Abstract
Objective: The objective of this study was to investigate the diagnostic power of apparent diffusion coefficient/coefficient of variance (ADCcV) as well as ADC parameters formed based on magnetic resonance images (MRI) in the distinction of molecular breast cancer subtypes.
Methods: The study involved 205 patients who had breast cancer at stages 1-3. Estrogen receptor (EsR), progesterone receptor (PrR), human epidermal growth factor receptor 2 (Her2), and proliferation index (Ki-67) were histologically analyzed in the tumor. The correlations between the immunohistochemistry and intrinsic subtypes were analyzed using ADC and ADCcV.
Results: The maximum whole tumor (WTu) ADC (p=0.004), minimum WTu ADC (p<0.001), and mean WTu ADC (p<0.001) values were significantly smaller in the EsR-positive tumors than those in the EsR-negative tumors. Compared to the PrR-negative tumors, the PrR-positive tumors showed significantly smaller maximum, minimum, and mean WTu ADC values (p=0.005, p=0.001, and p<0.001, respectively). In the comparisons of the molecular subtypes in terms of ADCcV, the p-values indicated statistically significant differences between the luminal A (lumA) group and the triple negative (TN) group, between the luminal B (lumB) group and the TN group, and between the Her2-enriched and TN groups (p<0.001, p=0.011, and p=0.004, respectively). Considering the luminal and non-luminal groups, while a significant difference was observed between the groups considering their minimum, maximum, and mean WTu ADC values, their ADCcV values were similar (p<0.001, p=0.004, and p<0.001, respectively).
Conclusion: Using ADCcV in addition to ADC parameters increased the diagnostic power of diffusion weighted-MRI (DW-MRI) in the distinction of molecular subtypes of breast cancer.
[1]
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2018; 68(6): 394-424.
[http://dx.doi.org/10.3322/caac.21492] [PMID: 30207593]
[http://dx.doi.org/10.3322/caac.21492] [PMID: 30207593]
[2]
Bickel H, Pinker K, Polanec S, et al. Diffusion-weighted imaging of breast lesions: Region-of-interest placement and different ADC parameters influence apparent diffusion coefficient values. Eur Radiol 2017; 27(5): 1883-92.
[http://dx.doi.org/10.1007/s00330-016-4564-3] [PMID: 27578047]
[http://dx.doi.org/10.1007/s00330-016-4564-3] [PMID: 27578047]
[3]
Baba S, Isoda T, Maruoka Y, et al. Diagnostic and prognostic value of pretreatment SUV in 18F-FDG/PET in breast cancer: comparison with apparent diffusion coefficient from diffusion-weighted MR imaging. J Nucl Med 2014; 55(5): 736-42.
[http://dx.doi.org/10.2967/jnumed.113.129395] [PMID: 24665089]
[http://dx.doi.org/10.2967/jnumed.113.129395] [PMID: 24665089]
[4]
Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: A looking glass for cancer? Nat Rev Cancer 2012; 12(5): 323-34.
[http://dx.doi.org/10.1038/nrc3261] [PMID: 22513401]
[http://dx.doi.org/10.1038/nrc3261] [PMID: 22513401]
[5]
Korkaya H, Kim G, Davis A, et al. Activation of an IL6 inflammatory loop mediates trastuzumab resistance in HER2+ breast cancer by expanding the cancer stem cell population. Mol Cell 2012; 47(4): 570-84.
[http://dx.doi.org/10.1016/j.molcel.2012.06.014] [PMID: 22819326]
[http://dx.doi.org/10.1016/j.molcel.2012.06.014] [PMID: 22819326]
[6]
Mann RM, Cho N, Moy L. Breast MRI: State of the art. Radiology 2019; 292(3): 520-36.
[http://dx.doi.org/10.1148/radiol.2019182947] [PMID: 31361209]
[http://dx.doi.org/10.1148/radiol.2019182947] [PMID: 31361209]
[7]
Partridge SC, Stone KM, Strigel RM, DeMartini WB, Peacock S, Lehman CD. Breast DCE-MRI. Acad Radiol 2014; 21(9): 1195-203.
[http://dx.doi.org/10.1016/j.acra.2014.04.013] [PMID: 24998690]
[http://dx.doi.org/10.1016/j.acra.2014.04.013] [PMID: 24998690]
[8]
Pinker K, Helbich TH, Morris EA. The potential of multiparametric MRI of the breast. Br J Radiol 2017; 90(1069): 20160715.
[http://dx.doi.org/10.1259/bjr.20160715] [PMID: 27805423]
[http://dx.doi.org/10.1259/bjr.20160715] [PMID: 27805423]
[9]
Cheeney S, Rahbar H, Dontchos BN, Javid SH, Rendi MH, Partridge SC. Apparent diffusion coefficient values may help predict which MRI-detected high-risk breast lesions will upgrade at surgical excision. J Magn Reson Imaging 2017; 46(4): 1028-36.
[http://dx.doi.org/10.1002/jmri.25656] [PMID: 28181343]
[http://dx.doi.org/10.1002/jmri.25656] [PMID: 28181343]
[10]
Robertson-Tessi M, Gillies RJ, Gatenby RA, Anderson ARA. Impact of metabolic heterogeneity on tumor growth, invasion, and treatment outcomes. Cancer Res 2015; 75(8): 1567-79.
[http://dx.doi.org/10.1158/0008-5472.CAN-14-1428] [PMID: 25878146]
[http://dx.doi.org/10.1158/0008-5472.CAN-14-1428] [PMID: 25878146]
[11]
Dong X, Wu P, Sun X, et al. Intra-tumour 18 F-FDG uptake heterogeneity decreases the reliability on target volume definition with positron emission tomography/computed tomography imaging. J Med Imaging Radiat Oncol 2015; 59(3): 338-45.
[http://dx.doi.org/10.1111/1754-9485.12289] [PMID: 25708154]
[http://dx.doi.org/10.1111/1754-9485.12289] [PMID: 25708154]
[12]
Bundschuh RA, Dinges J, Neumann L, et al. Textural parameters of tumor heterogeneity in 18F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. J Nucl Med 2014; 55(6): 891-7.
[http://dx.doi.org/10.2967/jnumed.113.127340] [PMID: 24752672]
[http://dx.doi.org/10.2967/jnumed.113.127340] [PMID: 24752672]
[13]
Elston CW, Ellis IO. pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 1991; 19(5): 403-10.
[http://dx.doi.org/10.1111/j.1365-2559.1991.tb00229.x] [PMID: 1757079]
[http://dx.doi.org/10.1111/j.1365-2559.1991.tb00229.x] [PMID: 1757079]
[14]
Goldhirsch A, Wood WC, Coates AS, Gelber RD, Thürlimann B, Senn HJ. Strategies for subtypes—dealing with the diversity of breast cancer: highlights of the St Gallen International Expert Consensus on the Primary Therapy of Early Breast Cancer 2011. Ann Oncol 2011; 22(8): 1736-47.
[http://dx.doi.org/10.1093/annonc/mdr304] [PMID: 21709140]
[http://dx.doi.org/10.1093/annonc/mdr304] [PMID: 21709140]
[15]
Wolff AC, Hammond MEH, Hicks DG, et al. Recommendations for human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists clinical practice guideline update. J Clin Oncol 2013; 31(31): 3997-4013.
[http://dx.doi.org/10.1200/JCO.2013.50.9984] [PMID: 24101045]
[http://dx.doi.org/10.1200/JCO.2013.50.9984] [PMID: 24101045]
[16]
Partridge SC, Mullins CD, Kurland BF, et al. Apparent diffusion coefficient values for discriminating benign and malignant breast MRI lesions: effects of lesion type and size. AJR Am J Roentgenol 2010; 194(6): 1664-73.
[http://dx.doi.org/10.2214/AJR.09.3534] [PMID: 20489111]
[http://dx.doi.org/10.2214/AJR.09.3534] [PMID: 20489111]
[17]
Sardanelli F, Boetes C, Borisch B, et al. Magnetic resonance imaging of the breast: Recommendations from the EUSOMA working group. Eur J Cancer 2010; 46(8): 1296-316.
[http://dx.doi.org/10.1016/j.ejca.2010.02.015] [PMID: 20304629]
[http://dx.doi.org/10.1016/j.ejca.2010.02.015] [PMID: 20304629]
[18]
Pinker K, Grabner G, Bogner W, et al. A combined high temporal and high spatial resolution 3 Tesla MR imaging protocol for the assessment of breast lesions: initial results. Invest Radiol 2009; 44(9): 553-8.
[http://dx.doi.org/10.1097/RLI.0b013e3181b4c127] [PMID: 19652611]
[http://dx.doi.org/10.1097/RLI.0b013e3181b4c127] [PMID: 19652611]
[19]
Tan SLL, Rahmat K, Rozalli FI, et al. Differentiation between benign and malignant breast lesions using quantitative diffusion-weighted sequence on 3 T MRI. Clin Radiol 2014; 69(1): 63-71.
[http://dx.doi.org/10.1016/j.crad.2013.08.007] [PMID: 24156797]
[http://dx.doi.org/10.1016/j.crad.2013.08.007] [PMID: 24156797]
[20]
Martincich L, Deantoni V, Bertotto I, et al. Correlations between diffusion-weighted imaging and breast cancer biomarkers. Eur Radiol 2012; 22(7): 1519-28.
[http://dx.doi.org/10.1007/s00330-012-2403-8] [PMID: 22411304]
[http://dx.doi.org/10.1007/s00330-012-2403-8] [PMID: 22411304]
[21]
Choi SY, Chang Y-W, Park HJ, Kim HJ, Hong SS, Seo DY. Correlation of the apparent diffusion coefficiency values on diffusion-weighted imaging with prognostic factors for breast cancer. Br J Radiol 2012; 85(1016): e474-9.
[http://dx.doi.org/10.1259/bjr/79381464] [PMID: 22128125]
[http://dx.doi.org/10.1259/bjr/79381464] [PMID: 22128125]
[22]
Kim SH, Cha ES, Kim HS, et al. Diffusion-weighted imaging of breast cancer: Correlation of the apparent diffusion coefficient value with prognostic factors. J Magn Reson Imaging 2009; 30(3): 615-20.
[http://dx.doi.org/10.1002/jmri.21884] [PMID: 19711411]
[http://dx.doi.org/10.1002/jmri.21884] [PMID: 19711411]
[23]
Partridge SC, DeMartini WB, Kurland BF, Eby PR, White SW, Lehman CD. Quantitative diffusion-weighted imaging as an adjunct to conventional breast MRI for improved positive predictive value. AJR Am J Roentgenol 2009; 193(6): 1716-22.
[http://dx.doi.org/10.2214/AJR.08.2139] [PMID: 19933670]
[http://dx.doi.org/10.2214/AJR.08.2139] [PMID: 19933670]
[24]
Karan B, Pourbagher A, Torun N. Diffusion-weighted imaging and 18 F-fluorodeoxyglucose positron emission tomography/computed tomography in breast cancer: Correlation of the apparent diffusion coefficient and maximum standardized uptake values with prognostic factors. J Magn Reson Imaging 2016; 43(6): 1434-44.
[http://dx.doi.org/10.1002/jmri.25112] [PMID: 26663655]
[http://dx.doi.org/10.1002/jmri.25112] [PMID: 26663655]
[25]
Park SH, Choi HY, Hahn SY. Correlations between apparent diffusion coefficient values of invasive ductal carcinoma and pathologic factors on diffusion-weighted MRI at 3.0 Tesla. J Magn Reson Imaging 2015; 41(1): 175-82.
[http://dx.doi.org/10.1002/jmri.24519] [PMID: 24353241]
[http://dx.doi.org/10.1002/jmri.24519] [PMID: 24353241]
[26]
Horvat JV, Bernard-Davila B, Helbich TH, et al. Diffusion‐weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping as a quantitative imaging biomarker for prediction of immunohistochemical receptor status, proliferation rate, and molecular subtypes of breast cancer. J Magn Reson Imaging 2019; 50(3): 836-46.
[http://dx.doi.org/10.1002/jmri.26697] [PMID: 30811717]
[http://dx.doi.org/10.1002/jmri.26697] [PMID: 30811717]
[27]
Lee HS, Kim SH, Kang BJ, Baek JE, Song BJ. Perfusion parameters in dynamic contrast-enhanced MRI and apparent diffusion coefficient value in diffusion-weighted MRI. Acad Radiol 2016; 23(4): 446-56.
[http://dx.doi.org/10.1016/j.acra.2015.12.011] [PMID: 26852247]
[http://dx.doi.org/10.1016/j.acra.2015.12.011] [PMID: 26852247]
[28]
Ozkul B, Koyuncu Sokmen B, Sever I, Ozkul O, Gurcan N. The role of simultaneous standardized uptake value and MRI diffusion weighted heterogeneity index from hybrid PET/MR in the evaluation of brain metastases. EJMI 2022; 6(4): 387-93.
[http://dx.doi.org/10.14744/ejmi.2022.11286]
[http://dx.doi.org/10.14744/ejmi.2022.11286]
[29]
Sokmen BK, Sokmen D, Comez Yİ, Eksi M. Prediction of prostate cancer aggressiveness using a novel multiparametric magnetic resonance imaging parameter: Tumor heterogeneity index. Urol Int 2022; 106(9): 946-53.
[http://dx.doi.org/10.1159/000521606] [PMID: 35152216]
[http://dx.doi.org/10.1159/000521606] [PMID: 35152216]
[30]
Stein D, Goldberg N, Domachevsky L, Bernstine H, Nidam M, Abadi-Korek I, et al. Quantitative biomarkers for liver metastases: comparison of MRI diffusion-weighted imaging heterogeneity index and fluorine-18-fluoro-deoxyglucose standardised uptake value in hybrid PET/MR. Clin Radiol 2018; 73(9): 832.e17-e22.
