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. 2024 Jun 20;25(7):623–633. doi: 10.3348/kjr.2023.1188

Diffusion-Weighted MRI for the Assessment of Molecular Prognostic Biomarkers in Breast Cancer

Mami Iima 1,2,✉, Masako Kataoka 2, Maya Honda 2,3, Denis Le Bihan 4
PMCID: PMC11214919  PMID: 38942456

Abstract

This study systematically reviewed the role of diffusion-weighted imaging (DWI) in the assessment of molecular prognostic biomarkers in breast cancer, focusing on the correlation of apparent diffusion coefficient (ADC) with hormone receptor status and prognostic biomarkers. Our meta-analysis includes data from 52 studies examining ADC values in relation to estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and Ki-67 status. The results indicated significant differences in ADC values among different receptor statuses, with ER-positive, PgR-positive, HER2-negative, and Ki-67-positive tumors having lower ADC values compared to their negative counterparts. This study also highlights the potential of advanced DWI techniques such as intravoxel incoherent motion and non-Gaussian DWI to provide additional insights beyond ADC. Despite these promising findings, the high heterogeneity among the studies underscores the need for standardized DWI protocols to improve their clinical utility in breast cancer management.

Keywords: Breast cancer, Apparent diffusion coefficient, Estrogen receptor, Progesterone receptor, HER-2, Ki-67

INTRODUCTION

Diffusion-weighted imaging (DWI), with more than 35 years of development, provides microstructural and functional information that complements the excellent anatomical details provided by MRI. Although dynamic contrast-enhanced breast MRI can detect malignancies with high sensitivity, its specificity is variable. It also requires the administration of a gadolinium contrast agent, which can cause nephrogenic systemic fibrosis in patients with renal dysfunction, possible tissue deposition with unknown long-term side effects, and contraindications in specific populations like pregnant women [1].

DWI enables the detection of lesions based on tissue microstructural features revealed by the diffusion of water molecules. The apparent diffusion coefficient (ADC), which eliminates the confounding T1 and T2 effects visible on DW images and provides a quantitative estimation of the water diffusion process in tissues, has been useful for differentiating between benign and malignant breast lesions [2]. Generally, DWI provides outstanding image contrast that reflects the architecture of cancer-specific tissue. Recent advances in MRI gradient hardware have enabled the study of diffusion time-dependent ADCs [3]. Within structured environments, such as cancers, interactions with barriers occur more frequently, thereby reducing the ADC. This makes time-dependent DWI particularly effective for cancer characterization.

Recently, the use of DWI as a complementary and potential alternative imaging technique for evaluating breast lesions has increased. In particular, DWI can provide information in vivo at a microscopic scale, even though DWI data are acquired at the millimeter scale in the form of an ADC that reflects the specific tissue features of cancer [4,5]. Breast DWI has many advantages that complement conventional breast MRI, thereby improving breast cancer diagnostic accuracy (particularly specificity) and reducing unnecessary biopsy rates in suspected cases of breast cancer.

Many radiologists include ADC in breast MRI diagnostic reports [6], although this parameter has not yet been included in the Breast Imaging Reporting and Data System (BI-RADS). There is a growing interest in making DWI a routine sequence in BI-RADS. However, the use of an ADC with specific cutoff values requires hospitals and institutions to standardize breast DWI or at least meet a minimum level of quality assurance. For these reasons, consensus and recommendations have been published by the International Breast DWI Working Group of the European Society of Breast Imaging [7] and Korean radiologists [8]. Some multicenter studies have revealed that the ADC has the potential to spare patients from unnecessary biopsies for breast cancer diagnosis [9,10], thus reducing patient anxiety and healthcare costs.

As numerous studies have highlighted, ADC is also pivotal in distinguishing hormone receptor statuses and prognostic biomarkers in breast cancer [11], particularly the estrogen receptor (ER), progesterone receptor (PgR), human epidermal growth factor receptor 2 (HER2), and the marker of proliferation Ki-67. In this context, we present a focused and comprehensive summary of evidence regarding the relationship between ADC and the status of crucial ER, PgR, HER-2, and Ki-67 biomarkers in breast cancer. Additionally, we discuss the use of DWI to determine hormone receptor status in breast cancer.

Relationship between ADC and Hormone Receptor Status of Breast Cancer

For a comprehensive, quantitative summary of the relevant data in the literature, we conducted a systematic review and meta-analysis following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. The following words were searched using the PubMed, Web of Science, and Google Scholar databases: “breast neoplasm or breast cancer or breast tumor” and “diffusion weighted MRI or diffusion-weighted imaging or diffusion weighted imaging or ADC or apparent diffusion coefficient” and “estrogen receptor or progesterone receptor” or “ER” or “PgR” or “human epidermal growth factor receptor 2” or “HER2 or HER-2.” We limited our search to English-language publications and data published between January 2008 and July 2023. The references cited by the relevant articles were manually scanned to search for other pertinent studies.

Two investigators selected eligible studies, and relevant data were independently extracted from the retrieved papers. The results from each study were examined cooperatively and a consensus was reached on all items through discussion and reexamination. All the authors approved the final decision regarding the studies to be admitted. The standardized mean difference in ADC with a 95% confidence interval was used as a summary statistic for ER, PgR, and HER2 categories. A total of 52 articles [3,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62] were finally reviewed and quantitatively summarized (Fig. 1).

Fig. 1. Flowchart of the screening process for the meta-analysis. MRI = magnetic resonance imaging, DWI = diffusion-weighted imaging, ADC = apparent diffusion coefficient, SD = standard deviation.

Fig. 1

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ADCs for Differentiation of ER Status

A forest plot of the mean differences in ADCs between ER-positive and -negative breast cancers from 44 studies [3,13,14,16,17,18,19,20,21,22,24,26,27,28,29,30,31,32,33,34,35,36,37,38,41,42,43,44,45,46,47,48,49,50,51,52,53,54,56,57,58,59,60,61] is given in Figure 2A. There was large heterogeneity among the studies (I2 = 80%), but overall, ER-positive cancers exhibited significantly lower ADCs than ER-negative cancers (P < 0.01).

Fig. 2. ADC differences in breast tumors by hormone receptor status. A: A forest plot of mean ADC (10-3 mm2/s) difference reported for ER-positive and ER-negative breast tumors. Tumors with positive ER status have significantly lower ADCs compared with receptor-negative tumors. B: Forest plot of mean ADC (10-3 mm2/s) difference reported for PgR-positive and PgR-negative breast tumors. Tumors with positive PgR status exhibit significantly lower ADCs compared with PgR-negative tumors. ADC = apparent diffusion coefficient, ER = estrogen receptor, PgR = progesterone receptor, SD = standard deviation, IV = weighted mean difference, CI = confidence interval, Chi2 = chi-squared test statistic, df = degrees of freedom, I2 = heterogeneity statistic, Z = Z-test statistic.

Fig. 2

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ADCs for Differentiation of PgR Status

A forest plot of the mean difference in ADCs between PgR-positive and -negative breast cancers from 41 studies [3,13,14,16,17,18,19,20,21,22,24,26,27,28,29,30,31,32,33,34,35,36,37,41,42,43,45,46,47,48,49,50,51,53,54,56,57,58,59,60,61] is provided in Figure 2B. It also showed large heterogeneity across the studies (I2 = 80%), but overall, PgR-positive cancers had significantly lower ADCs than PgR-negative cancers (P < 0.01).

ADCs for Differentiation of HER2 Status

A forest plot of the mean difference in ADCs between HER2-negative and positive cancers from 40 studies [3,15,16,17,18,20,21,24,25,26,27,28,29,31,32,33,34,36,37,38,41,42,43,44,45,46,47,48,49,50,51,52,53,54,56,57,58,59,60,61] is given in Figure 3A. Again, this showed a large heterogeneity across the studies (I2 = 92%). Overall, these results showed that HER2-negative cancers had significantly lower ADCs than HER2-positive cancers (P < 0.01).

Fig. 3. ADC differences in breast tumors by HER2 status and Ki-67 status. A: Forest plot of mean ADC (10-3 mm2/s) difference reported for breast tumors with negative and positive HER2 status. Tumors exhibiting negative HER2 status show significantly lower ADCs than those with positive HER2 status. B: Forest plot of mean ADC (10-3 mm2/s) difference reported for markers of proliferation Ki-67-positive and Ki-67-negative breast tumors. Tumors positive for Ki-67 show significantly lower ADCs compared to those with negative Ki-67 status. ADC = apparent diffusion coefficient, HER2 = human epidermal growth factor 2 receptor, SD = standard deviation, IV = weighted mean difference, CI = confidence interval, Chi2 = chi-squared test statistic, df = degrees of freedom, I2 = heterogeneity statistic, Z = Z-test statistic.

Fig. 3

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ADCs for Differentiation of Ki-67 Status

A forest plot of the mean difference in ADCs between Ki-67-positive and -negative cancers from 41 studies [3,12,13,14,15,16,17,18,20,21,23,24,26,27,28,31,32,33,34,35,36,37,38,39,40,42,43,45,46,47,48,51,52,53,54,55,57,59,60,61,62] is given in Figure 3B and shows that Ki-67-positive cancers have significantly lower ADCs than cancers with a negative Ki-67 status (P < 0.01), although a large heterogeneity was observed (I2 = 94%).

Clinical examples of different ER, PgR, HER2, and Ki-67 statuses are shown in Figure 4.

Fig. 4. Clinical examples of breast cancers (invasive ductal carcinomas). Contrast-enhanced images, diffusion-weighted images at a b value of 1000 sec/mm2, and ADC maps are shown. The ADC values and each ER, PgR, HER2, and Ki-67 status of breast cancers are also demonstrated. ADC = apparent diffusion coefficient, ER = estrogen receptor, PgR = progesterone receptor, HER-2 = human epidermal growth factor receptor 2.

Fig. 4

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ADCs for Differentiation of Breast Cancer Subtypes

The use of ADCs for the differentiation of breast cancer subtypes has yielded mixed findings, and a meta-analysis has shown that ADC cannot differentiate between breast cancer subtypes [63]. Further investigation is required to verify these results.

ADCs for Cancer Diagnosis, Predicting Treatment Response, and Prognosis

These results show that ER-positivity, PgR-positivity, and HER2-negativity are all related to lower ADC values, although there was a high degree of heterogeneity across the studies. Regarding the relationship between ADC and Ki-67 status, the overall results are consistent with recent clinical and preclinical studies showing that changes in ADC at different diffusion times may provide information on Ki-67 status [3,64] and suggesting that Ki-67 is a useful marker of tissue proliferation at a microscopic scale. In contrast, other studies have reported mixed results regarding the ability of ADCs to differentiate between high and low expression levels of the nuclear protein Ki-67 in breast cancer [2], and a recent multicenter study revealed a relatively low diagnostic performance (AUC: 0.6) of ADCs in such cases [11]. One reason for the variability in these findings might be the differing thresholds for considering Ki-67 as positive in breast cancer, which range from 10% to 50% [11]. Further research is needed to improve the accuracy and reliability of ADC-based differentiation of the Ki-67 status. In addition, although our meta-analysis elucidated the correlations between ADC values and the status of hormone receptors, including ER, PgR, HER2, and Ki-67 in breast cancer, the precise effect size of these correlations remains to be definitively established, warranting further research for more conclusive insights. Corrections based on the lesion size may be necessary to understand these relationships more accurately.

Specific ADC thresholds could help identify patients at higher risk, leading to a more personalized treatment for breast cancer. These findings pave the way for future studies to better use ADCs to determine the hormone receptor status. However, the interpretation of these results may have been influenced by the chosen diffusion time, histological threshold, and inconsistent acquisition parameters. The high inconsistency among studies, possibly exacerbated by small sample sizes, highlights the critical need for standardization in research methodologies. In addition, small mean differences in ADC values associated with hormone receptor status were observed in this study, which might have been influenced by the heterogeneity of the acquisition protocol. The standardization of ADC values, including reproducibility, is critical for verifying these changes.

Some multicenter studies have revealed that the ADC has the potential to spare patients from unnecessary biopsies for breast cancer diagnosis [9,10], thus reducing patient anxiety and healthcare costs. In addition to diagnosis, the ADC has been investigated as a potential predictor of treatment response in patients with breast cancer. The American College of Radiology Imaging Network conducted a multicenter study to assess the usefulness of ADCs in predicting the response to neoadjuvant chemotherapy in patients with breast cancer [65]. This study revealed that changes in ADCs during treatment could predict a pathological response to neoadjuvant chemotherapy and that mid-treatment changes in ADCs were predictive of hormone receptor-positive/HER2-negative cancers. Predicting treatment responses using the ADC may help guide treatment decisions and avoid ineffective therapies.

Beyond ADC: IVIM and Non-Gaussian DWI

Intravoxel incoherent motion (IVIM) and non-Gaussian DWI can provide additional information on tissue microstructure and microvasculature compared with standard ADC measurements. IVIM can be used to evaluate perfusion in capillaries, primarily at low b values (< 200 s/mm2). Large b values (> 1000 s/mm2) predominantly indicated hindered or restricted diffusion, and the non-normality of DWI could be quantified using the kurtosis model (Fig. 5). The ability to accurately characterize tissue microstructure and microvasculature can enhance the diagnostic accuracy of breast MRI, and IVIM has shown promise in differentiating between malignant and benign breast lesions [66,67]. Mean kurtosis values have also been found to be useful for differentiating invasive ductal carcinoma from ductal carcinoma in situ [66]. The difference between the maximum and minimum ADCs, together with the kurtosis value calculated from a non-Gaussian diffusion model, may also help predict metastatic breast cancer [68,69]. While IVIM and non-Gaussian DWI often require the acquisition of large sets of diffusion-weighted images and dedicated data processing, recent approaches have shown that IVIM and non-Gaussian diffusion information can be collected using ad hoc parameters, such as the shifted ADC [4] and signature index [4,70]. The latter, in particular, has been shown to provide information on cancer subtypes and hormone status [71]. However, it is clear that these advanced DWI techniques require standardization in terms of both acquisition and analysis.

Fig. 5. Example of a diffusion signal decay in typical breast lesions. IVIM offers insights into blood microcirculation and tissue vascularity, whereas the ADC reflects both intracellular and extracellular water diffusion, providing information about tissue cellularity. Kurtosis imaging measures the deviation from Gaussian water diffusion, offering information about hindrances caused by the tissue microstructure. IVIM = intravoxel incoherent motion, ADC = apparent diffusion coefficient.

Fig. 5

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CONCLUSION

Breast DWI is a rapidly growing and valuable methodology for detecting and characterizing breast cancer and predicting treatment responses. It is also safe for most women. Although ADCs have been reported to significantly correlate with some molecular prognostic biomarkers and some trends might be evident, as demonstrated in the meta-analysis, there remains a lack of consensus among studies. Additionally, the accuracy of breast cancer diagnosis using DWI may be influenced by the selected diffusion times and histological thresholds, underscoring the need for standardized DWI protocols for breast cancer diagnosis. Accumulating evidence suggests that several alternative measures, including IVIM and non-Gaussian DWI, can serve as useful imaging biomarkers in clinical settings.

Acknowledgments

We thank Eric Sigmund for providing valuable advice on the manuscript. We also thank Edanz Group for editing a draft of this manuscript.

Footnotes

Conflicts of Interest: The authors have no potential conflicts of interest to disclose.

Author Contributions:
  • Conceptualization: Mami Iima.
  • Data curation: Mami Iima, Masako Kataoka, Maya Honda.
  • Formal analysis: Mami Iima, Masako Kataoka.
  • Funding acquisition: Mami Iima.
  • Investigation: all authors.
  • Methodology: Mami Iima, Masako Kataoka, Maya Honda.
  • Project administration: Mami Iima, Masako Kataoka.
  • Resources: all authors.
  • Software: Mami Iima, Masako Kataoka, Maya Honda.
  • Supervision: Mami Iima, Denis Le Bihan.
  • Validation: all authors.
  • Visualization: all authors.
  • Writing—original draft: Mami Iima, Masako Kataoka, Maya Honda.
  • Writing—review & editing: all authors.

Funding Statement: This research was partially supported by AMED under grant number JP22he0422025j0001.

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