Diffusion Weighted Imaging of Different Breast Cancer Molecular Subtypes. A Systematic Review and Meta Analysis.

DOI: https://doi.org/10.21203/rs.3.rs-57672/v1

Abstract

Background: Magnetic resonance imaging can be used to diagnose breast cancer (BC)s. Diffusion weighted imaging and the apparent diffusion coefficient (ADC) can be used to reflect tumor microstructure. The present analysis sought to compare ADC values between molecular subtypes of BC based upon a large patient sample.

Methods: MEDLINE library and SCOPUS databases were screened for the associations between ADC and molecular suptype of BC to April 2020. Primary endpoint of the systematic review was the ADC value in different BC. Overall, 28 studies were suitable for the analysis and included into the present study.

Results: The included studies comprised a total of 2990 tumors. Luminal A type was diagnosed in 865 cases (28.9%), Luminal B in 899 cases (30.1%), Her-2 enriched in 597 cases (20.0%) and triple negative in 629 cases (21.0%). The mean ADC value of the Luminal A type was 0.99 × 10− 3 mm2/s [95% CI 0.94-1.04], of the Luminal B type was 0.99 × 10− 3 mm2/s [95% CI 0.89-1.05], of Her 2-enriched type was 1.02 × 10− 3 mm2/s [95% CI 0.95-1.08] and of the triple negative type was 0.99 × 10− 3 mm2/s [95% CI 0.91-1.07].

Conclusions: ADC values cannot be used to discriminate between molecular subtypes of BC. 

Introduction

Magnetic resonance imaging (MRI) has become a cornerstone for the diagnosis of breast cancer (BC) [1-4]. It has the highest sensitivity of all imaging modalities but is confronted with lacks in specificity [5]. To overcome this shortcoming, Diffusion-weighted imaging (DWI) was additionally introduced into the MRI protocol, which is a functional imaging modality based upon Brownian water movement in tissues [6, 7]. This sequence is directly correlated with cell density of tumors, which was utilized in several tumor entities with very promising results around oncology [7].

So, it was identified that benign breast tumors have significant higher apparent diffusion coefficients (ADC) than malignant tumors with a proposed threshold of 1.0 x10-3 mm²/s in a recent meta analysis [8]. Yet, diagnostic shortcomings were reported for discrimination of breast cancer subtypes with no clear significant differences of ADC values [9]. These results were predominantly reported by single center studies with different scanner technology and partial inconclusive results.

However, it would be crucial to discriminate different BC subtypes based upon the receptor status, as prognosis and treatment options differ substantially between types [10, 11]. So, Her2-enriched BC has a worse prognosis compared to hormone receptor positive types (Luminal A and B) but can be treated by Her2-targeted antibody therapy [12]. In clinically routine, the receptor status is defined by immunohistochemical stainings on bioptic specimen [11]. Yet, there might be possible clinical benefit by imaging defined BC receptor status as multifocal lesions or metastasized lesions can differ in receptor status [13]. This would result in different clinical decision making based upon functional imaging.

Therefore, the purpose of the present analysis was to systematically review the published literature regarding ADC values of BC in accordance to molecular subtype and perform a meta analysis to establish, whether ADC values can discriminate BC subtypes or not.

Methods

Search strategy and selection criteria

MEDLINE library and SCOPUS databases were screened for the associations between ADC values and BC up to April 2020.

The following search words were used: “DWI or diffusion weighted imaging or diffusion-weighted imaging or ADC or apparent diffusion coefficient AND breast cancer OR breast carcinoma”. Secondary references were also manually checked and recruited.

The primary endpoint of the systematic review was association between molecular subtype of BC and ADC values.

Studies (or subsets of studies) were included, if they satisfied all the following criteria: (1) patients with BC confirmed by histopathology, (2) pretreatment MRI with DWI and (3) reported mean and standard deviation of the ADC values.

Exclusion criteria were (1) reviews, (2) case reports, (3) studies without data of pretreatment DCE MRI, (4) studies with histopathology performed after treatment, (5) non-English language, and (6) experimental (xenograft or animals model) studies.

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was used for the analysis [14]. The paper acquisition is summarized in figure 1.

In total, 28 studies were suitable for the analysis and included into the present study [15-42].

Quality-Assessment

The methodological quality of the acquired studies was independently evaluated by two readers (A.S. and H.J.M.) using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) instrument [43]. Results of QUADAS-2 assessments are shown in figure 2.

Statistical analysis

The meta analysis was performed using RevMan 5.3 (2014; Cochrane Collaboration, Copenhagen, Denmark). Heterogeneity was calculated by means of the inconsistency index I2 [44, 45]. Finally, DerSimonian and Laird [46] random-effect models with inverse-variance weights were performed without any further correction.

Results

Risk of bias

Patient selection was generally well defined within the respective methodology; however, several studies did not report the inclusion criteria clearly which can account for potential bias.

All studies clearly reported methodology of the index test and were accordingly not considered a significant source of potential bias. All studies had as reference test the histopathology with immunohistochemical staining.

The acquired 28 studies comprised a total of 2990 BC. Of the included studies, 6 (21.4%) were of prospective and 22 (78.6%) of retrospective design. Different 1.5T scanners were used in 6 (21.4%) studies and 3T scanners in 20 (71.5%) studies and in 2 studies 1.5 and 3 T scanners were utilized (7.1%). Regarding b-values, most studies (n=19, 67.9%) used b-values 0 and 800 s/mm² or higher. In 3 studies (10.7%) b values 0 and 750 s/mm² and in 2 studies (7.1%) b values 0 and 600 s/mm² were used.

Luminal A type was diagnosed in 865 cases (28.9%), Luminal B in 899 cases (30.1%), Her-2 enriched in 597 cases (20.0%) and triple negative in 629 cases (21.0%).

The mean ADC value of the Luminal A type was 0.99 × 10− 3 mm2/s [95% CI 0.94-1.04, Tau²=0.01, Chi²=310.71, df=14, I²= 95%], of the Luminal B type was 0.99 × 10− 3 mm2/s [95% CI 0.89-1.05, Tau²=0.02, Chi²=715.49, df=12, I²= 98%], of Her 2-enriched type was 1.02 × 10− 3 mm2/s [95% CI 0.95-1.08, Tau²=0.02, Chi²=641.08, df=22, I²= 97%] and of the triple negative type was 0.99 × 10− 3 mm2/s [95% CI 0.91-1.07, Tau²=0.03, Chi²=962.41, df=20, I²= 98%] (figure 3). Figure 4 displays these results as a box plot graph. The ADC values of the BC groups overlapped significantly with no clear proposed threshold to distinguish between types.

Discussion

According to the present analysis, there were no differences of ADC values between the investigated BC types. Therefore, ADC cannot predict hormone receptor status of BC. This finding is very important.

The clinical importance of different BC molecular subtypes is without any questioning [10]. There are distinctive differences in prognosis and therapy in accordance to BC immunohistochemical subtype [10]. So, the BC type with expression of estrogen and progesterone receptors and low expression of proliferation marker Ki 67, namely Luminal A type, has the best prognosis with a 5-years overall survival rate of 95.1% to 78.5% of triple negative type [10, 47]. This is also caused by the possibility of endocrine hormone therapy as a treatment option. Luminal B type is defined by the presence of estrogen and progesterone receptors with additionally a high proliferation rate compared to Luminal A. Her2-enriched type is defined by the expression of the oncogene human epidermal growth factor receptor, which stimulates proliferation and inhibits apoptosis [48]. Importantly, it can be targeted by an antibody treatment, namely trastuzumab, which shows the utter importance of this receptor [48]. Lastly, the triple negative type is defined by the absence of any of these receptors, resulting in the worst prognosis and limited treatment options [10].

Previously, it was shown that ADC values are associated with cellularity and tumor microstructure [6]. Beyond sole cellularity, ADC values are associated with important histopathology parameters, reflecting proliferation potential (Ki 67) and tumor suppressor genes p53 [49, 50]. However, some immunohistochemical features of angiogenesis were not associated with ADC values [51]. In short, there is ongoing debate which features of tumors can be predicted by imaging.

There are inflicting published results regarding, whether ADC values can also reflect immunohistochemical features in BC. So, in some single center studies, there were reports that Her2-positive tumors have slightly higher ADC values compared to negative tumors [52]. However, Choi et al. could not identify an influence of the Her 2 status on the ADC values [53]. In another study by Montemezzi et al., Luminal A type showed the highest ADC values compared to all other subtypes (0.924 × 10−3± 0.033 mm2/s) [34]. According to other authors, triple negative type showed the highest ADC values [25, 38]. In a first multi center study comprising 661 patients, no significant differences were reported between BC subtypes corroborated by the present results [9].

Presumably, the reported differences in previous investigations were caused by different scanner technology, measurement, and patient samples. For example, it is a known fact that mucinous carcinoma alone has distinctive higher ADC values due to the histopathology which seems to be more important than the immunohistochemical subtype [54].

So, the present analysis can harmonize these reported differences that no significant differences of ADC values between molecular subtypes of BC can be assumed.

ADC values are reflective of tumor microstructure with a moderate inverse correlation of the cellularity of tumors [6-9]. Presumably, the histopathologic differences of the molecular subtypes are not strong enough that they can be reliably predicted by DWI.

There are other reports highlighting the importance of necrosis on ADC values which was the only independent influencing factor of the ADC values [27]. These relationships resulted in the highest ADC values of the triple negative type because of the high rate of necrosis.

Our results are also in accordance with a recently published meta analysis suggesting that ADC cannot predict outcome to neoadjuvant radiochemotherapy of BC [55].

Yet, there is clear evidence that ADC values can aid in the discrimination between benign and malignant tumors which was shown in a recent meta analysis [56]. So, ADC values can nevertheless aid in important clinical decision making despite the present negative results.

There are some inherent limitations of the present study to address. Firstly, the meta- analysis is based upon published results in the literature. There might be a certain publication bias because there is a trend to report positive or significant results; whereas studies with insignificant or negative results are often rejected or are not submitted. Secondly, there is the restriction to published papers in English language. Thirdly, the study investigated the widely used DWI technique using 2 b-values. However, more advanced MRI sequences, such as intravoxel incoherent motion and diffusion kurtosis imaging might show a better accuracy in discriminating BC phenotypes [57, 58]. Yet, there are few studies using these sequences and thus no comprehensive analysis can be made.

Conclusions

The present systematic review and meta analysis identified that ADC values cannot discriminate immunohistochemical molecular subtypes of BC. Therefore, ADC values cannot provide histopathological information in this regard.

Declarations

Not applicable

Not applicable”

The data that support the findings of this study are available from professor Surov but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of professor Surov

The authors declare that they have no competing interests

none

none

References

  1. Kuhl CK. MRI of breast tumors. Eur Radiol. 2000;10(1):46-58.
  2. Lehman CD, Gatsonis C, Kuhl CK, et al. MRI evaluation of the contralateral breast in women with recently diagnosed breast cancer. N Engl J Med. 2007;356(13):1295-1303.
  3. Kriege M, Brekelmans CT, Boetes C, et al. Efficacy of MRI and mammography for breast-cancer screening in women with a familial or genetic predisposition. N Engl J Med. 2004;351(5):427-37.
  4. Saadatmand S, Geuzinge HA, Rutgers EJT, et al. MRI versus mammography for breast cancer screening in women with familial risk (FaMRIsc): a multicentre, randomised, controlled trial. Lancet Oncol. 2019;20(8):1136-47. 
  5. Zhang Y, Ren H. Meta-analysis of diagnostic accuracy of magnetic resonance imaging and mammography for breast cancer. J Cancer Res Ther. 2017;13(5):862-8.
  6. Padhani AR, Liu G, Koh DM, et al. Diffusion-weighted magnetic resonance imaging as a cancer biomarker: consensus and recommendations. 2009;11(2):102-25.
  7. Surov A, Meyer HJ, Wienke A. Correlation between apparent diffusion coefficient (ADC) and cellularity is different in several tumors: a meta-analysis. 2017;8(35):59492-9. 
  8. Surov A, Meyer HJ, Wienke A. Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions. BMC Cancer. 2019;19(1):955.
  9. Surov A, Chang YW, Li L, et al. Apparent diffusion coefficient cannot predict molecular subtype and lymph node metastases in invasive breast cancer: a multicenter analysis. BMC Cancer. 2019;19(1):1043. 
  10. Hennigs A, Riedel F, Gondos A, et al. Prognosis of breast cancer molecular subtypes in routine clinical care: A large prospective cohort study. BMC Cancer. 2016;16(1):734.
  11. Testa U, Castelli G, Pelosi E. Breast Cancer: A Molecularly Heterogenous Disease Needing Subtype-Specific Treatments. Med Sci (Basel). 2020;8(1):18.
  12. Schettini F, Pascual T, Conte B, et al. HER2-enriched subtype and pathological complete response in HER2-positive breast cancer: A systematic review and meta-analysis. Cancer Treat Rev. 2020;84:101965.
  13. Cejalvo JM, Martínez de Dueñas E, Galván P, et al. Intrinsic Subtypes and Gene Expression Profiles in Primary and Metastatic Breast Cancer. Cancer Res. 2017;77(9):2213-21.
  14. Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.
  15. Arponen O, Sudah M, Masarwah A, et al. Diffusion-Weighted Imaging in 3.0 Tesla Breast MRI: Diagnostic Performance and Tumor Characterization Using Small Subregions vs. Whole Tumor Regions of Interest. Plos One 2015;10(10):e0141833.
  16. 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.
  17. Choi Y, Kim SH, Youn IK, Kang BJ, Park WC, Lee A. Rim sign and histogram analysis of apparent diffusion coefficient values on diffusion-weighted MRI in triple-negative breast cancer: Comparison with ER-positive subtype. PLoS One. 2017;12(5):e0177903.
  18. Cipolla V, Santucci D, Guerrieri D, Drudi FM, Meggiorini ML, de Felice C. Correlation between 3T apparent diffusion coefficient values and grading of invasive breast carcinoma. Eur J Radiol. 2014;83(12):2144-50.
  19. Costantini M, Belli P, Distefano D, et al. Magnetic resonance imaging features in triple-negative breast cancer: comparison with luminal and HER2-overexpressing tumors. Clin Breast Cancer. 2012;12(5):331-9.
  20. Durando M, Gennaro L, Cho GY, et al. Quantitative apparent diffusion coefficient measurement obtained by 3.0Tesla MRI as a potential noninvasive marker of tumor aggressiveness in breast cancer. Eur J Radiol. 2016;85(9):1651-8.
  21. Fan M, He T, Zhang P, et al. Diffusion-weighted imaging features of breast tumours and the surrounding stroma reflect intrinsic heterogeneous characteristics of molecular subtypes in breast cancer. NMR Biomed. 2018;31(2):10.1002/nbm.3869.
  22. Incoronato M, Grimaldi AM, Cavaliere C, et al. Relationship between functional imaging and immunohistochemical markers and prediction of breast cancer subtype: a PET/MRI study. Eur J Nucl Med Mol Imaging. 2018;45(10):1680-93.
  23. Jeh SK, Kim SH, Kim HS, et al. Correlation of the apparent diffusion coefficient value and dynamic magnetic resonance imaging findings with prognostic factors in invasive ductal carcinoma. J Magn Reson Imaging. 2011;33(1):102-9.
  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.
  25. Kato F, Kudo K, Yamashita H, et al. Differences in morphological features and minimum apparent diffusion coefficient values among breast cancer subtypes using 3-tesla MRI. Eur J Radiol. 2016;85(1):96-102.
  26. Kawashima H, Miyati T, Ohno N, et al. Differentiation Between Luminal-A and Luminal-B Breast Cancer Using Intravoxel Incoherent Motion and Dynamic Contrast-Enhanced Magnetic Resonance Imaging. Acad Radiol. 2017;24(12):1575-81.
  27. Kim SY, Shin J, Kim DH, et al. Correlation between electrical conductivity and apparent diffusion coefficient in breast cancer: effect of necrosis on magnetic resonance imaging. Eur Radiol. 2018;28(8):3204-14.
  28. Kim Y, Ko K, Kim D, et al. Intravoxel incoherent motion diffusion-weighted MR imaging of breast cancer: association with histopathological features and subtypes. Br J Radiol. 2016;89(1063):20160140.
  29. Kim EJ, Kim SH, Park GE, et al. Histogram analysis of apparent diffusion coefficient at 3.0t: Correlation with prognostic factors and subtypes of invasive ductal carcinoma. J Magn Reson Imaging. 2015;42(6):1666-78.
  30. Kitajima K, Yamano T, Fukushima K, et al. Correlation of the SUVmax of FDG-PET and ADC values of diffusion-weighted MR imaging with pathologic prognostic factors in breast carcinoma. Eur J Radiol. 2016;85(5):943-9.
  31. 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:: Association with Prognostic Factors in Breast Cancer. Acad Radiol. 2016;23(4):446-56.
  32. Liu S, Ren R, Chen Z, et al. Diffusion-weighted imaging in assessing pathological response of tumor in breast cancer subtype to neoadjuvant chemotherapy. J Magn Reson Imaging. 2015;42(3):779-87.
  33. Martincich L, Deantoni V, Bertotto I, et al. Correlations between diffusion-weighted imaging and breast cancer biomarkers. Eur Radiol. 2012;22(7):1519-28.
  34. Montemezzi S, Camera L, Giri MG, et al. Is there a correlation between 3T multiparametric MRI and molecular subtypes of breast cancer?. Eur J Radiol. 2018;108:120-7.
  35. Nakajo M, Kajiya Y, Kaneko T, et al. FDG PET/CT and diffusion-weighted imaging for breast cancer: prognostic value of maximum standardized uptake values and apparent diffusion coefficient values of the primary lesion. Eur J Nucl Med Mol Imaging. 2010;37(11):2011-20.
  36. 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.
  37. Park EK, Cho KR, Seo BK, Woo OH, Cho SB, Bae JW. Additional Value of Diffusion-Weighted Imaging to Evaluate Prognostic Factors of Breast Cancer: Correlation with the Apparent Diffusion Coefficient. Iran J Radiol. 2016;13(1):e33133.
  38. Sharma U, Sah RG, Agarwal K, Parshad R, Seenu V, Mathur SR, Hari S, Jagannathan NR.Potential of Diffusion-Weighted Imaging in the Characterization of Malignant, Benign, and Healthy Breast Tissues and Molecular Subtypes of Breast Cancer. Front Oncol. 2016;6:126.
  39. Shen L, Zhou G, Tong T, et al. ADC at 3.0 T as a noninvasive biomarker for preoperative prediction of Ki67 expression in invasive ductal carcinoma of breast. Clin Imaging. 2018;52:16-22.
  40. Song SE, Cho KR, Seo BK, et al. Intravoxel incoherent motion diffusion-weighted MRI of invasive breast cancer: Correlation with prognostic factors and kinetic features acquired with computer-aided diagnosis. J Magn Reson Imaging. 2019;49(1):118-30.
  41. Xie T, Zhao Q, Fu C, et al. Differentiation of triple-negative breast cancer from other subtypes through whole-tumor histogram analysis on multiparametric MR imaging. Eur Radiol. 2019;29(5):2535-44.
  42. Youk JH, Son EJ, Chung J, Kim JA, Kim EK. Triple-negative invasive breast cancer on dynamic contrast-enhanced and diffusion-weighted MR imaging: comparison with other breast cancer subtypes. Eur Radiol. 2012;22(8):1724-34.
  43. Whiting PF, Rutjes AW, Westwood ME, Mallett S, Deeks JJ, Reitsma JB, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med. 2011;155(8):529–36.
  44. Leeflang MM, Deeks JJ, Gatsonis C, Bossuyt PM. Systematic reviews of diagnostic test accuracy. Ann Intern Med. 2008;149(12):889–97.
  45. Zamora J, Abraira V, Muriel A, Khan K, Coomarasamy A. Meta-DiSc: a software for meta-analysis of test accuracy data. BMC Med Res Methodol. 2006;6:31.
  46. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88.
  47. Prat A, Pineda E, Adamo B, et al. Clinical implications of the intrinsic molecular subtypes of breast cancer. Breast. 2015;24 Suppl 2:S26-S35. 
  48. Krasniqi E, Barchiesi G, Pizzuti L, et al. Immunotherapy in HER2-positive breast cancer: state of the art and future perspectives. J Hematol Oncol. 2019;12(1):111.
  49. Surov A, Meyer HJ, Wienke A. Associations between apparent diffusion coefficient (ADC) and KI 67 in different tumors: a meta-analysis. Part 1: ADCmean. Oncotarget. 2017;8(43):75434-44.
  50. Meyer HJ, Leifels L, Hamerla G, Höhn AK, Surov A. ADC-histogram analysis in head and neck squamous cell carcinoma. Associations with different histopathological features including expression of EGFR, VEGF, HIF-1α, Her 2 and p53. A preliminary study. Magn Reson Imaging. 2018;54:214-17.
  51. Meyer HJ, Wienke A, Surov A. Association Between VEGF Expression and Diffusion Weighted Imaging in Several Tumors-A Systematic Review and Meta-Analysis. Diagnostics (Basel). 2019;9(4):126.
  52. Horvat JV, Iyer A, Morris EA, et al. Histogram Analysis and Visual Heterogeneity of Diffusion-Weighted Imaging with Apparent Diffusion Coefficient Mapping in the Prediction of Molecular Subtypes of Invasive Breast Cancers. Contrast Media Mol Imaging. 2019;2019:2972189.
  53. Choi BB, Kim SH, Kang BJ, et al. Diffusion-weighted imaging and FDG PET/CT: predicting the prognoses with apparent diffusion coefficient values and maximum standardized uptake values in patients with invasive ductal carcinoma. World J Surg Oncol. 2012;10:126.
  54. Guo Y, Kong QC, Zhu YQ, et al. Whole-lesion histogram analysis of the apparent diffusion coefficient: Evaluation of the correlation with subtypes of mucinous breast carcinoma. J Magn Reson Imaging. 2018;47(2):391-400.
  55. Surov A, Wienke A, Meyer HJ. Pretreatment apparent diffusion coefficient does not predict therapy response to neoadjuvant chemotherapy in breast cancer [published online ahead of print, 2020 Jun 26]. 2020;53:59-67.
  56. Surov A, Meyer HJ, Wienke A. Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions. BMC Cancer. 2019;19(1):955. 
  57. Li T, Hong Y, Kong D, Li K. Histogram analysis of diffusion kurtosis imaging based on whole-volume images of breast lesions. J Magn Reson Imaging. 2020;51(2):627-34.
  58. Zhao M, Fu K, Zhang L, 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 Lett. 2018;16(4):5100-12.