A Computed Tomography Radiomics-based model to Predict Survival of Patients with EGFR-Mutated Non-small-cell Lung Cancer_Chinese Journal of Respiratory and Critical Care Medicine

Authors：

ZHANG Fengmei ^1,2,3 , CAO Yubin ⁴ , ZHU Lei ⁵ , XIONG Qunli ⁶ , WANG Zhoufeng ^1,2,3,7 ,  ZHANG Qin ⁸ ,  LI Weimin ^1,2,3,7,9

1. Department of Respiratory and Critical Care Medicine, Institute of Respiratory Health, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R.China;
2. Institute of Respiratory Health, State Key Laboratory of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China;
3. Institute of Respiratory Health and Multimorbidity, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China;
4. Department of Gastroenterology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100000, P.R. China;
5. Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin 300000, P.R. China;
6. Department of Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong 510000, P.R. China;
7. Precision Medicine Center, Precision Medicine Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China;
8. Department of Postgraduate students, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, P.R. China;
9. The Research Units of West China, Chinese Academy of Medical Sciences, West China Hospital, Chengdu, Sichuan 610041, P.R. China;

Corresponding author：

ZHANG Qin, Email: zhangq@wchscu.cn; LI Weimin, Email: weimi003@scu.edu.cn

Keywords：

radiomics; prognosis; non-small-cell lung cancer

DOI：

10.7507/1671-6205.202410068

Video：

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Abstract Full text Figures/Tables Video References Cited by

Objective For potential patients with better prognosis of non-small-cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) mutations, a simpler and more effective model with easy-to-obtain histopathological parameters was established. Methods The computed tomography (CT) images of 158 patients with EGFR-mutant NSCLC who were first diagnosed in West China Hospital of Sichuan University were retrospectively analyzed, and the target areas of the lesions were described. Patients were randomly assigned to either a model training group or a test group.The radiomics features were extracted from the CT images, and the least absolute shrinkage and selection operator (LASSO) regression method was used to screen out the valuable radiomics features. The logistic regression method was used to establish a radiomic model, and the nomogram was used to evaluate the discrimination ability. Finally, the calibration curve, receiver characteristic curve (ROC), Kaplan-Meier curve and decision curve analysis (DCA) were employed to assess model efficacy. Results A nomogram combining three important clinical factors : gender, lesion location, treatment, and imaging risk score was established to predict the 3-year, 5-year, and 8-year survival rates of NSCLC patients with EGFR mutation. The calibration curve demonstrated highly consistent between model-predicted survival probabilities and observed overall survival (OS). The area under the curve (AUC) -ROC of the predicted 3-year, 5-year and 8-year OS was 0.70, 0.79 and 0.68, respectively. The Kaplan-Meier curve revealed significant OS disparities when comparing high- and low-risk patient subgroups. The DCA curve showed that the predicted 3-year and 5-year OS increased more clinical benefits than the treatment of all patients or no treatment.Conclusion The nomogram for predicting the survival prognosis of NSCLC patients with EGFR mutation was constructed and verified, which can effectively predict the survival time range of NSCLC patients, and provide a reference for more individualized treatment decisions for such patients in clinical practice.

Citation： ZHANG Fengmei, CAO Yubin, ZHU Lei, XIONG Qunli, WANG Zhoufeng, ZHANG Qin, LI Weimin. A Computed Tomography Radiomics-based model to Predict Survival of Patients with EGFR-Mutated Non-small-cell Lung Cancer. Chinese Journal of Respiratory and Critical Care Medicine, 2025, 24(2): 77-85. doi: 10.7507/1671-6205.202410068 Copy

1.	Wang F, Tan F, Shen S, et al. Risk-stratified approach for never- and ever-smokers in lung cancer screening: a prospective cohort study in china. Am J Respir Crit Care Med, 2023, 207(1): 77-88.
2.	Hirsch FR, Scagliotti GV, Mulshine JL, et al, Lung cancer: current therapies and new targeted treatments. Lancet, 2017, 389(10066): 299-311.
3.	Keith RL, Miller YE. Lung cancer chemoprevention: current status and future prospects. Nat Rev Clin Oncol, 2013, 10(6): 334-343.
4.	Wang J, Sun P, Wang Q, et al. (-)-Epigallocatechin-3-gallate derivatives combined with cisplatin exhibit synergistic inhibitory effects on non-small-cell lung cancer cells. Cancer Cell Int, 2019, 19: 266.
5.	Shi Y, Li J, Zhang S, Wang M, et al. Molecular epidemiology of EGFR mutations in Asian patients with advanced non-small-cell lung cancer of adenocarcinoma histology - mainland china subset analysis of the PIONEER study. PLoS One, 2015, 10(11): e0143515.
6.	Hess LM, Kern DM, Carter GC, et al. Real-World treatment sequences and outcomes among patients with non-small cell lung cancer (RESOUNDS) in the united states: study protocol. JMIR Res Protoc, 2017, 6(10): e195.
7.	Goto Y, Tanai C, Yoh K, et al. Continuing EGFR-TKI beyond radiological progression in patients with advanced or recurrent, EGFR mutation-positive non-small-cell lung cancer: an observational study. ESMO Open, 2017, 2(4): e000214.
8.	Dong ZY, Zhang JT, Liu SY, et al. EGFR mutation correlates with uninflamed phenotype and weak immunogenicity, causing impaired response to PD-1 blockade in non-small cell lung cancer. Oncoimmunology, 2017, 6(11): e1356145.
9.	Singh D, Attri BK, Gill RK, et al. Review on EGFR inhibitors: critical updates. Mini Rev Med Chem, 2016, 16(14): 1134-1166.
10.	Tan DS, Yom SS, Tsao MS, et al. The international association for the study of lung cancer consensus statement on optimizing management of EGFR mutation-positive non-small-cell lung cancer: status in 2016. J Thorac Oncol, 2016, 11(7): 946-963.
11.	Taniguchi H, Yamada T, Wang R, et al. AXL confers intrinsic resistance to osimertinib and advances the emergence of tolerant cells. Nat Commun, 2019, 10(1): 259.
12.	Majem M, Remon J. Tumor heterogeneity: evolution through space and time in EGFR mutant non small cell lung cancer patients. Transl Lung Cancer Res, 2013, 2(3): 226-237.
13.	R Rosell R, Moran T, Queralt C, et al. Screening for epidermal growth factor receptor mutations in lung cancer. N Engl J Med, 2009, 361(10): 958-967.
14.	Wang S, Yu H, Gan Y, et al. Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study. Lancet Digit Health, 2022, 4(5): e309-e319.
15.	Jiang AY, Zhang J, Luo HL, et al. Icotinib and whole-brain radiotherapy for the treatment in patients with brain metastases from EGFR-mutant nonsmall cell lung cancer: a retrospective study. Medicine, 2018, 97(15): e0312.
16.	Wei XL, Zhang DS, He MM, et al. The predictive value of alkaline phosphatase and lactate dehydrogenase for overall survival in patients with esophageal squamous cell carcinoma. Tumor Biol, 2016, 37(2): 1879-1887.
17.	Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
18.	Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumor phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006.
19.	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.
20.	Pan S, Ding Z, Zhang L, et al. A nomogram combined radiomic and semantic features as imaging biomarker for classification of ovarian cystadenomas. Frontiers in oncology, 2020, 10: 895.
21.	Yuan M, Zhang YD, Pu XH, et al. Comparison of a radiomic biomarker with volumetric analysis for decoding tumor phenotypes of lung adenocarcinoma with different disease-specific survival. Eur Radiol, 2017, 27(11): 4857-4865.
22.	Zheng X, Shao J, Zhou L, et al. A comprehensive nomogram combining CT imaging with clinical features for prediction of lymph node metastasis in stage I-IIIB non-small cell lung cancer. Ther Innov Regul Sci, 2022, 56(1): 155-167.
23.	Lu X, Li M, Zhang H, et al. A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma. Phys Med Biol, 2020, 65(5): 055012.
24.	Goldstraw P, Chansky K, Crowley J, et al. International association for the study of lung cancer staging and prognostic factors committee, advisory boards, and participating institutions; international association for the study of lung cancer staging and prognostic factors committee advisory boards and participating institutions. 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.
25.	Cuocolo R, Stanzione A, Faletti R, et al. MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study. Eur Radiol, 2021, 31(10): 7575-7583.
26.	Weng Q, Zhou L, Wang H, et al. A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules. Clinical radiology, 2019, 74(12): 933-943.
27.	Xie C, Li Q. A simple nomogram for predicting early complications in patients after primary knee arthroplasty. The Knee, 2020, 27(2): 518-526.
28.	Liu J, Huang X, Chen S, et al. Nomogram based on clinical characteristics for preoperative prediction of perineural invasion in gastric cancer. J Int Med Res, 2020, 48(1): 300060519895131.
29.	Xie W, Liu J, Huang X, et al. A nomogram to predict vascular invasion before resection of colorectal cancer. Oncology letters, 2019, 18(6): 5785-5792.
30.	Balachandran VP, Gonen M, Smith JJ, et al. Nomograms in oncology: more than meets the eye. Lancet Oncol, 2015, 16(4): e173-180.
31.	Iasonos A, Schrag D, Raj GV, et al. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol, 2008, 26(8): 1364-70.
32.	Park C, Lee I, Jang S, et al. Factors affecting tumor recurrence after curative surgery for NSCLC: impacts of lymphovascular invasion on early tumor recurrence. J Thorac Dis, 2014, 6(10): 1420-1428.
33.	陈柔倩, 樊俊玲, 陈亚娟. 动态胸片在呼吸系统疾病诊疗中的应用进展. 中国呼吸与危重监护杂志, 2024, 23(12): 905-908.
34.	Dighe S, Purkayastha S, Swift I, et al. Diagnostic precision of CT in local staging of colon cancers: a meta-analysis. Clin Radiol, 2010, 65(9): 708-719.
35.	刘欣欣, 周晓蕾, 贾要丽, 等. 构建CT结合血清学指标模型预测结缔组织病相关肺纤维化的进展. 中国呼吸与危重监护杂志, 2024, 23(6): 406-413.
36.	Wang H, Schabath MB, Liu Y, et al. Association between computed tomographic features and KRAS viral oncogene mutations in patients with stage I lung adenocarcinoma and their prognostic value. Clin Lung Cancer, 2016, 17(4): 271-278.
37.	Kattan M. Nomograms are difficult to beat. Eur Urol, 2008, 53(4): 671-672.

1. Wang F, Tan F, Shen S, et al. Risk-stratified approach for never- and ever-smokers in lung cancer screening: a prospective cohort study in china. Am J Respir Crit Care Med, 2023, 207(1): 77-88.
2. Hirsch FR, Scagliotti GV, Mulshine JL, et al, Lung cancer: current therapies and new targeted treatments. Lancet, 2017, 389(10066): 299-311.
3. Keith RL, Miller YE. Lung cancer chemoprevention: current status and future prospects. Nat Rev Clin Oncol, 2013, 10(6): 334-343.
4. Wang J, Sun P, Wang Q, et al. (-)-Epigallocatechin-3-gallate derivatives combined with cisplatin exhibit synergistic inhibitory effects on non-small-cell lung cancer cells. Cancer Cell Int, 2019, 19: 266.
5. Shi Y, Li J, Zhang S, Wang M, et al. Molecular epidemiology of EGFR mutations in Asian patients with advanced non-small-cell lung cancer of adenocarcinoma histology - mainland china subset analysis of the PIONEER study. PLoS One, 2015, 10(11): e0143515.
6. Hess LM, Kern DM, Carter GC, et al. Real-World treatment sequences and outcomes among patients with non-small cell lung cancer (RESOUNDS) in the united states: study protocol. JMIR Res Protoc, 2017, 6(10): e195.
7. Goto Y, Tanai C, Yoh K, et al. Continuing EGFR-TKI beyond radiological progression in patients with advanced or recurrent, EGFR mutation-positive non-small-cell lung cancer: an observational study. ESMO Open, 2017, 2(4): e000214.
8. Dong ZY, Zhang JT, Liu SY, et al. EGFR mutation correlates with uninflamed phenotype and weak immunogenicity, causing impaired response to PD-1 blockade in non-small cell lung cancer. Oncoimmunology, 2017, 6(11): e1356145.
9. Singh D, Attri BK, Gill RK, et al. Review on EGFR inhibitors: critical updates. Mini Rev Med Chem, 2016, 16(14): 1134-1166.
10. Tan DS, Yom SS, Tsao MS, et al. The international association for the study of lung cancer consensus statement on optimizing management of EGFR mutation-positive non-small-cell lung cancer: status in 2016. J Thorac Oncol, 2016, 11(7): 946-963.
11. Taniguchi H, Yamada T, Wang R, et al. AXL confers intrinsic resistance to osimertinib and advances the emergence of tolerant cells. Nat Commun, 2019, 10(1): 259.
12. Majem M, Remon J. Tumor heterogeneity: evolution through space and time in EGFR mutant non small cell lung cancer patients. Transl Lung Cancer Res, 2013, 2(3): 226-237.
13. R Rosell R, Moran T, Queralt C, et al. Screening for epidermal growth factor receptor mutations in lung cancer. N Engl J Med, 2009, 361(10): 958-967.
14. Wang S, Yu H, Gan Y, et al. Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study. Lancet Digit Health, 2022, 4(5): e309-e319.
15. Jiang AY, Zhang J, Luo HL, et al. Icotinib and whole-brain radiotherapy for the treatment in patients with brain metastases from EGFR-mutant nonsmall cell lung cancer: a retrospective study. Medicine, 2018, 97(15): e0312.
16. Wei XL, Zhang DS, He MM, et al. The predictive value of alkaline phosphatase and lactate dehydrogenase for overall survival in patients with esophageal squamous cell carcinoma. Tumor Biol, 2016, 37(2): 1879-1887.
17. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577.
18. Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumor phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006.
19. 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.
20. Pan S, Ding Z, Zhang L, et al. A nomogram combined radiomic and semantic features as imaging biomarker for classification of ovarian cystadenomas. Frontiers in oncology, 2020, 10: 895.
21. Yuan M, Zhang YD, Pu XH, et al. Comparison of a radiomic biomarker with volumetric analysis for decoding tumor phenotypes of lung adenocarcinoma with different disease-specific survival. Eur Radiol, 2017, 27(11): 4857-4865.
22. Zheng X, Shao J, Zhou L, et al. A comprehensive nomogram combining CT imaging with clinical features for prediction of lymph node metastasis in stage I-IIIB non-small cell lung cancer. Ther Innov Regul Sci, 2022, 56(1): 155-167.
23. Lu X, Li M, Zhang H, et al. A novel radiomic nomogram for predicting epidermal growth factor receptor mutation in peripheral lung adenocarcinoma. Phys Med Biol, 2020, 65(5): 055012.
24. Goldstraw P, Chansky K, Crowley J, et al. International association for the study of lung cancer staging and prognostic factors committee, advisory boards, and participating institutions; international association for the study of lung cancer staging and prognostic factors committee advisory boards and participating institutions. 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.
25. Cuocolo R, Stanzione A, Faletti R, et al. MRI index lesion radiomics and machine learning for detection of extraprostatic extension of disease: a multicenter study. Eur Radiol, 2021, 31(10): 7575-7583.
26. Weng Q, Zhou L, Wang H, et al. A radiomics model for determining the invasiveness of solitary pulmonary nodules that manifest as part-solid nodules. Clinical radiology, 2019, 74(12): 933-943.
27. Xie C, Li Q. A simple nomogram for predicting early complications in patients after primary knee arthroplasty. The Knee, 2020, 27(2): 518-526.
28. Liu J, Huang X, Chen S, et al. Nomogram based on clinical characteristics for preoperative prediction of perineural invasion in gastric cancer. J Int Med Res, 2020, 48(1): 300060519895131.
29. Xie W, Liu J, Huang X, et al. A nomogram to predict vascular invasion before resection of colorectal cancer. Oncology letters, 2019, 18(6): 5785-5792.
30. Balachandran VP, Gonen M, Smith JJ, et al. Nomograms in oncology: more than meets the eye. Lancet Oncol, 2015, 16(4): e173-180.
31. Iasonos A, Schrag D, Raj GV, et al. How to build and interpret a nomogram for cancer prognosis. J Clin Oncol, 2008, 26(8): 1364-70.
32. Park C, Lee I, Jang S, et al. Factors affecting tumor recurrence after curative surgery for NSCLC: impacts of lymphovascular invasion on early tumor recurrence. J Thorac Dis, 2014, 6(10): 1420-1428.
33. 陈柔倩, 樊俊玲, 陈亚娟. 动态胸片在呼吸系统疾病诊疗中的应用进展. 中国呼吸与危重监护杂志, 2024, 23(12): 905-908.
34. Dighe S, Purkayastha S, Swift I, et al. Diagnostic precision of CT in local staging of colon cancers: a meta-analysis. Clin Radiol, 2010, 65(9): 708-719.
35. 刘欣欣, 周晓蕾, 贾要丽, 等. 构建CT结合血清学指标模型预测结缔组织病相关肺纤维化的进展. 中国呼吸与危重监护杂志, 2024, 23(6): 406-413.
36. Wang H, Schabath MB, Liu Y, et al. Association between computed tomographic features and KRAS viral oncogene mutations in patients with stage I lung adenocarcinoma and their prognostic value. Clin Lung Cancer, 2016, 17(4): 271-278.
37. Kattan M. Nomograms are difficult to beat. Eur Urol, 2008, 53(4): 671-672.

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A Computed Tomography Radiomics-based model to Predict Survival of Patients with EGFR-Mutated Non-small-cell Lung Cancer

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