Tumor-infiltrating lymphocyte enrichment predicted by CT radiomic analysis is associated with clinical outcomes of immune checkpoint inhibitor in non–small cell lung cancer.

person Changhee Park Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea info_outline Changhee Park, Dong Young Jeong, Yeonu Choi, You Jin Oh, Jonghoon Kim, Sergio Pereira, Kyunghyun Paeng, Chan-Young Ock, Se-Hoon Lee, Ho Yun Lee

Authors person Changhee Park Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea info_outline Changhee Park, Dong Young Jeong, Yeonu Choi, You Jin Oh, Jonghoon Kim, Sergio Pereira, Kyunghyun Paeng, Chan-Young Ock, Se-Hoon Lee, Ho Yun Lee Organizations Department of Internal Medicine, Seoul National University Hospital, Seoul, South Korea, Department of Radiology, Incheon Regional Military Manpower Administration, Incheon, South Korea, Department of Radiology, Sungkyunkwan University School of Medicine (SKKU-SOM), Samsung Medical Center, Seoul, South Korea, Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul, South Korea, Department of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, South Korea, Lunit Inc., Seoul, South Korea, Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea, Department of Radiology, Samsung Medical Center, Seoul, South Korea Abstract Disclosures Research Funding No funding received 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 CT radiomics has the overcome the limitations of tissue-based assessment, including for TIL analysis. Here, we objectively assess TIL enrichment using an artificial intelligence-powered H&E analyzer, Lunit SCOPE IO, and analyze its association with advanced quantitative imaging features extracted via radiomic analysis. Clinical significance of the selected radiomic features (RFs) is then validated in independent NSCLC patients who received ICI. Methods: In the training cohort, which included 235 NSCLC patients with both tumor tissue and corresponding CT images obtained within 1 month, we extracted 86 RFs from the CT images. From tissue, a patient’s 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. From the corresponding CT images, the least absolute shrinkage and selection operator model was then developed using features that were significantly associated with TIL enrichment. The CT model was then applied to CT images from the validation cohort, which included 242 NSCLC patients who received ICI as ≥ second line. Results: Among the extracted RFs, 22 features were significantly associated with TILes (p < 0.005). After excluding features of multicollinearity and/or zero-coefficient, two features, gray level variance (coefficient 1.71 x 10 -3 ) and low gray level emphasis (coefficient -2.48 x 10 -5 ), were finally included in the model. The two features were both computed from the size-zone matrix (SZM), the idea of which is to break down a given tumor volume into smaller spatially contiguous compartments of different sizes. In the validation cohort, the patients with high predicted TILes (≥ median) had significantly prolonged progression-free survival compared with those with low predicted TILes (median 3.81 months [95% CI 2.14 – 5.69] versus 1.94 months [95% CI 1.58 – 2.93], hazard ratio 0.69 [95% CI 0.53 – 0.90], p = 0.007). Conclusions: This CT radiomics model is able to assess TIL enrichment in TME, which is significantly associated with favorable ICI outcomes in NSCLC. Analyzing the TME through radiomics may overcome limitations of tissue-based analysis and inform clinical decisions, particularly related to use of ICI.