Study on the value of mDixon-Quant technique in the diagnosis and prognosis evaluation of invasive ductal breast cancer

Share this content in WeChat

• Clinical Article •

WANG Zhuo WU Qi NING Ning LIANG Hongbing TIAN Jiahe ZHANG Lina SONG Qingwei

Cite this article as: WANG Z, WU Q, NING N, et al. Study on the value of mDixon-Quant technique in the diagnosis and prognosis evaluation of invasive ductal breast cancer[J]. Chin J Magn Reson Imaging, 2023, 14(3): 65-71. DOI:10.12015/issn.1674-8034.2023.03.012.

Abstract

[Abstract] Objective To explore the correlation of FF and T2^* values derived from mDixon-Quant imaging with pathological histological grade and prognostic factors of invasive breast carcinoma of the breast.Materials and Methods A total of 88 cases of invasive ductal carcinoma of the breast confirmed by pathology were retrospective analyzed (57 low-grade cases and 31 high-grade cases). The mDixon-Quant scanning was performed before operation, the FF, T2^* values and basic clinical information including with age, menopausal status, tumor size, tumor type, body mass index (BMI), amount of fibroglandular tissue (FGT) and background parenchymal enhancement (BPE) between the two groups. The FF and T2^* values were compared with some prognostic pathological features such as histological grade, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER-2), Ki-67 and molecular typing of breast cancer. The Mann-Whitney U test or t test was used for continuous variables; R×C χ² test was used for categorical variables; Spearman rank correlation test was used for correlation test. The Receiving operator characteristic (ROC) curve was plotted and the area under curve (AUC) was calculated to compare the diagnostic performance.Results Finally, 88 lesions of 88 patients with invasive ductal carcinoma of the breast (mean age 48.93±9.49, range 31-68 years) were included, and FF and T2^* values were measured with good inter- and intra-observer agreement with intra-class correlation coefficients (ICC) of 0.964 and 0.909, respectively. The study found that there was no statistical significance between the two groups in age, menopause situation, tumor size, maximum diameter of the lesion, tumor type, FGT, and BPE of breast parenchyma. Both FF and T2^* values were statistically significant between the two groups of different histological grades (P＜0.001). Compared with the low-grade group, the high-grade group had a smaller FF value (5.06%±2.56% vs. 8.33%±3.92%). FF value was negatively correlated with histological grade (r=-0.406, P＜0.001).The T2^* value of the high-grade group was higher than that of the low-grade group [(29.94±8.55) ms vs. (22.85±5.39) ms]. T2^* value was positively correlated with histological grade (r=0.397, P＜0.001). The AUC was 0.738 and 0.748 for FF value and T2^* value to predict histological grade, respectively. The combined diagnostic AUC of FF and T2^* values was 0.797, but there was no statistically significant difference with the diagnostic performance of a single parameter. There was a statistically significant difference in FF values in the distribution between different ER and molecular typing groups (P=0.038 and 0.005). FF values were positively correlated with ER (P=0.037) but not with PR, Ki-67, HER-2 and molecular typing (all P＞0.05). T2^* values were statistically significantly different between ER, PR, Ki-67 and molecular typing groups (all P＜0.05). In addition, it was negatively correlated with ER and PR (P＜0.001 and P=0.003), and positively correlated with Ki-67 and molecular typing (all P＜0.001).Conclusions FF and T2^* values derived from mDixon-Quant were independent predictors of histological grade of invasive ductal carcinoma of the breast, and the combination of the two parameter values helped to improve the specificity of the diagnosis and correlated with prognostic pathological features.

[Keywords] breast cancer；invasive ductal carcinoma；histological grade；molecular typing；fat quantification；mDixon-Quant；magnetic resonance imaging

Contributor Information

WANG Zhuo¹ WU Qi¹ NING Ning¹ LIANG Hongbing¹ TIAN Jiahe² ZHANG Lina^1* SONG Qingwei¹

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

² Zhongshan College of Dalian Medical University, Dalian 116085, China

Corresponding author: Zhang LN, E-mail: zln201045@163.com

Conflicts of interest None.

Publication Information

ACKNOWLEDGMENTS 2022 Liaoning Adult Education Association Continuing Education Teaching Reform Research Project (No. LCYJGZXYB22100); 2022 General Project of "Peak Climbing Plan" of Dalian City Key Specialty of Medicine (No. 2022DF042); 2021 Dalian Medical University Teaching Reform Research General Project (No. DYLX21036).

Received 2022-10-18

Accepted 2023-02-28

DOI: 10.12015/issn.1674-8034.2023.03.012

Text

References

[1]

BRAY F, FERLAY J, SOERJOMATARAM I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2018, 68(6): 394-424. DOI: 10.3322/caac.21492.

[2]

GALATI F, LUCIANI M L, CARAMANICO C, et al. Breast magnetic resonance spectroscopy at 3 T in biopsy-proven breast cancers: does choline peak correlate with prognostic factors?[J]. Invest Radiol, 2019, 54(12): 767-773. DOI: 10.1097/RLI.0000000000000597.

[3]

WANG Y, ACS B, ROBERTSON S, et al. Improved breast cancer histological grading using deep learning[J]. Ann Oncol, 2022, 33(1): 89-98. DOI: 10.1016/j.annonc.2021.09.007.

[4]

XU A Q, CHU X F, ZHANG S J, et al. Prediction breast molecular typing of invasive ductal carcinoma based on dynamic contrast enhancement magnetic resonance imaging radiomics characteristics: a feasibility study[J/OL]. Front Oncol, 2022, 12: 799232 [2022-07-03]. https://pubmed.ncbi.nlm.nih.gov/35664741/. DOI: 10.3389/fonc.2022.799232.

[5]

SHENG A Z, LI A J, XIA J B, et al. Application of MRI image based on computer semiautomatic segmentation algorithm in the classification prediction of breast cancer histology[J/OL]. J Healthc Eng, 2021, 2021: 6088322 [2022-07-04]. https://pubmed.ncbi.nlm.nih.gov/34868525/. DOI: 10.1155/2021/6088322.

[6]

THAKUR S B, BRENNAN S B, ISHILL N M, et al. Diagnostic usefulness of water-to-fat ratio and choline concentration in malignant and benign breast lesions and normal breast parenchyma: an in vivo (1) H MRS study[J]. J Magn Reson Imaging, 2011, 33(4): 855-863. DOI: 10.1002/jmri.22493.

[7]

BAINBRIDGE A, BRAY T J P, SENGUPTA R, et al. Practical approaches to bone marrow fat fraction quantification across magnetic resonance imaging platforms[J]. J Magn Reson Imaging, 2020, 52(1): 298-306. DOI: 10.1002/jmri.27039.

[8]

CRAFT M L, EDWARDS M, JAIN T P, et al. R2 and R2* MRI assessment of liver iron content in an undifferentiated diagnostic population with hyperferritinaemia, and impact on clinical decision making[J/OL]. Eur J Radiol, 2021, 135: 109473 [2022-07-06]. https://pubmed.ncbi.nlm.nih.gov/33373894/. DOI: 10.1016/j.ejrad.2020.109473.

[9]

BUUS T W, SIVESGAARD K, FRIS T L, et al. Fat fractions from high-resolution 3D radial Dixon MRI for predicting metastatic axillary lymph nodes in breast cancer patients[J/OL]. Eur J Radiol Open, 2020, 7: 100284 [2022-07-25]. https://pubmed.ncbi.nlm.nih.gov/33204769/. DOI: 10.1016/j.ejro.2020.100284.

[10]

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.

[11]

RAKHA E A, EL-SAYED M E, LEE A H, et al. Prognostic significance of Nottingham histologic grade in invasive breast carcinoma[J]. J Clin Oncol, 2008, 26(19): 3153-3158. DOI: 10.1200/JCO.2007.15.5986.

[12]

KAMIYA S, SATAKE H, HAYASHI Y, et al. Features from MRI texture analysis associated with survival outcomes in triple-negative breast cancer patients[J]. Breast Cancer, 2022, 29(1): 164-173. DOI: 10.1007/s12282-021-01294-1.

[13]

Breast Cancer Professional Committee of Chinese Anti-Cancer Association. Guidelines and norms for diagnosis and treatment of breast cancer of China Anti-Cancer Association (2021 edition)[J]. China Oncol, 2021, 31(10): 954-1040. DOI: 10.19401/j.cnki.1007-3639.2021.10.013.

[14]

KUKUK G M, HITTATIYA K, SPRINKART A M, et al. Comparison between modified Dixon MRI techniques, MR spectroscopic relaxometry, and different histologic quantification methods in the assessment of hepatic steatosis[J]. Eur Radiol, 2015, 25(10): 2869-2879. DOI: 10.1007/s00330-015-3703-6.

[15]

ZHANG Y, ZHOU Z, WANG C, et al. Reliability of measuring the fat content of the lumbar vertebral marrow and paraspinal muscles using MRI mDIXON-Quant sequence[J]. Diagn Interv Radiol, 2018, 24(5): 302-307. DOI: 10.5152/dir.2018.17323.

[16]

RODGE G A, GOENKA M K, GOENKA U, et al. Quantification of liver fat by MRI-PDFF imaging in patients with suspected non-alcoholic fatty liver disease and its correlation with metabolic syndrome, liver function test and ultrasonography[J]. J Clin Exp Hepatol, 2021, 11(5): 586-591. DOI: 10.1016/j.jceh.2020.11.004.

[17]

SUN M T, CHENG J L, REN C P, et al. Quantitative whole-body MR imaging for assessment of tumor burden in patients with multiple myeloma: correlation with prognostic biomarkers[J]. Quant Imaging Med Surg, 2021, 11(8): 3767-3780. DOI: 10.21037/qims-20-1361.

[18]

YU F Y, HE B, CHEN L, et al. Intermuscular fat content in young Chinese men with newly diagnosed type 2 diabetes: based on MR mDIXON-quant quantitative technique[J/OL]. Front Endocrinol (Lausanne), 2021, 12: 536018 [2022-07-15] .https://pubmed.ncbi.nlm.nih.gov/33868161/. DOI: 10.3389/fendo.2021.536018.

[19]

MAGGI L, MOSCATELLI M, FRANGIAMORE R, et al. Quantitative muscle MRI protocol as possible biomarker in Becker muscular dystrophy[J]. Clin Neuroradiol, 2021, 31(1): 257-266. DOI: 10.1007/s00062-019-00875-0.

[20]

LIDÉN M, ADRIAN D, WIDELL J, et al. Quantitative T2* imaging of iron overload in a non-dedicated center-Normal variation, repeatability and reader variation[J/OL]. Eur J Radiol Open, 2021, 8: 100357 [2022-07-13]. https://pubmed.ncbi.nlm.nih.gov/34095355/. DOI: 10.1016/j.ejro.2021.100357.

[21]

PEREIRA G A R, FOPPA M, EIFER D A, et al. Myocardial iron content by T2 Star cardiac magnetic resonance and serum markers of iron metabolism in patients with heart failure[J]. J Cardiovasc Med (Hagerstown), 2022, 23(1): e33-e35 [2022-07-20]. https://pubmed.ncbi.nlm.nih.gov/34580252/. DOI: 10.2459/JCM.0000000000001263.

[22]

D'ASSIGNIES G, PAISANT A, BARDOU-JACQUET E, et al. Non-invasive measurement of liver iron concentration using 3-Tesla magnetic resonance imaging: validation against biopsy[J]. Eur Radiol, 2018, 28(5): 2022-2030. DOI: 10.1007/s00330-017-5106-3.

[23]

HISANAGA S, AOKI T, SHIMAJIRI S, et al. Peritumoral fat content correlates with histological prognostic factors in breast carcinoma: a study using iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL)[J]. Magn Reson Med Sci, 2021, 20(1): 28-33. DOI: 10.2463/mrms.mp.2019-0201.

[24]

AGARWAL K, SHARMA U, MATHUR S, et al. Study of lipid metabolism by estimating the fat fraction in different breast tissues and in various breast tumor sub-types by in vivo 1H MR spectroscopy[J]. Magn Reson Imaging, 2018, 49: 116-122. DOI: 10.1016/j.mri.2018.02.004.

[25]

SEO M, RYU J K, JAHNG G H, et al. Estimation of T2* relaxation time of breast cancer: correlation with clinical, imaging and pathological features[J]. Korean J Radiol, 2017, 18(1): 238-248. DOI: 10.3348/kjr.2017.18.1.238.

[26]

WANG Y M, YIN C, ZHOU T, et al. The value of enhanced T2 star weighted angiography in the diagnosis of cervical cancer and the evaluation of radiotherapy effect[J]. Chin J Magn Reson Imaging, 2021, 12(10): 22-25, 31. DOI: 10.12015/issn.1674-8034.2021.10.005.

[27]

LI Q, XIAO Q, YANG M, et al. Histogram analysis of quantitative parameters from synthetic MRI: correlations with prognostic factors and molecular subtypes in invasive ductal breast cancer[J/OL]. Eur J Radiol, 2021, 139: 109697 [2022-07-14]. https://pubmed.ncbi.nlm.nih.gov/33857828/. DOI: 10.1016/j.ejrad.2021.109697.

[28]

QIU Z L, XIAO J F, ZHENG S K, et al. Associations between functional polychlorinated biphenyls in adipose tissues and prognostic biomarkers of breast cancer patients[J/OL]. Environ Res, 2020, 185: 109441 [2022-07-20]. https://pubmed.ncbi.nlm.nih.gov/32247153/. DOI: 10.1016/j.envres.2020.109441.

[29]

CHEN H, LI W, WAN C, et al. Correlation of dynamic contrast-enhanced MRI and diffusion-weighted MR imaging with prognostic factors and subtypes of breast cancers[J/OL]. Front Oncol, 2022, 12: 942943 [2022-07-20]. https://pubmed.ncbi.nlm.nih.gov/35992872/. DOI: 10.3389/fonc.2022.942943.

[30]

BOUGUERRA H, GUISSOUMA H, LABIDI S, et al. Breast cancer in Tunisia: association of body mass index with histopathological aspects of tumors[J]. Asian Pac J Cancer Prev, 2014, 15(16): 6805-6810. DOI: 10.7314/apjcp.2014.15.16.6805.

[31]

MOUTINHO-GUILHERME R, OYOLA J H, SANZ-ROSA D, et al. Correlation between apparent diffusion coefficient values in breast magnetic resonance imaging and prognostic factors of breast invasive ductal carcinoma[J]. Porto Biomed J, 2019, 4(1):e27 [2022-07-21]. https://pubmed.ncbi.nlm.nih.gov/31595254/. DOI: 10.1016/j.pbj.0000000000000027.

[32]

LI J, MO Y, HE B, et al. Association between MRI background parenchymal enhancement and lymphovascular invasion and estrogen receptor status in invasive breast cancer[J/OL]. Br J Radiol, 2019, 92(1103): 20190417 [2022-07-23]. https://pubmed.ncbi.nlm.nih.gov/31398071/. DOI: 10.1259/bjr.20190417.

[33]

BROOKS J D, CHRISTENSEN R A G, SUNG J S, et al. MRI background parenchymal enhancement, breast density and breast cancer risk factors: a cross-sectional study in pre- and post-menopausal women[J/OL]. NPJ Breast Cancer, 2022, 8(1): 97 [2022-07-23]. https://pubmed.ncbi.nlm.nih.gov/36008488/. DOI: 10.1038/s41523-022-00458-2.

PREV An integrated model based on feature fusion for classifying molecular subtypes of breast cancer

NEXT Study on the correlation between multi-parameter MRI and pathology in the tumor body and peritumoral area of breast cancer

LINK

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