Construction and validation of a risk stratification model based on Lung-RADS® v2022 and CT features for predicting the invasive pure ground-glass pulmonary nodules in China.

Abstract

Objectives: A novel risk stratification model based on Lung-RADS^® v2022 and CT features was constructed and validated for predicting invasive pure ground-glass nodules (pGGNs) in China.

Methods: Five hundred and twenty-six patients with 572 pulmonary GGNs were prospectively enrolled and divided into training (n = 169) and validation (n = 403) sets. Utilising the Lung-RADS^® v2022 framework and the types of GGN-vessel relationships (GVR), a complementary Lung-RADS^® v2022 was established, and the pGGNs were reclassified from categories 2, 3 and 4x of Lung-RADS^® v2022 into 2, 3, 4a, 4b, and 4x of cLung-RADS^® v2022. The cutoff value of invasive pGGNs was defined as the cLung-RADS^® v2022 4a-4x. Evaluation metrics like recall rate, precision, F1 score, accuracy, Matthews correlation coefficient (MCC), and the area under the receiver operating characteristic curve (AUC) were employed to assess the utility of the cLung-RADS^® v2022.

Results: In the training set, compared with the Lung-RADS 1.0, the AUC of Lung-RADS^® v2022 were decreased from 0.543 to 0.511 (p-value = 0.002), and compared to Lung-RADS 1.0 and Lung-RADS^® v2022, the cLung-RADS^® v2022 model exhibited the highest recall rate (94.9% vs 6.5%, 2.2%), MCC value (60.2% vs 5.4%, 6.3%), F1 score (92.5% vs 12.1%, 4.3%), accuracy (87.6% vs 23.1%, 19.5%), and AUC (0.718 vs 0.543, 0.511; p-value = 0.014, 0.0016) in diagnosing the invasiveness of pGGNs, and the similar performance was observed in the validation set.

Conclusion: The cLung-RADS^® v2022 can effectively predict the invasiveness of pGGNs in real-world scenarios.

Critical relevance statement: A complementary Lung-RADS^® v2022 based on the Lung-RADS^® v2022 and CT features can effectively predict the invasiveness of pulmonary pure ground-glass nodules and is applicable in clinical practice.

Trial registration: Establishment and application of a multi-scale low-dose CT Lung cancer screening model based on modified lung-RADS1.1 and deep learning technology, 2022-KY-0137. Registered 24 January 2022. https://www.medicalresearch.org.cn/search/research/researchView?id=a97e67d8-1ee6-40fb-aab1-e6238dbd8f29 .

Key points: Lung-RADS^® v2022 delayed lung cancer diagnosis for nodules appearing as pGGNs. Lung-RADS^® v2022 showed lower accuracy and AUC than Lung-RADS 1.0. cLung-RADS^® v2022 model effectively predicts the invasiveness of pulmonary pGGNs.