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Review

Application of Radiomics for Personalized Treatment of Cancer Patients

, , , , ORCID Icon &
Pages 10851-10858 | Published online: 30 Dec 2019

References

  • Wenya LB, Ahmed H, Matthew BS, et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J Clin. 2019;69(2 ):127–157. doi:10.3322/caac.2155230720861
  • Castro-Giner F, Gkountela S, Donato C, et al. Cancer diagnosis using a liquid biopsy: challenges and expectations. Diagnostics. 2018;8(2 ):31. doi:10.3390/diagnostics8020031
  • European Society of Radiology (ESR). Medical imaging in personalised medicine: a white paper of the research committee of the European Society of Radiology (ESR). Insights Imaging. 2015;6(2 ):141–155. doi:10.1007/s13244-015-0394-025763994
  • Lu M, Zhan X. The crucial role of multiomic approach in cancer research and clinically relevant outcomes. EPMA J. 2018;9(1 ):77–102. doi:10.1007/s13167-018-0128-829515689
  • Wang L. Pharmacogenomics: a systems approach. Wiley Interdiscip Rev Syst Biol Med. 2010;2(1 ):3–22. doi:10.1002/wsbm.v2:120836007
  • Zhang L, Hong H. Genomic discoveries and personalized medicine in neurological diseases. Pharmaceutics. 2015;7(4 ):542–553. doi:10.3390/pharmaceutics704054226690205
  • Maggi E, Patterson NE, Montagna C. Technological advances in precision medicine and drug development. Expert Rev Precis Med Drug Dev. 2016;1(3 ):331–343. doi:10.1080/23808993.2016.117652727622214
  • Ottlakan A, Borda B, Morvay Z, et al. The effect of diagnostic imaging on surgical treatment planning in diseases of the thymus. Contrast Media Mol Imaging. 2017;2017:9307292. doi:10.1155/2017/930729229097942
  • Pysz MA, Gambhir SS, Willmann JK. Molecular imaging: current status and emerging strategies. Clin Radiol. 2010;65(7 ):500–516. doi:10.1016/j.crad.2010.03.01120541650
  • Bagheri MH, Ahlman MA, Lindenberg L, et al. Advances in medical imaging for the diagnosis and management of common genitourinary cancers. Urol Oncol. 2017;35(7 ):473–491. doi:10.1016/j.urolonc.2017.04.01428506596
  • Morin O, Vallieres M, Jochems A, et al. A deep look into the future of quantitative imaging in oncology: a statement of working principles and proposal for change. Int J Radiat Oncol Biol Phys. 2018;102(4 ):1074–1082. doi:10.1016/j.ijrobp.2018.08.03230170101
  • Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumor phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006. doi:10.1038/ncomms500624892406
  • Gillies RJ, Anderson AR, Gatenby RA, et al. The biology underlying molecular imaging in oncology: from genome to anatome and back again. Clin Radiol. 2010;65:517–521. doi:10.1016/j.crad.2010.04.00520541651
  • Vallières M, Kay-Rivest E, Perrin LJ, et al. Radiomics strategies for risk assessment of tumor failure in head-and-neck cancer. Sci Rep. 2017;7:10117. doi:10.1038/s41598-017-10371-528860628
  • Parmar C, Leijenaar RTH, Grossmann P, et al. Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci Rep. 2015;5:11044. doi:10.1038/srep1104426251068
  • Elhalawani H, Kanwar A, Mohamed ASR, et al. Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients. Sci Rep. 2018;8:1524.29367653
  • Jethanandani A, Lin TA, Volpe S, et al. Exploring applications of radiomics in magnetic resonance imaging of head and neck cancer: a systematic review. Front Oncol. 2018;8:131. doi:10.3389/fonc.2018.0013129868465
  • Tseng HH, Luo Y, Cui S, et al. Deep reinforcement learning for automated radiation adaptation in lung cancer. Med Phys. 2017;44:6690–6705. doi:10.1002/mp.1262529034482
  • Dong Y, Feng Q, Yang W, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of t2-weighted fat-suppression and diffusion-weighted mri. Eur Radiol. 2018;28:582–591. doi:10.1007/s00330-017-5005-728828635
  • Cozzi L, Dinapoli N, Fogliata A, et al. Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy. BMC Cancer. 2017;17:829. doi:10.1186/s12885-017-3847-729207975
  • Lucia F, Visvikis D, Desseroit MC, et al. Prediction of outcome using pretreatment (18)f-fdg pet/ct and mri radiomics in locally advanced cervical cancer treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging. 2018;45:768–786. doi:10.1007/s00259-017-3898-729222685
  • Gnep K, Fargeas A, Gutierrez-Carvajal RE, et al. Haralick textural features on t2-weighted mri are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer. J Magn Reson Imaging. 2017;45:103–117. doi:10.1002/jmri.2533527345946
  • Vallieres M, Laberge S, Diamant A, et al. Enhancement of multimodality texture-based prediction models via optimization of pet and mr image acquisition protocols: A proof of concept. Phys Med Biol. 2017;62:8536–8565. doi:10.1088/1361-6560/aa8a4928872054
  • Chang K, Bai HX, Zhou H, et al. Residual convolutional neural network for the determination of idh status in low- and high-grade gliomas from mr imaging. Clin Cancer Res. 2018;24:1073–1081. doi:10.1158/1078-0432.CCR-17-223629167275
  • Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14:749–762. doi:10.1038/nrclinonc.2017.14128975929
  • European Society of Radiology (ESR); European Federation of Radiographer Societies (EFRS). Patient safety in medical imaging: a joint paper of the European Society of Radiology (ESR) and the European Federation of Radiographer Societies (EFRS). Insights Imaging. 2019;10(1 ):45. doi:10.1186/s13244-019-0721-y30949870
  • Dutta J, Ahn S, Li Q. Quantitative statistical methods for image quality assessment. Theranostics. 2013;3(10 ):741–756. doi:10.7150/thno.681524312148
  • Chen JE, Glover GH. Functional magnetic resonance imaging methods. Neuropsychol Rev. 2015;25(3 ):289–313.26248581
  • Jun Y, Eo T, Kim T, et al. Deep-learned 3D black-blood imaging using automatic labelling technique and 3D convolutional neural networks for detecting metastatic brain tumors. Sci Rep. 2018;8(1 ):9450. doi:10.1038/s41598-018-27742-129930257
  • Madabhushi A, Udupa JK. New methods of MRI image intensity standardization via generalized scale. Med Phys. 2006;33:3426–3434. doi:10.1118/1.233548717022239
  • Tangaro S, Amoroso N, Brescia M, et al. Feature selection based on machine learning in MRIs for hippocampal segmentation. Comput Math Methods Med. 2015;2015:814104. doi:10.1155/2015/81410426089977
  • Deo RC. Machine learning in medicine. Circulation. 2015;132(20 ):1920–1930. doi:10.1161/CIRCULATIONAHA.115.00159326572668
  • Mahobia NK, Patel RD, Sheikh NW, et al. Validation method used in quantitative structure activity relationship. Der Pharma Chem. 2010;2(5 ):260–271.
  • Lazzarini N, Runhaar J, Bay-Jensen AC, et al. A machine learning approach for the identification of new biomarkers for knee osteoarthritis development in overweight and obese women. Osteoarthritis Cartilage. 2017;25(12 ):2014–2021. doi:10.1016/j.joca.2017.09.00128899843
  • Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162:55–63. doi:10.7326/M14-069725560714
  • Ghasemi M, Nabipour I, Omrani A, et al. Precision medicine and molecular imaging: new targeted approaches toward cancer therapeutic and diagnosis. Am J Nucl Med Mol Imaging. 2016;6(6 ):310–327.28078184
  • Rutman AM, Kuo MD. Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur J Radiol. 2009;70(2 ):232–241. doi:10.1016/j.ejrad.2009.01.05019303233
  • Segal E, Sirlin CB, Ooi C, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol. 2007;25(6 ):675–680. doi:10.1038/nbt130617515910
  • Sala E, Mema E, Himoto Y, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging. Clin Radiol. 2017;72(1 ):3–10. doi:10.1016/j.crad.2016.09.01327742105
  • Liu Z, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges. Theranostics. 2019;9(5 ):1303–1322. doi:10.7150/thno.3030930867832
  • 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(1 ):307–315. doi:10.1148/radiol.201716184528727543
  • Badic B, Hatt M, Durand S, et al. Radiogenomics-based cancer prognosis in colorectal cancer. Sci Rep. 2019;9(1 ):9743. doi:10.1038/s41598-019-46286-631278324
  • Pascal ZO, Sanjay SK, Aikaterini K, et al. A coclinical radiogenomic validation study: conserved magnetic resonance radiomic appearance of periostin-expressing glioblastoma in patients and xenograft models. Clin Cancer Res. 2018;24:6288–6299. doi:10.1158/1078-0432.CCR-17-342030054278
  • Kesch C, Radtke JP, Wintsche A, et al. Correlation between genomic index lesions and mpMRI and 68Ga-PSMA-PET/CT imaging features in primary prostate cancer. Sci Rep. 2018;8(1 ):16708. doi:10.1038/s41598-018-35058-330420756
  • Visvikis D, Le Rest CC, Jaouen V, Hatt M. Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications. Eur J Nucl Med Mol Imaging. 2019;46:2630–2637. doi:10.1007/s00259-019-04373-w31280350
  • Su C, Jiang J, Zhang S, et al. Radiomics based on multicontrast MRI can precisely differentiate among glioma subtypes and predict tumour-proliferative behaviour. Eur Radiol. 2019;29(4 ):1986–1996. doi:10.1007/s00330-018-5704-830315419
  • Arimura H, Soufi M, Kamezawa H, et al. Radiomics with artificial intelligence for precision medicine in radiation therapy. J Radiat Res. 2019;60(1 ):150–157. doi:10.1093/jrr/rry07730247662
  • Emblem KE, Due-Tonnessen P, Hald JK, et al. Machine learning in preoperative glioma MRI: survival associations by perfusion-based support vector machine outperforms traditional MRI. J Magn Reson Imaging. 2014;40(1 ):47–54. doi:10.1002/jmri.2439024753371
  • Kravchenko J, Akushevich I, Seewaldt VL, et al. Breast cancer as heterogeneous disease: contributing factors and carcinogenesis mechanisms. Breast Cancer Res Treat. 2011;128(2 ):483–493. doi:10.1007/s10549-011-1347-z21225455
  • Drop B, Drop K. Problems with implementation of the Radiological Information System (RIS) and Picture Archiving and Communication System (PACS) in laboratories of diagnostic imaging. Collegium Econ Anal Ann. 2017;46:267–282.
  • Nitrosi A, Corazza A, Bertolini M, et al. Patient dose management solution directly integrated in the RIS: “gray detector” software. J Digit Imaging. 2014;27:786. doi:10.1007/s10278-014-9715-y24965275
  • Castiglioni I, Gallivanone F, Soda P, et al. AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics. Eur J Nucl Med Mol Imaging. 2019;3:1–27. doi:10.1007/s00259-019-04414-4
  • Rudie J, Rauschecker MA, Nick BR, et al. Emerging applications of artificial intelligence in neuro-oncology. Radiology. 2019;290:181928. doi:10.1148/radiol.2018181928
  • Visvikis D, Le Rest CC, Jaouen V, et al. Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications. Eur J Nucl Med Mol Imaging. 2019:1–8. doi:10.1007/s00259-019-04373-w
  • Strimbu K, Tavel JA. What are biomarkers? Curr Opin HIV AIDS. 2010;5(6 ):463–466. doi:10.1097/COH.0b013e32833ed17720978388
  • Liu H, Zhang C, Wang L, et al. MRI radiomics analysis for predicting preoperative synchronous distant metastasis in patients with rectal cancer. Eur Radiol. 2019;29:4418. doi:10.1007/s00330-018-5802-730413955
  • Duraiyan J, Govindarajan R, Kaliyappan K, Palanisamy M. Applications of immunohistochemistry. J Pharm Bioallied Sci. 2012;4(Suppl 2 ):S307–S309. doi:10.4103/0975-7406.10028123066277
  • Shin D, Arthur G, Caldwell C, et al. A pathologist-in-the-loop IHC antibody test selection using the entropy-based probabilistic method. J Pathol Inform. 2012;3:1. doi:10.4103/2153-3539.9339322439121
  • Prescott JW. Quantitative imaging biomarkers: the application of advanced image processing and analysis to clinical and preclinical decision making. J Digit Imaging. 2013;26(1 ):97–108. doi:10.1007/s10278-012-9465-722415112
  • Alvi E, Gupta R, Borok ZR, et al. Overview of established and emerging immunohistochemical biomarkers and their role in correlative studies in MRI. J Magn Reson Imaging. 2019. doi:10.1002/jmri.26763
  • Marshall HT, Djamgoz MBA. Immuno-oncology: emerging targets and combination therapies. Front Oncol. 2018;8:315. doi:10.3389/fonc.2018.0031530191140
  • Villanueva N, Bazhenova L. New strategies in immunotherapy for lung cancer: beyond PD-1/PD-L1. Ther Adv Respir Dis. 2018;12:1753466618794133. doi:10.1177/175346661879413330215300
  • Wrobel P, Ahmed S. Current status of immunotherapy in metastatic colorectal cancer. Int J Colorectal Dis. 2018;34. doi:10.1007/s00384-018-3202-8
  • El Naqa I, Ten Haken RK. Can radiomics personalise immunotherapy? Lancet Oncol. 2018;19(9 ):1138–1139. doi:10.1016/S1470-2045(18)30429-730120042
  • Sun R, Limkin EJ, Vakalopoulou M, 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 ):1180–1191. doi:10.1016/S1470-2045(18)30413-330120041
  • Chen S, Feng S, Wei J, et al. Pretreatment prediction of immunoscore in hepatocellular cancer: a radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging. Eur Radiol. 2019;29:4177. doi:10.1007/s00330-018-5986-x30666445
  • Paulson SS, Scruth E. Legal and ethical concerns of big data: predictive analytics. Clin Nurse Spec. 2017;31(5 ):237–239. doi:10.1097/NUR.000000000000031528806228
  • Marcu LG, Boyd C, Bezak E. Current issues regarding artificial intelligence in cancer and health care. Implications for Medical Physicists and Biomedical Engineers. Health Technol. 2019;9:375.
  • Dissaux G, Visvikis D, Do-Ano R, et al. Pre-treatment F-FDG PET/CT radiomics predict local recurrence in patients treated with stereotactic radiotherapy for early-stage non-small cell lung cancer: a multicentric study. J Nucl Med Technol. 2019. doi:10.2967/jnumed.119.228106