Research progress on the predictive value of artificial intelligence in pulmonary nodules with airway spread_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

NING Xianpu , KONG Weishuang , HUANG Zujun , LIANG Xun , WANG Dinglun ,  XIA Libo

Xuanwei Hospital Affiliated to Yunnan University of Chinese Medicine, Xuanwei, 655400, Yunnan, P. R. China;

Corresponding author：

XIA Libo, Email: 278039517@qq.com

Keywords：

Adenocarcinoma of the lung; spread through air space; radiological features; artificial intelligence; review

DOI：

10.7507/1007-4848.202503060

Video：

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[Abstract] With the widespread adoption of lung cancer screening, an increasing number of patients are being diagnosed with early-stage lung adenocarcinoma. For stage ⅠA lung adenocarcinoma, sublobar resection is the primary treatment approach. However, in patients with concomitant spread through air spaces (STAS), numerous studies advocate for lobectomy as the mainstay of treatment. Due to the limitations in preoperative prediction and intraoperative frozen section evaluation for assessing STAS, current research is largely restricted to using clinical and imaging features to predict STAS occurrence, with results that are inconsistent and unsatisfactory. Furthermore, most studies focus on individual clinical or imaging characteristics, and there is a lack of large-sample investigations. The rise of artificial intelligence in recent years has provided new insights into solving this problem, and existing studies have shown that artificial intelligence demonstrates better performance in STAS prediction compared to conventional methods. This article reviews the value of artificial intelligence in predicting STAS.

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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.	Li J, Wang Y, Li J, et al. Meta-analysis of lobectomy and sublobar resection for stage Ⅰ non-small cell lung cancer with spread through air spaces. Clin Lung Cancer, 2022, 23(3): 208-213.
4.	Thunnissen E, Marchevsky A, Rossi G, et al. RE: Spread through air spaces (STAS) is prognostic in atypical carcinoid, large cell neuroendocrine carcinoma, and small cell carcinoma of the Lung. J Thorac Oncol, 2020, 15(7): e116-e117.
5.	Chen S, Ye T, Yang S, et al. Prognostic implication of tumor spread through air spaces in patients with pathologic N0 lung adenocarcinoma. Lung Cancer, 2022 Feb: 164: 33-38.164-133.
6.	Altorki N, Wang X, Kozono D, et al. Lobar or sublobar resection for peripheral stage ⅠA non-small-cell lung cancer. N Engl J Med, 2023, 388(6): 489-498.
7.	Eguchi T, Kameda K, Lu S, et al. Lobectomy is associated with better outcomes than sublobar resection in spread through air spaces (STAS)-positive T1 lung adenocarcinoma: A propensity score-matched analysis. J Thorac Oncol, 2019, 14(1): 87-98.
8.	de Margerie-Mellon C, Onken A, Heidinger BH, et al. CT manifestations of tumor spread through airspaces in pulmonary adenocarcinomas presenting as subsolid nodules. J Thorac Imaging, 2018, 33(6): 402-408.
9.	Kim SK, Kim TJ, Chung MJ, et al. Lung adenocarcinoma: CT features associated with spread through air spaces. Radiology, 2018, 289(3): 831-840.
10.	Toyokawa G, Yamada Y, Tagawa T, et al. Computed tomography features of resected lung adenocarcinomas with spread through air spaces. J Thorac Cardiovasc Surg, 2018, 156(4): 1670-1676.
11.	Perez-Johnston R, Araujo-Filho JA, Connolly JG, et al. CT-based radiogenomic analysis of clinical stage Ⅰ lung adenocarcinoma with histopathologic features and oncologic outcomes. Radiology, 2022, 303(3): 664-672.
12.	Jiang C, Luo Y, Yuan J, et al. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma. Eur Radiol, 2020, 30(7): 4050-4057.
13.	Takehana K, Sakamoto R, Fujimoto K, et al. Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma. Sci Rep, 2022, 12(1): 10323.
14.	Bassi M, Russomando A, Vannucci J, et al. Role of radiomics in predicting lung cancer spread through air spaces in a heterogeneous dataset. Transl Lung Cancer Res, 2022, 11(4): 560-571.
15.	Wang X, Ma C, Jiang Q, et al. Performance of deep learning model and radiomics model for preoperative prediction of spread through air spaces in the surgically resected lung adenocarcinoma: A two-center comparative study. Transl Lung Cancer Res, 2024, 13(12): 3486-3499.
16.	Zhuo Y, Feng M, Yang S, et al. Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma. Transl Oncol, 2020, 13(10): 100820.
17.	Zhou J, Hu B, Feng W, et al. An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT. NPJ Digit Med, 2023, 6(1): 119.
18.	He K, Gkioxari G, Dollar P, et al. Mask R-CNN. IEEE Trans Pattern Anal Mach Intell, 2020, 42(2): 386-397.
19.	Wu L, Yang X, Cao W, et al. Multiple level CT radiomics features preoperatively predict lymph node metastasis in esophageal cancer: A multicentre retrospective study. Front Oncol, 2020 Jan 21: 9: 1548.
20.	Friedman RJ, Gutkowicz-Krusin D, Farber MJ, et al. The diagnostic performance of expert dermoscopists vs a computer-vision system on small-diameter melanomas. Arch Dermatol, 2008, 144(4): 476-482.
21.	Tao J, Liang C, Yin K, et al. 3D convolutional neural network model from contrast-enhanced CT to predict spread through air spaces in non-small cell lung cancer. Diagn Interv Imaging, 2022, 103(11): 535-544.
22.	Pan L, Liang Q, Zeng W, et al. Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer. NPJ Precis Oncol, 2024, 8(1): 285.
23.	Li J, Chen Z, Chen Y, et al. CT-based delta radiomics in predicting the prognosis of stage Ⅳ gastric cancer to immune checkpoint inhibitors. Front Oncol, 2023 Jan 4: 12: 1059874.
24.	Chen Z, Chen Y, Sun Y, et al. Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multi-modal data. Signal Transduct Target Ther, 2024, 9(1): 222.
25.	Zhang Z, Zhao Y, Ma YJ, et al. Prediction of STAS in lung adenocarcinoma with nodules ≤ 2 cm using machine learning: A multicenter retrospective study. BMC Cancer, 2025, 25(1): 417.
26.	Altinay S, Metovic J, Massa F, et al. Spread through air spaces (STAS) is a predictor of poor outcome in atypical carcinoids of the lung. Virchows Arch, 2019, 475(3): 325-334.
27.	Wang J, Yao Y, Tang D, et al. An individualized nomogram for predicting and validating spread through air space (STAS) in surgically resected lung adenocarcinoma: A single center retrospective analysis. J Cardiothorac Surg, 2023, 18(1): 337.
28.	Jin W, Shen L, Tian Y, et al. Improving the prediction of spreading through air spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model: A multicenter cohort study. Biomark Res, 2023, 11(1): 102.
29.	Liu C, Wang YF, Gong P, et al. Prediction of tumor spread through air spaces with an automatic segmentation deep learning model in peripheral stage Ⅰ lung adenocarcinoma. Respir Res, 2025, 26(1): 94.
30.	Ou DX, Lu CW, Chen LW, et al. Deep learning analysis for predicting tumor spread through air space in early-stage lung adenocarcinoma pathology images. Cancers (Basel), 2024, 16(11): 2132.
31.	Yang Z, Wei T, Liang Y, et al. A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images. Nat Commun, 2025, 16(1): 2366.
32.	Vaghjiani RG, Takahashi Y, Eguchi T, et al. Tumor spread through air spaces is a predictor of occult lymph node metastasis in clinical stage ⅠA lung adenocarcinoma. J Thorac Oncol, 2020, 15(5): 792-802.
33.	Ladbury C, Amini A, Govindarajan A, et al. Integration of artificial intelligence in lung cancer: Rise of the machine. Cell Rep Med, 2023, 4(2): 100933.
34.	Zhao T, Fu C, Tie M, et al. RGSB-UNet: Hybrid deep learning framework for tumour segmentation in digital pathology images. Bioengineering (Basel), 2023, 10(8): 957.
35.	Zhang S, Yuan Z, Zhou X, et al. VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images. Comput Methods Programs Biomed, 2024, 250: 108178.
36.	Su F, Zhang W, Liu Y, et al. The development and validation of pathological sections based U-Net deep learning segmentation model for the detection of esophageal mucosa and squamous cell neoplasm. J Gastrointest Oncol, 2023, 14(5): 1982-1992.
37.	Ding K, Zhou M, Wang H, et al. A large-scale synthetic pathological dataset for deep learning-enabled segmentation of breast cancer. Sci Data, 2023, 10(1): 231.
38.	Li H, Li L, Liu Y, et al. Predictive value of CT and 18F-FDG PET/CT features on spread through air space in lung adenocarcinoma. BMC Cancer, 2024, 24(1): 434.
39.	Guo D, Wang Y, Chen J, et al. Integration of multi-omics data for survival prediction of lung adenocarcinoma. Comput Methods Programs Biomed, 2024, 250: 108192.
40.	Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
41.	Kadota K, Kushida Y, Kagawa S, et al. limited resection is associated with a higher risk of locoregional recurrence than lobectomy in stage Ⅰ lung adenocarcinoma with tumor spread through air spaces. Am J Surg Pathol, 2019, 43(8): 1033-1041.
42.	Kawasaki H, Itoh T, Takaku Y, et al. The NanoSuit method: A novel histological approach for examining paraffin sections in a nondestructive manner by correlative light and electron microscopy. Lab Invest, 2020, 100(1): 161-173.
43.	Villalba JA, Shih AR, Sayo TMS, et al. Accuracy and reproducibility of intraoperative assessment on tumor spread through air spaces in stage 1 lung adenocarcinomas. J Thorac Oncol, 2021, 16(4): 619-629.
44.	Kim PJ, Hwang HS, Choi G, et al. A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma. Sci Rep, 2024, 14(1): 6366.
45.	Shi J, Sun D, Wu K, et al. Positional encoding-guided transformer-based multiple instance learning for histopathology whole slide images classification. Comput Methods Programs Biomed, 2025, 258: 108491.
46.	Huang D, Li Z, Jiang T, et al. Artificial intelligence in lung cancer: current applications, future perspectives, and challenges. Front Oncol, 2024, 14: 1486310.
47.	许万星, 王琳, 郭巧梅, et al. 多模态肺结节诊断模型的临床验证及应用价值探索. 上海交通大学学报(医学版), 2024, 44(08): 1030-6.
48.	Xiang J, Wang X, Zhang X, et al. A vision-language foundation model for precision oncology. Nature, 2025, 638(8051): 769-778.
49.	Chekroud AM, Hawrilenko M, Loho H, et al. Illusory generalizability of clinical prediction models. Science, 2024, 383(6679): 164-167.
50.	Qiu S, Joshi PS, Miller MI, et al. Development and validation of an interpretable deep learning framework for Alzheimer's disease classification. Brain, 2020, 143(6): 1920-1933.
51.	Qiu S, Miller MI, Joshi PS, et al. Multimodal deep learning for Alzheimer's disease dementia assessment. Nat Commun, 2022, 13(1): 3404.
52.	Ebrahimighahnavieh MA, Luo S, Chiong R. Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review. Comput Methods Programs Biomed, 2020, 187: 105242.
53.	Veneziani I, Marra A, Formica C, et al. Applications of artificial intelligence in the neuropsychological assessment of dementia: A systematic review. J Pers Med, 2024, 14(1): 113.
54.	Kampaktsis PN, Emfietzoglou M, Al Shehhi A, et al. Artificial intelligence in atherosclerotic disease: Applications and trends. Front Cardiovasc Med, 2023, 9: 949454.

1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin, 2021, 71(3): 209-249.
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. Li J, Wang Y, Li J, et al. Meta-analysis of lobectomy and sublobar resection for stage Ⅰ non-small cell lung cancer with spread through air spaces. Clin Lung Cancer, 2022, 23(3): 208-213.
4. Thunnissen E, Marchevsky A, Rossi G, et al. RE: Spread through air spaces (STAS) is prognostic in atypical carcinoid, large cell neuroendocrine carcinoma, and small cell carcinoma of the Lung. J Thorac Oncol, 2020, 15(7): e116-e117.
5. Chen S, Ye T, Yang S, et al. Prognostic implication of tumor spread through air spaces in patients with pathologic N0 lung adenocarcinoma. Lung Cancer, 2022 Feb: 164: 33-38.164-133.
6. Altorki N, Wang X, Kozono D, et al. Lobar or sublobar resection for peripheral stage ⅠA non-small-cell lung cancer. N Engl J Med, 2023, 388(6): 489-498.
7. Eguchi T, Kameda K, Lu S, et al. Lobectomy is associated with better outcomes than sublobar resection in spread through air spaces (STAS)-positive T1 lung adenocarcinoma: A propensity score-matched analysis. J Thorac Oncol, 2019, 14(1): 87-98.
8. de Margerie-Mellon C, Onken A, Heidinger BH, et al. CT manifestations of tumor spread through airspaces in pulmonary adenocarcinomas presenting as subsolid nodules. J Thorac Imaging, 2018, 33(6): 402-408.
9. Kim SK, Kim TJ, Chung MJ, et al. Lung adenocarcinoma: CT features associated with spread through air spaces. Radiology, 2018, 289(3): 831-840.
10. Toyokawa G, Yamada Y, Tagawa T, et al. Computed tomography features of resected lung adenocarcinomas with spread through air spaces. J Thorac Cardiovasc Surg, 2018, 156(4): 1670-1676.
11. Perez-Johnston R, Araujo-Filho JA, Connolly JG, et al. CT-based radiogenomic analysis of clinical stage Ⅰ lung adenocarcinoma with histopathologic features and oncologic outcomes. Radiology, 2022, 303(3): 664-672.
12. Jiang C, Luo Y, Yuan J, et al. CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma. Eur Radiol, 2020, 30(7): 4050-4057.
13. Takehana K, Sakamoto R, Fujimoto K, et al. Peritumoral radiomics features on preoperative thin-slice CT images can predict the spread through air spaces of lung adenocarcinoma. Sci Rep, 2022, 12(1): 10323.
14. Bassi M, Russomando A, Vannucci J, et al. Role of radiomics in predicting lung cancer spread through air spaces in a heterogeneous dataset. Transl Lung Cancer Res, 2022, 11(4): 560-571.
15. Wang X, Ma C, Jiang Q, et al. Performance of deep learning model and radiomics model for preoperative prediction of spread through air spaces in the surgically resected lung adenocarcinoma: A two-center comparative study. Transl Lung Cancer Res, 2024, 13(12): 3486-3499.
16. Zhuo Y, Feng M, Yang S, et al. Radiomics nomograms of tumors and peritumoral regions for the preoperative prediction of spread through air spaces in lung adenocarcinoma. Transl Oncol, 2020, 13(10): 100820.
17. Zhou J, Hu B, Feng W, et al. An ensemble deep learning model for risk stratification of invasive lung adenocarcinoma using thin-slice CT. NPJ Digit Med, 2023, 6(1): 119.
18. He K, Gkioxari G, Dollar P, et al. Mask R-CNN. IEEE Trans Pattern Anal Mach Intell, 2020, 42(2): 386-397.
19. Wu L, Yang X, Cao W, et al. Multiple level CT radiomics features preoperatively predict lymph node metastasis in esophageal cancer: A multicentre retrospective study. Front Oncol, 2020 Jan 21: 9: 1548.
20. Friedman RJ, Gutkowicz-Krusin D, Farber MJ, et al. The diagnostic performance of expert dermoscopists vs a computer-vision system on small-diameter melanomas. Arch Dermatol, 2008, 144(4): 476-482.
21. Tao J, Liang C, Yin K, et al. 3D convolutional neural network model from contrast-enhanced CT to predict spread through air spaces in non-small cell lung cancer. Diagn Interv Imaging, 2022, 103(11): 535-544.
22. Pan L, Liang Q, Zeng W, et al. Feature-interactive Siamese graph encoder-based image analysis to predict STAS from histopathology images in lung cancer. NPJ Precis Oncol, 2024, 8(1): 285.
23. Li J, Chen Z, Chen Y, et al. CT-based delta radiomics in predicting the prognosis of stage Ⅳ gastric cancer to immune checkpoint inhibitors. Front Oncol, 2023 Jan 4: 12: 1059874.
24. Chen Z, Chen Y, Sun Y, et al. Predicting gastric cancer response to anti-HER2 therapy or anti-HER2 combined immunotherapy based on multi-modal data. Signal Transduct Target Ther, 2024, 9(1): 222.
25. Zhang Z, Zhao Y, Ma YJ, et al. Prediction of STAS in lung adenocarcinoma with nodules ≤ 2 cm using machine learning: A multicenter retrospective study. BMC Cancer, 2025, 25(1): 417.
26. Altinay S, Metovic J, Massa F, et al. Spread through air spaces (STAS) is a predictor of poor outcome in atypical carcinoids of the lung. Virchows Arch, 2019, 475(3): 325-334.
27. Wang J, Yao Y, Tang D, et al. An individualized nomogram for predicting and validating spread through air space (STAS) in surgically resected lung adenocarcinoma: A single center retrospective analysis. J Cardiothorac Surg, 2023, 18(1): 337.
28. Jin W, Shen L, Tian Y, et al. Improving the prediction of spreading through air spaces (STAS) in primary lung cancer with a dynamic dual-delta hybrid machine learning model: A multicenter cohort study. Biomark Res, 2023, 11(1): 102.
29. Liu C, Wang YF, Gong P, et al. Prediction of tumor spread through air spaces with an automatic segmentation deep learning model in peripheral stage Ⅰ lung adenocarcinoma. Respir Res, 2025, 26(1): 94.
30. Ou DX, Lu CW, Chen LW, et al. Deep learning analysis for predicting tumor spread through air space in early-stage lung adenocarcinoma pathology images. Cancers (Basel), 2024, 16(11): 2132.
31. Yang Z, Wei T, Liang Y, et al. A foundation model for generalizable cancer diagnosis and survival prediction from histopathological images. Nat Commun, 2025, 16(1): 2366.
32. Vaghjiani RG, Takahashi Y, Eguchi T, et al. Tumor spread through air spaces is a predictor of occult lymph node metastasis in clinical stage ⅠA lung adenocarcinoma. J Thorac Oncol, 2020, 15(5): 792-802.
33. Ladbury C, Amini A, Govindarajan A, et al. Integration of artificial intelligence in lung cancer: Rise of the machine. Cell Rep Med, 2023, 4(2): 100933.
34. Zhao T, Fu C, Tie M, et al. RGSB-UNet: Hybrid deep learning framework for tumour segmentation in digital pathology images. Bioengineering (Basel), 2023, 10(8): 957.
35. Zhang S, Yuan Z, Zhou X, et al. VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images. Comput Methods Programs Biomed, 2024, 250: 108178.
36. Su F, Zhang W, Liu Y, et al. The development and validation of pathological sections based U-Net deep learning segmentation model for the detection of esophageal mucosa and squamous cell neoplasm. J Gastrointest Oncol, 2023, 14(5): 1982-1992.
37. Ding K, Zhou M, Wang H, et al. A large-scale synthetic pathological dataset for deep learning-enabled segmentation of breast cancer. Sci Data, 2023, 10(1): 231.
38. Li H, Li L, Liu Y, et al. Predictive value of CT and 18F-FDG PET/CT features on spread through air space in lung adenocarcinoma. BMC Cancer, 2024, 24(1): 434.
39. Guo D, Wang Y, Chen J, et al. Integration of multi-omics data for survival prediction of lung adenocarcinoma. Comput Methods Programs Biomed, 2024, 250: 108192.
40. Ardila D, Kiraly AP, Bharadwaj S, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med, 2019, 25(6): 954-961.
41. Kadota K, Kushida Y, Kagawa S, et al. limited resection is associated with a higher risk of locoregional recurrence than lobectomy in stage Ⅰ lung adenocarcinoma with tumor spread through air spaces. Am J Surg Pathol, 2019, 43(8): 1033-1041.
42. Kawasaki H, Itoh T, Takaku Y, et al. The NanoSuit method: A novel histological approach for examining paraffin sections in a nondestructive manner by correlative light and electron microscopy. Lab Invest, 2020, 100(1): 161-173.
43. Villalba JA, Shih AR, Sayo TMS, et al. Accuracy and reproducibility of intraoperative assessment on tumor spread through air spaces in stage 1 lung adenocarcinomas. J Thorac Oncol, 2021, 16(4): 619-629.
44. Kim PJ, Hwang HS, Choi G, et al. A new model using deep learning to predict recurrence after surgical resection of lung adenocarcinoma. Sci Rep, 2024, 14(1): 6366.
45. Shi J, Sun D, Wu K, et al. Positional encoding-guided transformer-based multiple instance learning for histopathology whole slide images classification. Comput Methods Programs Biomed, 2025, 258: 108491.
46. Huang D, Li Z, Jiang T, et al. Artificial intelligence in lung cancer: current applications, future perspectives, and challenges. Front Oncol, 2024, 14: 1486310.
47. 许万星, 王琳, 郭巧梅, et al. 多模态肺结节诊断模型的临床验证及应用价值探索. 上海交通大学学报(医学版), 2024, 44(08): 1030-6.
48. Xiang J, Wang X, Zhang X, et al. A vision-language foundation model for precision oncology. Nature, 2025, 638(8051): 769-778.
49. Chekroud AM, Hawrilenko M, Loho H, et al. Illusory generalizability of clinical prediction models. Science, 2024, 383(6679): 164-167.
50. Qiu S, Joshi PS, Miller MI, et al. Development and validation of an interpretable deep learning framework for Alzheimer's disease classification. Brain, 2020, 143(6): 1920-1933.
51. Qiu S, Miller MI, Joshi PS, et al. Multimodal deep learning for Alzheimer's disease dementia assessment. Nat Commun, 2022, 13(1): 3404.
52. Ebrahimighahnavieh MA, Luo S, Chiong R. Deep learning to detect Alzheimer's disease from neuroimaging: A systematic literature review. Comput Methods Programs Biomed, 2020, 187: 105242.
53. Veneziani I, Marra A, Formica C, et al. Applications of artificial intelligence in the neuropsychological assessment of dementia: A systematic review. J Pers Med, 2024, 14(1): 113.
54. Kampaktsis PN, Emfietzoglou M, Al Shehhi A, et al. Artificial intelligence in atherosclerotic disease: Applications and trends. Front Cardiovasc Med, 2023, 9: 949454.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

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