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• Clinical Article •
3D-MIP reconstruction and multi parameter evaluation of BI-RADS 4 breast tumors based on DCE-MRI
LIANG Hongbing  NING Ning  ZHAO Siqi  LI Yuanfei  WU Yueqi  SONG Qingwei  YANG Jie  GAO Xue  ZHANG Moyun  ZHANG Lina 

Cite this article as LIANG H B, NING N, ZHAO S Q, et al. 3D-MIP reconstruction and multi parameter evaluation of BI-RADS 4 breast tumors based on DCE-MRI[J]. Chin J Magn Reson Imaging, 2024, 15(5): 94-101. DOI:10.12015/issn.1674-8034.2024.05.016.


[Abstract] Objective To ascertain whether dynamic contrast-enhancement MRI (DCE-MRI) is a useful diagnostic tool for intratumoral and peritumoral vascular features in breast imaging reporting and data system (BI-RADS) 4 of tumors.Materials and Methods A retrospective collection of 102 female cases with BI-RADS4 breast MRI examination and clear pathological results from August 2018 to March 2023 at the First Affiliated Hospital of Dalian Medical University, 43 cases were benign group and 59 cases were breast malignant group. Record the patient's age, maximum lesion diameter (dmax), and basic imaging features of breast DCE-MRI, as well as peritumor vascular characteristics and intratumor hemodynamic parameters. Differences in several parameters between the two groups were analyzed by univariate and multivariate Logit models. The diagnostic efficacy of combining various peritumor vascular characteristic indexes and intratumor parameter values to differentiate between benign and malignant BI-RADS4 lesions in the breast was analysed using the receiver operating characteristic, (ROC) curves and the area under the curve (AUC). Evaluate AUC with the DeLong test.Results There were statistically significant differences in number of adjacent vascular signs (AVS), maximum diameter (dmax) of peritumoral blood vessels, the difference in diameter of blood vessels around the tumor between the affected and healthy sides (∆d), peritumoral vascular appearance phase, volume transfer constant (Ktrans), flux rate constant (Kep), maximum slope of enhancement (MSI), type of time signal intensity curve (TIC), background parenchymal enhancement (BPE) and fibrous glandular tissue (FGT) between the two groups of benign and malignant breast cases (P<0.05), while there was no statistically significant difference between signal enhancement ratio (SER) and volume fraction of extracellular space (Ve) (P>0.05). ∆d, dmax, MSI and Ktrans were independent factors that affected how the two groups differentiated from one another, according to multiple logistic regression analysis, with MSI values having the largest predominance ratio (AUC of 0.923). Comparing peritumor vascular characteristic indexes (∆d) with dmax, MSI, and Ktrans, the combined model of ∆d and MSI showed the highest diagnostic performance (AUC value of 0.933, sensitivity and specificity of 93.2% and 83.7%, respectively), and the difference between ∆d combined with MSI and ∆d combined with Ktrans was statistically significant (P=0.001); When comparing additional joint indicators pairwise, there was no statistically significant difference (P>0.05). The joint model performed better diagnostically than the individual MSI model.Conclusions The combination of peritumoral vascular characteristic indexes (∆d) and intratumoral semi quantitative parameters (MSI) has high application value in identifying benign and malignant breast lesions in BI-RADS4.
[Keywords] breast tumors；dynamic contrast-enhancement；peritumoral blood vessels；maximum intensity projection；magnetic resonance imaging

LIANG Hongbing1   NING Ning1   ZHAO Siqi1   LI Yuanfei1   WU Yueqi1   SONG Qingwei1   YANG Jie2   GAO Xue3   ZHANG Moyun1   ZHANG Lina1*  

1 Department of Radiology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

2 School of Public Health, Dalian Medical University, Dalian 116044, China

3 Department of Pathology, First Affiliated Hospital of Dalian Medical University, Dalian 116011, China

Corresponding author: ZHANG L N, E-mail: zln201045@163.com

Conflicts of interest   None.

Received  2024-01-22
Accepted  2024-04-23
DOI: 10.12015/issn.1674-8034.2024.05.016
Cite this article as LIANG H B, NING N, ZHAO S Q, et al. 3D-MIP reconstruction and multi parameter evaluation of BI-RADS 4 breast tumors based on DCE-MRI[J]. Chin J Magn Reson Imaging, 2024, 15(5): 94-101. DOI:10.12015/issn.1674-8034.2024.05.016.

[1]
WILKINSON L, GATHANI T. Understanding breast cancer as a global health concern[J/OL]. Br J Radiol, 2022, 95(1130): 20211033 [2024-01-12]. https://pubmed.ncbi.nlm.nih.gov/34905391/. DOI: 10.1259/bjr.20211033.
[2]
HONDA M, KATAOKA M, KAWAGUCHI K, et al. Subcategory classifications of Breast Imaging and Data System (BI-RADS) category 4 lesions on MRI[J]. Jpn J Radiol, 2021, 39(1): 56-65. DOI: 10.1007/s11604-020-01029-w.
[3]
DE ALMEIDA J R, GOMES A B, BARROS T P, et al. Predictive performance of BI-RADS magnetic resonance imaging descriptors in the context of suspicious (category 4) findings[J]. Radiol Bras, 2016, 49(3): 137-143. DOI: 10.1590/0100-3984.2015.0021.
[4]
FUJIWARA K, YAMADA T, KANEMAKI Y, et al. Grading system to categorize breast MRI in BI-RADS 5th edition: a multivariate study of breast mass descriptors in terms of probability of malignancy[J/OL]. AJR Am J Roentgenol, 2018, 210(3): W118-W127 [2024-01-12]. https://pubmed.ncbi.nlm.nih.gov/29381382/. DOI: 10.2214/AJR.17.17926.
[5]
SHIN S R, KO E Y, HAN B K, et al. Benign adenomyoepithelioma of the breast: imaging characteristics[J]. J Korean Soc Radiol, 2023, 84(2): 398-408. DOI: 10.3348/jksr.2022.0021.
[6]
LEITHNER D, WENGERT G, HELBICH T, et al. MRI in the Assessment of BI-RADS® 4 lesions[J]. Top Magn Reson Imaging, 2017, 26(5): 191-199. DOI: 10.1097/RMR.0000000000000138.
[7]
XING B Y, CHEN X Y, WANG Y L, et al. Evaluating breast ultrasound S-detect image analysis for small focal breast lesions[J/OL]. Front Oncol, 2022, 12: 1030624 [2024-01-12]. https://pubmed.ncbi.nlm.nih.gov/36582786/. DOI: 10.3389/fonc.2022.1030624.
[8]
JIN J, HE W, YU T F, et al. Application value of S-Detect combined with contrast-enhanced ultrasound in the Breast Imaging Reporting and Data System 4 breast lesions[J]. Chin J Ultrason, 2023, 32(5): 392-398. DOI: 10.3760/cma.j.cn131148-20221020-00709.
[9]
HONDA M, KATAOKA M, ONISHI N, et al. New parameters of ultrafast dynamic contrast-enhanced breast MRI using compressed sensing[J]. J Magn Reson Imaging, 2020, 51(1): 164-174. DOI: 10.1002/jmri.26838.
[10]
VARGHESE B A, LEE S, CEN S, et al. Characterizing breast masses using an integrative framework of machine learning and CEUS-based radiomics[J]. J Ultrasound, 2022, 25(3): 699-708. DOI: 10.1007/s40477-021-00651-2.
[11]
CUI Q, SUN L, ZHANG Y, et al. Value of breast MRI omics features and clinical characteristics in Breast Imaging Reporting and Data System (BI-RADS) category 4 breast lesions: an analysis of radiomics-based diagnosis[J/OL]. Ann Transl Med, 2021, 9(22): 1677. [2024-01-12]. https://pubmed.ncbi.nlm.nih.gov/34988186/. DOI: 10.21037/atm-21-5441.
[12]
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 [2024-01-12]. https://pubmed.ncbi.nlm.nih.gov/37052738/. DOI: 10.1186/s13244-023-01404-x.
[13]
SPAK D A, PLAXCO J S, SANTIAGO L, et al. BI-RADS® fifth edition: a summary of changes[J]. Diagn Interv Imaging, 2017, 98(3): 179-190. DOI: 10.1016/j.diii.2017.01.001.
[14]
WU Q, WANG Z, NING N, et al. Differential diagnostic value of IVIM combining with dynamic enhanced MRI in non-mass enhancement adenosis and breast cancer[J]. Chin J Magn Reson Imag, 2023, 14(2): 37-43, 49. DOI: 10.12015/issn.1674-8034.2023.02.007.
[15]
EDWARDS S D, LIPSON J A, IKEDA D M, et al. Updates and revisions to the BI-RADS magnetic resonance imaging lexicon[J]. Magn Reson Imaging Clin N Am, 2013, 21(3): 483-493. DOI: 10.1016/j.mric.2013.02.005.
[16]
KUL S, CANSU A, ALHAN E, et al. Contrast-enhanced MR angiography of the breast: evaluation of ipsilateral increased vascularity and adjacent vessel sign in the characterization of breast lesions[J]. AJR Am J Roentgenol, 2010, 195(5): 1250-1254. DOI: 10.2214/AJR.10.4368.
[17]
SARDANELLI F, IOZZELLI A, FAUSTO A, et al. Gadobenate dimeglumine-enhanced MR imaging breast vascular maps: association between invasive cancer and ipsilateral increased vascularity[J]. Radiology, 2005, 235(3): 791-797. DOI: 10.1148/radiol.2353040733.
[18]
ÇETINKAYA E, YıLDıZ Ş, OTÇU H, et al. The value of adjacent vessel sign in malignant breast tumors[J]. Diagn Interv Radiol, 2022, 28(5): 463-469. DOI: 10.5152/dir.2022.211228.
[19]
LIU D D, BA Z G, GAO Y, et al. Subcategorization of suspicious non-mass-like enhancement lesions(BI-RADS-MRI Category4)[J/OL]. BMC Med Imaging, 2023, 23(1): 182 [2024-01-12]. https://pubmed.ncbi.nlm.nih.gov/37950164/. DOI: 10.1186/s12880-023-01144-w.
[20]
LEI M F, DENG H L, JIN H K, et al. The value of ipsilateral breast vascularity in differential diagnosis of benign and\r malignant breast lesions by MRI[J]. Chin J CT MRI, 2020, 18(10): 70-73. DOI: 10.3969/j.issn.1672-5131.2020.10.021.
[21]
KIM J J, KIM J Y, HWANGBO L, et al. Ultrafast dynamic contrast-enhanced MRI using compressed sensing: associations of early kinetic parameters with prognostic factors of breast cancer[J]. AJR Am J Roentgenol, 2021, 217(1): 56-63. DOI: 10.2214/AJR.20.23457.
[22]
SCHULZE A K, HOSKIN T L, MOLDOVEANU D, et al. Tumor characteristics of bilateral breast cancer compared with unilateral breast cancer[J]. Ann Surg Oncol, 2024, 31(2): 947-956. DOI: 10.1245/s10434-023-14451-x.
[23]
HOU W Q. Evaluation of breast arterial vascular differences in patients with breast cancer by color ultrasound[J]. Mod Med Imageology, 2021, 30(11): 2137-2139. DOI: 10.3969/j.issn.1006-7035.2021.11.048.
[24]
KATAOKA M, HONDA M, OHASHI A, et al. Ultrafast dynamic contrast-enhanced MRI of the breast: how is it used?[J]. Magn Reson Med Sci, 2022, 21(1): 83-94. DOI: 10.2463/mrms.rev.2021-0157.
[25]
ONISHI N, KATAOKA M, KANAO S, et al. Ultrafast dynamic contrast-enhanced MRI of the breast using compressed sensing: breast cancer diagnosis based on separate visualization of breast arteries and veins[J]. J Magn Reson Imaging, 2018, 47(1): 97-104. DOI: 10.1002/jmri.25747.
[26]
TSAI W C, CHANG K M, KAO K J. Dynamic contrast enhanced MRI and intravoxel incoherent motion to identify molecular subtypes of breast cancer with different vascular normalization gene expression[J]. Korean J Radiol, 2021, 22(7): 1021-1033. DOI: 10.3348/kjr.2020.0760.
[27]
CHANG L, LAN H. Effect of Neoadjuvant Chemotherapy on Angiogenesis and Cell Proliferation of Breast Cancer Evaluated by Dynamic Enhanced Magnetic Resonance Imaging[J/OL]. Biomed Res Int, 2022, 3156093 [2024-01-12]. https://pubmed.ncbi.nlm.nih.gov/35915805/. DOI: 10.1155/2022/3156093.
[28]
ZHANG Q, SPINCEMAILLE P, DROTMAN M, et al. Quantitative transport mapping (QTM) for differentiating benign and malignant breast lesion: Comparison with traditional kinetics modeling and semi-quantitative enhancement curve characteristics[J/OL]. Magn Reson Imaging, 2022, 86: 86-93 [2024-01-12]. https://pubmed.ncbi.nlm.nih.gov/34748928/. DOI: 10.1016/j.mri.2021.10.039
[29]
XIAO J, RAHBAR H, HIPPE D S, et al. Dynamic contrast-enhanced breast MRI features correlate with invasive breast cancer angiogenesis[J/OL]. NPJ Breast Cancer, 2021, 7(1): 42 [2024-01-12]. https://pubmed.ncbi.nlm.nih.gov/33863924/. DOI: 10.1038/s41523-021-00247-3.
[30]
ZHANG H, WANG Y. The value of diagnosis of suspicious malignant breast lesions by magnetic resonance diffusion combined with perfusion imaging technique[J]. Chin J Magn Reson Imag, 2018, 9(4): 265-269. DOI: 10.12015/issn.1674-8034.2018.04.005.
[31]
AO F, YAN Y, ZHANG Z L, et al. The value of dynamic contrast-enhanced magnetic resonance imaging combined with apparent diffusion coefficient in the differentiation of benign and malignant diseases of the breast[J]. Acta Radiol, 2022, 63(7): 891-900. DOI: 10.1177/02841851211024002.
[32]
THAWANI R, GAO L, MOHINANI A, et al. Quantitative DCE-MRI prediction of breast cancer recurrence following neoadjuvant chemotherapy: a preliminary study[J/OL]. BMC Med Imaging, 2022, 22(1):182 [2024-01-12]. https://pubmed.ncbi.nlm.nih.gov/36266631/. DOI: 10.1186/s12880-022-00908-0
[33]
WANG J, JIANG G B, XIE X J. The value of IVIM and DCE-MRI semi-quantitative parameters in differentiating luminal epithelial types of breast cancers[J]. J Pract Radiol, 2022, 38(10): 1617-1620. DOI: 10.3969/j.issn.1002-1671.2022.10.013.
[34]
LIU H L, ZONG M, WEI H, et al. Differentiation between malignant and benign breast masses: combination of semi-quantitative analysis on DCE-MRI and histogram analysis of ADC maps[J]. Clin Radiol, 2018, 73(5): 460-466. DOI: 10.1016/j.crad.2017.11.026.
[35]
CHANG X, LU T, HUANG J C. Preliminary study on microvasculature normalization induced by peritumoral electroacupuncture in mice with breast cancer xenografts[J]. J Sichuan Univ Med Sci, 2023, 54(5): 972-977. DOI: 10.12182/20230960401.
[36]
MILOSEVIC V, EDELMANN R J, WINGE I, et al. Vessel size as a marker of survival in estrogen receptor positive breast cancer[J]. Breast Cancer Res Treat, 2023, 200(2): 293-304. DOI: 10.1007/s10549-023-06974-4.

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