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Research progress of MRI radiomics in lung cancer
YIN Meng  QIN Wenheng  SUN Zhanguo 

Cite this article as: YIN M, QIN W H, SUN Z G. Research progress of MRI radiomics in lung cancer[J]. Chin J Magn Reson Imaging, 2023, 14(6): 129-132, 150. DOI:10.12015/issn.1674-8034.2023.06.023.

[Abstract] The mortality rate of lung cancer ranks first among malignant tumors. Early accurate diagnosis and clinical intervention of lung cancer are significant to improve the survival rate of patients. Traditional imaging techniques such as CT, MRI and positron emission tomography/computed tomography (PET/CT) provide limited information in the clinical evaluation of lung cancer. However, radiomics can transform image data into feature space data to provide more comprehensive and in-depth information, which has become an emerging field of lung cancer research. This article aims to summarize the concept of radiomics and to review the research progress of radiomics in the diagnosis, differential diagnosis, pathological subtype classification, gene mutation status prediction, lymph node metastasis prediction, non-surgical treatment efficacy evaluation of lung cancer, in order to provide new imaging references for the diagnosis and treatment of lung cancer.
[Keywords] lung cancer;radiomics;magnetic resonance imaging;diagnosis and treatment;gene mutation;lymph node metastasis;therapeutic effect evaluation

YIN Meng1   QIN Wenheng2   SUN Zhanguo2*  

1 Clinical Medical College, Jining Medical University, Jining 272013, China

2 Department of Medical Imaging, Affiliated Hospital of Jining Medical University, Jining 272029, China

Corresponding author: Sun ZG, E-mail:

Conflicts of interest   None.

ACKNOWLEDGMENTS Shandong Medical and Health Science and Technology Development Program (No. 202009011151); Incubation Project of Affiliated Hospital of Jining Medical University (No. MP-ZD-2020-003).
Received  2022-12-14
Accepted  2023-05-06
DOI: 10.12015/issn.1674-8034.2023.06.023
Cite this article as: YIN M, QIN W H, SUN Z G. Research progress of MRI radiomics in lung cancer[J]. Chin J Magn Reson Imaging, 2023, 14(6): 129-132, 150. DOI:10.12015/issn.1674-8034.2023.06.023.

SIEGEL R L, MILLER K D, JEMAL A. Cancer statistics, 2016[J]. CA A Cancer J Clin, 2016, 66(1): 7-30. DOI: 10.3322/caac.21332.
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.
SCAPICCHIO C, GABELLONI M, BARUCCI A, et al. A deep look into radiomics[J]. Radiol Med, 2021, 126(10): 1296-1311. DOI: 10.1007/s11547-021-01389-x.
MAYERHOEFER M E, MATERKA A, LANGS G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
LAMBIN P, LEIJENAAR R T H, DEIST T M, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. DOI: 10.1038/nrclinonc.2017.141.
YAN Q Q, YI Y Q, SHEN J, et al. Preliminary study of 3 T-MRI native T1-mapping radiomics in differential diagnosis of non-calcified solid pulmonary nodules/masses[J/OL]. Cancer Cell Int, 2021, 21(1): 539 [2022-12-13]. DOI: 10.1186/s12935-021-02195-1.
PFAEHLER E, ZHOVANNIK I, WEI L S, et al. A systematic review and quality of reporting checklist for repeatability and reproducibility of radiomic features[J]. Phys Imaging Radiat Oncol, 2021, 20: 69-75. DOI: 10.1016/j.phro.2021.10.007.
WEYGAND J, FULLER C D, IBBOTT G S, et al. Spatial precision in magnetic resonance imaging-guided radiation therapy: the role of geometric distortion[J]. Int J Radiat Oncol Biol Phys, 2016, 95(4): 1304-1316. DOI: 10.1016/j.ijrobp.2016.02.059.
MIKAYAMA R, YABUUCHI H, SONODA S, et al. Comparison of intravoxel incoherent motion diffusion-weighted imaging between turbo spin-echo and echo-planar imaging of the head and neck[J]. Eur Radiol, 2018, 28(1): 316-324. DOI: 10.1007/s00330-017-4990-x.
LACROIX M, FROUIN F, DIRAND A S, et al. Correction for magnetic field inhomogeneities and normalization of voxel values are needed to better reveal the potential of MR radiomic features in lung cancer[J/OL]. Front Oncol, 2020, 10: 43 [2022-12-13]. DOI: 10.3389/fonc.2020.00043.
WAN Q, ZHOU J X, XIA X Y, et al. Diagnostic performance of 2D and 3D T2WI-based radiomics features with machine learning algorithms to distinguish solid solitary pulmonary lesion[J/OL]. Front Oncol, 2021, 11: 683587 [2022-12-13]. DOI: 10.3389/fonc.2021.683587.
PENG Q, HUANG Y, TANG W, et al. Comparison of parameters for diffusion-weighted intravoxel incoherent motion imaging in lung cancer patients with different histopathological subtypes[J]. Chin J Oncol, 2018, 40(11): 824-828. DOI: 10.3760/cma.j.issn.0253-3766.2018.11.005.
WANG X H, WAN Q, CHEN H J, et al. Classification of pulmonary lesion based on multiparametric MRI: utility of radiomics and comparison of machine learning methods[J]. Eur Radiol, 2020, 30(8): 4595-4605. DOI: 10.1007/s00330-020-06768-y.
LEE G, PARK H, BAK S H, et al. Radiomics in lung cancer from basic to advanced: current status and future directions[J]. Korean J Radiol, 2020, 21(2): 159-171. DOI: 10.3348/kjr.2019.0630.
BADE B C, DELA CRUZ C S. Lung cancer 2020[J]. Clin Chest Med, 2020, 41(1): 1-24. DOI: 10.1016/j.ccm.2019.10.001.
GUO Y B, DANG S, DUAN H F, et al. The value of MR-based radiomics signature for differentiating small cell lung cancer from non-small cell lung cancer[J]. J Clin Radiol, 2020, 39(9): 1776-1779. DOI: 10.11877/j.issn.1672-1535.2019.17.13.01.
TANG X, XU X P, HAN Z P, et al. Elaboration of a multimodal MRI-based radiomics signature for the preoperative prediction of the histological subtype in patients with non-small-cell lung cancer[J/OL]. BioMed Eng OnLine, 2020, 19(1): 5 [2022-09-13]. DOI: 10.1186/s12938-019-0744-0.
WANG M N, HERBST R S, BOSHOFF C. Toward personalized treatment approaches for non-small-cell lung cancer[J]. Nat Med, 2021, 27(8): 1345-1356. DOI: 10.1038/s41591-021-01450-2.
SANKAR K, GADGEEL S M, QIN A. Molecular therapeutic targets in non-small cell lung cancer[J]. Expert Rev Anticancer Ther, 2020, 20(8): 647-661. DOI: 10.1080/14737140.2020.1787156.
NAGASAKA M, GADGEEL S M. Role of chemotherapy and targeted therapy in early-stage non-small cell lung cancer[J]. Expert Rev Anticancer Ther, 2018, 18(1): 63-70. DOI: 10.1080/14737140.2018.1409624.
YUAN M, PU X H, XU X Q, et al. Lung adenocarcinoma: assessment of epidermal growth factor receptor mutation status based on extended models of diffusion-weighted image[J]. J Magn Reson Imaging, 2017, 46(1): 281-289. DOI: 10.1002/jmri.25572.
WANG Y Z, WAN Q, XIA X Y, et al. Value of radiomics model based on multi-parametric magnetic resonance imaging in predicting epidermal growth factor receptor mutation status in patients with lung adenocarcinoma[J]. J Thorac Dis, 2021, 13(6): 3497-3508. DOI: 10.21037/jtd-20-3358.
TANG X, BAI G Y, WANG H, et al. Predictive value for EGFR gene phenotype of lung adenocarcinoma based on multi-sequences MRI ra-diomics[J]. Radiol Pract, 2021, 36(8): 1010-1015. DOI: 10.13609/j.cnki.1000-0313.2021.08.012
EGUREN-SANTAMARIA I, SANMAMED M F, GOLDBERG S B, et al. PD-1/PD-L1 blockers in NSCLC brain metastases: challenging paradigms and clinical practice[J]. Clin Cancer Res, 2020, 26(16): 4186-4197. DOI: 10.1158/1078-0432.CCR-20-0798.
LI Y, LV X N, WANG B, et al. Differentiating EGFR from ALK mutation status using radiomics signature based on MR sequences of brain metastasis[J/OL]. Eur J Radiol, 2022, 155: 110499 [2022-10-12]. DOI: 10.1016/j.ejrad.2022.110499.
CHEN B T, JIN T H, YE N R, et al. Radiomic prediction of mutation status based on MR imaging of lung cancer brain metastases[J]. Magn Reson Imaging, 2020, 69: 49-56. DOI: 10.1016/j.mri.2020.03.002.
CAO R, DONG Y, WANG X Y, et al. MRI-based radiomics nomogram as a potential biomarker to predict the EGFR mutations in exon 19 and 21 based on thoracic spinal metastases in lung adenocarcinoma[J/OL]. Acad Radiol, 2022, 29(3): e9-e17 [2022-10-12]. DOI: 10.1016/j.acra.2021.06.004.
ETTINGER D S, WOOD D E, AISNER D L, et al. Non-small cell lung cancer, version 5.2017, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2017, 15(4): 504-535. DOI: 10.6004/jnccn.2017.0050.
LEE J, KIM Y K, SEO Y Y, et al. Clinical characteristics of false-positive lymph node on chest CT or PET-CT confirmed by endobronchial ultrasound-guided transbronchial needle aspiration in lung cancer[J]. Tuberc Respir Dis (Seoul), 2018, 81(4): 339-346. DOI: 10.4046/trd.2017.0121.
MAIGA A W, DEPPEN S A, MERCALDO S F, et al. Assessment of fluorodeoxyglucose F18-labeled positron emission tomography for diagnosis of high-risk lung nodules[J]. JAMA Surg, 2018, 153(4): 329-334. DOI: 10.1001/jamasurg.2017.4495.
HE Y F, GAO D P. Imaging evaluation of mediastinal lymph node metastasis in non-small cell lung cancer[J]. Radiol Pract, 2022, 37(1): 124-128. DOI: 10.13609/j.cnki.1000-0313.2022.01.023.
ZHU Y Q, JI H, ZHU Y F, et al. Predictive value of preoperative MRI-based nomogram for axillary lymph node metastasis in breast cancer[J]. Chin J Magn Reson Imaging, 2022, 13(5): 52-58. DOI: 10.12015/issn.1674-8034.2022.05.010
WEI Q R, YUAN W J, JIA Z Q, et al. Preoperative MR radiomics based on high-resolution T2-weighted images and amide proton transfer-weighted imaging for predicting lymph node metastasis in rectal adenocarcinoma[J]. Abdom Radiol (NY), 2023, 48(2): 458-470. DOI: 10.1007/s00261-022-03731-x.
HOU L N, ZHOU W, REN J L, et al. Radiomics analysis of multiparametric MRI for the preoperative prediction of lymph node metastasis in cervical cancer[J]. Front Oncol, 2020, 10: 1393 [2022-12-13]. https://doi.110.3389/fonc.2020.01393. DOI: 10.3389/fonc.2020.01393.
WANG Y, CUI Y Y, WANG X H, et al. 3D-ultrashort echo time MRI-based radiomics model facilitates the assessment of lymph node metastasis in non-small cell lung cancer[J]. Chin J Magn Reson Imaging, 2023, 14(3): 17-20, 41. DOI: 10.12015/issn.1674-8034.2023.03.004
Oncology Society of the Chinese Medical Association, Journal of Chinese Medical Association. Guidelines for clinical diagnosis and treatment of lung cancer by Chinese medical association (2022 edition)[J]. Natl Med J China, 2022, 102(23): 217-249. DOI: 10.3760/cma.j.cn112137-20220413-00795
ALEXANDER M, KIM S Y, CHENG H Y. Update 2020: management of non-small cell lung cancer[J].Lung, 2020, 198(6): 897-907. DOI: 10.1007/s00408-020-00407-5.
CHIOU V L, BUROTTO M. Pseudoprogression and immune-related response in solid tumors[J]. J Clin Oncol, 2015, 33(31): 3541-3543. DOI: 10.1200/jco.2015.61.6870.
WU L, LI J, FU C X, et al. Chemotherapy response of pancreatic cancer by diffusion-weighted imaging (DWI) and intravoxel incoherent motion DWI (IVIM-DWI) in an orthotopic mouse model[J]. MAGMA, 2019, 32(4): 501-509. DOI: 10.1007/s10334-019-00745-3.
YUAN Z, NIU X M, LIU X M, et al. Use of diffusion-weighted magnetic resonance imaging (DW-MRI) to predict early response to anti-tumor therapy in advanced non-small cell lung cancer (NSCLC): a comparison of intravoxel incoherent motion-derived parameters and apparent diffusion coefficient[J]. Transl Lung Cancer Res, 2021, 10(8): 3671-3681. DOI: 10.21037/tlcr-21-610.
JIANG J Q, CUI L, CAI R F, et al. The value of diffusion-weighted imaging based on monoexponential and biexponential model in predicting the response of chemotherapy in non-small cell lung cancer patients[J]. Chin J Radiol, 2018, 52(11): 829-835. DOI: 10.3760/cma.j.issn.1005-1201.2018.11.004
SHI C Z, LIU D X, XIAO Z Y, et al. Monitoring tumor response to antivascular therapy using non-contrast intravoxel incoherent motion diffusion-weighted MRI[J]. Cancer Res, 2017, 77(13): 3491-3501. DOI: 10.1158/0008-5472.CAN-16-2499.
EUN N L, KANG D, SON E J, et al. Texture analysis with 3.0-T MRI for association of response to neoadjuvant chemotherapy in breast cancer[J]. Radiology, 2020, 294(1): 31-41. DOI: 10.1148/radiol.2019182718.
JIANG J Z, DING Y Y, LI Z H. DWI-based radiomics for predicting response of lung cancer to chemotherapy: a pilot study[J]. Radiol Pract, 2017, 32(12): 1221-1224. DOI: 10.13609/j.cnki.1000-0313.2017.12.003
AVANZO M, WEI L S, STANCANELLO J, et al. Machine and deep learning methods for radiomics[J/OL]. Med Phys, 2020, 47(5): e185-e202 [2022-12-13]. DOI: 10.1002/mp.13678.
CHETAN M R, GLEESON F V. Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives[J]. Eur Radiol, 2021, 31(2): 1049-1058. DOI: 10.1007/s00330-020-07141-9.

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