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