Prediction of lymph node metastasis in invasive lung adenocarcinoma based on radiomics of the primary lesion, peritumoral region, and tumor habitat: A single-center retrospective study_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

WANG Hongchang¹ ¹ , GU Yan¹ ¹ , ZHANG Wenhao³ ³ , MU Guang¹ ¹ , XUE Wentao¹ ¹ , FU Chenghao¹ ¹ , CHEN Liang¹ ¹ , YUAN Mei² ¹ , WANG Jun¹ ² , 王俊 ¹

1. Department of Thoracic Surgery, Jiangsu Province People's Hospital (First Affiliated Hospital of Nanjing Medical University), Nanjing, 210029, P. R. China;
2. Department of Radiology, Jiangsu Province People's Hospital (First Affiliated Hospital of Nanjing Medical University), Nanjing, 210029, P. R. China;
3. Department of Thoracic Surgery, Taihe Hospital, Shiyan, 442012, Hubei, P. R. China;

Corresponding author：

Keywords：

Invasive lung adenocarcinoma; radiomics; stacking model; lymph node metastasis; machine learning

DOI：

10.7507/1007-4848.202503027

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Objective to develop machine learning models based on radiomics features extracted from the primary tumor, peritumoral region, and habitat region to predict lymph node metastasis status in patients with invasive pulmonary adenocarcinoma, and to evaluate the predictive performance and generalizability of different radiomic features. Methods A total of 1 263 patients with invasive pulmonary adenocarcinoma who underwent surgery at the Department of Thoracic Surgery, Jiangsu Provincial People’s Hospital from 2016 to 2019 were enrolled. The habitat regions on CT images were delineated by applying K-means clustering (with an average cluster number of 2) based on grayscale values. The peritumoral region was defined as a uniform 3 mm expansion around the primary tumor, which was segmented using an automatic V-net model combined with manual correction. Radiomic features were then extracted from these predefined regions respectively, and stacking machine learning models were constructed. The model performance was evaluated on the training, testing, and internal validation cohorts using metrics including the area under the receiver operating characteristic curve (AUC), F1 score, recall, and precision. Results After excluding patients who did not meet the inclusion criteria, 651 patients were finally included in the study, consisting of 181 males and 287 females, with a mean age of 58.39±11.23 years (range 29-78). Although the habitat radiomics-based model did not show the best performance in the training set, it demonstrated outstanding results in the internal validation set, achieving an AUC of 0.95 [95% CI (0.87, 1.00)], an F1 score of 0.85, and a precision-recall area under the curve (PR-AUC) of 0.89, outperforming models based on the primary tumor and peritumoral regions.Conclusion The model constructed based on habitat radiomics exhibited superior performance in the internal validation set, suggesting it may possess better generalizability and clinical utility in predicting lymph node metastasis status in pulmonary adenocarcinoma.

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14.	Bi Q, Miao K, Xu N, et al. Habitat radiomics based on MRI for predicting platinum resistance in patients with high-grade serous ovarian carcinoma: A multicenter study. Acad Radiol, 2024, 31(6): 2367-2380.
15.	Mu W, Liang Y, Hall LO, et al. (18)F-FDG PET/CT habitat radiomics predicts outcome of patients with cervical cancer treated with chemoradiotherapy. Radiol Artif Intell, 2020, 2(6): e190218.
16.	Cucchiara F, Petrini I, Romei C, et al. Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives. Pharmacol Res, 2021, 169: 105643.

1. Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2024, 74(3): 229-263.
2. Nicholson AG, Tsao MS, Beasley MB, et al. The 2021 WHO classification of lung tumors: Impact of advances since 2015. J Thorac Oncol, 2022, 17(3): 362-387.
3. Chen M, Copley SJ, Viola P, et al. Radiomics and artificial intelligence for precision medicine in lung cancer treatment. Semin Cancer Biol, 2023, 93: 97-113.
4. Mikubo M, Tamagawa S, Kondo Y, et al. Micropapillary and solid components as high-grade patterns in IASLC grading system of lung adenocarcinoma: Clinical implications and management. Lung Cancer, 2024, 187: 107445.
5. Sun J, Wu S, Jin Z, et al. Lymph node micrometastasis in non-small cell lung cancer. Biomed Pharmacother, 2022, 149: 112817.
6. Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol, 2017, 14(12): 749-762.
7. Xie C, Yang P, Zhang X, et al. Sub-region based radiomics analysis for survival prediction in oesophageal tumours treated by definitive concurrent chemoradiotherapy. EBioMedicine, 2019, 44: 289-297.
8. Zhao C, Xu Y, He Z, et al. Lung segmentation and automatic detection of COVID-19 using radiomic features from chest CT images. Pattern Recognit, 2021, 119: 108071.
9. van Griethuysen JJM, Fedorov A, Parmar C, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res, 2017, 77(21): e104-e107.
10. Saji H, Okada M, Tsuboi M, et al. Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer (JCOG0802/WJOG4607L): A multicentre, open-label, phase 3, randomised, controlled, non-inferiority trial. Lancet, 2022, 399(10335): 1607-1617.
11. Hou Y, Song W, Chen M, et al. The presence of lepidic and micropapillary/solid pathological patterns as minor components has prognostic value in patients with intermediate-grade invasive lung adenocarcinoma. Transl Lung Cancer Res, 2022, 11(1): 64-74.
12. He H, Hu W, Yi C, et al. Impact of imaging features on selecting limited lymph node resection for cT1N0M0 lung cancer. J Thorac Dis, 2024, 16(8): 5138-5151.
13. Zhang H, Li Y, Wu S, et al. Machine learning-based radiomics for guiding lymph node dissection in clinical stage Ⅰ lung adenocarcinoma: A multicenter retrospective study. Transl Lung Cancer Res, 2024, 13(12): 3579-3589.
14. Bi Q, Miao K, Xu N, et al. Habitat radiomics based on MRI for predicting platinum resistance in patients with high-grade serous ovarian carcinoma: A multicenter study. Acad Radiol, 2024, 31(6): 2367-2380.
15. Mu W, Liang Y, Hall LO, et al. (18)F-FDG PET/CT habitat radiomics predicts outcome of patients with cervical cancer treated with chemoradiotherapy. Radiol Artif Intell, 2020, 2(6): e190218.
16. Cucchiara F, Petrini I, Romei C, et al. Combining liquid biopsy and radiomics for personalized treatment of lung cancer patients. State of the art and new perspectives. Pharmacol Res, 2021, 169: 105643.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesPrediction of lymph node metastasis in invasive lung adenocarcinoma based on radiomics of the primary lesion, peritumoral region, and tumor habitat: A single-center retrospective study

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