Emerging research suggests that a modified version of the Lung-RADS v2022 classification system may significantly improve the system’s capability for predicting invasive pure ground glass nodules (pGGNs) on computed tomography (CT) scans.
For the prospective study, recently published in Insights into Imaging, researchers reviewed CT data from 526 patients (total of 572 pulmonary ground glass nodules (GGNs)) to compare the Lung-RADS 1.0 system, the Lung-RADS v2022 version and a complementary Lung-RADS v2022 (cLung-RADS v2022) that emphasizes GGN-vessel relationships (GVRs) in its categorization of nodules.
The training set for the cLung-RADS v2022 system included CT data for 169 pulmonary GGNs while the remaining 403 pulmonary GGNs were assessed in validation testing, according to the study.
In validation testing, the study authors found that the cLung-RADS v2022 system provided a significantly higher AUC and accuracy rate than the Lung-RADS 1.0 and Lung-RADS v2022 for predicting the invasiveness of pure GGNs. The accuracy rate was over 44 percent higher for cLung RADS v2022 (80.4 percent vs. 36 percent for Lung-RADS 1.0 and 31 percent for Lung-RADS v20222). The researchers also noted a greater than 16 percent area under the receiver operating characteristic curve (AUC) for cLung RADS v2022 (69.3 percent vs. 53.1 percent for Lung-RADS 1.0 and 50.9 percent for Lung-RADS v2022).
“Our findings suggest that the cLung-RADS® v2022 holds significant potential value for LC risk prediction and decision support in the management of pulmonary pGGNs. By effectively ruling out unnecessary imaging and invasive procedures through the (cLung-RADS v2022 model), we could potentially reduce the burden of excessive workups on a considerable number of patients,” wrote lead study author Qingcheng Meng, M.D., who is affiliated with the Department of Radiology at the Affiliated Cancer Hospital of Zhengzhou University and Henan Cancer Hospital in Zhengzhou, China, and colleagues.
The researchers pointed out that the use of the cLung-RADS v2022 systems led to significantly less false negative cases (26) in validation testing in comparison to the Lung-RADS 1.0 (256) and Lung-RADS v2022 systems (278).
Three Key Takeaways
1. Improved accuracy for invasive pGGN prediction. The cLung-RADS v2022 system demonstrated significantly higher accuracy (80.4 percent) compared to Lung-RADS 1.0 (36 percent) and Lung-RADS v2022 (31 percent) in predicting the invasiveness of pure ground glass nodules (pGGNs).
2. Reduction in false negatives and unnecessary procedures. The model led to a substantial decrease in false negatives (26 cases) versus Lung-RADS 1.0 (256 cases) and Lung-RADS v2022 (278 cases), reducing unnecessary imaging and invasive interventions.
3. Enhanced risk stratification with GVR consideration. By incorporating GGN-vessel relationships (GVRs) into its classification, cLung-RADS v2022 may improve early detection and intervention.
Noting that the cLung-RADS v2022 model emphasizes identification of vascular changes in GGNs, the study authors suggested this approach may facilitate earlier detection and appropriate intervention.
“The ability of the model to differentiate between invasive and noninvasive pGGNs with high precision could significantly enhance the management of patients with pulmonary pGGNs, potentially reducing the incidence of unnecessary procedures and ensuring the prompt treatment of invasive lesions,” maintained Meng and colleagues.
(Editor’s note: For related content, see “CT Study Links Better Five-Year Prognosis with Minor Ground Glass Opacity Component in NSCLC Lung Nodules,” “What Emerging CT Research Reveals About Pure Ground Glass Nodules” and “Computed Tomography Study Assesses Model for Predicting Recurrence of Non-Small Cell Lung Cancer.”)
Beyond the inherent limitations of a single-center study, the authors acknowledged the small sample size in the non-invasive cohort. They also noted the potential risk of overtreatment with the cLung-RADS model, pointing out that indolent and clinically insignificant pGGNs can occur even if they are invasive.
