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Application progress of MRI radiomics in the efficacy and prognosis of neoadjuvant chemotherapy for breast cancer
ZHU Xuelin  WU Jianlin 

Cite this article as: Zhu XL, Wu JL. Application progress of MRI radiomics in the efficacy and prognosis of neoadjuvant chemotherapy for breast cancer[J]. Chin J Magn Reson Imaging, 2022, 13(3): 159-161, 165. DOI:10.12015/issn.1674-8034.2022.03.038.

[Abstract] MRI radiomics extracts a large number of high-dimensional features from MRI images and analyzes the data in combination with machine learning, so as to non-invasively obtain information on the overall heterogeneity of tumors. It has been explorably used to predict the efficacy and prognosis of neoadjuvant chemotherapy (NAC) for breast cancer, and has shown good efficacy. Although its clinical application is currently limited by the lack of adequate standardized definitions and biological validation, it still has broad development prospects. This article will review the application progress, problems and application prospects of MRI radiomics in the efficacy and prognosis of NAC for breast cancer.
[Keywords] breast cancer;neoadjuvant chemotherapy;magnetic resonance imaging;radiomics;curative effect;prognosis

ZHU Xuelin1, 2   WU Jianlin1*  

1 Affiliated Zhongshan Hospital of Dalian University, Dalian 116001, China

2 Qingzhou People's Hospital, Weifang 262500, China

Wu JL, E-mail:

Conflicts of interest   None.

Received  2021-11-27
Accepted  2022-03-04
DOI: 10.12015/issn.1674-8034.2022.03.038
Cite this article as: Zhu XL, Wu JL. Application progress of MRI radiomics in the efficacy and prognosis of neoadjuvant chemotherapy for breast cancer[J]. Chin J Magn Reson Imaging, 2022, 13(3): 159-161, 165.DOI:10.12015/issn.1674-8034.2022.03.038

Wesdorp NJ, Hellingman T, Jansma EP, et al. Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment[J]. Eur J Nucl Med Mol Imaging, 2021, 48(6): 1785-1794. DOI: 10.1007/s00259-020-05142-w.
Slanetz PJ, Moy L, Baron P, et al. ACR appropriateness criteria® monitoring response to neoadjuvant systemic therapy for breast cancer[J]. J Am Coll Radiol, 2017, 14(11S): S462-S475. DOI: 10.1016/j.jacr.2017.08.037.
Liu WF, Chen W, Zhang XX, et al. Higher efficacy and reduced adverse reactions in neoadjuvant chemotherapy for breast cancer by using pegylated liposomal doxorubicin compared with pirarubicin[J]. Sci Rep, 2021, 11(1): 199. DOI: 10.1038/s41598-020-80415-w.
Pesapane F, Rotili A, Agazzi GM, et al. Recent radiomics advancements in breast cancer: lessons and pitfalls for the next future[J]. Curr Oncol, 2021, 28(4): 2351-2372. DOI: 10.3390/curroncol28040217.
Guiot J, Vaidyanathan A, Deprez L, et al. A review in radiomics: making personalized medicine a reality via routine imaging[J]. Med Res Rev, 2022, 42(1): 426-440. DOI: 10.1002/med.21846.
Tagliafico AS, Piana M, Schenone D, et al. Overview of radiomics in breast cancer diagnosis and prognostication[J]. Breast, 2020, 49: 74-80. DOI: 10.1016/j.breast.2019.10.018.
Pinker K, Chin J, Melsaether AN, et al. Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment[J]. Radiology, 2018, 287(3): 732-747. DOI: 10.1148/radiol.2018172171.
Leech M, Osman S, Jain S, et al. Mini review: Personalization of the radiation therapy management of prostate cancer using MRI-based radiomics[J]. Cancer Lett, 2021, 498: 210-216. DOI: 10.1016/j.canlet.2020.10.033.
Kim SY, Cho N, Choi Y, et al. Factors affecting pathologic complete response following neoadjuvant chemotherapy in breast cancer: development and validation of a predictive nomogram[J]. Radiology, 2021, 299(2): 290-300. DOI: 10.1148/radiol.2021203871.
Conti A, Duggento A, Indovina I, et al. Radiomics in breast cancer classification and prediction[J]. Semin Cancer Biol, 2021, 72: 238-250. DOI: 10.1016/j.semcancer.2020.04.002.
Chen SJ, Shu ZY, Li YF, 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.
Spring LM, Fell G, Arfe A, et al. Pathologic complete response after neoadjuvant chemotherapy and impact on breast cancer recurrence and survival: a comprehensive meta-analysis[J]. Clin Cancer Res, 2020, 26(12): 2838-2848. DOI: 10.1158/1078-0432.CCR-19-3492.
Hamy AS, Bonsang-Kitzis H, de Croze D, et al. Interaction between molecular subtypes and stromal immune infiltration before and after treatment in breast cancer patients treated with neoadjuvant chemotherapy[J]. Clin Cancer Res, 2019, 25(22): 6731-6741. DOI: 10.1158/1078-0432.CCR-18-3017.
Cullinane C, Creavin B, O'Leary DP, et al. Can the neutrophil to lymphocyte ratio predict complete pathologic response to neoadjuvant breast cancer treatment? A systematic review and meta-analysis[J]. Clin Breast Cancer, 2020, 20(6): e675-e681. DOI: 10.1016/j.clbc.2020.05.008.
di Cosimo S, Triulzi T, Pizzamiglio S, et al. The 41-gene classifier TRAR predicts response of HER2 positive breast cancer patients in the NeoALTTO study[J]. Eur J Cancer, 2019, 118: 1-9. DOI: 10.1016/j.ejca.2019.06.001.
Fu CF, Liu Y, Han XH, et al. An immune-associated genomic signature effectively predicts pathologic complete response to neoadjuvant paclitaxel and anthracycline-based chemotherapy in breast cancer[J]. Front Immunol, 2021, 12: 704655. DOI: 10.3389/fimmu.2021.704655.
Liu H, Zhang FX, Zhang F. Research status of DCE-MRI semi-quantitative and quantitative analysis in distinguishing benign and malignant cervical lymph nodes[J]. Chin J Magn Reson Imaging, 2021, 12(1): 103-105. DOI: 10.12015/issn.1674-8034.2021.01.024.
Liu J, Sun D, Chen LL, 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.
Eun NL, Kang D, Son EJ, 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.
Fan M, Chen H, You C, et al. Radiomics of tumor heterogeneity in longitudinal dynamic contrast-enhanced magnetic resonance imaging for predicting response to neoadjuvant chemotherapy in breast cancer[J]. Front Mol Biosci, 2021, 8: 622219. DOI: 10.3389/fmolb.2021.622219.
Marino MA, Helbich T, Baltzer P, et al. Multiparametric MRI of the breast: a review[J]. J Magn Reson Imaging, 2018, 47(2): 301-315. DOI: 10.1002/jmri.25790.
Chen XG, Chen XF, Yang JD, et al. Combining dynamic contrast-enhanced magnetic resonance imaging and apparent diffusion coefficient maps for a radiomics nomogram to predict pathological complete response to neoadjuvant chemotherapy in breast cancer patients[J]. J Comput Assist Tomogr, 2020, 44(2): 275-283. DOI: 10.1097/RCT.0000000000000978.
Bian TT, Wu ZJ, Lin Q, et al. Radiomic signatures derived from multiparametric MRI for the pretreatment prediction of response to neoadjuvant chemotherapy in breast cancer[J]. Br J Radiol, 2020, 93(1115): 20200287. DOI: 10.1259/bjr.20200287.
Liu ZY, Li ZL, Qu JR, 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(12): 3538-3547. DOI: 10.1158/1078-0432.CCR-18-3190.
Xiong QQ, Zhou XZ, Liu ZY, et al. Multiparametric MRI-based radiomics analysis for prediction of breast cancers insensitive to neoadjuvant chemotherapy[J]. Clin Transl Oncol, 2020, 22(1): 50-59. DOI: 10.1007/s12094-019-02109-8.
Zhuang XS, Chen C, Liu ZY, et al. Multiparametric MRI-based radiomics analysis for the prediction of breast tumor regression patterns after neoadjuvant chemotherapy[J]. Transl Oncol, 2020, 13(11): 100831. DOI: 10.1016/j.tranon.2020.100831.
Asaoka M, Narui K, Suganuma N, et al. Clinical and pathological predictors of recurrence in breast cancer patients achieving pathological complete response to neoadjuvant chemotherapy[J]. Eur J Surg Oncol, 2019, 45(12): 2289-2294. DOI: 10.1016/j.ejso.2019.08.001.
Valachis A, Mamounas EP, Mittendorf EA, et al. Risk factors for locoregional disease recurrence after breast-conserving therapy in patients with breast cancer treated with neoadjuvant chemotherapy: an international collaboration and individual patient meta-analysis[J]. Cancer, 2018, 124(14): 2923-2930. DOI: 10.1002/cncr.31518.
Yu YF, He ZF, Ouyang J, et al. Magnetic resonance imaging radiomics predicts preoperative axillary lymph node metastasis to support surgical decisions and is associated with tumor microenvironment in invasive breast cancer: a machine learning, multicenter study[J]. EBioMedicine, 2021, 69: 103460. DOI: 10.1016/j.ebiom.2021.103460.
Ellis MJ, Suman VJ, Hoog J, et al. Ki67 proliferation index as a tool for chemotherapy decisions during and after neoadjuvant aromatase inhibitor treatment of breast cancer: results from the American college of surgeons oncology group Z1031 trial (alliance)[J]. J Clin Oncol, 2017, 35(10): 1061-1069. DOI: 10.1200/JCO.2016.69.4406.
Cabrera-Galeana P, Muñoz-Montaño W, Lara-Medina F, et al. Ki67 changes identify worse outcomes in residual breast cancer tumors after neoadjuvant chemotherapy[J]. Oncologist, 2018, 23(6): 670-678. DOI: 10.1634/theoncologist.2017-0396.
Liang CS, Cheng ZX, Huang YQ, et al. An MRI-based radiomics classifier for preoperative prediction of ki-67 status in breast cancer[J]. Acad Radiol, 2018, 25(9): 1111-1117. DOI: 10.1016/j.acra.2018.01.006.
Li Q, Xiao Q, Li JW, et al. MRI-based radiomic signature as a prognostic biomarker for HER2-positive invasive breast cancer treated with NAC[J]. Cancer Manag Res, 2020, 12: 10603-10613. DOI: 10.2147/CMAR.S271876.
Liu ZY, Wang S, Dong D, et al. The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges[J]. Theranostics, 2019, 9(5): 1303-1322. DOI: 10.7150/thno.30309.
Tomaszewski MR, Gillies RJ. The biological meaning of radiomic features[J]. Radiology, 2021, 299(2): E256. DOI: 10.1148/radiol.2021219005.

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