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Cited by 297Radiomics and Machine Learning in Medical ImagingCancer Immunotherapy and BiomarkersImage Retrieval and Classification Techniques

Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non–Small Cell Lung Cancer

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Khorrami, Mohammadhadi, Prasanna, Prateek, Gupta, Amit, Patil, Pradnya, Velu, Priya D., Thawani, Rajat, Corredor, German, Alilou, Mehdi, Bera, Kaustav, Fu, Pingfu, Feldman, Michael, Velcheti, Vamsidhar, Madabhushi, Anant·2020·Cancer Immunology Research
DOI 10.1158/2326-6066.cir-19-0476PMC PMC7718609

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Abstract

Abstract No predictive biomarkers can robustly identify patients with non–small cell lung cancer (NSCLC) who will benefit from immune checkpoint inhibitor (ICI) therapies. Here, in a machine learning setting, we compared changes (“delta”) in the radiomic texture (DelRADx) of CT patterns both within and outside tumor nodules before and after two to three cycles of ICI therapy. We found that DelRADx patterns could predict response to ICI therapy and overall survival (OS) for patients with NSCLC. We retrospectively analyzed data acquired from 139 patients with NSCLC at two institutions, who were divided into a discovery set (D1 = 50) and two independent validation sets (D2 = 62, D3 = 27). Intranodular and perinodular texture descriptors were extracted, and the relative differences were computed. A linear discriminant analysis (LDA) classifier was trained with 8 DelRADx features to predict RECIST-derived response. Association of delta-radiomic risk score (DRS) with OS was determined. The association of DelRADx features with tumor-infiltrating lymphocyte (TIL) density on the diagnostic biopsies (n = 36) was also evaluated. The LDA classifier yielded an AUC of 0.88 ± 0.08 in distinguishing responders from nonresponders in D1, and 0.85 and 0.81 in D2 and D3. DRS was associated with OS [HR: 1.64; 95% confidence interval (CI), 1.22–2.21; P = 0.0011; C-index = 0.72). Peritumoral Gabor features were associated with the density of TILs on diagnostic biopsy samples. Our results show that DelRADx could be used to identify early functional responses in patients with NSCLC.

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  • Previous studies have primarily focused on tumor-infiltrating lymphocytes, which only provide a simplified and partial view of the TME.

    “First, previous studies have primarily focused on tumor-infiltrating lymphocytes,12,26 which only provide a simplified and partial view of the TME.”

    From Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics (2023) · cites this paper as [26]

    The Discussion (page unavailable) describes previous studies of tumor-infiltrating lymphocytes and their association with immunotherapy response. It does not establish that previous studies primarily focused on lymphocytes. The Introduction discusses other tumor-microenvironment factors, but the paper does not explicitly characterize lymphocytes as providing only a simplified, partial view. (LLM, conf=0.78)

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References 73 · Cited by 297

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  1. [1]Malhotra J, Jabbour SK, Aisner J. Current state of immunotherapy for non-small cell lung cancer. Translational Lung Cancer Research. 2017; 6(2): 196– 211. doi: 10.21037/tlcr.2017.03.01. 28529902 PMC5420529 · DOI
  2. [3]Mok TSK, Wu YL, Kudaba I, Kowalski DM, Pembrolizumab versus chemotherapy for previously untreated, PD-L1-expressing, locally advanced or metastatic non-small-cell lung cancer (KEYNOTE-042): a randomised, open-label, controlled, phase 3 trial. Lancet. 2019 5 4; 393(10183): 1819– 1830. doi: 10.1016/S0140-6736(18)32409-7. 30955977 · DOI
  3. [4]Reck M, Rodríguez–Abreu D, Robinson AG, Updated Analysis of KEYNOTE-024: Pembrolizumab Versus Platinum-Based Chemotherapy for Advanced Non–Small-Cell Lung Cancer With PD-L1 Tumor Proportion Score of 50% or Greater. Journal of Clinical Oncology. DOI: 10.1200/JCO.18.00149. 30620668 · DOI · Paper page
  4. [6]Fojo Antonio T. and Noonan Anne. Why RECIST Works and Why It Should Stay—Counterpoint. Cancer Res; 72(20); 5151– 7. DOI: 10.1158/0008-5472.CAN-12-0733 Published October 2012. 22952221 · DOI
  5. [7]Chiop VL., Burotto M. Pseudoprogression and Immune-Related Response in Solid Tumors. J Clin Oncol. 2015 11 1; 33(31): 3541– 3543. 26261262 PMC4622096
  6. [8]Wilson MK, Karakasis K, Oza AM Outcomes and endpoints in trials of cancer treatment: the past, present, and future. Lancet Oncol. 2015 1; 16(1): e32– 42. doi: 10.1016/S1470-2045(14)70375-4. Epub 2014 Dec 29. 25638553 · DOI
  7. [9]Braman NM,, Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI. Breast Cancer Res, 2017 19(1): p. 57. 28521821 PMC5437672
  8. [10]Khorrami M, Khunger M, Zagouras A, Combination of Peri- and Intratumoral Radiomic Features on Baseline CT Scans Predicts Response to Chemotherapy in Lung Adenocarcinoma. Radiology: Artificial IntelligenceVol. 1, No. 2 10.1148/ryai.2019180012. PMC6515986 32076657 · DOI
  9. [11]Khorrami M, Jain P, Pingfu F, Predicting pathologic response to neoadjuvant chemoradiation in resectable stage III non-small cell lung cancer patients using computed tomography radiomic features. Journal of Lung Cancer 10.1016/j.lungcan.2019.06.020. PMC6711393 31446979 · DOI
  10. [12]Lee Won-Chul, Diao Lixia, Wang Jing, Multiregion gene expression profiling reveals heterogeneity in molecular subtypes and immunotherapy response signatures in lung cancer. Modern Pathology (2018). doi: 10.1038/s41379-018-0029-3. 29410488 · DOI
  11. [13]Madore Jason, Vilain Ricardo E., Menzies Alexander M., Kakavand Hojabr. PD-L1 expression in melanoma shows marked heterogeneity within and between patients: implications for anti-PD-1/PD-L1 clinical trials. DOI: 10.1111/pcmr.12340. 25477049 · DOI
  12. [14]Munari Enrico, Zamboni Giuseppe, Marconi Marcella, PD-L1 expression heterogeneity in non-small cell lung cancer: evaluation of small biopsies reliability. Published online 2017 Oct 4. doi: 10.18632/oncotarget.21485. PMC5685736 29163815 · DOI
  13. [16]Robertson-Tessi M, Gillies RJ, Gatenby RA, and Anderson ARA, “ Impact of Metabolic Heterogeneity on Tumor Growth, Invasion, and Treatment Outcomes,” Cancer Research, vol. 75, no. 8, pp. 1567– 1579, 4 2015. 25878146 PMC4421891
  14. [17]Gerlee P and Anderson ARA, “ A hybrid cellular automaton model of clonal evolution in cancer: The emergence of the glycolytic phenotype,” Journal of Theoretical Biology, vol. 250, no. 4, pp. 705– 722, 2 2008. 18068192 PMC2846650
  15. [18]Trebeschi S, Drago SG, Birkbak NJ, Predicting Response to Cancer Immunotherapy using Non-invasive Radiomic Biomarkers. Annals of Oncology, mdz108, w 10.1093/annonc/mdz108. PMC6594459 30895304 · DOI
  16. [19]Ettinger DS, Akerley W, Borghaei H, Chang AC, Cheney RT, Chirieac LR, Non-small cell lung cancer, version 2.2013. J Natl Compr Canc Netw, 11 (2013), pp. 645– 653. 23744864
  17. [20]Beig N, Khorrami M, Alilou M, Perinodular and Intranodular Radiomic Features on Lung CT Images Distinguish Adenocarcinomas from Granulomas, Radiology, Vol. 290, No. 3 10.1148/radiol.2018180910. PMC6394783 30561278 · DOI
  18. [22]Orooji M, Alilou M, Rakshit S, “ Combination of computer extracted shape and texture features enables discrimination of granulomas from adenocarcinoma on chest computed tomography,” J. Med. Imag 5(2) 024501 (18 4 2018). PMC5904542 29721515
  19. [23]Tiwari P, Prasanna P, Wolansky L, Computer-Extracted Texture Features to Distinguish Cerebral Radionecrosis from Recurrent Brain Tumors on Multiparametric MRI: A Feasibility Study. American Journal of Neuroradiology 9 2016, 10.3174/ajnr.A4931. PMC5161689 27633806 · DOI
  20. [24]Armato S The reference image database to evaluate response to therapy in lung cancer (rider) project: A resource for the development of change-analysis software. Clinical Pharmacology & Therapeutics 84, 448– 456 (2008). 18754000 PMC4938843
  21. [25]Vincent L, Soille P, Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence. Volume: 13, Issue: 6, 6 1991 DOI: 10.1109/34.87344. · DOI
  22. [26]Xing F, Yang L. Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review. IEEE Rev Biomed Eng. 2016; 9: 234– 263. doi: 10.1109/RBME.2016.2515127. 26742143 PMC5233461 · DOI
  23. [27]Fisher RA (1922). “ On the interpretation of χ2 from contingency tables, and the calculation of P”. Journal of the Royal Statistical Society. 85 (1): 87– 94. doi: 10.2307/2340521 · DOI
  24. [28]Bewick V, Cheek L, Ball J Statistics review 12: Survival analysis. Crit Care. 2004; 8(5): 389– 394. Published online 2004 Sep 6. doi: 10.1186/cc2955. 15469602 PMC1065034 · DOI
  25. [29]Oken MM, Creech RH, Tormey DC, Horton J, Toxicity and response criteria of the Eastern Cooperative Oncology Group. Am J Clin Oncol, 5 (1982), pp. 649– 655. 7165009
  26. [30]Mirsadraee S The 7th lung cancer TNM classification and staging system: review of the changes and implications World J Radiol, 4 (2012). PMC3351680 22590666
  27. [31]Sornapudi S, Stanley RJ, Stoecker WV, Deep Learning Nuclei Detection in Digitized Histology Images by Superpixels. J Pathol Inform. 2018; 9: 5 Published 2018 Mar 5. doi: 10.4103/jpi.jpi_74_17. 29619277 PMC5869967 · DOI
  28. [32]Soo RA, Lim SM, Teng R, Immune checkpoint inhibitors in epidermal growth factor receptor mutant non-small cell lung cancer: Current controversies and future directions. Lung Cancer. 2018 1; 115: 12– 20. 29290252
  29. [34]Fogel I, Sagi D Gabor filters as texture discriminator. Biological Cybernetics. 61 (2). doi: 10.1007/BF00204594. ISSN 0340–1200. · DOI
  30. [35]Laws K “ Textured Image Segmentation”, Ph.D. Dissertation, University of Southern California, 1 1980.
  31. [36]Prasanna P, Tiwari P, Madabhushi A Co-occurrence of Local Anisotropic Gradient Orientations (CoLlAGe): A new radiomics descriptor. Sci Rep. 2016 11 22; 6: 37241. doi: 10.1038/srep37241. 27872484 PMC5118705 · DOI
  32. [37]Pilon-Thomas S, Kodumudi KN, and El-Kenawi AE, “ Neutralization of tumor acidity improves antitumor responses to immunotherapy,” Cancer Research, 2016. PMC4829106 26719539
  33. [38]Salem A, Asselin M, Reymen B, Targeting Hypoxia to Improve Non–Small Cell Lung Cancer Outcome. Journal of the National Cancer Institute, Volume 110, Issue 1, 1 1 2018, Pages 14– 30, 10.1093/jnci/djx160. 28922791 · DOI
  34. [39]Ganeshan B, Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology. 2013; 266: 326– 336. doi: 10.1148/radiol.12112428. 23169792 · DOI
  35. [40]Beig N, Patel J, Prasanna P, Hill V, Radiogenomic analysis of hypoxia pathway is predictive of overall survival in Glioblastoma. Scientific Reportsvolume 8, Article number: 7 (2018). 29311558 PMC5758516
  36. [41]Braman N, Prasanna P, Whitney J, Association of Peritumoral Radiomics With Tumor Biology and Pathologic Response to Preoperative Targeted Therapy for HER2 (ERBB2)–Positive Breast Cancer. JAMA Netw Open. 2019; 2(4): e192561. doi: 10.1001/jamanetworkopen.2019.2561. 31002322 PMC6481453 · DOI
  37. [43]Alilou M, Vaidya P, Khorrami M, “ Quantitative vessel tortuosity radiomics on baseline non-contrast lung CT predict response to immunotherapy and are prognostic of overall survival”, Proc. SPIE 10950, Medical Imaging 2019 10.1117/12.2513648. · DOI
  38. [44]Brahmer J, Reckamp KL, Bass P, Nivolumab versus Docetaxel in Advanced Squamous-Cell Non–Small-Cell Lung Cancer. N Engl J Med 2015; 373: 123– 135. DOI: 10.1056/NEJMoa1504627. 26028407 PMC4681400 · DOI · Paper page
  39. [45]Reck M, Rodríguez-Abreu D, Robinson A>G,, Pembrolizumab versus Chemotherapy for PD-L1–Positive Non–Small-Cell Lung Cancer. N Engl J Med 2016; 375: 1823– 1833. DOI: 10.1056/NEJMoa1606774. 27718847 · DOI · Paper page
  40. [47]Khunger M, Hernandez AV, Pasupuleti V, Programmed Cell Death 1 (PD-1) Ligand (PD-L1) Expression in Solid Tumors As a Predictive Biomarker of Benefit From PD-1/PD-L1 Axis Inhibitors: A Systematic Review and Meta-Analysis. JCO 5 18, 2017 DOI: 10.1200/PO.16.00030. 35172490 · DOI
  41. [48]Darvin P, Toor SM, Sasidharan Nair V, Elkord E. Immune checkpoint inhibitors: recent progress and potential biomarkers. Exp Mol Med. 2018; 50(12): 165 Published 2018 Dec 13. doi: 10.1038/s12276-018-0191-1. PMC6292890 30546008 · DOI · Paper page
  42. [49]Aguiar PN, De Mello RA, Barreto CMN, Immune checkpoint inhibitors for advanced non-small cell lung cancer: emerging sequencing for new treatment targets ESMO Open 2017; 2: e000200. doi: 10.1136/esmoopen-2017-000200. 29209522 PMC5703392 · DOI
  43. [50]Madore J, Vilain RE, Menzies AM, Kakavand H, Wilmott JS, Hyman J, Yearley JH, Kefford RF, Thompson JF, Long GV, Hersey P and Scolyer RA (2015), PD-L1 expression in melanoma shows marked heterogeneity within and between patients: implications for anti-PD-1/PD-L1 clinical trials. Pigment Cell Melanoma Res, 28: 245– 253. doi: 10.1111/pcmr.12340. 25477049 · DOI
  44. [51]Aguiar PN, Santoro IL, Tadokoro H., The role of PD-L1 expression as a predictive biomarker in advanced non-small-cell lung cancer: a network meta-analysis. Immunotherapy. 2016; 8(4): 479– 88. doi: 10.2217/imt-2015-0002. 26973128 · DOI
  45. [52]Aguiar PN Jr, De Mello RA, Hall P, PD-L1 expression as a predictive biomarker in advanced non-small-cell lung cancer: updated survival data. Immunotherapy. 2017 5; 9(6): 499– 506. doi: 10.2217/imt-2016-0150. 28472902 · DOI
  46. [53]Kerr Keith M. and Nicolson Marianne C. (2016) Non–Small Cell Lung Cancer, PD-L1, and the Pathologist. Archives of Pathology & Laboratory Medicine: 3 2016, Vol. 140, No. 3, pp. 249– 254. 26927720
  47. [54]Borghaei H, Paz-Ares L, Horn L Nivolumab versus Docetaxel in Advanced Nonsquamous Non-SmallCell Lung Cancer. N. Engl. J. Med 2015; 373(17): 1627– 1639. 26412456 PMC5705936
  48. [55]Mazzaschi G, Madeddu D, Falco A, Bocchialini G, Low PD-1 Expression in Cytotoxic CD8+ Tumor-Infiltrating Lymphocytes Confers an Immune-Privileged Tissue Microenvironment in NSCLC with a Prognostic and Predictive Value. Clin Cancer Res. 2018 1 15; 24(2): 407– 419. doi: 10.1158/1078-0432.CCR-17-2156. 29074606 · DOI
  49. [56]Zhang YL, Li J, Mo HY, Different subsets of tumor infiltrating lymphocytes correlate with NPC progression in different ways. Mol Cancer. 2010; 9: 4 Published 2010 Jan 10. doi: 10.1186/1476-4598-9-4. 20064222 PMC2818695 · DOI
  50. [57]Berghoff AS, Fuchs E, Ricken G, Density of tumor-infiltrating lymphocytes correlates with extent of brain edema and overall survival time in patients with brain metastases. Oncoimmunology. 2015; 5(1): e1057388 Published 2015 Jun 9. 26942067 PMC4760339
  51. [58]Goc Jérémy, Germain Claire, Dendritic Cells in Tumor-Associated Tertiary Lymphoid Structures Signal a Th1 Cytotoxic Immune Contexture and License the Positive Prognostic Value of Infiltrating CD8+ T Cells. Cancer Res. 2014 2 1; 74(3): 705– 15. 24366885
  52. [59]33rd Annual Meeting & Pre-Conference Programs of the Society for Immunotherapy of Cancer (SITC 2018). J Immunother Cancer. 2018; 6(Suppl 1): 114. 30400835 PMC6220480
  53. [60]Parra ER,, Image Analysis-based Assessment of PD-L1 and Tumor-Associated Immune Cells Density Supports Distinct Intratumoral Microenvironment Groups in Non-small Cell Lung Carcinoma Patients. Clin Cancer Res, 2016 22(24): p. 6278– 6289. 27252415 PMC5558040
  54. [61]Reynders K, De Ruysscher D. Tumor infiltrating lymphocytes in lung cancer: a new prognostic parameter. Journal of Thoracic Disease. 2016; 8(8): E833– E835. doi: 10.21037/jtd.2016.07.75. 27618931 PMC4999748 · DOI
  55. [62]Zeng D-Q, Yu Y-F, Ou Q-Y, Prognostic and predictive value of tumor-infiltrating lymphocytes for clinical therapeutic research in patients with non-small cell lung cancer. Oncotarget. 2016; 7(12): 13765– 13781. doi: 10.18632/oncotarget.7282. 26871598 PMC4924677 · DOI
  56. [63]Tunali Ilke, Gray Jhanelle, P1.01–041 Quantitative Imaging Features Predict Response of Immunotherapy in Non-Small Cell Lung Cancer Patients 10.1016/j.jtho.2016.11.565. · DOI
  57. [64]Tang C, Brian Hobbs, Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer, Scientific Reportsvolume 8, Article number: 1922 (2018). doi: 10.1038/s41598-018-20471-5. 29386574 PMC5792427 · DOI · Paper page
  58. [65]Saeed-Vafa D, Bravo R, Dean JA, Combining radiomics and mathematical modeling to elucidate mechanisms of resistance to immune checkpoint blockade in non-small cell lung cancer. 10.1101/190561. · DOI
  59. [68]Khorrami M, Alilou M, Prasanna P, A combination of intra- and peritumoral features on baseline CT scans is associated with overall survival in non-small cell lung cancer patients treated with immune checkpoint inhibitors: a multi-agent multi-site study. 10.1117/12.2513001. · DOI
  60. [69]Nishino M, Dahlberg SE, Adeni AE, Tumor Response Dynamics of Advanced Non–small cell Lung Cancer Patients Treated with PD-1 Inhibitors: Imaging Markers for Treatment Outcome. Clin Cancer Res. 2017. doi: 10.1158/1078-0432.CCR-17-1434. PMC5626605 28679767 · DOI
  61. [70]Nishino M, Ramaiya NH, Chambers ES, Immune-related response assessment during PD-1 inhibitor therapy in advanced non-small-cell lung cancer patients. Journal for ImmunoTherapy of Cancer 20164: 84. 28018599 PMC5168591

Authors
Khorrami, M., Prasanna, P., Gupta, A., Patil, P., Velu, P. D., Thawani, R., Corredor, G., Alilou, M., Bera, K., Fu, P., Feldman, M., Velcheti, V., & Madabhushi, A.
Affiliations
  • Case Western Reserve University (US)
  • University Hospitals of Cleveland (US)
  • Cleveland Clinic (US)
  • Weill Cornell Medicine (US)
  • Maimonides Medical Center (US)
  • 1Department of Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio.
  • Hospital of the University of Pennsylvania (US)
  • NYU Langone Health (US)
  • Louis Stokes Cleveland VA Medical Center (US)
Corresponding author
Madabhushi, Anant
ORCID
Venue
Cancer Immunology Research
Publisher
American Association for Cancer Research (AACR)
Published
2020-01-01
Volume
8
Issue
1
Pages
108-119
ISSN
  • 2326-6066 (ISSN-L)
  • 2326-6074
DOI
10.1158/2326-6066.cir-19-0476
PMC
PMC7718609
OpenAlex
W2983529249
Cited by
297
References
73
Citations by year
2026: 19 · 2025: 27 · 2024: 61 · 2023: 65 · 2022: 63 · 2021: 36 · 2020: 25
FWCI
20.49
Type
journal article
Language
en
Open access
Locations
  • Cancer Immunology Research (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • NCI NIH (1U24CA199374-01, R01CA202752-01A1, R01CA208236-01A1, R01CA216579-01A1, R01CA220581-01A1)
  • National Center for Research Resources (1 C06 RR12463-01)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
Keywords
non-small cell lung cancer, radiomics, immune checkpoint inhibitors, immunotherapy response prediction, tumor-infiltrating lymphocytes, CT texture analysis, overall survival, predictive biomarkers, RECIST response
MeSH terms
Adult, Aged, Aged, 80 and over, Carcinoma, Non-Small-Cell Lung, Female, Humans, Image Interpretation, Computer-Assisted, Immunotherapy, Lung Neoplasms, Male, Middle Aged, Predictive Value of Tests, Prognosis, Retrospective Studies, Tomography, X-Ray Computed, Biomarkers, Tumor, Survival Rate, Lymphocytes, Tumor-Infiltrating, Treatment Outcome, Programmed Cell Death 1 Receptor

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