The Rate of Apparent Diffusion Coefficient Change With Diffusion Time on Breast Diffusion-Weighted Imaging Depends on Breast Tumor Types and Molecular Prognostic Biomarker Expression

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

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.

Performance of Diffusion-weighted Imaging-based Noncontrast MRI Protocols for Diagnosis of Breast Cancer: A Systematic Review and Meta-Analysis

Background Diffusion-weighted imaging (DWI) is increasingly recognized as a powerful diagnostic tool and tested alternative to contrast-enhanced (CE) breast MRI. Purpose To perform a systematic review and meta-analysis that assesses the diagnostic performance of DWI-based noncontrast MRI protocols (ncDWI) for the diagnosis of breast cancer. Materials and Methods A systematic literature search in PubMed for articles published from January 1985 to September 2023 was performed. Studies were excluded if they investigated malignant lesions or selected patients and/or lesions only, used DWI as an adjunct technique to CE MRI, or were technical studies. Statistical analysis included pooling of diagnostic accuracy and investigating between-study heterogeneity. Additional subgroup comparisons of ncDWI to CE MRI and standard mammography were performed. Results A total of 28 studies were included, with 4406 lesions (1676 malignant, 2730 benign) in 3787 patients. The pooled sensitivity and specificity of ncDWI were 86.5% (95% CI: 81.4, 90.4) and 83.5% (95% CI: 76.9, 88.6), and both measures presented with high between-study heterogeneity (I 2 = 81.6% and 91.6%, respectively; P < .001). CE MRI (18 studies) had higher sensitivity than ncDWI (95.1% [95% CI: 92.9, 96.7] vs 88.9% [95% CI: 82.4, 93.1], P = .004) at similar specificity (82.2% [95% CI: 75.0, 87.7] vs 82.0% [95% CI: 74.8, 87.5], P = .97). Compared with ncDWI, mammography (five studies) showed no evidence of a statistical difference for sensitivity (80.3% [95% CI: 56.3, 93.3] vs 56.7%; [95% CI: 41.9, 70.4], respectively; P = .09) or specificity (89.9% [95% CI: 85.5, 93.1] vs 90% [95% CI: 61.3, 98.1], respectively; P = .62), but ncDWI had a higher area under the summary receiver operating characteristic curve (0.93 [95% CI: 0.91, 0.95] vs 0.78 [95% CI: 0.74, 0.81], P < .001). Conclusion A direct comparison with CE MRI showed a modestly lower sensitivity at similar specificity for ncDWI, and higher diagnostic performance indexes for ncDWI than standard mammography. Heterogeneity was high, thus these results must be interpreted with caution. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Kataoka and Iima in this issue.

Multiparametric Approach to Breast Cancer With Emphasis on Magnetic Resonance Imaging in the Era of Personalized Breast Cancer Treatment

ABSTRACT

A multiparametric approach to breast cancer imaging offers the advantage of integrating the diverse contributions of various parameters. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is the most important MRI sequence for breast imaging. The vascularity and permeability of lesions can be estimated through the use of semiquantitative and quantitative parameters. The increased use of ultrafast DCE-MRI has facilitated the introduction of novel kinetic parameters. In addition to DCE-MRI, diffusion-weighted imaging provides information associated with tumor cell density, with advanced diffusion-weighted imaging techniques such as intravoxel incoherent motion, diffusion kurtosis imaging, and time-dependent diffusion MRI opening up new horizons in microscale tissue evaluation. Furthermore, T2-weighted imaging plays a key role in measuring the degree of tumor aggressiveness, which may be related to the tumor microenvironment. Magnetic resonance imaging is, however, not the only imaging modality providing semiquantitative and quantitative parameters from breast tumors. Breast positron emission tomography demonstrates superior spatial resolution to whole-body positron emission tomography and allows comparable delineation of breast cancer to MRI, as well as providing metabolic information, which often precedes vascular and morphological changes occurring in response to treatment. The integration of these imaging-derived factors is accomplished through multiparametric imaging. In this article, we explore the relationship among the key imaging parameters, breast cancer diagnosis, and histological characteristics, providing a technical and theoretical background for these parameters. Furthermore, we review the recent studies on the application of multiparametric imaging to breast cancer and the significance of the key imaging parameters.

Radiogenomics: a key component of precision cancer medicine

Radiogenomics, focusing on the relationship between genomics and imaging phenotypes, has been widely applied to address tumour heterogeneity and predict immune responsiveness and progression. It is an inevitable consequence of current trends in precision medicine, as radiogenomics costs less than traditional genetic sequencing and provides access to whole-tumour information rather than limited biopsy specimens. By providing voxel-by-voxel genetic information, radiogenomics can allow tailored therapy targeting a complete, heterogeneous tumour or set of tumours. In addition to quantifying lesion characteristics, radiogenomics can also be used to distinguish benign from malignant entities, as well as patient characteristics, to better stratify patients according to disease risk, thereby enabling more precise imaging and screening. Here, we have characterised the radiogenomic application in precision medicine using a multi-omic approach. we outline the main applications of radiogenomics in diagnosis, treatment planning and evaluations in the field of oncology with the aim of developing quantitative and personalised medicine. Finally, we discuss the challenges in the field of radiogenomics and the scope and clinical applicability of these methods.

Evaluation of apparent diffusion coefficient of two-dimensional BLADE turbo gradient- and spin-echo diffusion-weighted imaging with a breast phantom

This study aimed to evaluate the reliability of apparent diffusion coefficient (ADC) values generated with two-dimensional turbo gradient- and spin-echo with BLADE trajectory diffusion-weighted imaging (TGSE-BLADE-DWI) sequence using a breast diffusion phantom. TGSE-BLADE-DWI and single-shot spin-echo echo-planar imaging (SS-EPI-DWI) were performed using a 3.0 T magnetic resonance imaging scanner. Concordance rates of ADC values and the signal-to-noise ratio (SNR) were compared between TGSE-BLADE-DWI and SS-EPI-DWI. TGSE-BLADE-DWI provided a higher concordance rate for ADC values than SS-EPI-DWI when b-values > 2000s/mm² and a slice thickness of 1 mm were used. TGSE-BLADE-DWI showed less image distortion than SS-EPI-DWI. The SNR of TGSE-BLADE-DWI was higher than that of SS-EPI-DWI, except at a number of excitations of 7 and a slice thickness of 1 mm. In conclusion, TGSE-BLADE-DWI can offer a better SNR, less distortion, and more reliable ADC measurements than SS-EPI-DWI in a breast phantom.

The road to breast cancer screening with diffusion MRI

Breast cancer is the leading cause of cancer in women with a huge medical, social and economic impact. Mammography (MMG) has been the gold standard method until now because it is relatively inexpensive and widely available. However, MMG suffers from certain limitations, such as exposure to X-rays and difficulty of interpretation in dense breasts. Among other imaging methods, MRI has clearly the highest sensitivity and specificity, and breast MRI is the gold standard for the investigation and management of suspicious lesions revealed by MMG. Despite this performance, MRI, which does not rely on X-rays, is not used for screening except for a well-defined category of women at risk, because of its high cost and limited availability. In addition, the standard approach to breast MRI relies on Dynamic Contrast Enhanced (DCE) MRI with the injection of Gadolinium based contrast agents (GBCA), which have their own contraindications and can lead to deposit of gadolinium in tissues, including the brain, when examinations are repeated. On the other hand, diffusion MRI of breast, which provides information on tissue microstructure and tumor perfusion without the use of contrast agents, has been shown to offer higher specificity than DCE MRI with similar sensitivity, superior to MMG. Diffusion MRI thus appears to be a promising alternative approach to breast cancer screening, with the primary goal of eliminating with a very high probability the existence of a life-threatening lesion. To achieve this goal, it is first necessary to standardize the protocols for acquisition and analysis of diffusion MRI data, which have been found to vary largely in the literature. Second, the accessibility and cost-effectiveness of MRI examinations must be significantly improved, which may become possible with the development of dedicated low-field MRI units for breast cancer screening. In this article, we will first review the principles and current status of diffusion MRI, comparing its clinical performance with MMG and DCE MRI. We will then look at how breast diffusion MRI could be implemented and standardized to optimize accuracy of results. Finally, we will discuss how a dedicated, low-cost prototype of breast MRI system could be implemented and introduced to the healthcare market.

Investigation of breast cancer microstructure and microvasculature from time-dependent DWI and CEST in correlation with histological biomarkers

We investigated the associations of time-dependent DWI, non-Gaussian DWI, and CEST parameters with histological biomarkers in a breast cancer xenograft model. 22 xenograft mice (7 MCF-7 and 15 MDA-MB-231) were scanned at 4 diffusion times [T_d = 2.5/5 ms with 11 b-values (0–600 s/mm²) and T_d = 9/27.6 ms with 17 b-values (0–3000 s/mm²), respectively]. The apparent diffusion coefficient (ADC) was estimated using 2 b-values in different combinations (ADC^0–600 using b = 0 and 600 s/mm² and shifted ADC [sADC^200–1500] using b = 200 and 1500 s/mm²) at each of those diffusion times. Then the change (Δ) in ADC/sADC between diffusion times was evaluated. Non-Gaussian diffusion and intravoxel incoherent motion (IVIM) parameters (ADC₀, the virtual ADC at b = 0; K, Kurtosis from non-Gaussian diffusion; f, the IVIM perfusion fraction) were estimated. CEST images were acquired and the amide proton transfer signal intensity (APT SI) were measured. The ΔsADC_9–27.6 (between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{sADC}}_{{9\,{\text{ms}}}}^{200{-}1500}$$\end{document}sADC9ms200-1500 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{sADC}}_{{27.6\,{\text{ms}}}}^{200{-}1500}$$\end{document}sADC27.6ms200-1500 and ΔADC_2.5_sADC_27.6 (between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{ADC}}_{{2.5\, {\text{ms}}}}^{0{-}600}$$\end{document}ADC2.5ms0-600 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{sADC}}_{{27.6\,{\text{ms}}}}^{200{-}1500}$$\end{document}sADC27.6ms200-1500) was significantly larger for MCF-7 groups, and ΔADC_2.5_sADC_27.6 was positively correlated with Ki67_max and APT SI. ADC₀ decreased significantly in MDA-MB-231 group and K increased significantly with T_d in MCF-7 group. APT SI and cellular area had a moderately strong positive correlation in MDA-MB-231 and MCF-7 tumors combined, and there was a positive correlation in MDA-MB-231 tumors. There was a significant negative correlation between APT SI and the Ki-67-positive ratio in MDA-MB-231 tumors and when combined with MCF-7 tumors. The associations of ΔADC_2.5_sADC_27.6 and API SI with Ki-67 parameters indicate that the T_d-dependent DW and CEST parameters are useful to predict the histological markers of breast cancers.

Repeatability and Reproducibility of ADC Measurements and MRI Signal Intensity Measurements of Bone Marrow in Monoclonal Plasma Cell Disorders: A Prospective Bi-institutional Multiscanner, Multiprotocol Study

BACKGROUND/OBJECTIVES

Apparent diffusion coefficient (ADC) and signal intensity (SI) measurements play an increasing role in magnetic resonance imaging (MRI) of monoclonal plasma cell disorders. The purpose of this study was to assess interrater variability, repeatability, and reproducibility of ADC and SI measurements from bone marrow (BM) under variation of MRI protocols and scanners.

PATIENTS AND METHODS

Fifty-five patients with suspected or confirmed monoclonal plasma cell disorder were prospectively included in this institutional review board-approved study and underwent several measurements after the standard clinical whole-body MR scan, including repeated scan after repositioning, scan with a second MRI protocol, scan at a second 1.5 T scanner with a harmonized MRI protocol, and scan at a 3 T scanner. For T1-weighted, T2-weighted STIR, B800 images, and ADC maps, regions of interest were placed in the BM of the iliac crest and sacral bone, and in muscle tissue for image normalization. Bland-Altman plots were constructed, and absolute bias, relative bias to mean, limits of agreement, and coefficients of variation were calculated.

RESULTS

Interrater variability and repeatability experiments showed a maximal relative bias of -0.077 and a maximal coefficient of variation of 16.2% for all sequences. Although the deviations at the second 1.5 T scanner with harmonized MRI protocol to the first 1.5 T scanner showed a maximal relative bias of 0.124 for all sequences, the variation of the MRI protocol and scan at the 3 T scanner led to large relative biases of up to -0.357 and -0.526, respectively. When comparing the 3 T scanner to the 1.5 T scanner, normalization to muscle reduced the bias of T1-weighted and T2-weighted sequences, but not of ADC maps.

CONCLUSIONS

The MRI scanners with identical field strength and harmonized MRI protocols can provide relatively stable quantitative measurements of BM ADC and SI. Deviations in MRI field strength and MRI protocol should be avoided when applying ADC cutoff values, which were established at other scanners or when performing multicentric imaging trials.

Breast Cancer Subtypes and Quantitative Magnetic Resonance Imaging: A Systemic Review

Magnetic resonance imaging (MRI) is the most sensitive imaging modality for breast cancer detection. This systematic review investigated the role of quantitative MRI features in classifying molecular subtypes of breast cancer. We performed a literature search of articles published on the application of quantitative MRI features in invasive breast cancer molecular subtype classification in PubMed from 1 January 2002 to 30 September 2021. Of the 1275 studies identified, 106 studies with a total of 12,989 patients fulfilled the inclusion criteria. Bias was assessed based using the Quality Assessment of Diagnostic Studies. All studies were case-controlled and research-based. Most studies assessed quantitative MRI features using dynamic contrast-enhanced (DCE) kinetic features and apparent diffusion coefficient (ADC) values. We present a summary of the quantitative MRI features and their correlations with breast cancer subtypes. In DCE studies, conflicting results have been reported; therefore, we performed a meta-analysis. Significant differences in the time intensity curve patterns were observed between receptor statuses. In 10 studies, including a total of 1276 lesions, the pooled difference in proportions of type Ⅲ curves (wash-out) between oestrogen receptor-positive and -negative cancers was not significant (95% confidence interval (CI): [−0.10, 0.03]). In nine studies, including a total of 1070 lesions, the pooled difference in proportions of type 3 curves between human epidermal growth factor receptor 2-positive and -negative cancers was significant (95% CI: [0.01, 0.14]). In six studies including a total of 622 lesions, the pooled difference in proportions of type 3 curves between the high and low Ki-67 groups was significant (95% CI: [0.17, 0.44]). However, the type 3 curve itself is a nonspecific finding in breast cancer. Many studies have examined the relationship between mean ADC and breast cancer subtypes; however, the ADC values overlapped significantly between subtypes. The heterogeneity of ADC using kurtosis or difference, diffusion tensor imaging parameters, and relaxation time was reported recently with promising results; however, current evidence is limited, and further studies are required to explore these potential applications.