External validation of clinical and imaging features for predicting high-grade subtypes of stage ⅠA invasive lung adenocarcinoma_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

RONG Yu ^1,2 , HAN Nianqiao ³ , HAO Yanbing ¹ , HU Jianli ¹ , NIU Yajin ¹ , ZHANG Lan ⁴ , DONG Yuehua ¹ , ZHANG Nan ⁵ ,  LIU Junfeng ⁵

1. Department of Thoracic Surgery, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, P. R. China;
2. Department of Thoracic Surgery, The First Hospital of Hebei Medical University, Shijiazhuang, 050023, P. R. China;
3. Department of Thoracic Surgery, Xiongan Xuanwu Hospital, Xiong’an, 070001, Hebei, P. R. China;
4. Department of Medical Imaging, The First Affiliated Hospital of Hebei North University, Zhangjiakou, 075000, Hebei, P. R. China;
5. Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050031, P. R. China;

Corresponding author：

LIU Junfeng, Email: liujf@hebmu.edu.cn

Keywords：

Clinical prediction model; lung adenocarcinoma; high-grade pattern; external validation

DOI：

10.7507/1007-4848.202502042

Video：

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Objective To validate the performance of a CT imaging feature-based prediction model for identifying high-grade histological patterns (HGP), specifically micropapillary and solid subtypes, in stage Ⅰ A invasive lung adenocarcinoma. Methods A previously developed prediction model was applied to a cohort of 650 patients with stage Ⅰ A lung adenocarcinoma from the Fourth Hospital of Hebei Medical University. The model’s ability to discriminate HGP (assessed by area under the receiver operating characteristic curve), calibration, and clinical utility were evaluated based on extracted imaging parameters including tumor size, density, and lobulation. Results Validation revealed that the model demonstrated good performance in discriminating HGP (area under the curve>0.7). Calibration of the original model improved its calibration performance. Decision curve analysis (DCA) indicated that the model’s predicted HGP patient population closely approximated the actual population when using a threshold probability>0.6. Conclusion This study confirms the effectiveness of a CT imaging feature-based prediction model for identifying HGP in stage Ⅰ A lung adenocarcinoma in a clinical setting. Successful application of this model may be significant for determining surgical strategies and improving patient prognosis. Despite certain limitations, these findings provide new directions for future research.

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2.	Succony L, Rassl DM, Barker AP, et al. Adenocarcinoma spectrum lesions of the lung: Detection, pathology and treatment strategies. Cancer Treat Rev, 2021, 99: 102237.
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7.	Bertolaccini L, Cara A, Chiari M, et al. Real-world survival outcomes of wedge resection versus lobectomy for cT1a/b cN0 cM0 non-small cell lung cancer: A single center retrospective analysis. Front Oncol, 2023, 13: 1226429.
8.	Nitadori J, Bograd AJ, Kadota K, et al. Impact of micropapillary histologic subtype in selecting limited resection vs lobectomy for lung adenocarcinoma of 2 cm or smaller. J Natl Cancer Inst, 2013, 105(16): 1212-1220.
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11.	Chen LW, Yang SM, Chuang CC, et al. Solid attenuation components attention deep learning model to predict micropapillary and solid patterns in lung adenocarcinomas on computed tomography. Ann Surg Oncol, 2022, 29(12): 7473-7482.
12.	Wang F, Wang CL, Yi YQ, et al. Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features. Sci Rep, 2023, 13(1): 9302.
13.	Yang Y, Tan M, Ma W, et al. Preoperative prediction of the degree of differentiation of lung adenocarcinoma presenting as sub-solid or solid nodules with a radiomics nomogram. Clin Radiol, 2022, 77(9): e680-e688.
14.	Dong H, Yin LK, Qiu YG, et al. Prediction of high-grade patterns of stage ⅠA lung invasive adenocarcinoma based on high-resolution CT features: A bicentric study. Eur Radiol, 2023, 33(6): 3931-3940.
15.	Goldstraw P, Chansky K, Crowley J, et al. The IASLC Lung Cancer Staging Project: Proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. J Thorac Oncol, 2016, 11(1): 39-51.
16.	Shiono S, Yanagawa N. Spread through air spaces is a predictive factor of recurrence and a prognostic factor in stageⅠ lung adenocarcinoma. Interact Cardiovasc Thorac Surg, 2016, 23(4): 567-572.
17.	Janssen KJ, Moons KG, Kalkman CJ, et al. Updating methods improved the performance of a clinical prediction model in new patients. J Clin Epidemiol, 2008, 61(1): 76-86.
18.	Yang Y, Tan J, He Y, et al. Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study. Front Endocrinol, 2022, 13: 1099302.
19.	Jeon HW, Kim YD, Sim SB, et al. Comparison of clinical results between high grade patterns in stage Ⅰ lung adenocarcinoma. Thorac Cancer, 2022, 13(17): 2473-2479.
20.	梁佳凤, 吴琼, 马胜林, 等. 肺微乳头腺癌病理学及分子学特征研究进展. 中国肺癌杂志, 2020, 23(11): 1007-1013.Liang J, Wu Q, Ma S, et al. Pathological and molecular features of lung micropapillary adenocarcinoma. Chin J Lung Cancer, 2020, 23(11): 1007-1013.
21.	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.
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23.	Rauh SP, Heymans MW, Mehr DR, et al. Predicting mortality in patients treated differently: Updating and external validation of a prediction model for nursing home residents with dementia and lower respiratory infections. BMJ Open, 2016, 6(8): e011380.
24.	Zhao Z, Wickersham N, Kangelaris KN, et al. External validation of a biomarker and clinical prediction model for hospital mortality in acute respiratory distress syndrome. Intensive Care Med, 2017, 43(8): 1123-1131.
25.	Gulati G, Upshaw J, Wessler BS, et al. Generalizability of cardiovascular disease clinical prediction models: 158 independent external validations of 104 unique models. Circ Cardiovasc Qual Outcomes, 2022, 15(4): e008487.
26.	Altorki NK, Wang X, Wigle D, et al. Perioperative mortality and morbidity after sublobar versus lobar resection for early-stage non-small-cell lung cancer: Post-hoc analysis of an international, randomised, phase 3 trial (CALGB/Alliance 140503). Lancet Respir Med, 2018, 6(12): 915-924.
27.	Fukui M, Matsunaga T, Hattori A, et al. Sublobar resection is not always superior for early-stage lung cancer in high-risk patients. Eur J Cardiothorac Surg, 2024, 65(1).
28.	Beasley MB, Dembitzer FR, Flores RM. Surgical pathology of early stage non-small cell lung carcinoma. Ann Transl Med, 2016, 4(12): 238.
29.	Peng B, Li G, Guo Y. Prognostic significance of micropapillary and solid patterns in stage ⅠA lung adenocarcinoma. Am J Transl Res, 2021, 13(9): 10562-10569.
30.	Liu W, Zhang Q, Zhang T, et al. Minor histological components predict the recurrence of patients with resected stageⅠ acinar- or papillary-predominant lung adenocarcinoma. Front Oncol, 2022, 12: 1090544.
31.	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.
32.	靳凯淇沈莹冉吴易沐, 等. 是否存在实体型或微乳头型成分与ⅠA期肺腺癌患者预后关系的回顾性队列研究. 中华外科杂志, 2022, 60(6): 587-592.Jin KQ, Shen YR, Wu YM, et al. Association between the presence of solid or micropapillary components and survival outcome in stage ⅠA lung adenocarcinoma cases: A retrospective cohort study. Chin J Surg, 2022, 60(6): 587-592.

1. Krist AH, Davidson KW, Mangione CM, et al. Screening for lung cancer: US Preventive Services Task Force recommendation statement. JAMA, 2021, 325(10): 962-970.
2. Succony L, Rassl DM, Barker AP, et al. Adenocarcinoma spectrum lesions of the lung: Detection, pathology and treatment strategies. Cancer Treat Rev, 2021, 99: 102237.
3. Takahashi Y, Eguchi T, Kameda K, et al. Histologic subtyping in pathologic stage Ⅰ-ⅡA lung adenocarcinoma provides risk-based stratification for surveillance. Oncotarget, 2018, 9(87): 35742-35751.
4. Choi SH, Jeong JY, Lee SY, et al. Clinical implication of minimal presence of solid or micropapillary subtype in early-stage lung adenocarcinoma. Thorac Cancer, 2021, 12(2): 235-244.
5. 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.
6. Shi Y, Wu S, Ma S, et al. Comparison between wedge resection and lobectomy/segmentectomy for early-stage non-small cell lung cancer: A Bayesian meta-analysis and systematic review. Ann Surg Oncol, 2022, 29(3): 1868-1879.
7. Bertolaccini L, Cara A, Chiari M, et al. Real-world survival outcomes of wedge resection versus lobectomy for cT1a/b cN0 cM0 non-small cell lung cancer: A single center retrospective analysis. Front Oncol, 2023, 13: 1226429.
8. Nitadori J, Bograd AJ, Kadota K, et al. Impact of micropapillary histologic subtype in selecting limited resection vs lobectomy for lung adenocarcinoma of 2 cm or smaller. J Natl Cancer Inst, 2013, 105(16): 1212-1220.
9. Zhao ZR, To KF, Mok TS, et al. Is there significance in identification of non-predominant micropapillary or solid components in early-stage lung adenocarcinoma? Interact Cardiovasc Thorac Surg, 2017, 24(1): 121-125.
10. Ding H, Xia W, Zhang L, et al. CT-Based deep learning model for invasiveness classification and micropapillary pattern prediction within lung adenocarcinoma. Front Oncol, 2020, 10: 1186.
11. Chen LW, Yang SM, Chuang CC, et al. Solid attenuation components attention deep learning model to predict micropapillary and solid patterns in lung adenocarcinomas on computed tomography. Ann Surg Oncol, 2022, 29(12): 7473-7482.
12. Wang F, Wang CL, Yi YQ, et al. Comparison and fusion prediction model for lung adenocarcinoma with micropapillary and solid pattern using clinicoradiographic, radiomics and deep learning features. Sci Rep, 2023, 13(1): 9302.
13. Yang Y, Tan M, Ma W, et al. Preoperative prediction of the degree of differentiation of lung adenocarcinoma presenting as sub-solid or solid nodules with a radiomics nomogram. Clin Radiol, 2022, 77(9): e680-e688.
14. Dong H, Yin LK, Qiu YG, et al. Prediction of high-grade patterns of stage ⅠA lung invasive adenocarcinoma based on high-resolution CT features: A bicentric study. Eur Radiol, 2023, 33(6): 3931-3940.
15. Goldstraw P, Chansky K, Crowley J, et al. The IASLC Lung Cancer Staging Project: Proposals for revision of the TNM stage groupings in the forthcoming (eighth) edition of the TNM classification for lung cancer. J Thorac Oncol, 2016, 11(1): 39-51.
16. Shiono S, Yanagawa N. Spread through air spaces is a predictive factor of recurrence and a prognostic factor in stageⅠ lung adenocarcinoma. Interact Cardiovasc Thorac Surg, 2016, 23(4): 567-572.
17. Janssen KJ, Moons KG, Kalkman CJ, et al. Updating methods improved the performance of a clinical prediction model in new patients. J Clin Epidemiol, 2008, 61(1): 76-86.
18. Yang Y, Tan J, He Y, et al. Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study. Front Endocrinol, 2022, 13: 1099302.
19. Jeon HW, Kim YD, Sim SB, et al. Comparison of clinical results between high grade patterns in stage Ⅰ lung adenocarcinoma. Thorac Cancer, 2022, 13(17): 2473-2479.
20. 梁佳凤, 吴琼, 马胜林, 等. 肺微乳头腺癌病理学及分子学特征研究进展. 中国肺癌杂志, 2020, 23(11): 1007-1013.Liang J, Wu Q, Ma S, et al. Pathological and molecular features of lung micropapillary adenocarcinoma. Chin J Lung Cancer, 2020, 23(11): 1007-1013.
21. 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.
22. He B, Song Y, Wang L, et al. A machine learning-based prediction of the micropapillary/solid growth pattern in invasive lung adenocarcinoma with radiomics. Transl Lung Cancer Res, 2021, 10(2): 955-964.
23. Rauh SP, Heymans MW, Mehr DR, et al. Predicting mortality in patients treated differently: Updating and external validation of a prediction model for nursing home residents with dementia and lower respiratory infections. BMJ Open, 2016, 6(8): e011380.
24. Zhao Z, Wickersham N, Kangelaris KN, et al. External validation of a biomarker and clinical prediction model for hospital mortality in acute respiratory distress syndrome. Intensive Care Med, 2017, 43(8): 1123-1131.
25. Gulati G, Upshaw J, Wessler BS, et al. Generalizability of cardiovascular disease clinical prediction models: 158 independent external validations of 104 unique models. Circ Cardiovasc Qual Outcomes, 2022, 15(4): e008487.
26. Altorki NK, Wang X, Wigle D, et al. Perioperative mortality and morbidity after sublobar versus lobar resection for early-stage non-small-cell lung cancer: Post-hoc analysis of an international, randomised, phase 3 trial (CALGB/Alliance 140503). Lancet Respir Med, 2018, 6(12): 915-924.
27. Fukui M, Matsunaga T, Hattori A, et al. Sublobar resection is not always superior for early-stage lung cancer in high-risk patients. Eur J Cardiothorac Surg, 2024, 65(1).
28. Beasley MB, Dembitzer FR, Flores RM. Surgical pathology of early stage non-small cell lung carcinoma. Ann Transl Med, 2016, 4(12): 238.
29. Peng B, Li G, Guo Y. Prognostic significance of micropapillary and solid patterns in stage ⅠA lung adenocarcinoma. Am J Transl Res, 2021, 13(9): 10562-10569.
30. Liu W, Zhang Q, Zhang T, et al. Minor histological components predict the recurrence of patients with resected stageⅠ acinar- or papillary-predominant lung adenocarcinoma. Front Oncol, 2022, 12: 1090544.
31. 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.
32. 靳凯淇沈莹冉吴易沐, 等. 是否存在实体型或微乳头型成分与ⅠA期肺腺癌患者预后关系的回顾性队列研究. 中华外科杂志, 2022, 60(6): 587-592.Jin KQ, Shen YR, Wu YM, et al. Association between the presence of solid or micropapillary components and survival outcome in stage ⅠA lung adenocarcinoma cases: A retrospective cohort study. Chin J Surg, 2022, 60(6): 587-592.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesExternal validation of clinical and imaging features for predicting high-grade subtypes of stage ⅠA invasive lung adenocarcinoma

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