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Advances in the application of magnetic resonance habitat imaging for the diagnosis and treatment of breast cancer
XUE Yufei  WANG Weiwei  MA Shu  SUN Zhanguo 

Cite this article as: XUE Y F, WANG W W, MA S, et al. Advances in the application of magnetic resonance habitat imaging for the diagnosis and treatment of breast cancer[J]. Chin J Magn Reson Imaging, 2025, 16(7): 154-159. DOI:10.12015/issn.1674-8034.2025.07.025.


[Abstract] Breast cancer is one of the most common malignancies worldwide, and early diagnosis combined with standardized treatment are critical for improving patient prognosis. Tumor heterogeneity, as a key challenge in breast cancer research and clinical management, profoundly impacts tumor progression, metastatic potential, and treatment response. Tumor habitat imaging, which performs cluster analysis on intra-tumoral regions and their microenvironment by identifying subregions with similar characteristics, provides new perspectives into intratumoral heterogeneity. Habitat imaging based on multiparametric magnetic resonance imaging (MRI), by virtue of its technical advantages such as non-invasiveness and high resolution, enables non-invasive quantification of tumor heterogeneity. This article reviews the research progress of habitat imaging in breast cancer MRI, covering its applications in predicting hormone receptor status, molecular subtypes, lymphovascular invasion, axillary lymph node metastasis, treatment response prediction and evaluation, and prognosis assessment. The aim is to provide new ideas for precision diagnosis and treatment of breast cancer (including early diagnosis, therapeutic efficacy assessment, etc.).
[Keywords] breast cancer;habitat imaging;tumor microenvironment;heterogeneity;tumor microenvironment;magnetic resonance imaging

XUE Yufei1   WANG Weiwei2   MA Shu3   SUN Zhanguo2*  

1 College of Medical Imaging and Laboratory,Jining Medical University, Jining 272002, China

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

3 Department of Breast Surgery, Affiliated Hospital of Jining Medical College, Jining 272029, China

Corresponding author: SUN Z G, E-mail: yingxiangszg@163.com

Conflicts of interest   None.

Received  2025-05-01
Accepted  2025-07-06
DOI: 10.12015/issn.1674-8034.2025.07.025
Cite this article as: XUE Y F, WANG W W, MA S, et al. Advances in the application of magnetic resonance habitat imaging for the diagnosis and treatment of breast cancer[J]. Chin J Magn Reson Imaging, 2025, 16(7): 154-159. DOI:10.12015/issn.1674-8034.2025.07.025.

[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]
OUYANG F, SHI L. Disease burden analysis and future trend prediction of female breast cancer in China and the worldwide from 1990 to 2021[J]. Chin J Evid Based Med, 2025, 25(5): 497-503. DOI: 10.7507/1672-2531.202409068.
[3]
YIN Y L, JIANG X, HE X Y, et al. Disease burden of breast cancer in China and the world from 1990 to 2021 and projections for the next 15 years[J]. Chin J Cancer Prev Treat, 2025, 32(5): 275-283. DOI: 10.16073/j.cnki.cjcpt.2025.05.03.
[4]
PASHA N D, TURNER N C. Understanding and overcoming tumor heterogeneity in metastatic breast cancer treatment[J]. Nat Cancer, 2021, 2(7): 680-692. DOI: 10.1038/s43018-021-00229-1.
[5]
DAGOGO-JACK I, SHAW A T. Tumour heterogeneity and resistance to cancer therapies[J]. Nat Rev Clin Oncol, 2018, 15(2): 81-94. DOI: 10.1038/nrclinonc.2017.166.
[6]
SALA E, MEMA E, HIMOTO Y, et al. Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging[J]. Clin Radiol, 2017, 72(1): 3-10. DOI: 10.1016/j.crad.2016.09.013.
[7]
RAHMAT K, MUMIN N A, HAMID M T R, et al. MRI breast: current imaging trends, clinical applications, and future research directions[J]. Curr Med Imaging, 2022, 18(13): 1347-1361. DOI: 10.2174/1573405618666220415130131.
[8]
BOUGIAS H, STOGIANNOS N. Breast MRI: where are we currently standing?[J]. J Med Imaging Radiat Sci, 2022, 53(2): 203-211. DOI: 10.1016/j.jmir.2022.03.072.
[9]
HAN M, LU H, ZHU Y, et al. Establishment and validation of breast cancer prognosis prediction model based on multimodal imaging and clinical feature fusion[J]. J Clin Radiol, 2024, 43(9): 1478-1484. DOI: 10.13437/j.cnki.jcr.2024.09.006.
[10]
TIAN T T, LI N N, ZHONG Z, et al. Value of combined clinical and imaging approaches in predicting the efficacy of neoadjuvant chemotherapy for breast cancer[J]. China J Gen Surg, 2024, 33(8): 1337-1342. DOI: 10.7659/j.issn.1005-6947.2024.08.015.
[11]
GILLIES R J, BALAGURUNATHAN Y. Perfusion MR imaging of breast cancer: insights using "habitat imaging"[J]. Radiology, 2018, 288(1): 36-37. DOI: 10.1148/radiol.2018180271.
[12]
LI S L, DAI Y M, CHEN J Y, et al. MRI-based habitat imaging in cancer treatment: current technology, applications, and challenges[J/OL]. Cancer Imag, 2024, 24(1): 107 [2025-04-30]. https://pubmed.ncbi.nlm.nih.gov/39148139/. DOI: 10.1186/s40644-024-00758-9.
[13]
PARK J E, KIM H S, KIM N, et al. Spatiotemporal heterogeneity in multiparametric physiologic MRI is associated with patient outcomes in IDH-wildtype glioblastoma[J]. Clin Cancer Res, 2021, 27(1): 237-245. DOI: 10.1158/1078-0432.CCR-20-2156.
[14]
CHEN Z H, ZHOU J X, WANG M J, ET AL. RESEARCH PROGRESS IN BREAST CANCER BASED ON MRI TUMOR HABITAT IMAGING[J]. CHIN J RADIOL, 2025, 59(4): 463-467. DOI: 10.3760/CMA.J.CN112149-20240512-00269.
[15]
CHENG W Q, QI X, YANG H K, et al. Research progresses in multi-parameter MRI habitat imaging of breast cancer[J]. Chin J Med Imag Technol, 2024, 40(11): 1798-1801. DOI: 10.13929/j.issn.1003-3289.2024.11.034.
[16]
SUJIT S J, AMINU M, KARPINETS T V, et al. Enhancing NSCLC recurrence prediction with PET/CT habitat imaging, ctDNA, and integrative radiogenomics-blood insights[J/OL]. Nat Commun, 2024, 15: 3152 [2025-04-30]. https://pubmed.ncbi.nlm.nih.gov/38605064/. DOI: 10.1038/s41467-024-47512-0.
[17]
NAPEL S, MU W, JARDIM-PERASSI B V, et al. Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats[J]. Cancer, 2018, 124(24): 4633-4649. DOI: 10.1002/cncr.31630.
[18]
LEE D H, PARK J E, KIM N, et al. Tumor habitat analysis by magnetic resonance imaging distinguishes tumor progression from radiation necrosis in brain metastases after stereotactic radiosurgery[J]. Eur Radiol, 2022, 32(1): 497-507. DOI: 10.1007/s00330-021-08204-1.
[19]
YUAN L, LI Y J, HUAN Y, et al. Application advances of tumor habitat imaging[J]. Int J Med Radiol, 2023, 46(6): 721-724. DOI: 10.19300/j.2023.z20836.
[20]
LIU Y H, GAO Y. Implications of habitat imaging-based multisequence MRI in adult-type diffuse glioma[J]. Chin J Magn Reson Imag, 2023, 14(9): 119-124. DOI: 10.12015/issn.1674-8034.2023.09.022.
[21]
XIONG X, ZHENG L W, DING Y, et al. Breast cancer: pathogenesis and treatments[J/OL]. Sig Transduct Target Ther, 2025, 10: 49 [2025-04-30]. https://pubmed.ncbi.nlm.nih.gov/39966355/. DOI: 10.1038/s41392-024-02108-4.
[22]
NOLAN E, LINDEMAN G J, VISVADER J E. Deciphering breast cancer: from biology to the clinic[J]. Cell, 2023, 186(8): 1708-1728. DOI: 10.1016/j.cell.2023.01.040.
[23]
WOLFF A C, SOMERFIELD M R, DOWSETT M, et al. Human epidermal growth factor receptor 2 testing in breast cancer[J]. Arch Pathol Lab Med, 2023, 147(9): 993-1000. DOI: 10.5858/arpa.2023-0950-SA.
[24]
ZHU X J, ZHANG H, ZHANG S, et al. Clinicopathological features and prognosis of breast cancer with human epidermal growth factor receptor 2 low expression[J]. J Peking Univ Health Sci, 2023, 55(2): 243-253. DOI: 10.19723/j.issn.1671-167X.2023.02.007.
[25]
TARANTINO P, CURIGLIANO G, TOLANEY S M. Navigating the HER2-low paradigm in breast oncology: new standards, future horizons[J]. Cancer Discov, 2022, 12(9): 2026-2030. DOI: 10.1158/2159-8290.CD-22-0703.
[26]
TARANTINO P, GANDINI S, NICOLÒ E, et al. Evolution of low HER2 expression between early and advanced-stage breast cancer[J/OL]. Eur J Cancer, 2022, 163: 35-43 [2025-04-30]. https://pubmed.ncbi.nlm.nih.gov/35032815/. DOI: 10.1016/j.ejca.2021.12.022.
[27]
LI Z H, CHEN X Y, ZHANG X L, et al. Prediction of different expression status of human epidermal growth factor receptor 2 in breast cancer by multi-parameter MRI habitat images[J]. Chin J Radiol, 2024, 58(9): 909-915. DOI: 10.3760/cma.j.cn112149-20240228-00091.
[28]
CHEN H Q, LIU Y L, ZHAO J Q, et al. Quantification of intratumoral heterogeneity using habitat-based MRI radiomics to identify HER2-positive, -low and-zero breast cancers: a multicenter study[J/OL]. Breast Cancer Res, 2024, 26(1): 160 [2025-04-30]. https://pubmed.ncbi.nlm.nih.gov/39578913/. DOI: 10.1186/s13058-024-01921-7.
[29]
LI L R, LI Y, SUN Q. Clinical trials and current progress in the treatment of triple-negative breast cancer[J]. Med J Peking Union Med Coll Hosp, 2023, 14(1): 177-183. DOI: 10.12290/xhyxzz.2022-0085.
[30]
XU R, YU D, LUO P, et al. Do habitat MRI and fractal analysis help distinguish triple-negative breast cancer from non-triple-negative breast carcinoma[J]. Can Assoc Radiol J, 2024, 75(3): 584-592. DOI: 10.1177/08465371241231573.
[31]
ZHANG W L, LIANG F R, ZHAO Y, et al. Multiparametric MR-based feature fusion radiomics combined with ADC maps-based tumor proliferative burden in distinguishing TNBC versus non-TNBC[J]. Phys Med Biol, 2024, 69(5) [2025-04-30]. https://pubmed.ncbi.nlm.nih.gov/38306970/. DOI: 10.1088/1361-6560/ad25c0.
[32]
ZHAO N N, ZHU Y, TANG X M, et al. Prediction of axillary lymph node metastasis in breast cancer based on intra-tumoral and peri-tumoral MRI radiomics nomogram[J]. Chin J Magn Reson Imag, 2023, 14(3): 81-87, 94. DOI: 10.12015/issn.1674-8034.2023.03.014.
[33]
CHENG S J, ZHAI X Y, ZHOU S H, et al. Nomogram for predicting lymphovascular invasion in breast cancer using MRI features and quantitative parameters[J]. Chin J Magn Reson Imag, 2024, 15(5): 111-118. DOI: 10.12015/issn.1674-8034.2024.05.018.
[34]
WANG Z Y, MAO N, XIE H Z. Research progress of radiomics based on MRI in breast cancer[J]. Chin J Magn Reson Imag, 2021, 12(1): 109-111. DOI: 10.12015/issn.1674-8034.2021.01.026.
[35]
KOLEOSO O, TOUMBACARIS N, BROGI E, et al. The presence of extensive lymphovascular invasion is associated with higher risks of local-regional recurrence compared with usual lymphovascular invasion in curatively treated breast cancer patients[J]. Int J Radiat Oncol Biol Phys, 2024, 120(3): 835-844. DOI: 10.1016/j.ijrobp.2024.04.073.
[36]
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 Imag, 2022, 13(5): 52-58. DOI: 10.12015/issn.1674-8034.2022.05.010.
[37]
GE W, FAN X, ZENG Y, et al. Exploring habitats-based spatial distributions: improving predictions of lymphovascular invasion in invasive breast cancer[J]. Acad Radiol, 2024, 31(11): 4317-4328. DOI: 10.1016/j.acra.2024.05.043.
[38]
WU P Q, GUO F L, WANG J, et al. Development and validation of a dynamic contrast-enhanced magnetic resonance imaging-based habitat and peritumoral radiomic model to predict axillary lymph node metastasis in patients with breast cancer: a retrospective study[J]. Quant Imaging Med Surg, 2024, 14(12): 8211-8226. DOI: 10.21037/qims-24-558.
[39]
TARANTINO P, HORTOBAGYI G, TOLANEY S M, et al. Heterogeneity of residual disease after neoadjuvant systemic therapy in breast cancer[J/OL]. JAMA Oncol, 2024, 10(11): 1578 [2025-04-30]. https://pubmed.ncbi.nlm.nih.gov/39264638/. DOI: 10.1001/jamaoncol.2024.3679.
[40]
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 Imag, 2024, 15(6): 207-211, 223. DOI: 10.12015/issn.1674-8034.2024.06.033.
[41]
MATTONEN S A, PALMA D A, JOHNSON C, et al. Detection of local cancer recurrence after stereotactic ablative radiation therapy for lung cancer: physician performance versus radiomic assessment[J]. Int J Radiat Oncol Biol Phys, 2016, 94(5): 1121-1128. DOI: 10.1016/j.ijrobp.2015.12.369.
[42]
XU C, WANG Z H, WANG A L, et al. Breast cancer: multi-b-value diffusion weighted habitat imaging in predicting pathologic complete response to neoadjuvant chemotherapy[J]. Acad Radiol, 2024, 31(12): 4733-4742. DOI: 10.1016/j.acra.2024.06.004.
[43]
GRADISHAR W J, MORAN M S, ABRAHAM J, et al. Breast cancer, version 3.2024, NCCN clinical practice guidelines in oncology[J]. J Natl Compr Canc Netw, 2024, 22(5): 331-357. DOI: 10.6004/jnccn.2024.0035.
[44]
WEINFURTNER R J, ABDALAH M, STRINGFIELD O, et al. Quantitative changes in intratumoral habitats on MRI correlate with pathologic response in early-stage ER/PR+ HER2- breast cancer treated with preoperative stereotactic ablative body radiotherapy[J]. J Breast Imaging, 2022, 4(3): 273-284. DOI: 10.1093/jbi/wbac013.
[45]
SUN X X, YU Q. Intra-tumor heterogeneity of cancer cells and its implications for cancer treatment[J]. Acta Pharmacol Sin, 2015, 36(10): 1219-1227. DOI: 10.1038/aps.2015.92.
[46]
TAR P D, THACKER N A, BABUR M, et al. Habitat imaging of tumors enables high confidence sub-regional assessment of response to therapy[J/OL]. Cancers, 2022, 14(9): 2159 [2025-04-30]. https://pubmed.ncbi.nlm.nih.gov/35565288/. DOI: 10.3390/cancers14092159.
[47]
SYED A K, WHISENANT J G, BARNES S L, et al. Multiparametric analysis of longitudinal quantitative MRI data to identify distinct tumor habitats in preclinical models of breast cancer[J/OL]. Cancers, 2020, 12(6): 1682 [2025-04-30]. https://pubmed.ncbi.nlm.nih.gov/32599906/. DOI: 10.3390/cancers12061682.
[48]
KAZEROUNI A S, HORMUTH D A II, DAVIS T, et al. Quantifying tumor heterogeneity via MRI habitats to characterize microenvironmental alterations in HER2+ breast cancer[J/OL]. Cancers, 2022, 14(7): 1837 [2025-04-30]. https://pubmed.ncbi.nlm.nih.gov/35406609/. DOI: 10.3390/cancers14071837.
[49]
ZHANG X L, CHEN X Y, FU Y, et al. Study on heterogeneity of vascularity and cellularity via multiparametric MRI habitat imaging in breast cancer[J/OL]. BMC Med Imag, 2025, 25(1): 159 [2025-04-30]. https://pubmed.ncbi.nlm.nih.gov/40361010/. DOI: 10.1186/s12880-025-01698-x.
[50]
XU P Y, HAN H J, CHU Z, et al. Evolutionary patterns and influencing factors of tumor cell subclones[J]. Tumor, 2018, 38(3): 278-282. DOI: 10.3781/j.issn.1000-7431.2018.55.470.
[51]
JUAN-ALBARRACÍN J, FUSTER-GARCIA E, PÉREZ-GIRBÉS A, et al. Glioblastoma: vascular habitats detected at preoperative dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging predict survival[J]. Radiology, 2018, 287(3): 944-954. DOI: 10.1148/radiol.2017170845.
[52]
CHO H H, KIM H, NAM S Y, et al. Measurement of perfusion heterogeneity within tumor habitats on magnetic resonance imaging and its association with prognosis in breast cancer patients[J/OL]. Cancers, 2022, 14(8): 1858 [2025-04-30]. https://pubmed.ncbi.nlm.nih.gov/35454768/. DOI: 10.3390/cancers14081858.
[53]
WU J, CAO G H, SUN X L, et al. Intratumoral spatial heterogeneity at perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy[J]. Radiology, 2018, 288(1): 26-35. DOI: 10.1148/radiol.2018172462.

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