Predictive value of intravoxel incoherent motion combined with diffusion kurtosis imaging for breast cancer axillary lymph node metastasis: a retrospective study
Restricted accessResearch articleFirst published online March, 2023
Predictive value of intravoxel incoherent motion combined with diffusion kurtosis imaging for breast cancer axillary lymph node metastasis: a retrospective study
Non-invasive imaging technologies for assessing axillary lymph node (ALN) metastasis of breast cancer are needed in clinical practice.
Purpose
To explore the clinical value of intravoxel incoherent motion (IVIM) and diffusional kurtosis imaging (DKI) for predicting ALN metastasis of breast cancer.
Material and Methods
A total of 194 patients with pathologically confirmed breast cancer who underwent IVIM and DKI examination were reviewed retrospectively. The IVIM derived parameters of D, D*, and f and DKI-derived parameters of MD and MK were measured. The independent samples t-test was used to compare the parameters between the ALN metastasis and non-ALN metastasis groups. Receiver operating characteristic (ROC) curve analysis was also performed.
Results
The D and MD in the ALN metastasis group were significantly lower than those in the non-ALN metastasis group (P < 0.001, P < 0.001). The D*, f, and MK were higher in the ALN metastasis group than in the non-ALN metastasis group (P = 0.015, P= 0.014, and P= 0.001, respectively). The area under the ROC curve (AUC) of D (0.768) was highest. In addition, the diagnostic efficiency of both IVIM and DKI were higher than that of the conventional MRI (P = 0.002, P = 0.048). The diagnostic efficiency of IVIM + DKI were higher than that of the IVIM or DKI alone (P = 0.021, P = 0.004).
Conclusion
IVIM and DKI can be used for predicting breast cancer ALN metastasis with D as the most meaningful parameter.
GipponiMBassettiCCanaveseG, et al.Sentinel lymph node as a new marker for therapeutic planning in breast cancer patients. J Surg Oncol2004;85:102–111.
2.
PeintingerFReitsamerRSmidtML, et al.Lymph nodes in breast cancer-what can we learn from translational research?Breast Care (Basel)2018;13:342–347.
3.
ChangJMLeungJWTMoyL, et al.Axillary nodal evaluation in breast cancer: state of the art. Radiology2020;295:500–515.
4.
MaeseeleNFaesJVan de PutteT, et al.Axillary lymph node dissection on the run?Facts Views Vis Obgyn2017;9:45–49.
5.
ChoiEJYoukJHChoiH, et al.Dynamic contrast-enhanced and diffffusion weighted MRI of invasive breast cancer for the prediction of sentinel lymph node status. J Magn Reson Imaging2020;51:615–626.
6.
AhmedMPurushothamADDouekM. Novel techniques for sentinel lymph node biopsy in breast cancer: a systematic review. Lancet Oncol2014;15:e351–e362.
7.
BaruahBPGoyalAYoungP, et al.Axillary node staging by ultrasonography and fine-needle aspiration cytology in patients with breast cancer. Br J Surg2010;97:680–683.
8.
DietzelMBaltzerPAVagT, et al.Application of breast MRI for prediction of lymph node metastases-systematic approach using 17 individual descriptors and a dedicated decision tree. Acta Radiol2010;51:885–894.
9.
DietzelMBaltzerPAVagT, et al.The necrosis sign in magnetic resonance-mammography: diagnostic accuracy in 1,084 histologically verified breast lesions. Breast J2010;16:603–608.
10.
GuoQDongZZhangL, et al.Ultrasound features of breast cancer for predicting axillary lymph node metastasis. J Ultrasound Med2018;37:1354–1353.
11.
GuvencIWhitmanGJLiuP, et al.Diffusion-weighted MR imaging increases diagnostic accuracy of breast MR imaging for predicting axillary metastases in breast cancer patients. Breast J2019;25:47–55.
12.
SinghVSaundersCWylieL, et al.New diagnostic techniques for breast cancer detection. Future Oncol2008;4:501–513.
13.
LowesSLeaverACoxK, et al.Evolving imaging techniques for staging axillary lymph nodes in breast cancer. Clin Radiol2018;73:396–409.
14.
PinkerKMoyLSuttonEJ, et al.Diffusion-weighted imaging with apparent diffusion coefficient mapping for breast cancer detection as a stand-alone parameter: comparison with dynamic contrast-enhanced and multiparametric magnetic resonance imaging. Invest Radiol2018;53:587–595.
15.
CoudertHMirafzalSDissardA, et al.Multiparametric magnetic resonance imaging of parotid tumors: a systematic review. Diagn Interv Imaging2021;102:121–130.
16.
DorriusMDDijkstraHOudkerkM, et al.Effect of b value and pre-admission of contrast on diagnostic accuracy of 1.5-T breast DWI: a systematic review and meta-analysis. Eur Radiol2014;24:2835–2847.
17.
ZhangQOuyangHYeF, et al.Feasibility of intravoxel incoherent motion diffusion-weighted imaging in distinguishing adenocarcinoma originated from uterine corpus or cervix. Abdom Radiol (NY)2021;46:732–744.
18.
PartridgeSCMcDonaldES. Diffusion weighted magnetic resonance imaging of the breast: protocol optimization, interpretation, and clinical applications. Magn Reson Imaging Clin N Am2013;21:601–624.
19.
Thomassin-NaggaraIDe BazelaireCChopierJ, et al.Diffusion-weighted MR imaging of the breast: advantages and pitfalls. Eur J Radiol2013;82:435–443.
20.
BrandãoACLehmanCDPartridgeSC. Breast magnetic resonance imaging: diffusion-weighted imaging. Magn Reson Imaging Clin N Am2013;21:321–336.
21.
LiuCLiangCLiuZ, et al.Intravoxel incoherent motion (IVIM) in evaluation of breast lesions: comparison with conventional DWI. Eur J Radiol2013;82:e782–e789.
22.
GuoY-LLiS-JZhangZ-P, et al.Parameters of diffusional kurtosis imaging for the diagnosis of acute cerebral infarction in different brain regions. Exp Ther Med2016;12:933–938.
23.
EngelJEckelRKerrJ, et al.The process of metastasisation for breast cancer. Eur J Cancer2003;39:1794–1806.
24.
CutuliBVeltenMMartinC. Assessment of axillary lymph node involvement in small breast cancer: analysis of 893 cases. Clin Breast Cancer2001;2:59–65, discussion 66.
25.
DongYFengQYangW, et al.Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI. Eur Radiol2018;28:582–591.
26.
CarrieroADi CredicoAMansourM, et al.Maximum intensity projection analysis in magnetic resonance of the breastJ Exp Clin Cancer Res2002;21:77–81.
27.
ColpaertCVermeulenPvan BeestP, et al.Intratumoral hypoxia resulting in the presence of a fibrotic focus is an independent predictor of early distant relapse in lymph node-negative breast cancer patients. Histopathology2001;39:416–425.
28.
NarisadaHAokiTSasaguriT, et al.Correlation between numeric gadolinium-enhanced dynamic MRI ratios and prognostic factors and histologic type of breast carcinoma. AJR Am J Roentgenol2006;187:297–306.
29.
YuanJWongOLLoGG, et al.Statistical assessment of bi-exponential diffusion weighted imaging signal characteristics induced by intravoxel incoherent motion in malignant breast tumors. Quant Imaging Med Surg2016;6:418–429.
30.
LeeYJKimSHKangBJ, et al.Intravoxel incoherent motion (IVIM)-derived parameters in diffusion-weighted MRI: associations with prognostic factors in invasive ductal carcinoma. J Magn Reson Imaging2017;45:1394–1406.
31.
LiuYYinZLiX, et al.The diagnostic accuracy of intravoxel incoherent motion and diffusion kurtosis imaging in the differentiation of malignant and benign soft-tissue masses: which is better?Acta Radiol2022;63:785–793.
32.
ZhaoMWuQGuoL, et al.Magnetic resonance imaging features for predicting axillary lymph node metastasis in patients with breast cancer. Eur J Radiol2020;129:109093.
33.
SeyfriedTNHuysentruytLC. On the origin of cancer metastasis. Crit Rev Oncog2013;18:43–73.
34.
CarmelietPJainRK. Angiogenesis in cancer and other diseases. Nature2000;407:249–257.
35.
ZhaoMFuKZhangL, et al.Intravoxel incoherent motion magnetic resonance imaging for breast cancer: a comparison with benign lesions and evaluation of heterogeneity in different tumor regions with prognostic factors and molecular classification. Oncol Lett2018;16:5100–5112.
36.
MengNWangXJSunJ, et al.Comparative study of amide proton transfer-weighted imaging and intravoxel incoherent motion imaging in breast cancer diagnosis and evaluation. J Magn Reson Imaging2020;52:1175–1186.
37.
LiZLiXPengC, et al.The diagnostic performance of diffusion kurtosis imaging in the characterization of breast tumors: a meta-analysis. Front Oncol2020;10:575272.
38.
HuangYLinYHuW, et al.Diffusion kurtosis at 3.0T as an in vivo imaging marker for breast cancer characterization: correlation with prognostic factors. J Magn Reson Imaging2019;49:845–856.
39.
UllahIKarthikG-MAlkodsiA, et al.Evolutionary history of metastatic breast cancer reveals minimal seeding from axillary lymph nodes. J Clin Invest2018;128:1355–1370.
40.
SuoSChengFCaoM, et al.Multiparametric diffusion-weighted imaging in breast lesions: Association with pathologic diagnosis and prognostic factors. J Magn Reson Imaging2017;46:740–750.
