*Result*: Non-invasive prediction of NSCLC immunotherapy efficacy and tumor microenvironment through unsupervised machine learning-driven CT radiomic subtypes: a multi-cohort study.
Title:
Non-invasive prediction of NSCLC immunotherapy efficacy and tumor microenvironment through unsupervised machine learning-driven CT radiomic subtypes: a multi-cohort study.
Authors:
Guo Y; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China.; Hubei Key Laboratory of Molecular Imaging, Wuhan, China., Gong B; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China.; Hubei Key Laboratory of Molecular Imaging, Wuhan, China., Li Y; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China.; Hubei Key Laboratory of Molecular Imaging, Wuhan, China., Mo P; Department of Radiotherapy, Fuzong Clinical Medical College (900th Hospital), Fujian Medical University, Fuzhou, China., Chen Y; Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China., Fan Q; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China.; Hubei Key Laboratory of Molecular Imaging, Wuhan, China., Sun Q; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China.; Hubei Key Laboratory of Molecular Imaging, Wuhan, China., Miao L; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China.; Hubei Key Laboratory of Molecular Imaging, Wuhan, China., Li Y; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China.; Hubei Key Laboratory of Molecular Imaging, Wuhan, China., Liu Y; School of Medicine, Wuhan College of Arts and Science, Wuhan, China., Tan W; Department of Medical Imaging, Geriatric Hospital Affiliated with Wuhan University of Science and Technology, Wuhan, China., Yang L; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China.; Hubei Key Laboratory of Molecular Imaging, Wuhan, China., Zheng C; Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Hubei Provincial Clinical Research Center for Precision Radiology & Interventional Medicine, Wuhan, China.; Hubei Key Laboratory of Molecular Imaging, Wuhan, China.
Source:
International journal of surgery (London, England) [Int J Surg] 2025 Oct 01; Vol. 111 (10), pp. 6592-6603. Date of Electronic Publication: 2025 Jun 24.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wolters Kluwer Health, Inc Country of Publication: United States NLM ID: 101228232 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1743-9159 (Electronic) Linking ISSN: 17439159 NLM ISO Abbreviation: Int J Surg Subsets: MEDLINE
Imprint Name(s):
Publication: 2023- : [Philadelphia] : Wolters Kluwer Health, Inc.
Original Publication: London : Surgical Associates Ltd., c2004-
Original Publication: London : Surgical Associates Ltd., c2004-
MeSH Terms:
Tumor Microenvironment*/immunology , Carcinoma, Non-Small-Cell Lung*/therapy , Carcinoma, Non-Small-Cell Lung*/diagnostic imaging , Carcinoma, Non-Small-Cell Lung*/immunology , Carcinoma, Non-Small-Cell Lung*/mortality , Lung Neoplasms*/therapy , Lung Neoplasms*/diagnostic imaging , Lung Neoplasms*/immunology , Lung Neoplasms*/mortality , Immunotherapy*/methods , Tomography, X-Ray Computed*/methods , Unsupervised Machine Learning*, Humans ; Male ; Female ; Middle Aged ; Aged ; Cohort Studies ; Prognosis ; Radiomics
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Horvat N, Veeraraghavan H, Khan M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 2018;287:833–43.
Zhu Y, Mao Y, Chen J, et al. Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection. Sci Rep 2021;11:18347.
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Li Y, Wu X, Yang P, Jiang G, Luo Y. Machine learning for lung cancer diagnosis, treatment, and prognosis. Genomics Proteomics Bioinf 2022;20:850–66.
Xin H, Lai Q, Liu Y, et al. Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: a multi-cohort study. Pharmacol Res 2024;210:107535.
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Wang L, Saci A, Szabo PM, et al. EMT- and stroma-related gene expression and resistance to PD-1 blockade in urothelial cancer. Nat Commun 2018;9:3503.
Taylor K, Loo Yau H, Chakravarthy A, et al. An open-label, phase II multicohort study of an oral hypomethylating agent CC-486 and durvalumab in advanced solid tumors. J Immunother Cancer 2020;8:e000883.
Gevaert O, Xu J, Hoang CD, et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data–methods and preliminary results. Radiology 2012;264:387–96.
Sleeboom JJF, van Tienderen GS, Schenke-Layland K, van der Laan LJW, Khalil AA, Verstegen MMA. The extracellular matrix as hallmark of cancer and metastasis: from biomechanics to therapeutic targets. Sci Transl Med 2024;16:eadg3840.
Naik A, Leask A. Tumor-associated fibrosis impairs the response to immunotherapy. Matrix Biol 2023;119:125–40.
Zhou M, Leung A, Echegaray S, et al. Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology 2018;286:307–15.
Taki M, Abiko K, Ukita M, et al. Tumor immune microenvironment during epithelial-mesenchymal transition. Clin Cancer Res 2021;27:4669–79.
Bai H, Duan J, Li C, et al. EPHA mutation as a predictor of immunotherapeutic efficacy in lung adenocarcinoma. J Immunother Cancer 2020;8:e001315.
Ziv E, Erinjeri JP, Yarmohammadi H, et al. Lung adenocarcinoma: predictive value of KRAS mutation status in assessing local recurrence in patients undergoing image-guided ablation. Radiology 2017;282:251–58.
Song J, Wang L, Ng NN, et al. Development and validation of a machine learning model to explore tyrosine kinase inhibitor response in patients with stage IV EGFR variant-positive non-small cell lung cancer. JAMA Network Open 2020;3:e2030442.
Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021;71:209–49.
Zhang H, Hou X, Lin M, et al. The study on the preparation and characterization of gene-loaded immunomagnetic albumin nanospheres and their anti-cell proliferative effect combined with magnetic fluid hyperthermia on GLC-82 cells. Drug Des Devel Ther 2015;9:6445–60.
Wan X, Xie B, Sun H, et al. Exosomes derived from M2 type tumor-associated macrophages promote osimertinib resistance in non-small cell lung cancer through MSTRG.292666.16-miR-6836-5p-MAPK8IP3 axis. Cancer Cell Int 2022;22:83.
Jiang T, Chen J, Xu X, et al. On-treatment blood TMB as predictors for camrelizumab plus chemotherapy in advanced lung squamous cell carcinoma: biomarker analysis of a phase III trial. Mol Cancer 2022;21:4.
Kennedy LB, Salama AKS. A review of cancer immunotherapy toxicity. CA Cancer J Clin 2020;70:86–104.
Zhou F, Qiao M, Zhou C. The cutting-edge progress of immune-checkpoint blockade in lung cancer. Cell Mol Immunol 2021;18:279–93.
Oosting LT, Franke K, Martin MV, et al. Development of a personalized tumor neoantigen based vaccine formulation (FRAME-001) for use in a phase II trial for the treatment of advanced non-small cell lung cancer. Pharmaceutics 2022;14:1515.
Parma B, Wurdak H, Ceppi P. Harnessing mitochondrial metabolism and drug resistance in non-small cell lung cancer and beyond by blocking heat-shock proteins. Drug Resist Updat 2022;65:100888.
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016;278:563–77.
Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014;5:4006.
Guo Y, Li T, Gong B, et al. From images to genes: radiogenomics based on artificial intelligence to achieve non-invasive precision medicine in cancer patients. Adv Sci (Weinh) 2025;12:e2408069.
Chen K, Li X, Liu L, et al. Correlation of noninvasive imaging of tumour-infiltrating lymphocytes with survival and BCG immunotherapy response in patients with bladder cancer: a multicentre cohort study. Int J Surg 2025;111:920–31.
Horvat N, Veeraraghavan H, Khan M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology 2018;287:833–43.
Zhu Y, Mao Y, Chen J, et al. Radiomics-based model for predicting early recurrence of intrahepatic mass-forming cholangiocarcinoma after curative tumor resection. Sci Rep 2021;11:18347.
Lin Z, Wang W, Yan Y, Ma Z, Xiao Z, Mao K. A deep learning-based clinical-radiomics model predicting the treatment response of immune checkpoint inhibitors (ICIs)-based conversion therapy in potentially convertible hepatocelluar carcinoma patients: a tumor marker prognostic study. Int J Surg 2025;111:3342–55.
Zheng J, Xu S, Wang G, Shi Y. Applications of CT-based radiomics for the prediction of immune checkpoint markers and immunotherapeutic outcomes in non-small cell lung cancer. Front Immunol 2024;15:1434171.
Mu W, Tunali I, Gray JE, Qi J, Schabath MB, Gillies RJ. Radiomics of (18)F-FDG PET/CT images predicts clinical benefit of advanced NSCLC patients to checkpoint blockade immunotherapy. Eur J Nucl Med Mol Imaging 2020;47:1168–82.
Tankyevych O, Trousset F, Latappy C, et al. Development of radiomic-based model to predict clinical outcomes in non-small cell lung cancer patients treated with immunotherapy. Cancers (Basel) 2022;14:5931.
Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Bmj 2015;350:g7594.
Agha RA, Mathew G, Rashid R, et al. Strengthening the reporting of cohort, cross-sectional and case-control studies in surgery (STROCSS) guideline: an update for the age of artificial intelligence. Prem J Sci 2025;10:100081.
Schwartz LH, Litière S, de Vries E, et al. RECIST 1.1-update and clarification: from the RECIST committee. Eur J Cancer 2016;62:132–37.
Clark K, Vendt B, Smith K, et al. The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2013;26:1045–57.
Isensee F, Jaeger PF, Kohl SAA, Petersen J, Maier-Hein KH. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 2021;18:203–11.
Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 2007;8:118–27.
Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 2005;102:15545–50.
Butler A, Hoffman P, Smibert P, Papalexi E, Satija R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol 2018;36:411–20.
Chu Y, Dai E, Li Y, et al. Pan-cancer T cell atlas links a cellular stress response state to immunotherapy resistance. Nat Med 2023;29:1550–62.
Li Y, Wu X, Yang P, Jiang G, Luo Y. Machine learning for lung cancer diagnosis, treatment, and prognosis. Genomics Proteomics Bioinf 2022;20:850–66.
Xin H, Lai Q, Liu Y, et al. Integrative radiomics analyses identify universal signature for predicting prognosis and therapeutic vulnerabilities across primary and secondary liver cancers: a multi-cohort study. Pharmacol Res 2024;210:107535.
Liu X, Chen C, Li J, Li L, Ma M. Identification of tumor-specific T cell signature predicting cancer immunotherapy response in bladder cancer by multi-omics analysis and experimental verification. Cancer Cell Int 2024;24:255.
Sun R, Henry T, Laville A, et al. Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy? J Immunother Cancer 2022;10:e004848.
Rios Velazquez E, Parmar C, Liu Y, et al. Somatic mutations drive distinct imaging phenotypes in lung cancer. Cancer Res 2017;77:3922–30.
Nqk L, Kha QH, Nguyen VH, Chen YC, Cheng SJ, Chen CY. Machine learning-based radiomics signatures for EGFR and KRAS mutations prediction in non-small-cell lung cancer. Int J Mol Sci 2021;22:9254.
Son JY, Lee HY, Lee KS, et al. Quantitative CT analysis of pulmonary ground-glass opacity nodules for the distinction of invasive adenocarcinoma from pre-invasive or minimally invasive adenocarcinoma. PLoS One 2014;9:e104066.
Wang L, Saci A, Szabo PM, et al. EMT- and stroma-related gene expression and resistance to PD-1 blockade in urothelial cancer. Nat Commun 2018;9:3503.
Taylor K, Loo Yau H, Chakravarthy A, et al. An open-label, phase II multicohort study of an oral hypomethylating agent CC-486 and durvalumab in advanced solid tumors. J Immunother Cancer 2020;8:e000883.
Gevaert O, Xu J, Hoang CD, et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data–methods and preliminary results. Radiology 2012;264:387–96.
Sleeboom JJF, van Tienderen GS, Schenke-Layland K, van der Laan LJW, Khalil AA, Verstegen MMA. The extracellular matrix as hallmark of cancer and metastasis: from biomechanics to therapeutic targets. Sci Transl Med 2024;16:eadg3840.
Naik A, Leask A. Tumor-associated fibrosis impairs the response to immunotherapy. Matrix Biol 2023;119:125–40.
Zhou M, Leung A, Echegaray S, et al. Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology 2018;286:307–15.
Taki M, Abiko K, Ukita M, et al. Tumor immune microenvironment during epithelial-mesenchymal transition. Clin Cancer Res 2021;27:4669–79.
Bai H, Duan J, Li C, et al. EPHA mutation as a predictor of immunotherapeutic efficacy in lung adenocarcinoma. J Immunother Cancer 2020;8:e001315.
Ziv E, Erinjeri JP, Yarmohammadi H, et al. Lung adenocarcinoma: predictive value of KRAS mutation status in assessing local recurrence in patients undergoing image-guided ablation. Radiology 2017;282:251–58.
Song J, Wang L, Ng NN, et al. Development and validation of a machine learning model to explore tyrosine kinase inhibitor response in patients with stage IV EGFR variant-positive non-small cell lung cancer. JAMA Network Open 2020;3:e2030442.
Contributed Indexing:
Keywords: immunotherapy; non-small cell lung cancer; radiomics; tumor immune microenvironment; unsupervised machine learning
Entry Date(s):
Date Created: 20250624 Date Completed: 20251018 Latest Revision: 20260110
Update Code:
20260130
PubMed Central ID:
PMC12527701
DOI:
10.1097/JS9.0000000000002839
PMID:
40552903
Database:
MEDLINE
*Further Information*
*Background: Radiomics analyzes quantitative features from medical images to reveal tumor heterogeneity, offering new insights for diagnosis, prognosis, and treatment prediction. This study explored radiomics based biomarkers to predict immunotherapy response and its association with the tumor microenvironment in non-small cell lung cancer (NSCLC) using unsupervised machine learning models derived from CT imaging.
Materials and Methods: This study included 1539 NSCLC patients from seven independent cohorts. For 1834 radiomic features extracted from 869 NSCLC patients, K-means unsupervised clustering was applied to identify radiomic subtypes. A random forest model extended subtype classification to external cohorts, model accuracy, sensitivity, and specificity were evaluated. By conducting bulk RNA sequencing (RNA-seq) and single-cell transcriptome sequencing (scRNA-seq) of tumors, the immune microenvironment characteristics of tumors can be obtained to evaluate the association between radiomic subtypes and immunotherapy efficacy, immune scores, and immune cells infiltration.
Results: Unsupervised clustering stratified NSCLC patients into two subtypes (Cluster 1 and Cluster 2). Principal component analysis confirmed significant distinctions between subtypes across all cohorts. Cluster 2 exhibited significantly longer median overall survival (35 vs. 30 months, P = 0.006) and progression-free survival (19 vs. 16 months, P = 0.020) compared to Cluster 1. Multivariate Cox regression identified radiomic subtype as an independent predictor of overall survival (HR: 0.738, 95% CI 0.583-0.935, P = 0.012), validated in two external cohorts. Bulk RNA seq showed elevated interaction signaling and immune scores in Cluster 2 and scRNA-seq demonstrated higher proportions of T cells, B cells, and NK cells in Cluster 2.
Conclusion: This study establishes a radiomic subtype associated with NSCLC immunotherapy efficacy and tumor immune microenvironment. The findings provide a non-invasive tool for personalized treatment, enabling early identification of immunotherapy-responsive patients and optimized therapeutic strategies.
(Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc.)*