Skip to main content

Advertisement

SpringerLink
Log in

Radiogenomics in Interventional Oncology

  • Interventional Oncology (DC Madoff, Section Editor)
  • Published:
Current Oncology Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Radiogenomics is a growing field that has garnered immense interest over the past decade, owing to its numerous applications in the field of oncology and its potential value in improving patient outcomes. Current applications have only begun to delve into the potential of radiogenomics, and particularly in interventional oncology, there is room for development and increased value of these applications.

Recent Findings

The field of interventional oncology (IO) has seen valuable radiogenomic applications, from prediction of response to locoregional therapies in hepatocellular carcinoma to identification of genetic mutations in non-small cell lung cancer. Future directions that can increase the value of radiogenomics include applications that address tumor heterogeneity, predict immune responsiveness of tumors, and differentiate between oligoprogression and early widespread progression, among others.

Summary

Radiogenomics, whether in terms of methodologies or applications, is still in the early stages of development and far from maturation. Future applications, particularly in the field of interventional oncology, will allow realization of its full potential.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. Pinker K, Shitano F, Sala E, Do RK, Young RJ, Wibmer AG, et al. Background, current role, and potential applications of radiogenomics. J Magn Reson Imaging. 2018;47(3):604–20.

    Article  Google Scholar 

  2. Mazurowski MA. Radiogenomics: what it is and why it is important. J Am Coll Radiol. 2015;12(8):862–6. Available from:. https://doi.org/10.1016/j.jacr.2015.04.019.

    Article  PubMed  Google Scholar 

  3. Barnett GC, Coles CE, Elliott RM, Baynes C, Luccarini C, Conroy D, et al. Independent validation of genes and polymorphisms reported to be associated with radiation toxicity: A prospective analysis study. Lancet Oncol. 2012;13(1):65–77. Available from:. https://doi.org/10.1016/S1470-2045(11)70302-3.

    Article  CAS  PubMed  Google Scholar 

  4. Cherukuri AR, Lubner MG, Zea R, Hinshaw JL, Lubner SJ, Matkowskyj KA, et al. Tissue sampling in the era of precision medicine: comparison of percutaneous biopsies performed for clinical trials or tumor genomics versus routine clinical care. Abdom Radiol (NY). 2019;44(6):2074–80. Available from:. https://doi.org/10.1007/s00261-018-1702-1.

    Article  Google Scholar 

  5. Esteva A, Kuprel B, Novoa R, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–8.

    Article  CAS  Google Scholar 

  6. Lo Gullo R, Daimiel I, Morris EA, Pinker K. Combining molecular and imaging metrics in cancer: radiogenomics. Insights Imaging. 2020;11(1):1 Available from: https://pubmed.ncbi.nlm.nih.gov/31901171.

    Article  Google Scholar 

  7. Bai HX, Lee AM, Yang L, Zhang P, Davatzikos C, Maris JM, et al. Imaging genomics in cancer research: Limitations and promises. Br J Radiol. 2016;89(1061):20151030. https://doi.org/10.1259/bjr.20151030

  8. Bodalal Z, Trebeschi S, Nguyen-Kim TDL, Schats W, Beets-Tan R. Radiogenomics: bridging imaging and genomics. Abdom Radiol. 2019;44(6):1960–84. Available from:. https://doi.org/10.1007/s00261-019-02028-w.

    Article  Google Scholar 

  9. Thawani R, McLane M, Beig N, Ghose S, Prasanna P, Velcheti V, et al. Radiomics and radiogenomics in lung cancer: a review for the clinician. Lung Cancer. 2018;115(October 2017):34–41. Available from:. https://doi.org/10.1016/j.lungcan.2017.10.015.

    Article  PubMed  Google Scholar 

  10. Xia W, Chen Y, Zhang R, Yan Z, Zhou X, Zhang B, et al. Radiogenomics of hepatocellular carcinoma: multiregion analysis-based identification of prognostic imaging biomarkers by integrating gene data - a preliminary study. Phys Med Biol. 2018;63(3):035044. https://doi.org/10.1088/1361-6560/aaa609.

  11. Karlo CA, Di Paolo PL, Chaim J, Hakimi AA, Ostrovnaya I, Russo P, et al. Radiogenomics of clear cell renal cell carcinoma: associations between CT imaging features and mutations. Radiology. 2014;270(2):464–71.

    Article  Google Scholar 

  12. Yamamoto S, Maki DD, Korn RL, Kuo MD. Radiogenomic analysis of breast cancer using MRI: a preliminary study to define the landscape. Am J Roentgenol. 2012;199(3):654–63.

    Article  Google Scholar 

  13. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77.

    Article  Google Scholar 

  14. Parwani A. Next generation diagnostic pathology: use of digital pathology and artificial intelligence tools to augment a pathological diagnosis. Diagn Pathol. 2019;14(1):138.

    Article  CAS  Google Scholar 

  15. Kim H, Park C, Lee M, et al. Impact of reconstruction algorithms on CT radiomic features of pulmonary tumors: analysis of intra- and inter-reader variability and inter-reconstruction algorithm variability. PLoS One. 2016;11(10):e0164924.

    Article  Google Scholar 

  16. Lu L, Ehmke RC, Schwartz LH, Zhao B. Assessing agreement between radiomic features computed for multiple CT imaging settings. PLoS One. 2016;11(12):e0166550. Available from:. https://doi.org/10.1371/journal.pone.0166550.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Clarke LP, Nordstrom RJ, Zhang H, Tandon P, Zhang Y, Redmond G, et al. The Quantitative Imaging Network: NCI’s Historical Perspective and Planned Goals. Transl Oncol. 2014;7(1):1–4 Available from: https://pubmed.ncbi.nlm.nih.gov/24772201. This is one of the important international initiatives launched to standardize image acquisition protocols among institutions, which affects the data modeling stage of the radiomics process.

    Article  Google Scholar 

  18. • Buckler AJ, Bresolin L, Dunnick NR, Sullivan DC. A Collaborative Enterprise for Multi-Stakeholder Participation in the Advancement of Quantitative Imaging. Radiology. 2011;258(3):906–14. Available from: https://doi.org/10.1148/radiol.10100799. This is one of the important international initiatives launched to standardize image acquisition protocols among institutions, which affects the data modeling stage of the radiomics process.

  19. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–44. Available from:. https://doi.org/10.1038/nature14539.

    Article  CAS  PubMed  Google Scholar 

  20. Benitez JM, Castro JL, Requena I. Are artificial neural networks black boxes? IEEE Trans Neural Netw. 1997;8(5):1156–64.

    Article  CAS  Google Scholar 

  21. Kim J, Seo S, Ashrafinia S, Rahmim A, Sossi V, Klyuzhin I. Training of deep convolutional neural nets to extract radiomic signatures of tumors. J Nucl Med. 2019;60(supplement 1):406 Available from: http://jnm.snmjournals.org/content/60/supplement_1/406.abstract.

    Google Scholar 

  22. Kang J, Rancati T, Lee S, Oh JH, Kerns SL, Scott JG, et al. Machine learning and radiogenomics: lessons learned and future directions. Front Oncol. 2018;8:228 Available from: https://pubmed.ncbi.nlm.nih.gov/29977864.

    Article  Google Scholar 

  23. Llovet JM, Fuster J, Bruix J. The Barcelona approach: diagnosis, staging, and treatment of hepatocellular carcinoma. Liver Transpl. 2004;10(S2)):S115–20. Available from:. https://doi.org/10.1002/lt.20034.

    Article  PubMed  Google Scholar 

  24. Ziv E, Yarmohammadi H, Boas FE, Petre EN, Brown KT, Solomon SB, et al. Gene Signature Associated with Upregulation of the Wnt/β-Catenin Signaling Pathway Predicts Tumor Response to Transarterial Embolization. J Vasc Interv Radiol. 2017;28(3):349–355.e1 Available from: http://www.sciencedirect.com/science/article/pii/S1051044316307370.

    Article  Google Scholar 

  25. Gaba RC, Groth JV, Parvinian A, Guzman G, Casadaban LC. Gene expression in hepatocellular carcinoma: pilot study of potential transarterial chemoembolization response biomarkers. J Vasc Interv Radiol. 2015;26(5):723–32 Available from: http://www.sciencedirect.com/science/article/pii/S1051044314018478.

    Article  Google Scholar 

  26. Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol. 2007;25(6):675–80. Available from: https://doi.org/10.1038/nbt1306. An early radiogenomics study demonstrating correlations of CT imaging features and global gene expression programs of primary liver cancers.

  27. Kuo MD, Gollub J, Sirlin CB, Ooi C, Chen X. Radiogenomic analysis to identify Imaging phenotypes associated with drug response Gene Expression programs in hepatocellular carcinoma. J Vasc Interv Radiol. 2007;18(7):821–30. Available from: https://doi.org/10.1016/j.jvir.2007.04.031. An early radiogenomics study demonstrating capacity of imaging features to predict treatment response in primary liver cancers.

  28. Taouli B, Hoshida Y, Kakite S, Chen X, Tan PS, Sun X, et al. Imaging-based surrogate markers of transcriptome subclasses and signatures in hepatocellular carcinoma: preliminary results. Eur Radiol. 2017;27(11):4472–81. Available from:. https://doi.org/10.1007/s00330-017-4844-6.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Hsu H-C, Wu T-T, Wu M-Z, Sheu J-C, Lee C-S, Chen D-S. Tumor invasiveness and prognosis in resected hepatocellular carcinoma. Clinical and pathogenetic implications. Cancer. 1988;61(10):2095–9. Available from. https://doi.org/10.1002/1097-0142(19880515)61:10<2095::AID-CNCR2820611027>3.0.CO.

    Article  CAS  PubMed  Google Scholar 

  30. Chen X, Cheung ST, So S, Fan ST, Barry C, Higgins J, et al. Gene expression patterns in human liver cancers. Mol Biol Cell. 2002;13(6):1929–39. Available from:. https://doi.org/10.1091/mbc.02-02-0023.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Banerjee S, Wang DS, Kim HJ, Sirlin CB, Chan MG, Korn RL, et al. A computed tomography radiogenomic biomarker predicts microvascular invasion and clinical outcomes in hepatocellular carcinoma. Hepatology. 2015;62(3):792–800. Available from:. https://doi.org/10.1002/hep.27877.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Renzulli M, Brocchi S, Cucchetti A, Mazzotti F, Mosconi C, Sportoletti C, et al. Can current preoperative imaging be used to detect microvascular invasion of hepatocellular carcinoma? Radiology. 2015;279(2):432–42. Available from:. https://doi.org/10.1148/radiol.2015150998.

    Article  PubMed  Google Scholar 

  33. Tacher V, Lin M, Duran R, Yarmohammadi H, Lee H, Chapiro J, et al. Comparison of existing response criteria in patients with hepatocellular carcinoma treated with transarterial chemoembolization using a 3D quantitative approach. Radiology. 2015;278(1):275–84. Available from:. https://doi.org/10.1148/radiol.2015142951.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Chapiro J, Wood LD, Lin M, Duran R, Cornish T, Lesage D, et al. Radiologic-pathologic analysis of contrast-enhanced and diffusion-weighted MR imaging in patients with HCC after TACE: diagnostic accuracy of 3D quantitative image analysis. Radiology. 2014;273(3):746–58. Available from:. https://doi.org/10.1148/radiol.14140033.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Herber S, Biesterfeld S, Franz U, Schneider J, Thies J, Schuchmann M, et al. Correlation of multislice CT and histomorphology in HCC following TACE: predictors of outcome. Cardiovasc Intervent Radiol. 2008;31(4):768–77. Available from:. https://doi.org/10.1007/s00270-007-9270-8.

    Article  CAS  PubMed  Google Scholar 

  36. Monsky WL, Kim I, Loh S, Li C-S, Greasby TA, Deutsch L-S, et al. Semiautomated segmentation for volumetric analysis of intratumoral ethiodol uptake and subsequent tumor necrosis after chemoembolization. Am J Roentgenol. 2010;195(5):1220–30. Available from:. https://doi.org/10.2214/AJR.09.3964.

    Article  Google Scholar 

  37. Wang Z, Chen R, Duran R, Zhao Y, Yenokyan G, Chapiro J, et al. Intraprocedural 3D quantification of lipiodol deposition on cone-beam CT predicts tumor response after transarterial chemoembolization in patients with hepatocellular carcinoma. Cardiovasc Intervent Radiol. 2015;38:1548–56.

    Article  CAS  Google Scholar 

  38. Dupuy DE, Fernando HC, Hillman S, Ng T, Tan AD, Sharma A, et al. Radiofrequency ablation of stage IA non–small cell lung cancer in medically inoperable patients: results from the American College of Surgeons Oncology Group Z4033 (Alliance) trial. Cancer. 2015;121(19):3491–8. Available from:. https://doi.org/10.1002/cncr.29507.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Yu HA, Sima CS, Huang J, Solomon SB, Rimner A, Paik P, et al. Local therapy with continued EGFR tyrosine kinase inhibitor therapy as a treatment strategy in EGFR-mutant advanced lung cancers that have developed acquired resistance to EGFR tyrosine kinase inhibitors. J Thorac Oncol. 2013;8(3):346–51 Available from: https://pubmed.ncbi.nlm.nih.gov/23407558.

    Article  CAS  Google Scholar 

  40. Weickhardt AJ, Scheier B, Burke JM, Gan G, Lu X, Bunn PA Jr, et al. Local ablative therapy of oligoprogressive disease prolongs disease control by tyrosine kinase inhibitors in oncogene-addicted non-small-cell lung cancer. J Thorac Oncol. 2012;7(12):1807–14 Available from: https://pubmed.ncbi.nlm.nih.gov/23154552.

    Article  CAS  Google Scholar 

  41. Kim C, Hoang CD, Kesarwala AH, Schrump DS, Guha U, Rajan A. Role of local ablative therapy in patients with oligometastatic and oligoprogressive non–small cell lung cancer. J Thorac Oncol. 2017;12(2):179–93 Available from: http://www.sciencedirect.com/science/article/pii/S1556086416311753.

    Article  Google Scholar 

  42. Ziv E, Erinjeri JP, Yarmohammadi H, Boas FE, Petre EN, Gao S, et al. Lung adenocarcinoma: predictive value of KRAS mutation status in assessing local recurrence in patients undergoing image-guided ablation. Radiology. 2016;282(1):251–8. Available from:. https://doi.org/10.1148/radiol.2016160003.

    Article  PubMed  PubMed Central  Google Scholar 

  43. Gao S, Stein S, Petre EN, Shady W, Durack JC, Ridge C, et al. Micropapillary and/or solid histologic subtype based on pre-treatment biopsy predicts local recurrence after thermal ablation of lung adenocarcinoma. Cardiovasc Intervent Radiol. 2018;41(2):253–9. Available from:. https://doi.org/10.1007/s00270-017-1760-8.

    Article  PubMed  Google Scholar 

  44. Kim TH, Buonocore D, Petre EN, Durack JC, Maybody M, Johnston RP, et al. Utility of core biopsy specimen to identify histologic subtype and predict outcome for lung adenocarcinoma. Ann Thorac Surg. 2019;108(2):392–8 Available from: http://www.sciencedirect.com/science/article/pii/S0003497519305363.

    Article  Google Scholar 

  45. Gevaert O, Echegaray S, Khuong A, Hoang CD, Shrager JB, Jensen KC, et al. Predictive radiogenomics modeling of EGFR mutation status in lung cancer. Sci Rep. 2017;7:1–8. Available from:. https://doi.org/10.1038/srep41674.

    Article  CAS  Google Scholar 

  46. Sawan P, Plodkowski AJ, Li AE, Li BT, Drilon A, Capanu M, et al. CT features of HER2-mutant lung adenocarcinomas. Clin Imaging. 2018;51(May):279–83. Available from:. https://doi.org/10.1016/j.clinimag.2018.05.028.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Halpenny DF, Riely GJ, Hayes S, Yu H, Zheng J, Moskowitz CS, et al. Are there imaging characteristics associated with lung adenocarcinomas harboring ALK rearrangements? Lung Cancer. 2014;86(2):190–4 Available from: https://pubmed.ncbi.nlm.nih.gov/25312988.

    Article  Google Scholar 

  48. Mendoza DP, Stowell J, Muzikansky A, Shepard J-AO, Shaw AT, Digumarthy SR. Computed tomography imaging characteristics of non–small-cell lung cancer with anaplastic lymphoma kinase rearrangements: a systematic review and meta-analysis. Clin Lung Cancer. 2019;20(5):339–49 Available from: http://www.sciencedirect.com/science/article/pii/S152573041930110X.

    Article  Google Scholar 

  49. Koo HJ, Kim MY, Park S, Lee HN, Kim HJ, Lee JC, et al. Non–small cell lung cancer with resistance to EGFR-TKI therapy: CT characteristics of T790M mutation–positive cancer. Radiology. 2018;289(1):227–37. Available from:. https://doi.org/10.1148/radiol.2018180070.

    Article  PubMed  Google Scholar 

  50. Chae H-D, Park CM, Park SJ, Lee SM, Kim KG, Goo JM. Computerized texture analysis of ersistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas. Radiology. 2014;273(1):285–93. Available from:. https://doi.org/10.1148/radiol.14132187.

    Article  PubMed  Google Scholar 

  51. Wu H, Sun T, Wang J, Li X, Wang W, Huo D, et al. Combination of radiological and gray level co-occurrence matrix textural features used to distinguish solitary pulmonary nodules by Computed tomography. J Digit Imaging. 2013;26(4):797–802. Available from:. https://doi.org/10.1007/s10278-012-9547-6.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Firmino M, Angelo G, Morais H, Dantas MR, Valentim R. Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. Biomed Eng Online. 2016;15(1):2. Available from:. https://doi.org/10.1186/s12938-015-0120-7.

    Article  PubMed  PubMed Central  Google Scholar 

  53. Park S, Lee SM, Noh HN, Hwang HJ, Kim S, Do K-H, et al. Differentiation of predominant subtypes of lung adenocarcinoma using a quantitative radiomics approach on CT. Eur Radiol. 2020; Available from:. https://doi.org/10.1007/s00330-020-06805-w.

  54. Liu J, Cui J, Liu F, Yuan Y, Guo F, Zhang G. Multi-subtype classification model for non-small cell lung cancer based on radiomics: SLS model. Med Phys. 2019;46(7):3091–100. Available from:. https://doi.org/10.1002/mp.13551.

    Article  PubMed  Google Scholar 

  55. Young S, Golzarian J, Anderson J. Thermal ablation of T1a renal cell carcinoma: the clinical evidence. Semin Interv Radiol. 2019;36(5):367–73.

    Article  Google Scholar 

  56. da Costa WH, da Cunha IW, Fares AF, Bezerra SM, Shultz L, Clavijo DA, et al. Prognostic impact of concomitant loss of PBRM1 and BAP1 protein expression in early stages of clear cell renal cell carcinoma. Urol Oncol. 2018;36(5):243.e1–8 Available from: http://www.sciencedirect.com/science/article/pii/S1078143918300024.

    Article  Google Scholar 

  57. Lee WH, Cho H, Joung J-G, Jeon GH, Jeong CB, Jeon SS, et al. Integrative radiogenomics approach for risk assessment of post-operative metastasis in pathological T1 renal cell carcinoma: a pilot retrospective cohort study. Cancers (Basel). 2020;12(4):866. https://doi.org/10.3390/cancers12040866.

  58. Lahti SJ, Xing M, Zhang D, Lee JJ, Magnetta MJ, Kim HS. KRAS status as an independent prognostic factor for survival after Yttrium-90 radioembolization therapy for unresectable colorectal cancer liver metastases. J Vasc Interv Radiol. 2015;26(8):1102–11 Available from: http://www.sciencedirect.com/science/article/pii/S1051044315005436.

    Article  Google Scholar 

  59. Shady W, Petre EN, Vakiani E, Ziv E, Gonen M, Brown KT, et al. KRAS mutation is a marker of worse oncologic outcomes after percutaneous radiofrequency ablation of colorectal liver metastases. Oncotarget. 2017;8(39):66117–27 Available from: https://pubmed.ncbi.nlm.nih.gov/29029497.

    Article  Google Scholar 

  60. Ziv E, Bergen M, Yarmohammadi H, Boas FE, Petre EN, Sofocleous CT, et al. PI3K pathway mutations are associated with longer time to local progression after radioembolization of colorectal liver metastases. Oncotarget. 2017;8(14):23529–38 Available from: https://pubmed.ncbi.nlm.nih.gov/28206962.

    Article  Google Scholar 

  61. Pershad Y, Govindan S, Hara AK, Borad MJ, Bekaii-Saab T, Wallace A, et al. Using naïve bayesian analysis to determine imaging characteristics of KRAS mutations in metastatic colon cancer. Diagnostics (Basel). 2017;7(3):50 Available from: https://pubmed.ncbi.nlm.nih.gov/28869500.

    Article  Google Scholar 

  62. Simpson AL, Doussot A, Creasy JM, Adams LB, Allen PJ, DeMatteo RP, et al. Computed tomography image texture: a noninvasive prognostic marker of hepatic recurrence after hepatectomy for metastatic colorectal cancer. Ann Surg Oncol. 2017;24(9):2482–90 Available from: https://pubmed.ncbi.nlm.nih.gov/28560599.

    Article  Google Scholar 

  63. Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, et al. Activating mutations in the epidermal growth factor receptor underlying responsiveness of non–small-cell lung cancer to gefitinib. N Engl J Med. 2004;350(21):2129–39. Available from:. https://doi.org/10.1056/NEJMoa040938.

    Article  CAS  PubMed  Google Scholar 

  64. Kwak EL, Bang Y-J, Camidge DR, Shaw AT, Solomon B, Maki RG, et al. Anaplastic lymphoma kinase inhibition in non–small-cell lung cancer. N Engl J Med. 2010;363(18):1693–703. Available from:. https://doi.org/10.1056/NEJMoa1006448.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. de Bruin EC, McGranahan N, Mitter R, Salm M, Wedge DC, Yates L, et al. Spatial and temporal diversity in genomic instability processes defines lung cancer evolution. Science. 2014;346(6206):251–6 Available from: https://pubmed.ncbi.nlm.nih.gov/25301630.

    Article  Google Scholar 

  66. Marchetti A, Del Grammastro M, Felicioni L, Malatesta S, Filice G, Centi I, et al. Assessment of EGFR mutations in circulating tumor cell preparations from NSCLC patients by next generation sequencing: toward a real-time liquid biopsy for treatment. PLoS One. 2014;9(8):e103883 Available from: https://pubmed.ncbi.nlm.nih.gov/25137181.

    Article  Google Scholar 

  67. Barajas RF Jr, Phillips JJ, Parvataneni R, Molinaro A, Essock-Burns E, Bourne G, et al. Regional variation in histopathologic features of tumor specimens from treatment-naive glioblastoma correlates with anatomic and physiologic MR Imaging. Neuro-Oncology. 2012;14(7):942–54 Available from: https://pubmed.ncbi.nlm.nih.gov/22711606.

    Article  CAS  Google Scholar 

  68. Zhou M, Hall L, Goldgof D, Russo R, Balagurunathan Y, Gillies R, et al. Radiologically defined ecological dynamics and clinical outcomes in glioblastoma multiforme: preliminary results. Transl Oncol. 2014;7(1):5–13 Available from: https://pubmed.ncbi.nlm.nih.gov/24772202. This study identified the presence of tumor heterogeneity and attempted to use radiogenomic solutions to identify different habitats within the tumor.

    Article  CAS  Google Scholar 

  69. Emaminejad N, Qian W, Guan Y, Tan M, Qiu Y, Liu H, et al. Fusion of quantitative image and genomic biomarkers to improve prognosis assessment of early stage lung cancer patients. IEEE Trans Biomed Eng. 2016;63(5):1034–43.

    Article  Google Scholar 

  70. Tamez-Peña J-G, Rodriguez-Rojas J-A, Gomez-Rueda H, Celaya-Padilla J-M, Rivera-Prieto R-A, Palacios-Corona R, et al. Radiogenomics analysis identifies correlations of digital mammography with clinical molecular signatures in breast cancer. PLoS One. 2018;13(3):e0193871 Available from: https://pubmed.ncbi.nlm.nih.gov/29596496.

    Article  Google Scholar 

  71. Hirsch FR, McElhinny A, Stanforth D, Ranger-Moore J, Jansson M, Kulangara K, et al. PD-L1 immunohistochemistry assays for lung cancer: results from phase 1 of the Blueprint PD-L1 IHC Assay Comparison Project. J Thorac Oncol. 2017;12(2):208–22. Available from:. https://doi.org/10.1016/j.jtho.2016.11.2228.

    Article  PubMed  Google Scholar 

  72. Khunger M, Hernandez AV, Pasupuleti V, Rakshit S, Pennell NA, Stevenson J, et al. 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 Precis Oncol. 2017;18(1):1–15. Available from:. https://doi.org/10.1200/PO.16.00030.

    Article  Google Scholar 

  73. Khunger M, Bordeaux J, Dakappagari N, Vaupel C, Khunger A, Hu B, et al. Tumor PD-L1 heterogeneity in non-small cell lung cancer: does biopsy size and volume matter? J Clin Oncol. 2018;36(15_suppl):12058. Available from:. https://doi.org/10.1200/JCO.2018.36.15_suppl.12058.

    Article  Google Scholar 

  74. Munari E, Zamboni G, Marconi M, Sommaggio M, Brunelli M, Martignoni G, et al. PD-L1 expression heterogeneity in non-small cell lung cancer: evaluation of small biopsies reliability. Oncotarget. 2017;8(52):90123–31 Available from: https://pubmed.ncbi.nlm.nih.gov/29163815.

    Article  Google Scholar 

  75. Gniadek TJ, Li QK, Tully E, Chatterjee S, Nimmagadda S, Gabrielson E. Heterogeneous expression of PD-L1 in pulmonary squamous cell carcinoma and adenocarcinoma: implications for assessment by small biopsy. Mod Pathol. 2017;30(4):530–8. Available from:. https://doi.org/10.1038/modpathol.2016.213.

    Article  CAS  PubMed  Google Scholar 

  76. Zhang AW, McPherson A, Milne K, Kroeger DR, Hamilton PT, Miranda A, et al. Interfaces of malignant and immunologic clonal dynamics in ovarian cancer. Cell. 2018;173(7):1755–1769.e22 Available from: http://www.sciencedirect.com/science/article/pii/S0092867418304458.

    Article  CAS  Google Scholar 

  77. Tang C, Hobbs B, Amer A, Li X, Behrens C, Canales JR, et al. Development of an immune-pathology informed radiomics model for non-small cell lung cancer. Sci Rep. 2018;8(1):1922 Available from: https://pubmed.ncbi.nlm.nih.gov/29386574.

    Article  Google Scholar 

  78. Tripathi R, Jajodia A, Chaturvedi A, Koyyala VPB, Pasricha S, Goyal S, et al. EP1.01-31 PET CT radiogenomic depiction in PDL1 expression in lung cancer in Indian population. J Thorac Oncol. 2019;14(10):S923. Available from:. https://doi.org/10.1016/j.jtho.2019.08.2003.

    Article  Google Scholar 

  79. Seoane J, De Mattos-Arruda L. The challenge of intratumour heterogeneity in precision medicine. J Intern Med. 2014;276(1):41–51. Available from:. https://doi.org/10.1111/joim.12240.

    Article  CAS  PubMed  Google Scholar 

  80. Moertel S, Ackermann H, Baghi M, Eckardt A, Wagenblast J, Stöver T, et al. Heterogeneity of primary site biopsies in head and neck squamous cell carcinoma. Anticancer Res. 2011;31(2):665–9 Available from: http://ar.iiarjournals.org/content/31/2/665.abstract.

    PubMed  Google Scholar 

  81. Sun R, Limkin EJ, Vakalopoulou M, Dercle L, Champiat S, Han SR, et al. 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. 2018;19(9):1180–91 Available from: http://www.sciencedirect.com/science/article/pii/S1470204518304133. This study investigated correlating imaging features with gene expression to identify predictor of response to immunotherapy.

    Article  CAS  Google Scholar 

  82. Trebeschi S, Drago SG, Birkbak NJ, Kurilova I, Cǎlin AM, Delli Pizzi A, et al. Predicting response to cancer immunotherapy using noninvasive radiomic biomarkers. Ann Oncol. 2019;30(6):998–1004 Available from: https://pubmed.ncbi.nlm.nih.gov/30895304.

    Article  CAS  Google Scholar 

  83. Marcoux N, Gettinger SN, O’Kane G, Arbour KC, Neal JW, Husain H, et al. EGFR-mutant adenocarcinomas that transform to small-cell lung cancer and other neuroendocrine carcinomas: clinical outcomes. J Clin Oncol. 2018;37(4):278–85. Available from. https://doi.org/10.1200/JCO.18.01585.

    Article  PubMed  PubMed Central  Google Scholar 

  84. Jiang S-Y, Zhao J, Wang M-Z, Huo Z, Zhang J, Zhong W, et al. Small-cell lung cancer transformation in patients with pulmonary adenocarcinoma: a case report and review of literature. Medicine (Baltimore). 2016;95(6):e2752. https://doi.org/10.1097/MD.0000000000002752.

  85. Oser MG, Niederst MJ, Sequist LV, Engelman JA. Transformation from non-small-cell lung cancer to small-cell lung cancer: molecular drivers and cells of origin. Lancet Oncol. 2015;16(4):e165–72. Available from:. https://doi.org/10.1016/S1470-2045(14)71180-5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Chen L, Smith DA, Somarouthu B, Gupta A, Gilani KA, Ramaiya NH. A radiologist’s guide to the changing treatment paradigm of advanced non–small cell lung cancer: The ASCO 2018 Molecular Testing Guidelines and Targeted Therapies. Am J Roentgenol. 2019;213(5):1047–58. Available from:. https://doi.org/10.2214/AJR.19.21135.

    Article  Google Scholar 

  87. Lu H, Mu W, Balagurunathan Y, Qi J, Abdalah MA, Garcia AL, et al. Multi-window CT based radiomic signatures in differentiating indolent versus aggressive lung cancers in the National Lung Screening Trial: a retrospective study. Cancer Imaging. 2019;19(1):45. Available from: https://doi.org/10.1186/s40644-019-0232-6. This study investigated the potential of radiomics in predicting the natural course of lung tumors, which can allow stratification of patients into groups, with more aggressive tumors getting more aggressive treatment early in the disease process.

Download references

Author information

Authors and Affiliations

  1. Department of Interventional Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, Suite H-112, New York, NY, 10065, USA

    Amgad M. Moussa & Etay Ziv

Authors
  1. Amgad M. Moussa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Etay Ziv
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Etay Ziv.

Ethics declarations

Conflict of Interest

Amgad M. Moussa declares that he has no conflict of interest. Etay Ziv has received research funding from the Society of Interventional Radiology, Radiological Society of North America, North American Neuroendocrine Society, Memorial Sloan Kettering Functional Genomics Initiative, Memorial Sloan Kettering Society, Cycle for Survival, Druckenmiller Center for Lung Cancer Research, American Association for Cancer Research-Neuroendocrine Tumor Research Foundation (AACR-NETRF), and Ethicon.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Interventional Oncology

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Moussa, A.M., Ziv, E. Radiogenomics in Interventional Oncology. Curr Oncol Rep 23, 9 (2021). https://doi.org/10.1007/s11912-020-00994-9

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11912-020-00994-9

Keywords

Search

Navigation