Share this content in WeChat
Research progress in the application of radiomics in targeted therapy of tumors
LIU Qian  WANG Ning  LIU Yulin 

Cite this article as: Liu Q, Wang N, Liu YL. Research progress in the application of radiomics in targeted therapy of tumors[J]. Chin J Magn Reson Imaging, 2022, 13(8): 166-170. DOI:10.12015/issn.1674-8034.2022.08.038.

[Abstract] Molecularly targeted therapy plays an important role in the precision treatment of various malignant tumors. Based on the method of radiomics, valuable features are obtained from medical images to analyze tumor phenotype to identify targets, monitor tumor phenotype changes during treatment, and evaluate patients' treatment efficacy and prognosis, so as to achieve the purpose of precision treatment. The continuous development of technologies such as deep learning and artificial intelligence has also breathed new life into the development of radiomics, this paper aims to review the research progress of radiomics in lung cancer, breast cancer and other malignant tumors in targeted therapy and the current prospects, and summarize the current problems and solutions of radiomics.
[Keywords] radiomics;targeted therapy;lung cancer;colorectal cancer;breast cancer;precision treatment;targets;prognostic analysis

LIU Qian   WANG Ning   LIU Yulin*  

Department of Radiology, Hubei Cancer Hospital, the Affiliated Cancer of Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430079, China

Liu YL, E-mail:

Conflicts of interest   None.

Received  2022-04-16
Accepted  2022-07-29
DOI: 10.12015/issn.1674-8034.2022.08.038
Cite this article as: Liu Q, Wang N, Liu YL. Research progress in the application of radiomics in targeted therapy of tumors[J]. Chin J Magn Reson Imaging, 2022, 13(8): 166-170.DOI:10.12015/issn.1674-8034.2022.08.038

Orzetti S, Tommasi F, Bertola A, et al. Genetic Therapy and Molecular Targeted Therapy in Oncology: Safety, Pharmacovigilance, and Perspectives for Research and Clinical Practice[J/OL]. Int J Mol Sci, 2022 [2022-04-16]. DOI: 10.3390/ijms23063012.
Bedard PL, Hyman DM, Davids MS, et al. Small molecules, big impact: 20 years of targeted therapy in oncology[J]. Lancet, 2020, 395(10229): 1078-1088. DOI: 10.1016/S0140-6736(20)30164-1.
Tsimberidou AM, Fountzilas E, Nikanjam M, et al. Review of precision cancer medicine: Evolution of the treatment paradigm[J/OL]. Cancer Treat Rev, 2020 [2022-04-16]. DOI: 10.1016/j.ctrv.2020.102019.
Zhou ZJ, Li M. Targeted therapies for cancer[J/OL]. BMC Med, 2022 [2022-04-16]. DOI: 10.1186/s12916-022-02287-3.
Ramagopalan S, Leahy TP, Ray J, et al. The value of innovation: association between improvements in survival of advanced and metastatic non-small cell lung cancer and targeted and immunotherapy[J/OL]. BMC Med, 2021 [2022-04-16]. DOI: 10.1186/s12916-021-02070-w.
Kilgour E, Rothwell DG, Brady G, et al. Liquid biopsy-based biomarkers of treatment response and resistance[J]. Cancer Cell, 2020, 37(4): 485-495. DOI: 10.1016/j.ccell.2020.03.012.
Shur JD, Doran SJ, Kumar S, et al. Radiomics in oncology: a practical Guide[J]. Radiographics, 2021, 41(6): 1717-1732. DOI: 10.1148/rg.2021210037.
Kroschke J, von Stackelberg O, Heußel CP, et al. Imaging biomarkers in thoracic oncology: current advances in the use of radiomics in lung cancer patients and its potential use for therapy response prediction and monitoring[J]. Rofo, 2022, 194(7): 720-727. DOI: 10.1055/a-1729-1516.
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
Dong C, Zheng YM, Li J, et al. A CT-based radiomics nomogram for differentiation of squamous cell carcinoma and non-Hodgkin's lymphoma of the palatine tonsil[J]. Eur Radiol, 2022, 32(1): 243-253. DOI: 10.1007/s00330-021-08153-9.
Schell M, Pflüger I, Brugnara G, et al. Validation of diffusion MRI phenotypes for predicting response to bevacizumab in recurrent glioblastoma: post-hoc analysis of the EORTC-26101 trial[J]. Neuro Oncol, 2020, 22(11): 1667-1676. DOI: 10.1093/neuonc/noaa120.
Lo GR, Daimiel I, Morris EA, et al. Combining molecular and imaging metrics in cancer: radiogenomics[J/OL]. Insights Imaging, 2020 [2022-04-16]. DOI: 10.1186/s13244-019-0795-6.
Yan J, Zhang B, Zhang S, et al. Quantitative MRI-based radiomics for noninvasively predicting molecular subtypes and survival in glioma patients[J/OL]. NPJ Precis Oncol, 2021 [2022-04-16]. DOI: 10.1038/s41698-021-00205-z.
Jiang YM, Chen CL, Xie JJ, et al. Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer[J]. EBioMedicine, 2018, 36: 171-182. DOI: 10.1016/j.ebiom.2018.09.007.
Zhao LN, Gong J, Xi YB, et al. MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma[J]. Eur Radiol, 2020, 30(1): 537-546. DOI: 10.1007/s00330-019-06211-x.
Ramalingam SS, Vansteenkiste J, Planchard D, et al. Overall survival with osimertinib in untreated, EGFR-mutated advanced NSCLC[J]. N Engl J Med, 2020, 382(1): 41-50. DOI: 10.1056/NEJMoa1913662.
Mok T, Camidge DR, Gadgeel SM, et al. Updated overall survival and final progression-free survival data for patients with treatment-naive advanced ALK-positive non-small-cell lung cancer in the ALEX study[J]. Ann Oncol, 2020, 31(8): 1056-1064. DOI: 10.1016/j.annonc.2020.04.478.
Ettinger DS, Aisner DL, Wood DE, et al. NCCN guidelines insights: non-small cell lung cancer, version 5.2018[J]. J Natl Compr Canc Netw, 2018, 16(7): 807-821. DOI: 10.6004/jnccn.2018.0062.
Imyanitov EN, Iyevleva AG, Levchenko EV. Molecular testing and targeted therapy for non-small cell lung cancer: Current status and perspectives[J/OL]. Crit Rev Oncol Hematol, 2021 [2022-04-16]. DOI: 10.1016/j.critrevonc.2020.103194.
Agazzi GM, Ravanelli M, Roca E, et al. CT texture analysis for prediction of EGFR mutational status and ALK rearrangement in patients with non-small cell lung cancer[J]. Radiol Med, 2021, 126(6): 786-794. DOI: 10.1007/s11547-020-01323-7.
Wang XX, Kong C, Xu WZ, et al. Decoding tumor mutation burden and driver mutations in early stage lung adenocarcinoma using CT-based radiomics signature[J]. Thorac Cancer, 2019, 10(10): 1904-1912. DOI: 10.1111/1759-7714.13163.
Aerts HJ, Grossmann P, Tan YQ, et al. Defining a Radiomic Response Phenotype: a Pilot Study using targeted therapy in NSCLC[J/OL]. Sci Rep, 2016 [2022-04-16]. DOI: 10.1038/srep33860.
Zhang JY, Zhao XM, Zhao Y, et al. Value of pre-therapy 18F-FDG PET/CT radiomics in predicting EGFR mutation status in patients with non-small cell lung cancer[J]. Eur J Nucl Med Mol Imaging, 2020, 47(5): 1137-1146. DOI: 10.1007/s00259-019-04592-1.
Tang X, Li Y, Yan WF, et al. Machine Learning-Based CT Radiomics Analysis for Prognostic Prediction in Metastatic Non-Small Cell Lung Cancer Patients With EGFR-T790M Mutation Receiving Third-Generation EGFR-TKI Osimertinib Treatment[J/OL]. Front Oncol, 2021 [2022-04-16]. DOI: 10.3389/fonc.2021.719919.
Zhu JM, Sun L, Wang L, et al. Radiomics combined with clinical characteristics predicted the progression-free survival time in first-line targeted therapy for advanced non-small cell lung cancer with EGFR mutation[J/OL]. BMC Res Notes, 2022 [2022-04-16]. DOI: 10.1186/s13104-022-06019-x.
Li HL, Zhang R, Wang SW, et al. CT-based radiomic signature as a prognostic factor in stage IV ALK-positive non-small-cell lung cancer treated with TKI Crizotinib: a proof-of-concept study[J/OL]. Front Oncol, 2020 [2022-04-16]. DOI: 10.3389/fonc.2020.00057.
Siegel RL, Miller KD, Jemal A. Cancer statistics, 2016[J]. CA A Cancer J Clin, 2016, 66(1): 7-30. DOI: 10.3322/caac.21332.
Reid S, Haddad D, Tezak A, et al. Impact of molecular subtype and race on HR+, HER2- breast cancer survival[J]. Breast Cancer Res Treat, 2021, 189(3): 845-852. DOI: 10.1007/s10549-021-06342-0.
Waks AG, Winer EP. Breast cancer treatment: a review[J]. JAMA, 2019, 321(3): 288-300. DOI: 10.1001/jama.2018.19323.
Phillips KA, Marshall DA, Haas JS, et al. Clinical practice patterns and cost effectiveness of human epidermal growth receptor 2 testing strategies in breast cancer patients[J]. Cancer, 2009, 115(22): 5166-5174. DOI: 10.1002/cncr.24574.
Zhou J, Tan HN, Bai Y, et al. Evaluating the HER-2 status of breast cancer using mammography radiomics features[J/OL]. Eur J Radiol, 2019 [2022-04-16]. DOI: 10.1016/j.ejrad.2019.108718.
Zhou J, Tan HN, Li W, et al. Radiomics signatures based on multiparametric MRI for the preoperative prediction of the HER2 status of patients with breast cancer[J]. Acad Radiol, 2021, 28(10): 1352-1360. DOI: 10.1016/j.acra.2020.05.040.
Li CL, Yin JD. Radiomics Nomogram Based on Radiomics Score from Multiregional Diffusion-Weighted MRI and Clinical Factors for Evaluating HER-2 2+ Status of Breast Cancer[J/OL]. Diagnostics, 2021 [2022-04-16]. DOI: 10.3390/diagnostics11081491.
Li CL, Song LR, Yin JD. Intratumoral and peritumoral radiomics based on functional parametric maps from breast DCE-MRI for prediction of HER-2 and ki-67 status[J]. J Magn Reson Imaging, 2021, 54(3): 703-714. DOI: 10.1002/jmri.27651.
Braman NM, Etesami M, Prasanna P, et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI[J/OL]. Breast Cancer Res, 2017 [2022-04-16]. DOI: 10.1186/s13058-017-0846-1.
Li Q, Xiao Q, Li J, et al. MRI-Based Radiomic Signature as a Prognostic Biomarker for HER2-Positive Invasive Breast Cancer Treated with NAC[J/OL]. Cancer Manag Res, 2020 [2022-04-16]. DOI: 10.2147/CMAR.S271876.
Benson AB, Venook AP, Al-Hawary MM, et al. NCCN guidelines insights: rectal cancer, version 6.2020[J]. J Natl Compr Canc Netw, 2020, 18(7): 806-815. DOI: 10.6004/jnccn.2020.0032.
Yang L, Dong D, Fang MJ, et al. Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?[J]. Eur Radiol, 2018, 28(5): 2058-2067. DOI: 10.1007/s00330-017-5146-8.
Shi RC, Chen WX, Yang BW, et al. Prediction of KRAS, NRAS and BRAF status in colorectal cancer patients with liver metastasis using a deep artificial neural network based on radiomics and semantic features[J]. Am J Cancer Res, 2020, 10(12): 4513-4526.
Cui YF, Liu HH, Ren JL, et al. Development and validation of a MRI-based radiomics signature for prediction of KRAS mutation in rectal cancer[J]. Eur Radiol, 2020, 30(4): 1948-1958. DOI: 10.1007/s00330-019-06572-3.
Negreros-Osuna AA, Parakh A, Corcoran RB, et al. Radiomics Texture Features in Advanced Colorectal Cancer: Correlation with BRAF Mutation and 5-year Overall Survival[J/OL]. Radiol Imaging Cancer, 2020 [2022-04-16]. DOI: 10.1148/rycan.2020190084.
Dercle L, Lu L, Schwartz LH, et al. Radiomics response signature for identification of metastatic colorectal cancer sensitive to therapies targeting EGFR pathway[J]. J Natl Cancer Inst, 2020, 112(9): 902-912. DOI: 10.1093/jnci/djaa017.
Prados MD, Byron SA, Tran NL, et al. Toward precision medicine in glioblastoma: the promise and the challenges[J]. Neuro Oncol, 2015, 17(8): 1051-1063. DOI: 10.1093/neuonc/nov031.
Le RE, Preusser M, Roth P, et al. Molecular targeted therapy of glioblastoma[J/OL]. Cancer Treat Rev. 2019 [2022-04-16]. DOI: 10.1016/j.ctrv.2019.101896.
Sun ZY, Li YM, Wang YY, et al. Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas[J/OL]. Cancer Imaging, 2019 [2022-04-16]. DOI: 10.1186/s40644-019-0256-y.
Grossmann P, Narayan V, Chang K, et al. Quantitative imaging biomarkers for risk stratification of patients with recurrent glioblastoma treated with bevacizumab[J]. Neuro Oncol, 2017, 19(12): 1688-1697. DOI: 10.1093/neuonc/nox092.
Ammari S, Sallé DCR, Assi T, et al. Machine-Learning-Based Radiomics MRI Model for Survival Prediction of Recurrent Glioblastomas Treated with Bevacizumab[J/OL]. Diagnostics, 2021 [2022-04-16]. DOI: 10.3390/diagnostics11071263.
Kickingereder P, Götz M, Muschelli J, et al. Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response[J]. Clin Cancer Res, 2016, 22(23): 5765-5771. DOI: 10.1158/1078-0432.CCR-16-0702.
Oh JE, Kim MJ, Lee J, et al. Magnetic resonance-based texture analysis differentiating KRAS mutation status in rectal cancer[J]. Cancer Res Treat, 2020, 52(1): 51-59. DOI: 10.4143/crt.2019.050.
Mackin D, Fave X, Zhang LF, et al. Measuring computed tomography scanner variability of radiomics features[J]. Invest Radiol, 2015, 50(11): 757-765. DOI: 10.1097/RLI.0000000000000180.
van Velden FH, Kramer GM, Frings V, et al. Repeatability of radiomic features in non-small-cell lung cancer [(18)F]FDG-PET/CT studies: impact of reconstruction and delineation[J]. Mol Imaging Biol, 2016, 18(5): 788-795. DOI: 10.1007/s11307-016-0940-2.
Zwanenburg A, Vallières M, Abdalah MA, et al. The image biomarker standardization initiative: standardized quantitative radiomics for high-throughput image-based phenotyping[J]. Radiology, 2020, 295(2): 328-338. DOI: 10.1148/radiol.2020191145.

PREV Research status of mesenteric lymph node imaging and pathologic localization
NEXT Regional homogeneity changes after MR-guided focused ultrasound thalamotomy in essential tremor: A rs-fMRI study

Tel & Fax: +8610-67113815    E-mail: