Construction and validation of a risk score system for diagnosing invasive adenocarcinoma presenting as pulmonary pure ground-glass nodules: a multi-center cohort study in China.

Background: Anxiety-driven clinical interventions have been queried due to the nondeterminacy of pure ground-glass nodules (pGGNs). Although radiomics and radiogenomics aid diagnosis, standardization and reproducibility challenges persist. We aimed to assess a risk score system for invasive adenocarcinoma in pGGNs. Methods: In a retrospective, multi-center study, 772 pGGNs from 707 individuals in The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital were grouped into training (509 patients with 558 observations) and validation (198 patients with 214 observations) sets consecutively from January 2017 to November 2021. An additional test set of 143 observations in Hainan Cancer Hospital was analyzed in the same period. Computed tomography (CT) signs and clinical features were manually collected, and the quantitative parameters were achieved by artificial intelligence (AI). The positive cutoff score was ≥3. Risk scores system 3 combined carcinoma history, chronic obstructive pulmonary disease (COPD), maximum diameters, nodule volume, mean CT values, type II or III vascular supply signs, and other radiographic characteristics. The evaluation included the area under the curves (AUCs), accuracy, sensitivity, specificity, positive predictive values (PPV), and negative predictive values (NPV) for both the risk score systems 1, 2, 3 and the AI model. Results: The risk score system 3 [AUC, 0.840; 95% confidence interval (CI): 0.789-0.890] outperformed the AI model (AUC, 0.553; 95% CI: 0.487-0.619), risk score system 1 (AUC, 0.802; 95% CI: 0.754-0.851), and risk score system 2 (AUC, 0.816; 95% CI: 0.766-0.867), with 88.0% (0.850-0.904) accuracy, 95.6% (0.932-0.972) PPV, 0.620 (0.535-0.702) NPV, 89.6% (0.864-0.920) sensitivity, and 80.6% (0.717-0.872) specificity in the training sets. In the validation and test sets, risk score system 3 performed best with AUCs of 0.769 (0.678-0.860) and 0.801 (0.669-0.933). Conclusions: An AI-based risk scoring system using quantitative image parameters, clinical features, and radiographic characteristics effectively predicts invasive adenocarcinoma in pulmonary pGGNs.

Development and validation of qualitative and quantitative models to predict invasiveness of lung adenocarcinomas manifesting as pure ground-glass nodules based on low-dose computed tomography during lung cancer screening.

Background: Due to different management strategy and prognosis of different subtypes of lung adenocarcinomas appearing as pure ground-glass nodules (pGGNs), it is important to differentiate invasive adenocarcinoma (IA) from adenocarcinoma in situ/minimally invasive adenocarcinoma (AIS/MIA) during lung cancer screening. The aim of this study was to develop and validate the qualitative and quantitative models to predict the invasiveness of lung adenocarcinoma appearing as pGGNs based on low-dose computed tomography (LDCT) and compare their diagnostic performance with that of intraoperative frozen section (FS). Methods: A total of 223 consecutive pathologically confirmed pGGNs from March 2018 to December 2020 were divided into a primary cohort (96 IAs and 64 AIS/MIAs) and validation cohort (39 IAs and 24 AIS/MIAs) according to scans (Brilliance iCT and Somatom Definition Flash) performed at Sichuan Cancer Hospital and Institute. The following LDCT features of pGGNs were analyzed: the qualitative features included nodule location, shape, margin, nodule-lung interface, lobulation, spiculation, pleural indentation, air bronchogram, vacuole, and vessel type, and the quantitative features included the diameter, volume, and mean attenuation. Multivariate logistic regression analysis was used to build a qualitative model, quantitative model, and combined qualitative and quantitative model. The diagnostic performance was assessed according to the following factors: the area under curve (AUC) of the receiver operating characteristic (ROC) curve, sensitivity, specificity, and accuracy. Results: The AUCs of the qualitative model, quantitative model, combined qualitative and quantitative model, and the FS diagnosis were 0.854, 0.803, 0.873, and 0.870, respectively, in the primary cohort and 0.884, 0.855, 0.875, and 0.946, respectively, in the validation cohort. No significant difference of the AUCs was found among the radiological models and the FS diagnosis in the primary or validation cohort (all corrected P>0.05). Among the radiological models, the combined qualitative and quantitative model consisting of vessel type and volume showed the highest accuracy in both the primary and validation cohorts (0.831 and 0.889, respectively). Conclusions: The diagnostic performances of the qualitative and quantitative models based on LDCT to differentiate IA from AIS/MIA in pGGNs are equivalent to that of intraoperative FS diagnosis. The vessel type and volume can be preoperative and non-invasive biomarkers to assess the invasive risk of pGGNs in lung cancer screening.

Computed tomography radiomics-based distinction of invasive adenocarcinoma from minimally invasive adenocarcinoma manifesting as pure ground-glass nodules with bubble-like signs.

BACKGROUND: To explore an effective model based on radiomics features extracted from nonenhanced computed tomography (CT) images to distinguish invasive adenocarcinoma (IAC) from minimally invasive adenocarcinoma (MIA) presenting as pure ground-glass nodules (pGGNs) with bubble-like (B-pGGNs) signs. PATIENTS AND METHODS: We retrospectively reviewed 511 nodules (MIA, n = 288; IAC, n = 223) between November 2012 and June 2018 from almost all pGGNs pathologically confirmed MIA or IAC. Eventually, a total of 109 B-pGGNs (MIA, n = 55; IAC, n = 54) from 109 patients fulfilling the criteria were randomly assigned to the training and test cluster at a ratio of 7:3. The gradient boosting decision tree (GBDT) method and logistic regression (LR) analysis were applied to feature selection (radiomics, semantic, and conventional CT features). LR was performed to construct three models (the conventional, radiomics and combined model). The performance of the predictive models was evaluated using the area under the curve (AUC). RESULTS: The radiomics model had good AUCs of 0.947 in the training cluster and of 0.945 in the test cluster. The combined model produced an AUC of 0.953 in the training cluster and of 0.945 in the test cluster. The combined model yielded no performance improvement (vs. the radiomics model). The rad_score was the only independent predictor of invasiveness. CONCLUSION: The radiomics model showed excellent predictive performance in discriminating IAC from MIA presenting as B-pGGNs and may provide a necessary reference for extending clinical practice.

Discriminating invasive adenocarcinoma among lung pure ground-glass nodules: a multi-parameter prediction model.

BACKGROUND: Patients 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. METHODS: The 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. RESULTS: In 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). CONCLUSIONS: Our 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.

CT Characteristics for Predicting Invasiveness in Pulmonary Pure Ground-Glass Nodules.

OBJECTIVE. The objective of our study was to investigate the differences in the CT features of atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IA) manifesting as a pure ground-glass nodule (pGGN) with the aim of determining parameters predictive of invasiveness. MATERIALS AND METHODS. A total of 161 patients with 172 pGGNs (14 AAHs, 59 AISs, 68 MIAs, and 31 IAs) were retrospectively enrolled. The following CT features of each histopathologic subtype of nodule were analyzed and compared: lesion location, diameter, area, shape, attenuation, uniformity of density, margin, nodule-lung interface, and internal and surrounding changes. RESULTS. ROC curves revealed that nodule diameter and area (cutoff value, 10.5 mm and 86.5 mm2; sensitivity, 87.1% and 87.1%; specificity, 70.9% and 65.2%) were significantly larger in IAs than in AAHs, AISs, and MIAs (p < 0.001), whereas the latter three were similar in size (p > 0.050). CT attenuation higher than -632 HU in pGGNs indicated invasiveness (sensitivity, 78.8%; specificity, 59.8%). As opposed to noninvasive pGGNs (AAHs and AISs), invasive pGGNs (MIAs and IAs) usually had heterogeneous density, irregular shape, coarse margin, lobulation, spiculation, pleural indentation, and dilated or distorted vessels (each, p < 0.050). Multivariate analysis showed that mean CT attenuation and presence of lobulation were predictors for invasive pGGNs (p ≤ 0.001). CONCLUSION. The likelihood of invasiveness is greater in pGGNs with larger size (> 10.5 mm or > 86.5 mm2), higher attenuation (> -632 HU), heterogeneous density, irregular shape, coarse margin, spiculation, lobulation, pleural indentation, and dilated or distorted vessels.

Quantitative features can predict further growth of persistent pure ground-glass nodule.

BACKGROUND: To evaluate whether quantitative features of persistent pure ground-glass nodules (PGGN) on the initial computed tomography (CT) scans can predict further nodule growth. METHODS: This retrospective study included 59 patients with 101 PGGNs from 2011 to 2012, who received regular CT follow-up for lung nodule surveillance. Nineteen quantitative image features consisting of 8 volumetric and 11 histogram parameters were calculated to detect lung nodule growth. For the extraction of the quantitative features, semi-automatic GrowCut segmentation was implemented on chest CT images in 3D slicer platform. Univariate and multivariate analyses were performed to identify risk factors for nodule growth. RESULTS: With a median follow-up of 52 months, nodule growth was detected in 10 nodules by radiological assessment and in 16 nodules by quantitative features. In univariate analysis, 3D maximum diameter (MD), volume, mass, surface area, 90% percentile, and standard deviation value (SD) of PGGN on the initial CT scan were significantly different between stable nodules and nodules with further growth. In multivariate analysis, MD [hazard ratio (HR), 3.75; 95% confidence interval (CI), 2.14-6.55] and SD (HR, 2.06; 95% CI, 1.35-3.14) were independent predictors of further nodule growth. Also, the area under the curve was 0.896 (95% CI: 0.820-0.948) and 0.813 (95% CI: 0.723-0.883) for MD with a cut-off value of 10.2mm and SD of 50.0 Hounsfield Unit (HU). Besides, the growth rate was 55.6% (n=15) of PGGNs with MD >10.2 mm and SD >50.0 HU. CONCLUSIONS: Based on the initial CT scan, the quantitative features can predict PGGN growth more precisely. PGGN with MD >10.2 mm and SD >50.0 HU may require close follow-up or surgical intervention for the high incidence of growth.