LUO Yanwen, ZHU Qingli. Application of Radiomics in Breast Cancer[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(6): 983-988. DOI: 10.12290/xhyxzz.2021-0011
Citation: LUO Yanwen, ZHU Qingli. Application of Radiomics in Breast Cancer[J]. Medical Journal of Peking Union Medical College Hospital, 2021, 12(6): 983-988. DOI: 10.12290/xhyxzz.2021-0011

Application of Radiomics in Breast Cancer

Funds: 

National Natural Science Foundation of China 81771855

CAMS Innovation Fund for Medical Science 2020-I2M-C&T-B-033

More Information
  • Corresponding author:

    ZHU Qingli  Tel: 86-10-69155494, E-mail: zqlpumch@126.com

  • Received Date: January 04, 2021
  • Accepted Date: February 22, 2021
  • Available Online: November 03, 2021
  • Issue Publish Date: November 29, 2021
  • Breast cancer is the most common malignant tumor in Chinese women. Early diagnosis, treatment, and prognosis assessment are important clinical problems to be solved. Radiomics is a non-invasive method for high-throughput extraction and analysis of lesion features of images to provide more potential information of tumors, and guide precise diagnosis and treatment. Recently, it has been widely concerned and studied in breast cancer, covering every stage in the care of patients with breast cancer. In terms of diagnosis, the research has reached the level of maturity and gradually proceeded to the clinical setting. Regarding efficacy evaluation and prognostic prediction, although in its infancy, radiomics shows promising potential. This paper mainly reviews the application of radiomics in the diagnosis, response evaluation, and prognosis prediction of breast cancer.
  • [1]
    Chen W, Zheng R, Baade PD, et al. Cancer statistics in China, 2015[J]. CA Cancer J Clin, 2016, 66: 115-132. DOI: 10.3322/caac.21338
    [2]
    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: 441-446. DOI: 10.1016/j.ejca.2011.11.036
    [3]
    Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data[J]. Radiology, 2016, 278: 563-577. DOI: 10.1148/radiol.2015151169
    [4]
    Hooley RJ, Scoutt LM, Philpotts LE. Breast ultrasonography: state of the art[J]. Radiology, 2013, 268: 642-659. DOI: 10.1148/radiol.13121606
    [5]
    Chitalia RD, Kontos D. Role of texture analysis in breast MRI as a cancer biomarker: A review[J]. J Magn Reson Imaging, 2019, 49: 927-938. DOI: 10.1002/jmri.26556
    [6]
    Jiang Y, Edwards AV, Newstead GM. Artificial Intelli-gence Applied to Breast MRI for Improved Diagnosis[J]. Radiology, 2021, 298: 38-46. DOI: 10.1148/radiol.2020200292
    [7]
    Li J, Sang T, Yu WH, et al. The value of S-Detect for the differential diagnosis of breast masses on ultrasound: a systematic review and pooled meta-analysis[J]. Med Ultrason, 2020, 22: 211-219. DOI: 10.11152/mu-2402
    [8]
    Mao N, Yin P, Wang Q, et al. Added Value of Radiomics on Mammography for Breast Cancer Diagnosis: A Feasibility Study[J]. J Am Coll Radiol, 2019, 16: 485-491. DOI: 10.1016/j.jacr.2018.09.041
    [9]
    McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening[J]. Nature, 2020, 577: 89-94. DOI: 10.1038/s41586-019-1799-6
    [10]
    Li H, Mendel KR, Lan L, et al. Digital Mammography in Breast Cancer: Additive Value of Radiomics of Breast Parenchyma[J]. Radiology, 2019, 291: 15-20. DOI: 10.1148/radiol.2019181113
    [11]
    Zhou J, Zhang Y, Chang KT, et al. Diagnosis of Benign and Malignant Breast Lesions on DCE-MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue[J]. J Magn Reson Imaging, 2020, 51: 798-809. DOI: 10.1002/jmri.26981
    [12]
    Lee SE, Han K, Kwak JY, et al. Radiomics of US texture features in differential diagnosis between triple-negative breast cancer and fibroadenoma[J]. Sci Rep, 2018, 8: 13546. DOI: 10.1038/s41598-018-31906-4
    [13]
    Luo WQ, Huang QX, Huang XW, et al. Predicting Breast Cancer in Breast Imaging Reporting and Data System (BI-RADS) Ultrasound Category 4 or 5 Lesions: A Nomogram Combining Radiomics and BI-RADS[J]. Sci Rep, 2019, 9: 11921. DOI: 10.1038/s41598-019-48488-4
    [14]
    Hao W, Gong J, Wang S, et al. Application of MRI Radiomics-Based Machine Learning Model to Improve Contralateral BI-RADS 4 Lesion Assessment[J]. Front Oncol, 2020, 10: 531476. DOI: 10.3389/fonc.2020.531476
    [15]
    Zhang X, Liang M, Yang Z, et al. Deep Learning-Based Radiomics of B-Mode Ultrasonography and Shear-Wave Elastography: Improved Performance in Breast Mass Classifica-tion[J]. Front Oncol, 2020, 10: 1621. DOI: 10.3389/fonc.2020.01621
    [16]
    Wilke LG, McCall LM, Posther KE, et al. Surgical complications associated with sentinel lymph node biopsy: results from a prospective international cooperative group trial[J]. Ann Surg Oncol, 2006, 13: 491-500. DOI: 10.1245/ASO.2006.05.013
    [17]
    Hindié E, Groheux D, Brenot-Rossi I, et al. The sentinel node procedure in breast cancer: nuclear medicine as the starting point[J]. J Nucl Med, 2011, 52: 405-414. DOI: 10.2967/jnumed.110.081711
    [18]
    Cui X, Wang N, Zhao Y, et al. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Radiomics Features of DCE-MRI[J]. Sci Rep, 2019, 9: 2240. DOI: 10.1038/s41598-019-38502-0
    [19]
    Liu J, Sun D, Chen L, et al. Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer[J]. Front Oncol, 2019, 9: 980. DOI: 10.3389/fonc.2019.00980
    [20]
    Schiano C, Franzese M, Pane K, et al. Hybrid (18)F-FDG-PET/MRI Measurement of Standardized Uptake Value Coupled with Yin Yang 1 Signature in Metastatic Breast Cancer. A Preliminary Study[J]. Cancers (Basel), 2019, 11: 1444. DOI: 10.3390/cancers11101444
    [21]
    Yang J, Wang T, Yang L, et al. Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer Using Mammography-Based Radiomics Method[J]. Sci Rep, 2019, 9: 4429. DOI: 10.1038/s41598-019-40831-z
    [22]
    Mao N, Yin P, Li Q, et al. Radiomics nomogram of contrast-enhanced spectral mammography for prediction of axillary lymph node metastasis in breast cancer: a multicenter study[J]. Eur Radiol, 2020, 30: 6732-6739. DOI: 10.1007/s00330-020-07016-z
    [23]
    Giuliano AE, Ballman KV, McCall L, et al. Effect of Axillary Dissection vs No Axillary Dissection on 10-Year Overall Survival Among Women With Invasive Breast Cancer and Sentinel Node Metastasis: The ACOSOG Z0011 (Alliance) Randomized Clinical Trial[J]. JAMA, 2017, 318: 918-926. DOI: 10.1001/jama.2017.11470
    [24]
    Zheng X, Yao Z, Huang Y, et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer[J]. Nat Commun, 2020, 11: 1236. DOI: 10.1038/s41467-020-15027-z
    [25]
    Gao Y, Luo Y, Zhao C, et al. Nomogram based on radiomics analysis of primary breast cancer ultrasound images: prediction of axillary lymph node tumor burden in patients[J]. Eur Radiol, 2021, 31: 928-937. DOI: 10.1007/s00330-020-07181-1
    [26]
    Guo X, Liu Z, Sun C, et al. Deep learning radiomics of ultrasonography: Identifying the risk of axillary non-sentinel lymph node involvement in primary breast cancer[J]. EBioMedicine, 2020, 60: 103018. DOI: 10.1016/j.ebiom.2020.103018
    [27]
    Liu C, Ding J, Spuhler K, et al. Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI[J]. J Magn Reson Imaging, 2019, 49: 131-140. DOI: 10.1002/jmri.26224
    [28]
    Goetz MP, Gradishar WJ, Anderson BO, et al. NCCN Guidelines Insights: Breast Cancer, Version 3.2018[J]. J Natl Compr Canc Netw, 2019, 17: 118-126. DOI: 10.6004/jnccn.2019.0009
    [29]
    Li P, Wang X, Xu C, et al. (18)F-FDG PET/CT radiomic predictors of pathologic complete response (pCR) to neoadjuvant chemotherapy in breast cancer patients[J]. Eur J Nucl Med Mol Imaging, 2020, 47: 1116-1126. DOI: 10.1007/s00259-020-04684-3
    [30]
    Chen S, Shu Z, Li Y, et al. Machine Learning-Based Radiomics Nomogram Using Magnetic Resonance Images for Prediction of Neoadjuvant Chemotherapy Efficacy in Breast Cancer Patients[J]. Front Oncol, 2020, 10: 1410. DOI: 10.3389/fonc.2020.01410
    [31]
    Liu Z, Li Z, Qu J, et al. Radiomics of Multiparametric MRI for Pretreatment Prediction of Pathologic Complete Response to Neoadjuvant Chemotherapy in Breast Cancer: A Multicenter Study[J]. Clin Cancer Res, 2019, 25: 3538-3547. DOI: 10.1158/1078-0432.CCR-18-3190
    [32]
    Antunovic L, De Sanctis R, Cozzi L, et al. PET/CT radiomics in breast cancer: promising tool for prediction of pathological response to neoadjuvant chemotherapy[J]. Eur J Nucl Med Mol Imaging, 2019, 46: 1468-1477. DOI: 10.1007/s00259-019-04313-8
    [33]
    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]. Breast Cancer Res, 2017, 19: 57. DOI: 10.1186/s13058-017-0846-1
    [34]
    Sutton EJ, Onishi N, Fehr DA, et al. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy[J]. Breast Cancer Res, 2020, 22: 57. DOI: 10.1186/s13058-020-01291-w
    [35]
    Bitencourt AGV, Gibbs P, Rossi Saccarelli C, et al. MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer[J]. EBioMedicine, 2020, 61: 103042. DOI: 10.1016/j.ebiom.2020.103042
    [36]
    Moscoso A, Ruibal A, Dominguez-Prado I, et al. Texture analysis of high-resolution dedicated breast (18) F-FDG PET images correlates with immunohistochemical factors and subtype of breast cancer[J]. Eur J Nucl Med Mol Imaging, 2018, 45: 196-206. DOI: 10.1007/s00259-017-3830-1
    [37]
    Antunovic L, Gallivanone F, Sollini M, et al. [(18)F]FDG PET/CT features for the molecular characterization of primary breast tumors[J]. Eur J Nucl Med Mol Imaging, 2017, 44: 1945-1954. DOI: 10.1007/s00259-017-3770-9
    [38]
    Fan M, Yuan W, Zhao W, et al. Joint Prediction of Breast Cancer Histological Grade and Ki-67 Expression Level Based on DCE-MRI and DWI Radiomics[J]. IEEE J Biomed Health Inform, 2020, 24: 1632-1642. DOI: 10.1109/JBHI.2019.2956351
    [39]
    Yang X, Wu L, Zhao K, et al. Evaluation of human epidermal growth factor receptor 2 status of breast cancer using preoperative multidetector computed tomography with deep learning and handcrafted radiomics features[J]. Chin J Cancer Res, 2020, 32: 175-185. DOI: 10.21147/j.issn.1000-9604.2020.02.05
    [40]
    Leithner D, Bernard-Davila B, Martinez DF, et al. Radiomic Signatures Derived from Diffusion-Weighted Imaging for the Assessment of Breast Cancer Receptor Status and Molecular Subtypes[J]. Mol Imaging Biol, 2020, 22: 453-461. DOI: 10.1007/s11307-019-01383-w
    [41]
    Parekh VS, Jacobs MA. Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging[J]. Breast Cancer Res Treat, 2020, 180: 407-421. DOI: 10.1007/s10549-020-05533-5
    [42]
    Leithner D, Mayerhoefer ME, Martinez DF, et al. Non-Invasive Assessment of Breast Cancer Molecular Subtypes with Multiparametric Magnetic Resonance Imaging Radiomics[J]. J Clin Med, 2020, 9: 1853. DOI: 10.3390/jcm9061853
    [43]
    Xie T, Wang Z, Zhao Q, et al. Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer[J]. Front Oncol, 2019, 9: 505. DOI: 10.3389/fonc.2019.00505
    [44]
    Huang SY, Franc BL, Harnish RJ, et al. Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis[J]. NPJ Breast Cancer, 2018, 4: 24. DOI: 10.1038/s41523-018-0078-2
    [45]
    Park H, Lim Y, Ko ES, et al. Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer[J]. Clin Cancer Res, 2018, 24: 4705-4714. DOI: 10.1158/1078-0432.CCR-17-3783
    [46]
    Kim S, Kim MJ, Kim EK, et al. MRI Radiomic Features: Association with Disease-Free Survival in Patients with Triple-Negative Breast Cancer[J]. Sci Rep, 2020, 10: 3750. DOI: 10.1038/s41598-020-60822-9
    [47]
    Koh J, Lee E, Han K, et al. Three-dimensional radiomics of triple-negative breast cancer: Prediction of systemic recurrence[J]. Sci Rep, 2020, 10: 2976. DOI: 10.1038/s41598-020-59923-2
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