Predictive value of a machine learning model integrating intratumoral, peritumoral radiomics, and clinical features for preoperative differentiation of minimally invasive and invasive lung adenocarcinoma_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

GUO Lintao ^1,2 , WANG Qi ^1,2 ,  SHI Hongcan ^1,2

1. Department of Thoracic Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, Yangzhou, 225001, Jiangsu,P. R. China;
2. Institute of Translational Medicine, Medical College, Yangzhou University, Yangzhou, 225009, Jiangsu, P. R. China;

Corresponding?author：

SHI Hongcan, Email: shihongcan@yzu.edu.cn

Keywords：

Minimally invasive lung adenocarcinoma; invasive lung adenocarcinoma; radiomics; peritumoral region; machine learning; preoperative differentiation; prediction model; fusion model

DOI：

10.7507/1007-4848.202512036

Video：

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Objective To investigate and compare the value of clinical, intratumoral, and peritumoral radiomic features in the preoperative differentiation of minimally invasive adenocarcinoma (MIA) from invasive adenocarcinoma (IAC) of the lung, and to develop a high-performance integrated predictive model to guide surgical decision-making. Methods We retrospectively enrolled patients with postoperative pathologically confirmed MIA or IAC at the Department of Thoracic Surgery, Northern Jiangsu People's Hospital Affiliated to Yangzhou University, from 2020 to 2022. Clinical data and preoperative CT images were collected. Intratumoral and peritumoral (a 3 mm extension from the tumor margin) regions of interest were delineated, and high-throughput radiomic features were extracted using PyRadiomics. After feature selection, various machine learning algorithms were employed to construct predictive models based on clinical features, intratumoral features, peritumoral features, and combined features. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), decision curve analysis, and calibration curves. Results A total of 665 patients were included. There were 208 males and 457 females with a mean age of (57.67±11.21) years. The radiomics model combining intratumoral and peritumoral features (AUC=0.924) outperformed the single-region models. After further integrating the optimal radiomics model with the clinical model, the AUC of the resulting fusion model increased to 0.935 in the validation set. Furthermore, Delong's test, the net reclassification improvement, and the integrated discrimination improvement all confirmed that the predictive efficacy of the fusion model was significantly superior to that of any individual model. Conclusion The machine learning model integrating clinical and radiomic features can effectively differentiate MIA from IAC preoperatively, providing reliable decision support for the selection of personalized surgical strategies.

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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.	Liu S, Wang R, Zhang Y, et al. Precise diagnosis of intraoperative frozen section is an effective method to guide resection strategy for peripheral small-sized lung adenocarcinoma. J Clin Oncol, 2016, 34(4): 307-313.
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7.	Yuan L, Shen Z, Shan Y, et al. Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis. Front Oncol, 2024, 14: 1432212.
8.	Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging, 2012, 30(9): 1323-1341.
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11.	Bgatova N, Skudin N, Shatruk A, et al. Phenotypic and ultrastructural heterogeneity of fibroblasts and vasculogenic mimicry in rectal adenocarcinoma following neoadjuvant chemoradiotherapy. Histochem Cell Biol, 2025, 163(1): 118.
12.	Wang R, Zhang J, Chen S, et al. Tumor-associated macrophages provide a suitable microenvironment for non-small lung cancer invasion and progression. Lung Cancer, 2011, 74(2): 188-196.
13.	Li S, Lu E, Mi L, et al. Research on the interactive mechanisms between mitochondrial variations and immune responses in gliomas based on integrated visualization analysis. Discov Oncol, 2025, 16(1): 2078.
14.	Shang H, Xia D, Geng R, et al. Thermosensitive resiquimod-loaded lipid nanoparticles promote the polarization of tumor-associated macrophages to enhance bladder cancer immunotherapy. ACS Nano, 2025, 19(21): 19599-19621.
15.	Sun M, Yao S, Fan L, et al. Fibroblast activation protein-α responsive peptide assembling prodrug nanoparticles for remodeling the immunosuppressive microenvironment and boosting cancer immunotherapy. Small, 2022, 18(9): e2106296.
16.	Lubner MG, Smith AD, Sandrasegaran K, et al. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics, 2017, 37(5): 1483-1503.
17.	Long S, Li M, Chen J, et al. Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study. J Immunother Cancer, 2024, 12(12): e009879.
18.	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.
19.	Zhao S, Li Y, Ning N, et al. Association of peritumoral region features assessed on breast MRI and prognosis of breast cancer: a systematic review and meta-analysis. Eur Radiol, 2024, 34(9): 6108-6120.
20.	Altorki NK, Borczuk AC, Harrison S, et al. Global evolution of the tumor microenvironment associated with progression from preinvasive invasive to invasive human lung adenocarcinoma. Cell Rep, 2022, 39(1): 110639.
21.	Shang Y, Zeng Y, Luo S, et al. Habitat imaging with tumoral and peritumoral radiomics for prediction of lung adenocarcinoma invasiveness on preoperative chest CT: a multicenter study. AJR Am J Roentgenol, 2024, 223(4): e2431675.
22.	Wu L, Lou X, Kong N, et al. Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review. Eur Radiol, 2023, 33(3): 2105-2117.
23.	Wang X, Xue H, Ding W, et al. Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules. Sci Rep, 2025, 15(1): 14112.

1. Travis WD, Brambilla E, Noguchi M, et al. International Association for the Study of Lung cancer/American Thoracic Society/European Respiratory Society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol, 2011, 6(2): 244-285.
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. Liu S, Wang R, Zhang Y, et al. Precise diagnosis of intraoperative frozen section is an effective method to guide resection strategy for peripheral small-sized lung adenocarcinoma. J Clin Oncol, 2016, 34(4): 307-313.
4. Zhu J, Wang W, Xiong Y, et al. Evolution of lung adenocarcinoma from preneoplasia to invasive adenocarcinoma. Cancer Med, 2023, 12(5): 5545-5557.
5. 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.
6. Li X, Gao Z, Diao H, et al. Lung adenocarcinoma: selection of surgical approaches in solid adenocarcinoma from the viewpoint of clinicopathologic features and tumor microenvironmental heterogeneity. Front Oncol, 2024, 14: 1326626.
7. Yuan L, Shen Z, Shan Y, et al. Unveiling the landscape of pathomics in personalized immunotherapy for lung cancer: a bibliometric analysis. Front Oncol, 2024, 14: 1432212.
8. Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging, 2012, 30(9): 1323-1341.
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. Fujiwara A, Funaki S, Fukui E, et al. Effects of pirfenidone targeting the tumor microenvironment and tumor-stroma interaction as a novel treatment for non-small cell lung cancer. Sci Rep, 2020, 10(1): 10900.
11. Bgatova N, Skudin N, Shatruk A, et al. Phenotypic and ultrastructural heterogeneity of fibroblasts and vasculogenic mimicry in rectal adenocarcinoma following neoadjuvant chemoradiotherapy. Histochem Cell Biol, 2025, 163(1): 118.
12. Wang R, Zhang J, Chen S, et al. Tumor-associated macrophages provide a suitable microenvironment for non-small lung cancer invasion and progression. Lung Cancer, 2011, 74(2): 188-196.
13. Li S, Lu E, Mi L, et al. Research on the interactive mechanisms between mitochondrial variations and immune responses in gliomas based on integrated visualization analysis. Discov Oncol, 2025, 16(1): 2078.
14. Shang H, Xia D, Geng R, et al. Thermosensitive resiquimod-loaded lipid nanoparticles promote the polarization of tumor-associated macrophages to enhance bladder cancer immunotherapy. ACS Nano, 2025, 19(21): 19599-19621.
15. Sun M, Yao S, Fan L, et al. Fibroblast activation protein-α responsive peptide assembling prodrug nanoparticles for remodeling the immunosuppressive microenvironment and boosting cancer immunotherapy. Small, 2022, 18(9): e2106296.
16. Lubner MG, Smith AD, Sandrasegaran K, et al. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics, 2017, 37(5): 1483-1503.
17. Long S, Li M, Chen J, et al. Spatial patterns and MRI-based radiomic prediction of high peritumoral tertiary lymphoid structure density in hepatocellular carcinoma: a multicenter study. J Immunother Cancer, 2024, 12(12): e009879.
18. 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.
19. Zhao S, Li Y, Ning N, et al. Association of peritumoral region features assessed on breast MRI and prognosis of breast cancer: a systematic review and meta-analysis. Eur Radiol, 2024, 34(9): 6108-6120.
20. Altorki NK, Borczuk AC, Harrison S, et al. Global evolution of the tumor microenvironment associated with progression from preinvasive invasive to invasive human lung adenocarcinoma. Cell Rep, 2022, 39(1): 110639.
21. Shang Y, Zeng Y, Luo S, et al. Habitat imaging with tumoral and peritumoral radiomics for prediction of lung adenocarcinoma invasiveness on preoperative chest CT: a multicenter study. AJR Am J Roentgenol, 2024, 223(4): e2431675.
22. Wu L, Lou X, Kong N, et al. Can quantitative peritumoral CT radiomics features predict the prognosis of patients with non-small cell lung cancer? A systematic review. Eur Radiol, 2023, 33(3): 2105-2117.
23. Wang X, Xue H, Ding W, et al. Peritumoral features for assessing invasiveness of lung adenocarcinoma manifesting as ground-glass nodules. Sci Rep, 2025, 15(1): 14112.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

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