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.
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.
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).
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.
Changhee Park1, Dong Young Jeong2, Yeonu Choi3, You Jin Oh4, Jonghoon Kim5, Sergio Pereira6, Kyunghyun Paeng6, Chan-Young Ock6, Se-Hoon Lee7, Ho Yun Lee3.
1Department of Internal Medicine, Seoul National University Hospital, Seoul. 2Department of Radiology, Incheon Regional Military Manpower Administration, Incheon. 3Department of Radiology, Sungkyunkwan University School of Medicine (SKKU-SOM), Samsung Medical Center, Seoul. 4Department of Health Sciences and Technology, SAIHST, Sungkyunkwan University, Seoul. 5Department of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea. 6Lunit Inc., Seoul, Republic of Korea. 7Division of Hematology Oncology, Department of Medicine, Samsung Medical Center.