Background: In the era of immunotherapy, immune checkpoint inhibitor (ICI) has changed the treatment paradigm in metastatic non-small cell lung cancer (NSCLC). Along with clinical trials, there is an ongoing investigation to discover the predictive biomarker of ICI which so far has unsatisfactory reliability. As an effort to enhance the predictive value of ICI treatment, we applied deep learning and developed artificial intelligent (AI) score (range from 0 to 1) to analyze the specific context of immune-tumor microenvironment (TME) extracted by scanned images from H&E slides.
Methods: As a ground work, deep learning-based H&E image analyzer, Lunit SCOPE, has been trained with H&E images (n = 1824) from ICI naive NSCLC samples. For the calculation of AI score, training was conducted using responder/non-responder labeled ICI treated samples from the exploratory cohort. The ICI responder was defined as the patient with a best overall response of partial or complete response and stable disease for more than 6 months. The positivity of PD-L1 immunohistochemistry (IHC) was assessed manually by pathologists.
Results: The exploratory cohort is composed of NSCLC patients treated with ICI (n = 189) in Samsung Medical Center, and response to ICI was observed in 72 (38.1%) patients. Median follow-up duration was 6.8 months (6.6~8.2). Samples with PD-L1 IHC positive, defined by ≥ 1%, was observed in 138 (73.0%) patients. AI score was significant higher in the responder group (median: 0.391 vs 0.205, P = 6.14e-5), and the patients with AI score above the cut-off (0.337) showed a better response to ICI (odds ratio [OR] 3.47 P = 7.34e-5) which is higher than patients with PD-L1 ≥ 1% (OR 1.92, P = 0.069). High AI score group (n = 83) showed significantly favorable PFS compared to low AI score group (n = 106, median PFS: 5.1m vs 1.9m, hazard ratio [HR] 0.51, P = 9.6e-5) and this outcome was independent with PD-L1 status (P = 6.0e-5). In subgroup analysis, PFS of PD-L1 high / AI score high group (n = 63) had longer median PFS (6.7m) compared to both PD-L1 high / AI score low group (n = 70, 4.0m, P = 0.001) and PD-L1 low/AI score low group (n = 35, 1.9m, P = 4.0e-6). Tumor infiltrating lymphocyte (TIL) density of cancer epithelium was significantly correlated with AI score (Pearson’s r = 0.310, P = 1.43e-5), which suggests that AI score may partly reflect TME represented by TIL.
Conclusions: The AI score by machine-learned information, extracted from H&E images without additional IHC stain, could predict responsiveness and PFS of ICI treatment independent of PD-L1 IHC positivity.
Sehhoon Park, Chang Ho Ahn, Geunyoung Jung, Sarah Lee, Kyunghyun Paeng, Jiwon Shin, Inwan Yoo, Hyun Ae Jung, Jong-Mu Sun, Jin Seok Ahn, Myung-Ju Ahn, Keunchil Park, Yoon La Choi, Sang-Yong Song, Se-Hoon Lee
Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Lunit Inc., Seoul, South Korea; Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea; Department of Pathology, Samsung Medical Centre, Sungkyunkwan University, Seoul, South Korea; Department of Pathology and Translational Genomics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, South Korea
Abstract: Deep learning-based predictive biomarker for adjuvant chemotherapy in early-stage hormone receptor-positive breast cancer
Abstract: Pan-cancer analysis of tumor microenvironment using deep learning-based cancer stroma and immune profiling in H&E images
Abstract: Comprehensive deep learning analysis of H&E tissue phenomics reveals distinct immune landscape and transcriptomic enrichment profile among immune inflamed, excluded and desert subtypes...
Abstract: Deep-learning analysis of H&E images to define three immune phenotypes to reveal loss-of-target in excluded immune cells as a novel resistance mechanism of immune checkpoint inhibitor...
Abstract: Deep learning-based immune phenotype analysis reveals distinct resistance pattern of immune checkpoint inhibitor in non-small cell lung cancer
Reducing Domain Gap by Reducing Style Bias
Predicting breast tumor proliferation from whole-slide images: The TUPAC16 challenge
Accuracy and efficiency of an artificial intelligence tool when counting breast mitoses
Abstract: Artificial intelligence-powered spatial analysis of tumor infiltrating lymphocytes (TIL) to reflect target gene expressions of novel immuno-oncology agents.
Abstract: Distinct subset of immune cells assessed by multiplex immunohistochemistry correlates with immune phenotype classified by ...
Abstract : Pathologic validation of artificial intelligence-powered prediction of combined positive score of PD-L1 immunohistochemistry in urothelial carcinoma.
Abstract : Clinical performance of artificial intelligence-powered annotation of tumor cell PD-L1 expression for treatment of immune-checkpoint inhibitor (ICI) ...
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes predicts survival after immune checkpoint inhibitor therapy across multiple cancer types.
Abstract : Deep learning based radiomic biomarker for predicting the presence of high-grade histologic patterns in lung adenocarcinoma
Abstract : Artificial intelligence-powered tissue analysis reveals distinct tumor-infiltrating lymphocyte profile as a potential biomarker of molecular subtypes in endometrial cancer
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals distinct genomic profile of immune excluded phenotype in pan-carcinoma
Deep learning from HE slides predicts the clinical benefit from adjuvant chemotherapy in hormone receptor-positive breast cancer patients
Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems
Abstract : Interim results of phase I dose escalation study of YBL-006: A novel anti-PD-1 monoclonal antibody in advanced solid tumors
Abstract : Assistance with an artificial intelligence-powered PD-L1 analyzer reduces interobserver variation in pathologic reading of tumor proportion score in non-small cell lung cancer
Abstract : AI-powered whole-slide image analysis of tumor-infiltrating lymphocytes for prediction of prognosis in colorectal cancer
Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma
Artificial Intelligence–Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as Complementary Biomarker for Immune Checkpoint Inhibition in Non–Small-Cell Lung Cancer
Artificial Intelligence–Powered Hematoxylin and Eosin Analyzer Reveals Distinct Immunologic and Mutational Profiles among Immune Phenotypes in Non–Small-Cell Lung Cancer
Abstract : Artificial Intelligence-Powered Analyzer Reduces Inter-observer Variation in PD-L1 Tumor Proportion Score of Non-Small Cell Lung Cancer
Abstract : Artificial Intelligence-Powered Tumor Purity Assessment From H&E Whole Slide Images Correlates...
Abstract : Deep learning-based H&E analyzer reveals distinct immune profiles and clinical outcomes among immune phenotypes in uterine corpus endometrial carcinoma
Abstract : Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes reveals immune-excluded phenotype is correlated with TGF-beta pathway related genomic aberrations
Observer Performance Study to Examine the Feasibility of the AI-powered PD-L1 Analyzer to Assist Pathologists’ Assessment of PD-L1 Expression Using Tumor Proportion Score in Non-Small Cell Lung Cancer
Artificial intelligence-powered human epidermal growth factor receptor 2 (HER2) analyzer in breast cancer as an assistance tool for pathologists to reduce interobserver variation
Artificial intelligence-powered whole-slide image analyzer reveals a distinctive distribution of tumor-infiltrating lymphocytes in neuroendocrine tumors and carcinomas
Artificial Intelligence (AI) - powered spatial analysis of tumor-infiltrating lymphocytes (TIL) for prediction of response to neoadjuvant chemotherapy (NAC) in triple negative breast cancer (TNBC)
Artificial intelligence (AI)-powered pathology image analysis merged with spatial transcriptomics reveals distinct TIGIT expression in the immune-excluded tumor-infiltrating lymphocytes
Trastuzumab plus FOLFOX for Gemcitabine/Cisplatin refractory HER2-positive biliary tract cancer: a multi-institutional phase II trial of the Korean Cancer Study Group (KCSG-HB19-14)
The Inflamed Immune Phenotype (IIP): a clinically actionable artificial intelligence (AI)-based biomarker predictive of immune checkpoint inhibitor (ICI) outcomes across >16 primary tumor types
Safety and efficacy of YBL-006, an anti-PD-1 monoclonal antibody in advanced solid tumors: a phase I study
Robust artificial intelligence-powered imaging biomarker based on mammography for risk prediction of breast cancer.