To develop models to predict programmed death ligand 1 (PD-L1) expression in pulmonary squamous cell carcinoma (SCC) using CT.
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.
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).
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.
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