Abstract
PurposeThe assessment of Programmed death-ligand 1 (PD-L1) expression has become a game changer in the treatment of patients with advanced non-small cell lung cancer (NSCLC). We aimed to investigate the ability of Radiomics applied to computed tomography (CT) in predicting PD-L1 expression in patients with advanced NSCLC.MethodsBy applying texture analysis, we retrospectively analyzed 72 patients with advanced NSCLC. The datasets were randomly split into a training cohort (2/3) and a validation cohort (1/3). Forty radiomic features were extracted by manually drawing tumor volumes of interest (VOIs) on baseline contrast-enhanced CT. After selecting features on the training cohort, two predictive models were created using binary logistic regression, one for PD-L1 values ≥ 50% and the other for values between 1 and 49%. The two models were analyzed with ROC curves and tested in the validation cohort.ResultsThe Radiomic Score (Rad-Score) for PD-L1 values ≥ 50%, which consisted of Skewness and Low Gray-Level Zone Emphasis (GLZLM_LGZE), presented a cut-off value of − 0.745 with an area under the curve (AUC) of 0.811 and 0.789 in the training and validation cohort, respectively. The Rad-Score for PD-L1 values between 1 and 49% consisted of Sphericity, Skewness, Conv_Q3 and Gray Level Non-Uniformity (GLZLM_GLNU), showing a cut-off value of 0.111 with AUC of 0.763 and 0.806 in the two population, respectively.ConclusionRad-Scores obtained from CT texture analysis could be useful for predicting PD-L1 expression and guiding the therapeutic choice in patients with advanced NSCLC.
Highlights
Lung cancer is the leading cause for cancer death worldwide in both male and female patients with 2,093,876 estimated new cases in 2018 [1]
According to the National Comprehensive Cancer Network (NCCN) guideline version 4.2021, Pembrolizumab is considered the therapy of choice for patients without mutations of Epidermal Growth Factor Receptor (EGFR) and Anaplastic Lymphoma Kinase (ALK), if Programmed death-ligand 1 (PD-L1) is expressed by ≥ 50% of neoplastic cells
The aim of our study was to build two predictive models of PD-L1 expression values ≥ 1 and ≥ 50%, respectively, both based on a score formed by radiomic characteristics from baseline contrast-enhanced computed tomography (CT) images of patients with advanced non-small cell lung cancer (NSCLC), in order to noninvasively identify patients who may benefit from immunotherapy as first-line treatment in a pre-operative or pre-biopsy phase
Summary
Lung cancer is the leading cause for cancer death worldwide in both male and female patients with 2,093,876 estimated new cases in 2018 [1]. Eighty-five percent of lung cancer is represented by non-small cell lung cancer (NSCLC) with the majority of patients presenting with advanced disease at diagnosis [2]. Several clinical studies have shown that Pembrolizumab, a monoclonal antibody that prevents the PD-1/PD-L1 linking, is associated with better disease control and improved OS, with a reduced toxicity profile compared to chemotherapy in patients affected by advanced NSCLC [5,6,7,8,9]. According to the National Comprehensive Cancer Network (NCCN) guideline version 4.2021, Pembrolizumab is considered the therapy of choice for patients without mutations of Epidermal Growth Factor Receptor (EGFR) and Anaplastic Lymphoma Kinase (ALK), if PD-L1 is expressed by ≥ 50% of neoplastic cells
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
