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Research progress of MRI radiomics in the efficacy evaluation and prognosis of neoadjuvant therapy for breast cancer
BAI Yingnan  ZHOU Rongyan  NING Zirui  LI Zhuolin 

Cite this article as: BAI Y N, ZHOU R Y, NING Z R, et al. Research progress of MRI radiomics in the efficacy evaluation and prognosis of neoadjuvant therapy for breast cancer[J]. Chin J Magn Reson Imaging, 2024, 15(6): 207-211, 223. DOI:10.12015/issn.1674-8034.2024.06.033.


[Abstract] The incidence of breast cancer has jumped to the top of the global neoplastic lesions,and the incidence is still on the rise. Neoadjuvant therapy (NAT) has been widely used as the first-line treatment for patients with locally advanced breast cancer. Magnetic resonance imaging (MRI), with its good soft tissue and spatial resolution, is increasingly important in assessing lesion extent, early diagnosis, efficacy prediction, and prognostic assessment. MRI-based radiomics can analyze internal texture features that cannot be distinguished by the naked eye, which has great advantages in assessing tumor heterogeneity. Studies have demonstrated the advantages and disadvantages of MRI assessment at different time points of the NAT course in breast cancer, with multi-temporal assessment being more advantageous than single-temporal. The aim of this review is to investigate the current research status, controversies and application prospects of longitudinal temporal MRI images for evaluating neoadjuvant efficacy with the support of imaging histology, as well as the unique advantages of MRI imaging histology for predicting the long-term prognosis of neoadjuvant therapy in breast cancer patients. This study suggests the superiority of the multiseries longitudinal imaging histology model to evaluate tumor response, and provides ideas for more analysis methods of imaging histology in the future.
[Keywords] radiomics；breast cancer；neoadjuvant therapy；efficacy；prognosis；magnetic resonance imaging

BAI Yingnan   ZHOU Rongyan   NING Zirui   LI Zhuolin*  

Department of Radiology, Yunnan Cancer Hospital (the Third Affiliated Hospital of Kunming Medical University), Kunming 650118, China

Corresponding author: LI Z L, E-mail: lizhuolin0327@163.com

Conflicts of interest   None.

Received  2024-01-26
Accepted  2024-06-05
DOI: 10.12015/issn.1674-8034.2024.06.033
Cite this article as: BAI Y N, ZHOU R Y, NING Z R, et al. Research progress of MRI radiomics in the efficacy evaluation and prognosis of neoadjuvant therapy for breast cancer[J]. Chin J Magn Reson Imaging, 2024, 15(6): 207-211, 223. DOI:10.12015/issn.1674-8034.2024.06.033.

[1]
SUNG H, FERLAY J, SIEGEL R L, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
[2]
SHAO Z M, WU J, JIANG Z F, et al. Expert consensus on neoadjuvant treatment of breast cancer in China(2021 edition)[J]. China Oncol, 2022, 32(1): 80-89. DOI: 10.19401/j.cnki.1007-3639.2022.01.011.
[3]
WEKKING D, PORCU M, SILVA P D, et al. Breast MRI: clinical indications, recommendations, and future applications in breast cancer diagnosis[J]. Curr Oncol Rep, 2023, 25(4): 257-267. DOI: 10.1007/s11912-023-01372-x.
[4]
MING W L, LI F Y, ZHU Y H, et al. Unsupervised analysis based on DCE-MRI radiomics features revealed three novel breast cancer subtypes with distinct clinical outcomes and biological characteristics[J/OL]. Cancers, 2022, 14(22): 5507 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/36428600/. DOI: 10.3390/cancers14225507.
[5]
EWAIDAT H A, AYASRAH M. A concise review on the utilization of abbreviated protocol breast MRI over full diagnostic protocol in breast cancer detection[J/OL]. Int J Biomed Imaging, 2022, 2022: 8705531 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/35528224/. DOI: 10.1155/2022/8705531.
[6]
CHOUDHERY S, GOMEZ-CARDONA D, FAVAZZA C P, et al. MRI radiomics for assessment of molecular subtype, pathological complete response, and residual cancer burden in breast cancer patients treated with neoadjuvant chemotherapy[J/OL]. Acad Radiol, 2022, 29(Suppl 1): S145-S154 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/33160859/. DOI: 10.1016/j.acra.2020.10.020.
[7]
RIZZO S, BOTTA F, RAIMONDI S, et al. Radiomics: the facts and the challenges of image analysis[J/OL]. Eur Radiol Exp, 2018, 2(1): 36 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/30426318/. DOI: 10.1186/s41747-018-0068-z.
[8]
WOO J, RYU J M, JUNG S M, et al. Breast radiologic complete response is associated with favorable survival outcomes after neoadjuvant chemotherapy in breast cancer[J]. Eur J Surg Oncol, 2021, 47(2): 232-239. DOI: 10.1016/j.ejso.2020.08.023.
[9]
SQUIFFLET P, SAAD E D, LOIBL S, et al. Re-evaluation of pathologic complete response as a surrogate for event-free and overall survival in human epidermal growth factor receptor 2-positive, early breast cancer treated with neoadjuvant therapy including anti-human epidermal growth factor receptor 2 therapy[J]. J Clin Oncol, 2023, 41(16): 2988-2997. DOI: 10.1200/JCO.22.02363.
[10]
HAQUE W, VERMA V, HATCH S, et al. Response rates and pathologic complete response by breast cancer molecular subtype following neoadjuvant chemotherapy[J]. Breast Cancer Res Treat, 2018, 170(3): 559-567. DOI: 10.1007/s10549-018-4801-3.
[11]
PANICO C, FERRARA F, WOITEK R, et al. Staging breast cancer with MRI, theT. A key role in the neoadjuvant setting[J/OL]. Cancers, 2022, 14(23): 5786 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/36497265/. DOI: 10.3390/cancers14235786.
[12]
ROMEO V, ACCARDO G, PERILLO T, et al. Assessment and prediction of response to neoadjuvant chemotherapy in breast cancer: a comparison of imaging modalities and future perspectives[J/OL]. Cancers, 2021, 13(14): 3521 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/34298733/. DOI: 10.3390/cancers13143521.
[13]
KUERER H M, SMITH B D, KRISHNAMURTHY S, et al. Eliminating breast surgery for invasive breast cancer in exceptional responders to neoadjuvant systemic therapy: a multicentre, single-arm, phase 2 trial[J]. Lancet Oncol, 2022, 23(12): 1517-1524. DOI: 10.1016/S1470-2045(22)00613-1.
[14]
PENG S Y, CHEN L Q, TAO J, et al. Radiomics analysis of multi-phase DCE-MRI in predicting tumor response to neoadjuvant therapy in breast cancer[J/OL]. Diagnostics, 2021, 11(11): 2086 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/34829433/. DOI: 10.3390/diagnostics11112086.
[15]
LIU Z Y, LI Z L, QU J R, 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.
[16]
BIAN T T, WU Z J, LIN Q, et al. Radiomic signatures derived from multiparametric MRI for the pretreatment prediction of response to neoadjuvant chemotherapy in breast cancer[J/OL]. Br J Radiol, 2020, 93(1115): 20200287 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/32822542/. DOI: 10.1259/bjr.20200287.
[17]
BITENCOURT A G V, 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/OL]. EBioMedicine, 2020, 61: 103042 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/33039708/. DOI: 10.1016/j.ebiom.2020.103042.
[18]
YANG W, LI Z Z, LIU K H, et al. Nomogram based on clinical-pathological-imaging model for predicting the pathological complete response of breast cancer patients undergoing neoadjuvant chemotherapy[J]. Chin J Med Imag, 2023, 31(7): 734-742. DOI: 10.3969/j.issn.1005-5185.2023.07.012.
[19]
SHI Z W, HUANG X M, CHENG Z L, et al. MRI-based quantification of intratumoral heterogeneity for predicting treatment response to neoadjuvant chemotherapy in breast cancer[J/OL]. Radiology, 2023, 308(1): e222830 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/37432083/. DOI: 10.1148/radiol.222830.
[20]
O'DONNELL J P M, GASIOR S A, DAVEY M G, et al. The accuracy of breast MRI radiomic methodologies in predicting pathological complete response to neoadjuvant chemotherapy: a systematic review and network meta-analysis[J/OL]. Eur J Radiol, 2022, 157: 110561 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/36308849/. DOI: 10.1016/j.ejrad.2022.110561.
[21]
SUTTON E J, ONISHI N, FEHR D A, et al. A machine learning model that classifies breast cancer pathologic complete response on MRI post-neoadjuvant chemotherapy[J/OL]. Breast Cancer Res, 2020, 22(1): 57 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/32466777/. DOI: 10.1186/s13058-020-01291-w.
[22]
XIONG Q Q, ZHOU X Z, LIU Z Y, 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.
[23]
PESAPANE F, ROTILI A, BOTTA F, et al. Radiomics of MRI for the prediction of the pathological response to neoadjuvant chemotherapy in breast cancer patients: a single referral centre analysis[J/OL]. Cancers, 2021, 13(17): 4271 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/34503081/. DOI: 10.3390/cancers13174271.
[24]
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.
[25]
HUSSAIN L, HUANG P, NGUYEN T, et al. Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response[J/OL]. Biomed Eng Online, 2021, 20(1): 63 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/34183038/. DOI: 10.1186/s12938-021-00899-z.
[26]
CHEN Z G, HUANG M X, LYU J B, et al. Machine learning for predicting breast-conserving surgery candidates after neoadjuvant chemotherapy based on DCE-MRI[J/OL]. Front Oncol, 2023, 13: 1174843 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/37621690/. DOI: 10.3389/fonc.2023.1174843.
[27]
HOTTAT N A, BADR D A, LECOMTE S, et al. Value of diffusion-weighted MRI in predicting early response to neoadjuvant chemotherapy of breast cancer: comparison between ROI-ADC and whole-lesion-ADC measurements[J]. Eur Radiol, 2022, 32(6): 4067-4078. DOI: 10.1007/s00330-021-08462-z.
[28]
ZHENG D, HE X J, JING J. Overview of artificial intelligence in breast cancer medical imaging[J/OL]. J Clin Med, 2023, 12(2): 419 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/36675348/. DOI: 10.3390/jcm12020419.
[29]
CHITALIA R D, ROWLAND J, MCDONALD E S, et al. Imaging phenotypes of breast cancer heterogeneity in preoperative breast dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) scans predict 10-year recurrence[J]. Clin Cancer Res, 2020, 26(4): 862-869. DOI: 10.1158/1078-0432.CCR-18-4067.
[30]
LU H C, YIN J D. Texture analysis of breast DCE-MRI based on intratumoral subregions for predicting HER2 2+ status[J/OL]. Front Oncol, 2020, 10: 543 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/32373531/. DOI: 10.3389/fonc.2020.00543.
[31]
EUN N L, KANG D, SON E J, et al. Texture analysis using machine learning-based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy[J]. Eur Radiol, 2021, 31(9): 6916-6928. DOI: 10.1007/s00330-021-07816-x.
[32]
MA M M, GAN L Y, LIU Y H, et al. Radiomics features based on automatic segmented MRI images: prognostic biomarkers for triple-negative breast cancer treated with neoadjuvant chemotherapy[J/OL]. Eur J Radiol, 2022, 146: 110095 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/34890936/. DOI: 10.1016/j.ejrad.2021.110095.
[33]
VALACHIS A, MAMOUNAS E P, MITTENDORF E A, 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.
[34]
RAO R, EUHUS D, MAYO H G, et al. Axillary node interventions in breast cancer: a systematic review[J]. JAMA, 2013, 310(13): 1385-1394. DOI: 10.1001/jama.2013.277804.
[35]
YU Y F, TAN Y J, XIE C M, et al. Development and validation of a preoperative magnetic resonance imaging radiomics-based signature to predict axillary lymph node metastasis and disease-free survival in patients with early-stage breast cancer[J/OL]. JAMA Netw Open, 2020, 3(12): e2028086 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/33289845/. DOI: 10.1001/jamanetworkopen.2020.28086.
[36]
YU Y F, HE Z F, 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/OL]. EBioMedicine, 2021, 69: 103460 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/34233259/. DOI: 10.1016/j.ebiom.2021.103460.
[37]
RABINOVICI-COHEN S, FERNÁNDEZ X M, GRANDAL REJO B, et al. Multimodal prediction of five-year breast cancer recurrence in women who receive neoadjuvant chemotherapy[J/OL]. Cancers, 2022, 14(16): 3848 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/36010844/. DOI: 10.3390/cancers14163848.
[38]
ROMEO V, CUOCOLO R, SANDUZZI L, et al. MRI radiomics and machine learning for the prediction of oncotype dx recurrence score in invasive breast cancer[J/OL]. Cancers, 2023, 15(6): 1840 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/36980724/. DOI: 10.3390/cancers15061840.
[39]
KOH J, LEE E, HAN K, et al. Three-dimensional radiomics of triple-negative breast cancer: prediction of systemic recurrence[J/OL]. Sci Rep, 2020, 10(1): 2976 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/32076078/. DOI: 10.1038/s41598-020-59923-2.
[40]
XIA B Q, WANG H, WANG Z, et al. A combined nomogram model to predict disease-free survival in triple-negative breast cancer patients with neoadjuvant chemotherapy[J/OL]. Front Genet, 2021, 12: 783513 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/34868273/. DOI: 10.3389/fgene.2021.783513.
[41]
LI Q, XIAO Q, LI J W, 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, 12: 10603-10613 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/33149669/. DOI: 10.2147/CMAR.S271876.
[42]
PESAPANE F, DE MARCO P, RAPINO A, et al. How radiomics can improve breast cancer diagnosis and treatment[J/OL]. J Clin Med, 2023, 12(4): 1372 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/36835908/. DOI: 10.3390/jcm12041372.
[43]
GRANZIER R W Y, IBRAHIM A, PRIMAKOV S P, et al. MRI-based radiomics analysis for the pretreatment prediction of pathologic complete tumor response to neoadjuvant systemic therapy in breast cancer patients: a multicenter study[J/OL]. Cancers, 2021, 13(10): 2447 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/34070016/. DOI: 10.3390/cancers13102447.
[44]
SHUR J, BLACKLEDGE M, D'ARCY J, et al. MRI texture feature repeatability and image acquisition factor robustness, a phantom study and in silico study[J/OL]. Eur Radiol Exp, 2021, 5(1): 2 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/33462642/. DOI: 10.1186/s41747-020-00199-6.
[45]
LIANG Y H, TANG W J, WANG T, et al. HRadNet: a hierarchical radiomics-based network for multicenter breast cancer molecular subtypes prediction[J]. IEEE Trans Med Imaging, 2024, 43(3): 1225-1236. DOI: 10.1109/TMI.2023.3331301.
[46]
JI Y, WHITNEY H M, LI H, et al. Differences in molecular subtype reference standards impact AI-based breast cancer classification with dynamic contrast-enhanced MRI[J/OL]. Radiology, 2023, 307(1): e220984 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/36594836/. DOI: 10.1148/radiol.220984.
[47]
CHOE J, LEE S M, DO K H, et al. Deep learning-based image conversion of CT reconstruction kernels improves radiomics reproducibility for pulmonary nodules or masses[J]. Radiology, 2019, 292(2): 365-373. DOI: 10.1148/radiol.2019181960.
[48]
SU G H, XIAO Y, YOU C, et al. Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets[J/OL]. Sci Adv, 2023, 9(40): eadf0837 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/37801493/. DOI: 10.1126/sciadv.adf0837.
[49]
MEHMOOD S, FAHEEM M, ISMAIL H, et al. Breast cancer resistance likelihood and personalized treatment through integrated multiomics[J/OL]. Front Mol Biosci, 2022, 9: 783494 [2024-01-25]. https://pubmed.ncbi.nlm.nih.gov/35495618/. DOI: 10.3389/fmolb.2022.783494.

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