대한영상의학회지 (Journal of the Korean Society of Radiology)

  • 제85권2호
  • /
  • Pages.394-408
  • /
  • 2024
  • /
  • 1738-2637(pISSN)
  • /
  • 2951-0805(eISSN)
대한영상의학회 (The Korean Society of Radiology)
권호 표지
DOI QR코드

DOI QR Code

편평세포폐암에서 CT 영상 소견을 이용한 PD-L1 발현 예측

Predictions of PD-L1 Expression Based on CT Imaging Features in Lung Squamous Cell Carcinoma

  • 여성희 (중앙보훈병원 영상의학과) ;
  • 윤현정 (중앙보훈병원 영상의학과) ;
  • 김인중 (중앙보훈병원 영상의학과) ;
  • 김여진 (중앙보훈병원 영상의학과) ;
  • 이영 (중앙보훈병원 보훈의학연구소) ;
  • 차윤기 (성균관대학교 의과대학 삼성서울병원 영상의학과) ;
  • 박소현 (울산대학교 의과대학 서울아산병원 영상의학과)
  • Seong Hee Yeo (Department of Radiology, Veterans Health Service Medical Center) ;
  • Hyun Jung Yoon (Department of Radiology, Veterans Health Service Medical Center) ;
  • Injoong Kim (Department of Radiology, Veterans Health Service Medical Center) ;
  • Yeo Jin Kim (Department of Radiology, Veterans Health Service Medical Center) ;
  • Young Lee (Veterans Medical Research Institute, Veterans Health Service Medical Center) ;
  • Yoon Ki Cha (Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine) ;
  • So Hyeon Bak (Department of Radiology, Asan Medical Center, University of Ulsan College of Medicine)
  • 투고 : 2023.01.25
  • 심사 : 2023.08.10
  • 발행 : 2024.03.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

목적 CT 영상 소견을 이용하여 편평세포폐암에서 programmed death ligand 1 (이하 PD-L1)의 발현을 예측하는 모델을 구축해 보고자 하였다. 대상과 방법 PD-L1 발현검사 결과를 포함하고 있는 97명의 편평세포폐암 환자를 포함하였고 종양 치료 전 시행한 CT 영상 소견을 분석하였다. 전체 환자군과 40명의 진행성(≥ stage IIIB) 병기 환자군에 대하여 PD-L1 발현 예측을 위한 다중 로지스틱 회귀 분석 모델 구축을 시행하였다. 각각의 환자군에 대하여 곡선 아래 면적(areas under the receiver operating characteristic curves; 이하 AUCs)을 분석하여 예측력을 평가하였다. 결과 전체 환자군에서 '전체 유의인자 모델'(종양병기, 종양크기, 흉막결절, 폐전이)의 AUC 값은 0.652이며, '선택 유의인자 모델'(흉막결절)은 0.556이었다. 진행성 병기 환자군에서 '선택 유의인자 모델'(종양크기, 흉막결절, 폐소수전이, 간질성폐렴의 부재)의 AUC 값은 0.897이었다. 이러한 인자들 중 흉막결절과 폐소수전이는 높은 오즈비를 보였다(각각, 8.78과 16.35). 결론 본 연구에서의 모델은 편평세포폐암의 PD-L1 발현예측의 가능성을 보여주었으며 흉막결절과 폐소수전이는 PD-L1 발현을 예측하는데 중요한 CT 예측인자였다.

Purpose To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT. Materials and Methods A total of 97 patients diagnosed with SCC who underwent PD-L1 expression assay were included in this study. We performed a CT analysis of the tumors using pretreatment CT images. Multiple logistic regression models were constructed to predict PD-L1 positivity in the total patient group and in the 40 advanced-stage (≥ stage IIIB) patients. The area under the receiver operating characteristic curve (AUC) was calculated for each model. Results For the total patient group, the AUC of the 'total significant features model' (tumor stage, tumor size, pleural nodularity, and lung metastasis) was 0.652, and that of the 'selected feature model' (pleural nodularity) was 0.556. For advanced-stage patients, the AUC of the 'selected feature model' (tumor size, pleural nodularity, pulmonary oligometastases, and absence of interstitial lung disease) was 0.897. Among these factors, pleural nodularity and pulmonary oligometastases had the highest odds ratios (8.78 and 16.35, respectively). Conclusion Our model could predict PD-L1 expression in patients with lung SCC, and pleural nodularity and pulmonary oligometastases were notable predictive CT features of PD-L1.

키워드

과제정보

This study was supported by a VHS Medical Center Research Grant, Republic of Korea (grant number: VHSMC 21007). The funders had no role in study design, data collection, and analysis, decision to publish, or preparation of the manuscript.

참고문헌

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2019. CA Cancer J Clin 2019;69:7-34  https://doi.org/10.3322/caac.21551
  2. Meza R, Meernik C, Jeon J, Cote ML. Lung cancer incidence trends by gender, race and histology in the United States, 1973-2010. PLoS One 2015;10:e0121323 
  3. Zhang M, Wang D, Sun Q, Pu H, Wang Y, Zhao S, et al. Prognostic significance of PD-L1 expression and 18F-FDG PET/CT in surgical pulmonary squamous cell carcinoma. Oncotarget 2017;8:51630-51640  https://doi.org/10.18632/oncotarget.18257
  4. Chen RL, Zhou JX, Cao Y, Li SH, Li YH, Jiang M, et al. The efficacy of PD-1/PD-L1 inhibitors in advanced squamous-cell lung cancer: a meta-analysis of 3112 patients. Immunotherapy 2019;11:1481-1490  https://doi.org/10.2217/imt-2019-0101
  5. Brahmer J, Reckamp KL, Baas P, Crino L, Eberhardt WE, Poddubskaya E, et al. Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N Engl J Med 2015;373:123-135  https://doi.org/10.1056/NEJMoa1504627
  6. Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, et al. Safety, activity, and immune correlates of anti-PD-1 antibody in cancer. N Engl J Med 2012;366:2443-2454  https://doi.org/10.1056/NEJMoa1200690
  7. Taube JM, Klein A, Brahmer JR, Xu H, Pan X, Kim JH, et al. Association of PD-1, PD-1 ligands, and other features of the tumor immune microenvironment with response to anti-PD-1 therapy. Clin Cancer Res 2014;20:5064-5074  https://doi.org/10.1158/1078-0432.CCR-13-3271
  8. Aguiar PN Jr, Santoro IL, Tadokoro H, de Lima Lopes G, Filardi BA, Oliveira P, et al. The role of PD-L1 expression as a predictive biomarker in advanced non-small-cell lung cancer: a network meta-analysis. Immunotherapy 2016;8:479-488  https://doi.org/10.2217/imt-2015-0002
  9. Wang GX, Guo LQ, Gainor JF, Fintelmann FJ. Immune checkpoint inhibitors in lung cancer: imaging considerations. AJR Am J Roentgenol 2017;209:567-575  https://doi.org/10.2214/AJR.16.17770
  10. Yoon J, Suh YJ, Han K, Cho H, Lee HJ, Hur J, et al. Utility of CT radiomics for prediction of PD-L1 expression in advanced lung adenocarcinomas. Thorac Cancer 2020;11:993-1004  https://doi.org/10.1111/1759-7714.13352
  11. Reck M, Rodriguez-Abreu D, Robinson AG, Hui R, Cso"szi T, Fulop A, et al. Pembrolizumab versus chemotherapy for PD-L1-positive non-small-cell lung cancer. N Engl J Med 2016;375:1823-1833  https://doi.org/10.1056/NEJMoa1606774
  12. McLaughlin J, Han G, Schalper KA, Carvajal-Hausdorf D, Pelekanou V, Rehman J, et al. Quantitative assessment of the heterogeneity of PD-L1 expression in non-small-cell lung cancer. JAMA Oncol 2016;2:46-54  https://doi.org/10.1001/jamaoncol.2015.3638
  13. Nimmagadda S. Quantifying PD-L1 expression to monitor immune checkpoint therapy: opportunities and challenges. Cancers (Basel) 2020;12:3173 
  14. Wu T, Zhou F, Soodeen-Lalloo AK, Yang X, Shen Y, Ding X, et al. The association between imaging features of TSCT and the expression of PD-L1 in patients with surgical resection of lung adenocarcinoma. Clin Lung Cancer 2019;20:e195-e207  https://doi.org/10.1016/j.cllc.2018.10.012
  15. Takada K, Toyokawa G, Okamoto T, Shimokawa M, Kozuma Y, Matsubara T, et al. A comprehensive analysis of programmed cell death ligand-1 expression with the clone SP142 antibody in non-small-cell lung cancer patients. Clin Lung Cancer 2017;18:572-582.e1  https://doi.org/10.1016/j.cllc.2017.02.004
  16. Sacher AG, Gandhi L. Biomarkers for the clinical use of PD-1/PD-L1 inhibitors in non-small-cell lung cancer: a review. JAMA Oncol 2016;2:1217-1222  https://doi.org/10.1001/jamaoncol.2016.0639
  17. Takada K, Okamoto T, Shoji F, Shimokawa M, Akamine T, Takamori S, et al. Clinical significance of PD-L1 protein expression in surgically resected primary lung adenocarcinoma. J Thorac Oncol 2016;11:1879-1890  https://doi.org/10.1016/j.jtho.2016.06.006
  18. Pan Y, Zheng D, Li Y, Cai X, Zheng Z, Jin Y, et al. Unique distribution of programmed death ligand 1 (PD-L1) expression in East Asian non-small cell lung cancer. J Thorac Dis 2017;9:2579-2586  https://doi.org/10.21037/jtd.2017.08.61
  19. Shukuya T, Carbone DP. Predictive markers for the efficacy of anti-PD-1/PD-L1 antibodies in lung cancer. J Thorac Oncol 2016;11:976-988  https://doi.org/10.1016/j.jtho.2016.02.015
  20. Fernandez-Bussy S, Pires Y, Labarca G, Vial MR. PD-L1 expression in a non-small cell lung cancer specimen obtained by EBUS-TBNA. Arch Bronconeumol (Engl Ed) 2018;54:290-292  https://doi.org/10.1016/j.arbr.2017.10.019
  21. Chen YB, Mu CY, Huang JA. Clinical significance of programmed death-1 ligand-1 expression in patients with non-small cell lung cancer: a 5-year-follow-up study. Tumori 2012;98:751-755  https://doi.org/10.1177/030089161209800612
  22. Toyokawa G, Takada K, Okamoto T, Kawanami S, Kozuma Y, Matsubara T, et al. Relevance between programmed death ligand 1 and radiologic invasiveness in pathologic stage I lung adenocarcinoma. Ann Thorac Surg 2017;103:1750-1757  https://doi.org/10.1016/j.athoracsur.2016.12.025
  23. Rizzo S, Petrella F, Buscarino V, De Maria F, Raimondi S, Barberis M, et al. CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer. Eur Radiol 2016;26:32-42  https://doi.org/10.1007/s00330-015-3814-0
  24. Liu Y, Kim J, Qu F, Liu S, Wang H, Balagurunathan Y, et al. CT features associated with epidermal growth factor receptor mutation status in patients with lung adenocarcinoma. Radiology 2016;280:271-280  https://doi.org/10.1148/radiol.2016151455
  25. Goldstraw P, Chansky K, Crowley J, Rami-Porta R, Asamura H, Eberhardt WE, 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;11:39-51  https://doi.org/10.1016/j.jtho.2015.09.009
  26. Herbst RS, Soria JC, Kowanetz M, Fine GD, Hamid O, Gordon MS, et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 2014;515:563-567  https://doi.org/10.1038/nature14011
  27. Sholl L. Molecular diagnostics of lung cancer in the clinic. Transl Lung Cancer Res 2017;6:560-569  https://doi.org/10.21037/tlcr.2017.08.03
  28. Kim H, Chung JH. PD-L1 testing in non-small cell lung cancer: past, present, and future. J Pathol Transl Med 2019;53:199-206  https://doi.org/10.4132/jptm.2019.04.24
  29. Yu H, Boyle TA, Zhou C, Rimm DL, Hirsch FR. PD-L1 expression in lung cancer. J Thorac Oncol 2016;11:964-975  https://doi.org/10.1016/j.jtho.2016.04.014
  30. Zhang M, Li G, Wang Y, Wang Y, Zhao S, Haihong P, et al. PD-L1 expression in lung cancer and its correlation with driver mutations: a meta-analysis. Sci Rep 2017;7:10255 
  31. Firth D. Bias reduction of maximum likelihood estimates. Biometrika 1993;80:27-38  https://doi.org/10.1093/biomet/80.1.27
  32. Heinze G, Schemper M. A solution to the problem of separation in logistic regression. Stat Med 2002;21:2409-2419  https://doi.org/10.1002/sim.1047
  33. Akaike H. Information theory and an extension of the maximum likelihood principle. In Parzen E, Tanabe K, Kitagawa G, eds. Selected papers of Hirotugu Akaike. New York: Springer 1998:199-213 
  34. Hastie TJ, Pregibon D. Chapter 6: generalized linear models. In Hastie TJ, ed. Statistical models in S. 1st ed. New York: Taylor & Francis 1992:195-248 
  35. Venables WN, Ripley BD. Modern applied statistics with S. 4th ed. New York: Springer 2002:498