Deep learning–based chest CT model to predict treatment response to immune checkpoint inhibitors in non-small cell lung cancer independently and additively to histopathological biomarkers.

person Chang Ho Ahn Oncology, Lunit Inc., Seoul, South Korea info_outline Chang Ho Ahn, Dong Young Jeong, Jimin Moon, Jongchan Park, Jisoo Shin, Seungje Lee, Yoojoo Lim, Sehhoon Park, Sehoon Lee, Chan-Young Ock, Ho Yun Lee

Authors person Chang Ho Ahn Oncology, Lunit Inc., Seoul, South Korea info_outline Chang Ho Ahn, Dong Young Jeong, Jimin Moon, Jongchan Park, Jisoo Shin, Seungje Lee, Yoojoo Lim, Sehhoon Park, Sehoon Lee, Chan-Young Ock, Ho Yun Lee Organizations Oncology, Lunit Inc., Seoul, South Korea, Samsung Medical Center, Seoul, South Korea Abstract Disclosures Research Funding No funding sources reported Background: Immune checkpoint inhibitors (ICI) are an integral part of the treatment for advanced or metastatic non-small cell lung cancer (NSCLC). Chest CT and pathology data is gathered in routine clinical practice, and each modality provides complementary information. Tumor microenvironment is spatially heterogeneous and temporally dynamic. CT images provide macro-level characteristics of multiple tumors, and tissue-based histopathologic biomarkers analyze a single representative tumor microenvironment in-depth. We developed a deep learning-based chest CT prediction model to predict response to ICIs and validated its performance independently and in combination with histopathologic biomarkers. Methods: NSCLC patients treated with ICI monotherapy were retrospectively enrolled from Samsung Medical Center. A deep learning CT prediction (DL-CT) model was trained on the data of 1876 patients where time to next treatment (TTNT) was used as a proxy. A pre-trained CT nodule detection model was used to extract intermediate features from chest CT taken at a time closest to the initiation of ICI. Histopathologic biomarkers include tumor infiltrating lymphocytes (TIL) and immune phenotype analyzed by an AI analyzer (Lunit SCOPE IO) in H&E whole slide images and PD-L1 tumor proportion score (TPS) interpreted by human pathologists. The performance of DL-CT model was validated in an independent test set of 489 NSCLC patients. Results: In the test set, median follow up was 11.3 months. Patients predicted as a responder by DL-CT model (CT-responder) had prolonged TTNT (median TTNT: 7.0m vs. 2.5m, hazard ratio [HR] 0.588, p <0.001) and overall survival (OS) (median OS: 16.5m vs 7.6m, HR 0.581, p <0.001). The responder group predicted by DL-CT model was associated with better ECOG performance score (p = 0.012) and current smoking (p <0.001), but not with TIL-based immune phenotype or PD-L1 expression (p = 0.201 and p = 0.312, respectively). CT-responder showed favorable outcome across all PD-L1 subgroups (HR for TTNT 0.509, p = 0.005 for TPS 0%, HR 0.616, P = 0.015 for TPS 1-49% and HR 0.585, p <0.001 for TPS ≥50%) and immune phenotypes (HR for TTNT 0.543, p <0.001 for inflamed and HR 0.611, p <0.001 for non-inflamed). Combining DL-CT and histopathologic biomarkers, patients having all three biomarkers positive (CT-responder, inflamed and PDL1 TPS ≥50%) had substantially longer TTNT and OS compared to the other patients (mTTNT 9.4m vs. 3.5m, p<0.001 and mOS 32.0m vs. 10.0m vs. p <0.001). Conclusions: The AI model based on chest CT data was a strong independent biomarker to predict treatment response to ICI, which possibly reflects patients’ clinical characteristics. Combining AI CT model and histopathologic biomarkers showed additive impact on the prediction of the treatment response to ICI.