Skip to main content
SpringerLink
Log in

The Utility of Radiomics in Predicting Response to Cancer Immunotherapy

  • Living reference work entry
  • First Online:
Handbook of Cancer and Immunology

  • 71 Accesses

Abstract

Immunotherapy, particularly immune checkpoint inhibitor therapy, has made remarkable advancements in the treatment of patients with cancer in the last decade. However, not all patients respond to immunotherapy, which is a therapeutic approach associated with high costs and adverse events. As such, there is an unmet need for identifying biomarkers that can accurately predict treatment response. The potential of currently identified biomarkers such as programmed cell death ligand 1 (PD-L1) and tumor mutational burden (TMB) is debated. Recently, noninvasive radiomics approaches have showed promise in predicting response to immunotherapy, further facilitating precision oncology. In this chapter, we establish a general understanding of the utility of radiomics in predicting immunotherapy response in patients with cancer, in a way that medical professionals with limited understanding of computer science can comprehend. We explore various cancers for which this novel approach has been applied and discuss the challenges that need to be addressed in future investigations.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Similar content being viewed by others

References

  • Alilou M, Vaidya P, Khorrami M, Zagouras A, Patil P, Bera K, Fu P, Velcheti V, Madabhushi A (2019) Quantitative vessel tortuosity radiomics on baseline non-contrast lung CT predict response to immunotherapy and are prognostic of overall survival. Medical imaging 2019: computer-aided diagnosis. International Society for Optics and Photonics, 109501F

    Google Scholar 

  • Bera K, Velcheti V, Madabhushi A (2018) Novel quantitative imaging for predicting response to therapy: techniques and clinical applications. Am Soc Clin Oncol Educ Book 38:1008–1018

    Article  Google Scholar 

  • Bhatia A, Birger M, Veeraraghavan H, Um H, Tixier F, McKenney AS, Cugliari M, Caviasco A, Bialczak A, Malani R, Flynn J, Zhang Z, Yang TJ, Santomasso BD, Shoushtari AN, Young RJ (2019) MRI radiomic features are associated with survival in melanoma brain metastases treated with immune checkpoint inhibitors. Neuro Oncol 21:1578–1586

    Article  CAS  Google Scholar 

  • Bi WL, Hosny A, Schabath MB, Giger ML, Birkbak NJ, Mehrtash A, Allison T, Arnaout O, Abbosh C, Dunn IF, Mak RH, Tamimi RM, Tempany CM, Swanton C, Hoffmann U, Schwartz LH, Gillies RJ, Huang RY, Aerts H (2019) Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin 69:127–157

    Google Scholar 

  • Bian Y, Liu C, Li Q, Meng Y, Liu F, Zhang H, Fang X, Li J, Yu J, Feng X, Ma C, Zhao Z, Wang L, Xu J, Shao C, Lu J (2021) Preoperative radiomics approach to evaluating tumor-infiltrating CD8+ T cells in patients with pancreatic ductal adenocarcinoma using noncontrast magnetic resonance imaging. J Magn Reson Imaging 55(3):803–814

    Article  Google Scholar 

  • Bogowicz M, Vuong D, Huellner MW, Pavic M, Andratschke N, Gabrys HS, Guckenberger M, Tanadini-Lang S (2019) CT radiomics and PET radiomics: ready for clinical implementation? Q J Nucl Med Mol Imaging 63:355–370

    Article  Google Scholar 

  • Borhani AA, Catania R, Velichko YS, Hectors S, Taouli B, Lewis S (2021) Radiomics of hepatocellular carcinoma: promising roles in patient selection, prediction, and assessment of treatment response. Abdom Radiol 46:3674–3685

    Article  Google Scholar 

  • Brendlin AS, Peisen F, Almansour H, Afat S, Eigentler T, Amaral T, Faby S, Calvarons AF, Nikolaou K, Othman AE (2021) A machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma. J Immunother Cancer 9:e003261

    Article  Google Scholar 

  • Chaddad A, Daniel P, Zhang M, Rathore S, Sargos P, Desrosiers C, Niazi T (2022) Deep radiomic signature with immune cell markers predicts the survival of glioma patients. Neurocomputing 469:366–375

    Article  Google Scholar 

  • Chen Q, Zhang L, Mo X, You J, Chen L, Fang J, Wang F, Jin Z, Zhang B, Zhang S (2021a) Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis. Eur J Nucl Med Mol Imaging 49:345–360

    Article  Google Scholar 

  • Chen X, Zhou M, Wang Z, Lu S, Chang S, Zhou Z (2021b) Immunotherapy treatment outcome prediction in metastatic melanoma through an automated multi-objective delta-radiomics model. Comput Biol Med 138:104916

    Article  Google Scholar 

  • Colen RR, Rolfo C, Ak M, Ayoub M, Ahmed S, Elshafeey N, Mamindla P, Zinn PO, Ng C, Vikram R (2021) Radiomics analysis for predicting pembrolizumab response in patients with advanced rare cancers. J Immunother Cancer 9:e001752

    Article  Google Scholar 

  • Cuccarini V, Aquino D, Gioppo A, Anghileri E, Pellegatta S, Schettino C, Mazzi F, Finocchiaro G, Bruzzone MG, Eoli M (2019) Advanced MRI assessment during dendritic cell immunotherapy added to standard treatment against glioblastoma. J Clin Med 8:2007

    Article  CAS  Google Scholar 

  • Cui Y, Li Y, Li X (2018) The impact of PD-L1 on glucose metabolism of lung adenocarcinoma cells. J Nucl Med 59(Suppl 1):1252

    Google Scholar 

  • Cunliffe A, Armato SG III, Castillo R, Pham N, Guerrero T, Al-Hallaq HA (2015) Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. Int J Radiat Oncol Biol Phys 91:1048–1056

    Article  Google Scholar 

  • Damgaci S, Ibrahim-Hashim A, Enriquez-Navas PM, Pilon-Thomas S, Guvenis A, Gillies RJ (2018) Hypoxia and acidosis: immune suppressors and therapeutic targets. Immunology 154:354–362

    Article  CAS  Google Scholar 

  • Doroshow DB, Sanmamed MF, Hastings K, Politi K, Rimm DL, Chen L, Melero I, Schalper KA, Herbst RS (2019) Immunotherapy in non-small cell lung cancer: facts and hopes. Clin Cancer Res 25:4592–4602

    Article  CAS  Google Scholar 

  • Dromain C, Beigelman C, Pozzessere C, Duran R, Digklia A (2020) Imaging of tumour response to immunotherapy. Eur Radiol Exp 4:2–2

    Article  Google Scholar 

  • Ettinger DS, Wood DE, Aisner DL, Akerley W, Bauman JR, Bharat A, Bruno DS, Chang JY, Chirieac LR, D’Amico TA, Dilling TJ, Dowell J, Gettinger S, Gubens MA, Hegde A, Hennon M, Lackner RP, Lanuti M, Leal TA, Lin J, Loo BW Jr, Lovly CM, Martins RG, Massarelli E, Morgensztern D, Ng T, Otterson GA, Patel SP, Riely GJ, Schild SE, Shapiro TA, Singh AP, Stevenson J, Tam A, Yanagawa J, Yang SC, Gregory KM, Hughes M (2021) NCCN guidelines insights: non-small cell lung cancer, version 2.2021. J Natl Compr Cancer Netw 19:254–266

    Article  CAS  Google Scholar 

  • García-Figueiras R, Baleato-González S, Luna A, Muñoz-Iglesias J, Oleaga L, Vallejo Casas JA, Martín-Noguerol T, Broncano J, Areses MC, Vilanova JC (2020) Assessing immunotherapy with functional and molecular imaging and radiomics. Radiographics 40:1987–2010

    Article  Google Scholar 

  • Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577

    Article  Google Scholar 

  • Granata V, Fusco R, Costa M, Picone C, Cozzi D, Moroni C, Casella L, Viola G, Montanino A, Monti R (2021) Preliminary report on computed tomography radiomics features as biomarkers to immunotherapy selection in lung adenocarcinoma patients. Cancers 13:3992

    Article  Google Scholar 

  • He B, Di Dong YS, Zhou C, Fang M, Zhu Y, Zhang H, Huang Z, Jiang T, Tian J, Chen C (2020) Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker. J Immunother Cancer 8:e000550

    Article  Google Scholar 

  • Herbst RS, Soria J-C, Kowanetz M, Fine GD, Hamid O, Gordon MS, Sosman JA, McDermott DF, Powderly JD, Gettinger SN, Kohrt HEK, Horn L, Lawrence DP, Rost S, Leabman M, Xiao Y, Mokatrin A, Koeppen H, Hegde PS, Mellman I, Chen DS, Hodi FS (2014) Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515:563–567

    Article  CAS  Google Scholar 

  • Himoto Y, Veeraraghavan H, Zheng J, Zamarin D, Snyder A, Capanu M, Nougaret S, Vargas HA, Shitano F, Callahan M (2019) Computed tomography-derived radiomic metrics can identify responders to immunotherapy in ovarian cancer. JCO Precis Oncol 3:1–13

    Google Scholar 

  • Huang Y, Liu Z, He L, Chen X, Pan D, Ma Z, Liang C, Tian J, Liang C (2016) Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281:947–957

    Article  Google Scholar 

  • Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, Berent-Maoz B, Pang J, Chmielowski B, Cherry G, Seja E, Lomeli S, Kong X, Kelley MC, Sosman JA, Johnson DB, Ribas A, Lo RS (2016) Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell 165:35–44

    Article  CAS  Google Scholar 

  • Ji Z, Cui Y, Peng Z, Gong J, Zhu H-T, Zhang X, Li J, Lu M, Lu Z, Shen L (2020) Use of radiomics to predict response to immunotherapy of malignant tumors of the digestive system. Med Sci Monit 26:e924671

    Article  CAS  Google Scholar 

  • Jiang M, Sun D, Guo Y, Guo Y, Xiao J, Wang L, Yao X (2020) Assessing PD-L1 expression level by radiomic features from PET/CT in nonsmall cell lung cancer patients: an initial result. Acad Radiol 27:171–179

    Article  Google Scholar 

  • Khorrami M, Prasanna P, Gupta A, Patil P, Velu PD, Thawani R, Corredor G, Alilou M, Bera K, Fu P (2020) Changes in CT radiomic features associated with lymphocyte distribution predict overall survival and response to immunotherapy in non-small cell lung cancer. Cancer Immunol Res 8:108–119

    Article  CAS  Google Scholar 

  • Koh YW, Han JH, Park SY, Yoon DH, Suh C, Huh J (2017) GLUT1 as a prognostic factor for classical Hodgkin’s lymphoma: correlation with PD-L1 and PD-L2 expression. J Pathol Transl Med 51:152–158

    Article  Google Scholar 

  • Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48:441–446

    Article  Google Scholar 

  • Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong EEC, van Timmeren J, Sanduleanu S, Larue R, Even AJG, Jochems A, van Wijk Y, Woodruff H, van Soest J, Lustberg T, Roelofs E, van Elmpt W, Dekker A, Mottaghy FM, Wildberger JE, Walsh S (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  Google Scholar 

  • Larimer BM, Wehrenberg-Klee E, Dubois F, Mehta A, Kalomeris T, Flaherty K, Boland G, Mahmood U (2017) Granzyme B PET imaging as a predictive biomarker of immunotherapy response. Cancer Res 77:2318–2327

    Article  CAS  Google Scholar 

  • Liang C, Huang Y, He L, Chen X, Ma Z, Dong D, Tian J, Liang C, Liu Z (2016) The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I–II and stage III–IV colorectal cancer. Oncotarget 7:31401–31412

    Article  Google Scholar 

  • Liao H, Zhang Z, Chen J, Liao M, Xu L, Wu Z, Yuan K, Song B, Zeng Y (2019) Preoperative radiomic approach to evaluate tumor-infiltrating CD8+ T cells in hepatocellular carcinoma patients using contrast-enhanced computed tomography. Ann Surg Oncol 26:4537–4547

    Article  Google Scholar 

  • Ligero M, Garcia-Ruiz A, Viaplana C, Villacampa G, Raciti MV, Landa J, Matos I, Martin-Liberal J, Ochoa-de-Olza M, Hierro C, Mateo J, Gonzalez M, Morales-Barrera R, Suarez C, Rodon J, Elez E, Braña I, Muñoz-Couselo E, Oaknin A, Fasani R, Nuciforo P, Gil D, Rubio-Perez C, Seoane J, Felip E, Escobar M, Tabernero J, Carles J, Dienstmann R, Garralda E, Perez-Lopez R (2021) A CT-based radiomics signature is associated with response to immune checkpoint inhibitors in advanced solid Tumors. Radiology 299:109–119

    Article  Google Scholar 

  • Lopci E, Toschi L, Grizzi F, Rahal D, Olivari L, Castino GF, Marchetti S, Cortese N, Qehajaj D, Pistillo D, Alloisio M, Roncalli M, Allavena P, Santoro A, Marchesi F, Chiti A (2016) Correlation of metabolic information on FDG-PET with tissue expression of immune markers in patients with non-small cell lung cancer (NSCLC) who are candidates for upfront surgery. Eur J Nucl Med Mol Imaging 43:1954–1961

    Article  CAS  Google Scholar 

  • Macyszyn L, Akbari H, Pisapia JM, Da X, Attiah M, Pigrish V, Bi Y, Pal S, Davuluri RV, Roccograndi L, Dahmane N, Martinez-Lage M, Biros G, Wolf RL, Bilello M, O’Rourke DM, Davatzikos C (2016) Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro Oncol 18:417–425

    Article  Google Scholar 

  • Makuku R, Khalili N, Razi S, Keshavarz-Fathi M, Rezaei N (2021) Current and future perspectives of PD-1/PDL-1 blockade in cancer immunotherapy. J Immunol Res 2021:6661406

    Article  Google Scholar 

  • Mathios D, Srivastava S, Kim T, Bettegowda C, Lim M (2021) Emerging technologies for non-invasive monitoring of treatment response to immunotherapy for brain tumors. Neuromol Med 24(2):74–87

    Article  Google Scholar 

  • McArthur SL (2019) Repeatability, reproducibility, and replicability: tackling the 3R challenge in biointerface science and engineering. Biointerphases 14:020201

    Article  Google Scholar 

  • Mu W, Jiang L, Shi Y, Tunali I, Gray JE, Katsoulakis E, Tian J, Gillies RJ, Schabath MB (2021) Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images. J Immunother Cancer 9:e002118

    Article  Google Scholar 

  • Park KJ, Lee J-L, Yoon S-K, Heo C, Park BW, Kim JK (2020) Radiomics-based prediction model for outcomes of PD-1/PD-L1 immunotherapy in metastatic urothelial carcinoma. Eur Radiol 30:5392–5403

    Article  CAS  Google Scholar 

  • Parmar C, Grossmann P, Bussink J, Lambin P, Aerts HJWL (2015) Machine learning methods for quantitative radiomic biomarkers. Sci Rep 5:13087

    Article  CAS  Google Scholar 

  • Polverari G, Ceci F, Bertaglia V, Reale ML, Rampado O, Gallio E, Passera R, Liberini V, Scapoli P, Arena V (2020) 18F-FDG pet parameters and radiomics features analysis in advanced NSCLC treated with immunotherapy as predictors of therapy response and survival. Cancers 12:1163

    Article  CAS  Google Scholar 

  • Porcu M, Solinas C, Mannelli L, Micheletti G, Lambertini M, Willard-Gallo K, Neri E, Flanders AE, Saba L (2020) Radiomics and “radi-… omics” in cancer immunotherapy: a guide for clinicians. Crit Rev Oncol Hematol 154:103068

    Article  Google Scholar 

  • Qin L, Li X, Stroiney A, Qu J, Helgager J, Reardon DA, Young GS (2017) Advanced MRI assessment to predict benefit of anti-programmed cell death 1 protein immunotherapy response in patients with recurrent glioblastoma. Neuroradiology 59:135–145

    Article  Google Scholar 

  • Ralli M, Botticelli A, Visconti IC, Angeletti D, Fiore M, Marchetti P, Lambiase A, de Vincentiis M, Greco A (2020) Immunotherapy in the treatment of metastatic melanoma: current knowledge and future directions. J Immunol Res 2020:9235638

    Article  Google Scholar 

  • Rundo F, Spampinato C, Banna GL, Conoci S (2019) Advanced deep learning embedded motion radiomics pipeline for predicting anti-PD-1/PD-L1 immunotherapy response in the treatment of bladder cancer: preliminary results. Electronics 8:1134

    Article  CAS  Google Scholar 

  • Sah B-R, Owczarczyk K, Siddique M, Cook GJR, Goh V (2019) Radiomics in esophageal and gastric cancer. Abdom Radiol 44:2048–2058

    Article  Google Scholar 

  • Schraag A, Klumpp B, Afat S, Gatidis S, Nikolaou K, Eigentler TK, Othman AE (2019) Baseline clinical and imaging predictors of treatment response and overall survival of patients with metastatic melanoma undergoing immunotherapy. Eur J Radiol 121:108688

    Article  Google Scholar 

  • Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, Verlingue L, Brandao D, Lancia A, Ammari S, Hollebecque A, Scoazec J-Y, Marabelle A, Massard C, Soria J-C, Robert C, Paragios N, Deutsch E, Ferté C (2018) A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 19:1180–1191

    Article  CAS  Google Scholar 

  • Thawani R, McLane M, Beig N, Ghose S, Prasanna P, Velcheti V, Madabhushi A (2018) Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung Cancer 115:34–41

    Article  Google Scholar 

  • Tixier F, Cheze-le-Rest C, Schick U, Simon B, Dufour X, Key S, Pradier O, Aubry M, Hatt M, Corcos L, Visvikis D (2020) Transcriptomics in cancer revealed by positron emission tomography radiomics. Sci Rep 10:5660

    Article  CAS  Google Scholar 

  • Traverso A, Wee L, Dekker A, Gillies R (2018) Repeatability and reproducibility of radiomic features: a systematic review. Int J Radiat Oncol Biol Phys 102:1143–1158

    Article  Google Scholar 

  • Trebeschi S, Drago S, Birkbak N, Kurilova I, Cǎlin A, Pizzi AD, Lalezari F, Lambregts D, Rohaan M, Parmar C (2019) Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol 30:998–1004

    Article  CAS  Google Scholar 

  • Tunali I, Tan Y, Gray JE, Katsoulakis E, Eschrich SA, Saller J, Aerts HJWL, Boyle T, Qi J, Guvenis A, Gillies RJ, Schabath MB (2021) Hypoxia-related radiomics and immunotherapy response: a multicohort study of non-small cell lung cancer. JNCI Cancer Spectr 5:pkab048

    Article  Google Scholar 

  • Twomey JD, Zhang B (2021) Cancer immunotherapy update: FDA-approved checkpoint inhibitors and companion diagnostics. AAPS J 23:39

    Article  Google Scholar 

  • van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, Beets-Tan RGH, Fillion-Robin JC, Pieper S, Aerts H (2017) Computational radiomics system to decode the radiographic phenotype. Cancer Res 77:e104–e107

    Article  Google Scholar 

  • Wang Z-L, Mao L-L, Zhou Z-G, Si L, Zhu H-T, Chen X, Zhou M-J, Sun Y-S, Guo J (2020) Pilot study of CT-based radiomics model for early evaluation of response to immunotherapy in patients with metastatic melanoma. Front Oncol 10:1524

    Article  Google Scholar 

  • Wang JH, Wahid KA, van Dijk LV, Farahani K, Thompson RF, Fuller CD (2021) Radiomic biomarkers of tumor immune biology and immunotherapy response. Clin Transl Radiat Oncol 28:97–115

    Article  CAS  Google Scholar 

  • Yoon J, Suh YJ, Han K, Cho H, Lee HJ, Hur J, Choi BW (2020) Utility of CT radiomics for prediction of PD-L1 expression in advanced lung adenocarcinomas. Thorac Cancer 11:993–1004

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Cancer Immunology Project (CIP), Universal Scientific Education and Research Network (USERN), Tehran, Iran

    Neda Khalili

  2. Research Center for Immunodeficiencies, Children’s Medical Center, Tehran University of Medical Sciences, Tehran, Iran

    Nima Rezaei

  3. Department of Immunology, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

    Nima Rezaei

  4. Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Stockholm, Sweden

    Nima Rezaei

Authors
  1. Neda Khalili
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nima Rezaei
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nima Rezaei .

Editor information

Editors and Affiliations

  1. Division of Clinical Immunology, Karolinska University Hospital, Huddinge, Sweden

    Nima Rezaei

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this entry

Check for updates. Verify currency and authenticity via CrossMark

Cite this entry

Khalili, N., Rezaei, N. (2023). The Utility of Radiomics in Predicting Response to Cancer Immunotherapy. In: Rezaei, N. (eds) Handbook of Cancer and Immunology. Springer, Cham. https://doi.org/10.1007/978-3-030-80962-1_136-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-80962-1_136-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-80962-1

  • Online ISBN: 978-3-030-80962-1

  • eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences

Publish with us

Policies and ethics

Search

Navigation