Share：
Share this content in WeChat
X
[Chinese][PDF]61
• Review •
Overview of MRI-based radiomics in breast cancer diagnosis and treatment
LI Xiaoguang  TIAN Jing  ZHANG Chunlai  XIE Zongyu  WANG Yi 

Cite this article as: LI X G, TIAN J, ZHANG C L, et al. Overview of MRI-based radiomics in breast cancer diagnosis and treatment[J]. Chin J Magn Reson Imaging, 2024, 15(7): 196-203. DOI:10.12015/issn.1674-8034.2024.07.033.


[Abstract] Breast cancer has become the world's highest incidence and mortality of female malignant tumors. Providing accurate and efficient diagnosis, risk stratification and timely adjustment of treatment strategies is an important step in achieving precision medicine for breast cancer. Radiomics is a new and high-throughput image quantitative analysis method, which aims to extract mineable high-dimensional data from clinical medical images. Currently, various studies from different fields of imaging medicine have shown the potential of radiomics in improving clinical decision-making of breast cancer. This paper will introduce the application of MRI radiomics in breast cancer differentiation, molecular subtyping prediction, efficacy evaluation of neoadjuvant chemotherapy, status of axillary lymph nodes, Ki-67 expression, prognosis assessment and recurrence risk, and discuss the limitations and challenges of current radiomics, in order to provide new ideas for optimizing treatment decisions and promoting the development of precision medicine for breast cancer.
[Keywords] breast tumor；magnetic resonance imaging；radiomics；habitat imaging；diagnosis；prediction；prognosis

LI Xiaoguang1   TIAN Jing1   ZHANG Chunlai1   XIE Zongyu2   WANG Yi3*  

1 Department of Radiology, Daping Hospital, Army Medical University, Chongqing 400042, China

2 Department of Radiology, the First Affiliated Hospital of Bengbu Medical university, Bengbu 233004, China

3 Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing 400042, China

Corresponding author: WANG Y, E-mail: ywhxl@qq.com

Conflicts of interest   None.

Received  2024-04-02
Accepted  2024-06-25
DOI: 10.12015/issn.1674-8034.2024.07.033
Cite this article as: LI X G, TIAN J, ZHANG C L, et al. Overview of MRI-based radiomics in breast cancer diagnosis and treatment[J]. Chin J Magn Reson Imaging, 2024, 15(7): 196-203. DOI:10.12015/issn.1674-8034.2024.07.033.

[1]
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.
[2]
KATAOKA M, IIMA M, MIYAKE K K, et al. Multiparametric imaging of breast cancer: an update of current applications[J]. Diagn Interv Imaging, 2022, 103(12): 574-583. DOI: 10.1016/j.diii.2022.10.012.
[3]
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.
[4]
GILLIES R J, ANDERSON A R, GATENBY R A, et al. The biology underlying molecular imaging in oncology: from genome to anatome and back again[J]. Clin Radiol, 2010, 65(7): 517-521. DOI: 10.1016/j.crad.2010.04.005.
[5]
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.
[6]
ZHAO X, BAI J W, GUO Q, et al. Clinical applications of deep learning in breast MRI[J/OL]. Biochim Biophys Acta Rev Cancer, 2023, 1878(2): 188864 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/36822377/. DOI: 10.1016/j.bbcan.2023.188864.
[7]
ZHANG X, SU G H, CHEN Y, et al. Decoding Intratumoral Heterogeneity: clinical Potential of Habitat Imaging based on Radiomics[J/OL]. Radiology, 2023, 309(3): e232047 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38085080/. DOI: 10.1148/radiol.232047.
[8]
JI Y, LI H, EDWARDS A V, et al. Independent validation of machine learning in diagnosing breast Cancer on magnetic resonance imaging within a single institution[J/OL]. Cancer Imaging, 2019, 19(1): 64 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/36991474/. DOI: 10.1186/s40644-019-0252-2.
[9]
DEBBI K, HABERT P, GROB A, et al. Radiomics model to classify mammary masses using breast DCE-MRI compared to the BI-RADS classification performance[J/OL]. Insights Imaging, 2023, 14(1): 64 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37052738/. DOI: 10.1186/s13244-023-01404-x.
[10]
XU H, LIU J K, CHEN Z, et al. Intratumoral and peritumoral radiomics based on dynamic contrast-enhanced MRI for preoperative prediction of intraductal component in invasive breast cancer[J]. Eur Radiol, 2022, 32(7): 4845-4856. DOI: 10.1007/s00330-022-08539-3.
[11]
FAN W, SUN W, XU M Z, et al. Diagnosis of benign and malignant nodules with a radiomics model integrating features from nodules and mammary regions on DCE-MRI[J/OL]. Front Oncol, 2024, 14: 1307907 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38450180/. DOI: 10.3389/fonc.2024.1307907.
[12]
SUN K, JIAO Z C, ZHU H, et al. Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR[J/OL]. J Transl Med, 2021, 19(1): 443 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/34689804/. DOI: 10.1186/s12967-021-03117-5.
[13]
LIU Y L, JIA X X, ZHAO J Q, et al. A machine learning-based unenhanced radiomics approach to distinguishing between benign and malignant breast lesions using T2-weighted and diffusion-weighted MRI[J/OL]. J Magn Reson Imaging, 2023 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37933890/. DOI: 10.1002/jmri.29111.
[14]
WANG G S, GUO Q, SHI D F, et al. Clinical breast MRI-based radiomics for distinguishing benign and malignant lesions: an analysis of sequences and enhanced phases[J/OL]. J Magn Reson Imaging, 2023 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38006286/. DOI: 10.1002/jmri.29150.
[15]
TURNER K M, YEO S K, HOLM T M, et al. Heterogeneity within molecular subtypes of breast cancer[J/OL]. Am J Physiol Cell Physiol, 2021, 321(2): C343-C354 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/34191627/. DOI: 10.1152/ajpcell.00109.2021.
[16]
HUANG T, FAN B, QIU Y Y, et al. Application of DCE-MRI radiomics signature analysis in differentiating molecular subtypes of luminal and non-luminal breast cancer[J/OL]. Front Med, 2023, 10: 1140514 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37181350/. DOI: 10.3389/fmed.2023.1140514.
[17]
YUE W Y, ZHANG H T, GAO S, et al. Predicting breast cancer subtypes using magnetic resonance imaging based radiomics with automatic segmentation[J]. J Comput Assist Tomogr, 2023, 47(5): 729-737. DOI: 10.1097/RCT.0000000000001474.
[18]
ZHANG L L, FAN M, WANG S W, et al. Radiomic analysis of pharmacokinetic heterogeneity within tumor based on the unsupervised decomposition of dynamic contrast-enhanced MRI for predicting histological characteristics of breast cancer[J]. J Magn Reson Imaging, 2022, 55(6): 1636-1647. DOI: 10.1002/jmri.27993.
[19]
ZHANG L, ZHOU X X, LIU L, et al. Comparison of dynamic contrast-enhanced MRI and non-mono-exponential model-based diffusion-weighted imaging for the prediction of prognostic biomarkers and molecular subtypes of breast cancer based on radiomics[J]. J Magn Reson Imaging, 2023, 58(5): 1590-1602. DOI: 10.1002/jmri.28611.
[20]
ZHOU J, TAN H N, LI W, et al. Radiomics signatures based on multiparametric MRI for the preoperative prediction of the HER2 status of patients with breast cancer[J]. Acad Radiol, 2021, 28(10): 1352-1360. DOI: 10.1016/j.acra.2020.05.040.
[21]
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/OL]. Phys Med Biol, 2024, 69(5) [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38306970/. DOI: 10.1088/1361-6560/ad25c0.
[22]
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/OL]. J L'association Can Des Radiol, 2024: 8465371241231573 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38389194/. DOI: 10.1177/08465371241231573.
[23]
VEMURU S, HUANG J, COLBORN K, et al. Clinical implications of receptor conversions in breast cancer patients who have undergone neoadjuvant chemotherapy[J]. Breast Cancer Res Treat, 2023, 200(2): 247-256. DOI: 10.1007/s10549-023-06978-0.
[24]
LIU H Q, LIN S Y, SONG Y D, et al. Machine learning on MRI radiomic features: identification of molecular subtype alteration in breast cancer after neoadjuvant therapy[J]. Eur Radiol, 2023, 33(4): 2965-2974. DOI: 10.1007/s00330-022-09264-7.
[25]
ZHENG S Y, YANG Z H, DU G Z, et al. Discrimination between HER2-overexpressing, -low-expressing, and -zero-expressing statuses in breast cancer using multiparametric MRI-based radiomics[J/OL]. Eur Radiol, 2024 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38363315/. DOI: 10.1007/s00330-024-10641-7.
[26]
YU Y M, WANG Z B, WANG Q, et al. Radiomic model based on magnetic resonance imaging for predicting pathological complete response after neoadjuvant chemotherapy in breast cancer patients[J/OL]. Front Oncol, 2023, 13: 1249339 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38357424/. DOI: 10.3389/fonc.2023.1249339.
[27]
LI Q, XIAO Q, LI J W, et al. Value of machine learning with multiphases CE-MRI radiomics for early prediction of pathological complete response to neoadjuvant therapy in HER2-positive invasive breast cancer[J/OL]. Cancer Manag Res, 2021, 13: 5053-5062 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/34234550/. DOI: 10.2147/CMAR.S304547.
[28]
PARK J, KIM M J, YOON J H, et al. Machine learning predicts pathologic complete response to neoadjuvant chemotherapy for ER+HER2- breast cancer: integrating tumoral and peritumoral MRI radiomic features[J/OL]. Diagnostics, 2023, 13(19): 3031 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37835774/. DOI: 10.3390/diagnostics13193031.
[29]
XU H M, DAI Y, MA Y Z, et al. MR T1WI intratumoral and peritumoral radiomics combined with clinical features for predicting effect of neoadjuvant chemotherapy for breast cancer[J]. Chin J Med Imag Technol, 2023, 39(10): 1520-1525. DOI: 10.13929/j.issn.1003-3289.2023.10.016.
[30]
ZHENG G Y, PENG J X, SHU Z Y, et al. Predicting pathological complete response to neoadjuvant chemotherapy in breast cancer patients: use of MRI radiomics data from three regions with multiple machine learning algorithms[J/OL]. J Cancer Res Clin Oncol, 2024, 150(3): 147 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38512406/. DOI: 10.1007/s00432-024-05680-y.
[31]
GUO L C, DU S Y, GAO S, et al. Delta-radiomics based on dynamic contrast-enhanced MRI predicts pathologic complete response in breast cancer patients treated with neoadjuvant chemotherapy[J/OL]. Cancers, 2022, 14(14): 3515 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/35884576/. DOI: 10.3390/cancers14143515.
[32]
YU Y L, WANG X, ZHA X M, et al. Whole volume ROI radiomics analysis of mass-like breast cancer based on pretreatment ADC images for the prediction of pathological complete response to neoadjuvant chemotherapy[J]. Radiol Pract, 2022, 37(8): 987-994. DOI: 10.13609/j.cnki.1000-0313.2022.08.012.
[33]
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 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37432083/. DOI: 10.1148/radiol.222830.
[34]
HWANG K P, ELSHAFEEY N A, KOTROTSOU A, et al. A radiomics model based on synthetic MRI acquisition for predicting neoadjuvant systemic treatment response in triple-negative breast cancer[J/OL]. Radiol Imaging Cancer, 2023, 5(4): e230009 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37505106/. DOI: 10.1148/rycan.230009.
[35]
WANG Y X, SHANG Y Y, GUO Y X, et al. Value of DCE-MRI based radiomics features for prediction of axillary lymph node metastasis in breast carcinoma[J]. Chin J Magn Reson Imag, 2023, 14(3): 21-27. DOI: 10.12015/issn.1674-8034.2023.03.005.
[36]
LI L, YU T, SUN J Q, et al. Prediction of the number of metastatic axillary lymph nodes in breast cancer by radiomic signature based on dynamic contrast-enhanced MRI[J]. Acta Radiol, 2022, 63(8): 1014-1022. DOI: 10.1177/02841851211025857.
[37]
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.
[38]
ZHAN C N, HU Y Q, WANG X R, et al. Prediction of Axillary Lymph Node Metastasis in Breast Cancer using Intra-peritumoral Textural Transition Analysis based on Dynamic Contrast-enhanced Magnetic Resonance Imaging[J/OL]. Acad Radiol, 2022, 29(Suppl 1): S107-S115 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/33712393/. DOI: 10.1016/j.acra.2021.02.008.
[39]
LIN G H, CHEN W Y, FAN Y Y, et al. Machine learning radiomics-based prediction of non-sentinel lymph node metastasis in Chinese breast cancer patients with 1-2 positive sentinel lymph nodes: a multicenter study[J/OL]. Acad Radiol, 2024: S1076-S6332(24)00080-1 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38490840/. DOI: 10.1016/j.acra.2024.02.010.
[40]
HARAGUCHI T, KOBAYASHI Y, HIRAHARA D, et al. Radiomics model of diffusion-weighted whole-body imaging with background signal suppression (DWIBS) for predicting axillary lymph node status in breast cancer[J]. J Xray Sci Technol, 2023, 31(3): 627-640. DOI: 10.3233/XST-230009.
[41]
SONG S E, WOO O H, CHO Y, et al. Prediction of axillary lymph node metastasis in early-stage triple-negative breast cancer using multiparametric and radiomic features of breast MRI[J/OL]. Acad Radiol, 2023, 30(Suppl 2): S25-S37 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37331865/. DOI: 10.1016/j.acra.2023.05.025.
[42]
CHEN Y S, LI J P, ZHANG J, et al. Radiomic nomogram for predicting axillary lymph node metastasis in patients with breast cancer[J]. Acad Radiol, 2024, 31(3): 788-799. DOI: 10.1016/j.acra.2023.10.026.
[43]
ZHANG B Y, YU Y M, MAO Y, et al. Development of MRI-based deep learning signature for prediction of axillary response after NAC in breast cancer[J]. Acad Radiol, 2024, 31(3): 800-811. DOI: 10.1016/j.acra.2023.10.004.
[44]
ZHANG L, HUANG X H, SHEN M Y, et al. The value of radiomics models based on different machine learning in predicting ki-67 expression in invasive breast cancer[J]. Chin Comput Med Imag, 2024, 30(1): 39-44. DOI: 10.19627/j.cnki.cn31-1700/th.2024.01.016.
[45]
MING J, CHEN Y, LIU Y, et al. Value of preoperative prediction of Ki-67 expression in breast cancer based on DCE-MRI intratumoral combined with peritumoral radiomics model[J]. Chin J Magn Reson Imag, 2022, 13(10): 132-137, 149. DOI: 10.12015/issn.1674-8034.2022.10.020.
[46]
LIU X D, WANG X Y, NING G. MRI radiomics for preoperative predicting Ki-67 expression of breast invasive ductal carcinoma[J]. Chin J Med Imag Technol, 2022, 38(2): 210-214. DOI: 10.13929/j.issn.1003-3289.2022.02.012.
[47]
FENG S Q, YIN J D. Radiomics of dynamic contrast-enhanced magnetic resonance imaging parametric maps and apparent diffusion coefficient maps to predict Ki-67 status in breast cancer[J/OL]. Front Oncol, 2022, 12: 847880 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/36895526/. DOI: 10.3389/fonc.2022.847880.
[48]
ZHANG L, SHEN M Y, ZHANG D Y, et al. Radiomics nomogram based on dual-sequence MRI for assessing ki-67 expression in breast cancer[J/OL]. J Magn Reson Imaging, 2023 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/38088478/. DOI: 10.1002/jmri.29149.
[49]
TABNAK P, HAJIESMAILPOOR Z, BARADARAN B, et al. MRI-based radiomics methods for predicting ki-67 expression in breast cancer: a systematic review and meta-analysis[J]. Acad Radiol, 2024, 31(3): 763-787. DOI: 10.1016/j.acra.2023.10.010.
[50]
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 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/34890936/. DOI: 10.1016/j.ejrad.2021.110095.
[51]
LEE J, KIM S H, KIM Y, et al. Radiomics nomogram: prediction of 2-year disease-free survival in young age breast cancer[J/OL]. Cancers, 2022, 14(18): 4461 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/36139620/. DOI: 10.3390/cancers14184461.
[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 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/35454768/. DOI: 10.3390/cancers14081858.
[53]
PARK G E, KIM S H, LEE E B, et al. Ipsilateral recurrence of DCIS in relation to radiomics features on contrast enhanced breast MRI[J]. Tomography, 2022, 8(2): 596-606. DOI: 10.3390/tomography8020049.
[54]
YU Y F, REN W, HE Z F, et al. Machine learning radiomics of magnetic resonance imaging predicts recurrence-free survival after surgery and correlation of LncRNAs in patients with breast cancer: a multicenter cohort study[J/OL]. Breast Cancer Res, 2023, 25(1): 132 [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/37915093/. DOI: 10.1186/s13058-023-01688-3.
[55]
CUI Y J, FAN M, LI L H. Prediction of Oncotype DX RS in breast cancer by integrating of DCE-MRI radiomics and clinicopathologic data[J]. J Hangzhou Dianzi Univ Nat Sci, 2022, 42(1): 67-73. DOI: 10.13954/j.cnki.hdu.2022.01.011.
[56]
CHEN Y, TANG W, LIU W, et al. Multiparametric MR imaging radiomics signatures for assessing the recurrence risk of ER+/HER2- breast cancer quantified with 21-gene recurrence score[J]. J Magn Reson Imaging, 2023, 58(2): 444-453. DOI: 10.1002/jmri.28547.
[57]
PAQUIER Z, CHAO S L, ACQUISTO A, et al. Radiomics software comparison using digital phantom and patient data: IBSI-compliance does not guarantee concordance of feature values[J/OL]. Biomed Phys Eng Express, 2022, 8(6) [2024-04-01]. https://pubmed.ncbi.nlm.nih.gov/36049399/. DOI: 10.1088/2057-1976/ac8e6f.

PREV Research progress of MRI in the evaluation of neoadjuvant chemotherapy efficacy for triple-negative breast cancer
NEXT Application status of tumor regression grading method after neoadjuvant chemoradiotherapy based on magnetic resonance imaging for locally advanced rectal cancer
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn