Journal of the Korean Society of Radiology (대한영상의학회지)

The Korean Society of Radiology (대한영상의학회)
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Predictions of PD-L1 Expression Based on CT Imaging Features in Lung Squamous Cell Carcinoma

편평세포폐암에서 CT 영상 소견을 이용한 PD-L1 발현 예측

  • 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)
  • 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)
  • Received : 2023.01.25
  • Accepted : 2023.08.10
  • Published : 2024.03.01
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Abstract

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.

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.

Keywords

Acknowledgement

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.

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