Utility of CT radiomics for prediction of PD‐L1 expression in advanced lung adenocarcinomas

Abstract

Background We aimed to assess if quantitative radiomic features can predict programmed death ligand 1 (PD‐L1) expression in advanced stage lung adenocarcinoma. Methods This retrospective study included 153 patients who had advanced stage (>IIIA by TNM classification) lung adenocarcinoma with pretreatment thin section computed tomography (CT) images and PD‐L1 expression test results in their pathology reports. Clinicopathological data were collected from electronic medical records. Visual analysis and radiomic feature extraction of the tumor from pretreatment CT were performed. We constructed two models for multivariate logistic regression analysis (one based on clinical variables, and the other based on a combination of clinical variables and radiomic features), and compared c‐statistics of the receiver operating characteristic curves of each model to identify the model with the higher predictability. Results Among 153 patients, 53 patients were classified as PD‐L1 positive and 100 patients as PD‐L1 negative. There was no significant difference in clinical characteristics or imaging findings on visual analysis between the two groups (P > 0.05 for all). Rad‐score by radiomic analysis was higher in the PD‐L1 positive group than in the PD‐L1 negative group with a statistical significance (−0.378 ± 1.537 vs. −1.171 ± 0.822, P = 0.0008). A prediction model that uses clinical variables and CT radiomic features showed higher performance compared to a prediction model that uses clinical variables only (c‐statistic = 0.646 vs. 0.550, P = 0.0299). Conclusions Quantitative CT radiomic features can predict PD‐L1 expression in advanced stage lung adenocarcinoma. A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD‐L1 expression. Key points Significant findings of the study Quantitative CT radiomic features can help predict PD‐L1 expression, whereas none of the qualitative imaging findings is associated with PD‐L1 positivity. What this study adds A prediction model composed of clinical variables and CT radiomic features may facilitate noninvasive assessment of PD‐L1 expression.

<p>Pretreatment CT-Based Radiomics Signature as a Potential Imaging Biomarker for Predicting the Expression of PD-L1 and CD8+TILs in ESCC</p>

“…48 Jiang et al reported that PET/CT radiomics features could be used to assess PD-L1 expression levels in NSCLC, 21 and a similar conclusion was confirmed by Yoon et al in patients with lung adenocarcinoma. 22 Our results demonstrated that radiomics-based features were superior to conventional clinical models in identifying PD-L1 expression, but the mechanism of the relationship between radiomics and underlying driving biology must be validated.…”

“…Cases with PD-L1-stained cells ranging from 1% to 10% of the total tumor cells were considered PD-L1positive in pancreatic cancer, 36 and Yoon et al applied CT radiomics for predicting PD-L1 expression and defined PD-L1 positive as ≥50% (TPS) with any intensity in NSCLC. 22 Some research ignored the intensity of staining to a certain extent, which influenced on the PD-L1 predictive accuracy and response to immunotherapy. Consequently, standard evaluation criteria and accurate cut-off values are urgently needed in the future.…”

“…19,20 Jiang et al reported that PET/ CT radiomics features were able to assess PD-L1 expression levels in NSCLC, 21 and Yoon et al confirmed these results in patients with lung adenocarcinoma. 22 Liao et al developed and validated a radiomics-based biomarker (Rad score) to predict the infiltration of tumor-infiltrating CD8+TILs in hepatocellular carcinoma (HCC). 23 However, no investigation has confirmed the relationship between CT quantitative features and PD-L1 or CD8+TILs expression status in esophageal cancer.…”

A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts ALK Rearrangement Status in Lung Adenocarcinoma

“…A large number of studies have shown that the radiomic analysis can be used to predict the mutation status of several oncogenes (16,17). Currently, most studies in lung cancer have been done in primary tumors using computed tomography (CT) images (18)(19)(20)(21)(22). For example, Gevaert et al used CT images-based signature of primary lung tumors to predict EGFR mutation status (23).…”

Functions and Clinical Significance of KLRG1 in the Development of Lung Adenocarcinoma and Immunotherapy

“…Although monotherapy with PD-1 or PD-L1 drugs is usually well tolerated, the combination treatment increases the risk of immune-related adverse events [8]. So, biomarkers with predictive role need to be developed to augment patient bene t, diminish the risk of toxicity, and guide the combination approaches [9,10]. Although the expression of PD-L1 on tumor cells positivity improves the clinical bene t population, PD-L1 detection alone is not satisfactory for patient selection and e cacy prediction in most malignancies [11].…”