AUTHOR=Park Changhee , Jeong Dong Young , Choi Yeonu , Oh You Jin , Kim Jonghoon , Ryu Jeongun , Paeng Kyunghyun , Lee Se-Hoon , Ock Chan-Young , Lee Ho Yun 

TITLE=Tumor-infiltrating lymphocyte enrichment predicted by CT radiomics analysis is associated with clinical outcomes of non-small cell lung cancer patients receiving immune checkpoint inhibitors

JOURNAL=Frontiers in Immunology

VOLUME=Volume 13 - 2022

YEAR=2023

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.1038089

DOI=10.3389/fimmu.2022.1038089

ISSN=1664-3224

ABSTRACT=Background: Enrichment of tumor-infiltrating lymphocytes (TIL) in the tumor microenvironment (TME) is a reliable biomarker of immune checkpoint inhibitors (ICI) in non-small cell lung cancer (NSCLC). Phenotyping through computed tomography (CT) radiomics has the overcome the limitations of tissue-based assessment, including for TIL analysis. Here, we assess TIL enrichment objectively using an AI-powered TIL analysis in H&E image and analyze its association with quantitative radiomic features (RFs). Clinical significance of the selected RFs is then validated in the independent NSCLC patients who received ICI.

Methods: In the training cohort containing both tumor tissue samples and corresponding CT images obtained within 1 month, we extracted 86 RFs from the CT images. The TIL enrichment score (TILes) was defined as the fraction of tissue area with high intra-tumoral or stromal TIL density divided by the whole TME area, as measured on an H&E slide. We filtered the insignificant RFs and developed a statistical model that predicted TILes using RFs. The model was applied to CT images from the validation cohort, which included NSCLC patients who received ICI monotherapy.

Results: A total of 220 NSCLC samples were included in the training cohort. After filtering the RFs, two features, gray level variance (coefficient 1·71 x 10-3) and large area low gray level emphasis (coefficient -2·48 x 10-5), were included in the model. The two features were both computed from the size-zone matrix, which has strength in reflecting intralesional texture heterogeneity. In the validation cohort, the patients with high predicted TILes (≥ median) had significantly prolonged progression-free survival compared to those with low predicted TILes (median 4·0 months versus 2·1 months, p = 0·002). Patients who experienced a response to ICI or stable disease with ICI had higher predicted TILes compared with the patients who experienced progressive disease as the best response (p = 0·001, p = 0·036, respectively). Predicted TILes was significantly associated with progression-free survival independent of PD-L1 status.

Conclusions: In this CT radiomics model, predicted TILes was significantly associated with ICI outcomes in NSCLC patients. Analyzing TME through radiomics may overcome the limitations of tissue-based analysis and assist clinical decisions regarding ICI.