WANG Hongchang 1 , GU Yan 1 , ZHANG Wenhao 3 , MU Guang 1 , XUE Wentao 1 , WANG Meng’en 1 , FU Chenghao 1 , CHEN Liang 1 , YUAN Mei 2 , WANG Jun 1
  • 1. Department of Thoracic Surgery, Jiangsu Province Hospital (the First Affiliated Hospital with Nanjing Medical University), Nanjing, 210029, P. R. China;
  • 2. Department of Radiology, Jiangsu Province Hospital (the First Affiliated Hospital with Nanjing Medical University), Nanjing, 210029,P. R. China;
  • 3. Department of Thoracic Surgery, Taihe Hospital, Shiyan, 442012, Hubei, P. R. China;
WANG Jun, Email: drwangjun@njmu.edu.cn
Invasive lung adenocarcinoma; radiomics; stacking model; lymph node metastasis; machine learning
10.7507/1007-4848.202503027
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Objective  To predict the lymph node metastasis status of patients with invasive pulmonary adenocarcinoma by constructing machine learning models based on primary tumor radiomics, peritumoral radiomics, and habitat radiomics, and to evaluate the predictive performance and generalization ability of different imaging features. Methods  A retrospective analysis was performed on the clinical data of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Province Hospital, from 2016 to 2019. Habitat regions were delineated by applying K-means clustering (average cluster number of 2) to the grayscale values of CT images. The peritumoral region was defined as a uniformly expanded area of 3 mm around the primary tumor. The primary tumor region was automatically segmented using V-net combined with manual correction and annotation. Subsequently, radiomics features were extracted based on these regions, and stacked machine learning models were constructed. Model performance was evaluated on the training, testing, and internal validation sets using the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results  After excluding patients who did not meet the screening criteria, a total of 651 patients were included, consisting of 181 males and 287 females, with an average age of 29-78 (58.39±11.23) years. Although the habitat radiomics model did not show the optimal performance in the training set, it exhibited superior performance in the internal validation set, with an AUC of 95.19% [95%CI (0.87, 1.00)], an F1 score of 0.85, and a precision-recall AUC (PR-AUC) of 89.22%, outperforming the models based on the primary tumor and peritumoral regions. Conclusion The model constructed based on habitat radiomics demonstrated superior performance in the internal validation set, suggesting its potential for better generalization ability and clinical application in predicting lymph node metastasis status in pulmonary adenocarcinoma.

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