A Multi-Classification Model for Predicting the Invasiveness of Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules

Fan Song; Lan Song; Tongtong Xing; Youdan Feng; Xiao Song; Peng Zhang; Tianyi Zhang; Zhenchen Zhu; Wei Song; Guanglei Zhang

doi:10.3389/fonc.2022.800811

A Multi-Classification Model for Predicting the Invasiveness of Lung Adenocarcinoma Presenting as Pure Ground-Glass Nodules

Front Oncol. 2022 Apr 28:12:800811. doi: 10.3389/fonc.2022.800811. eCollection 2022.

Authors

Fan Song¹, Lan Song², Tongtong Xing¹, Youdan Feng¹, Xiao Song³, Peng Zhang¹, Tianyi Zhang¹, Zhenchen Zhu^{2

4}, Wei Song², Guanglei Zhang¹

Affiliations

¹ Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
² Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
³ School of Medical Imaging, Shanxi Medical University, Taiyuan, China.
⁴ 4 + 4 MD Program, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

Abstract

Objectives: To establish a multi-classification model for precisely predicting the invasiveness (pre-invasive adenocarcinoma, PIA; minimally invasive adenocarcinoma, MIA; invasive adenocarcinoma, IAC) of lung adenocarcinoma manifesting as pure ground-glass nodules (pGGNs).

Methods: By the inclusion and exclusion criteria, this retrospective study enrolled 346 patients (female, 297, and male, 49; age, 55.79 ± 10.53 (24-83)) presenting as pGGNs from 1292 consecutive patients with pathologically confirmed lung adenocarcinoma. A total of 27 clinical were collected and 1409 radiomics features were extracted by PyRadiomics package on python. After feature selection with L2,1-norm minimization, logistic regression (LR), extra w(ET) and gradient boosting decision tree (GBDT) were used to construct the three-classification model. Then, an ensemble model of the three algorithms based on model ensemble strategy was established to further improve the classification performance.

Results: After feature selection, a hybrid of 166 features consisting of 1 clinical (short-axis diameter, ranked 27th) and 165 radiomics (4 shape, 71 intensity and 90 texture) features were selected. The three most important features are wavelet-HLL_firstorder_Minimum, wavelet-HLL_ngtdm_Busyness and square_firstorder_Kurtosis. The hybrid-ensemble model based on hybrid clinical-radiomics features and the ensemble strategy showed more accurate predictive performance than other models (hybrid-LR, hybrid-ET, hybrid-GBDT, clinical-ensemble and radiomics-ensemble). On the training set and test set, the model can obtain the accuracy values of 0.918 ± 0.022 and 0.841, and its F1-scores respectively were 0.917 ± 0.024 and 0.824.

Conclusion: The multi-classification of invasive pGGNs can be precisely predicted by our proposed hybrid-ensemble model to assist patients in the early diagnosis of lung adenocarcinoma and prognosis.

Keywords: adenocarcinoma of lung; computer-assisted diagnosis; early diagnosis; neoplasm invasiveness; prognosis; pure ground-glass nodule.