Diffusion-weighted imaging of breast tumours at 3 Tesla and 7 Tesla: a comparison

Diffusion-Weighted MRI-Based Virtual Elastography and Shear-Wave Elastography for the Assessment of Breast Lesions

BACKGROUND

Diffusion-weighted imaging (DWI)-based virtual MR elastography (DWI-vMRE) in the assessment of breast lesions is still in the research stage.

PURPOSE

To investigate the usefulness of elasticity values on DWI-vMRE in the evaluation of breast lesions, and the correlation with the values calculated from shear-wave elastography (SWE).

STUDY TYPE

Prospective.

POPULATION/SUBJECTS

153 patients (mean age ± standard deviation: 55 ± 12 years) with 153 pathological confirmed breast lesions (24 benign and 129 malignant lesions).

FIELD STRENGTH/SEQUENCE

1.5-T MRI, multi-b readout segmented echo planar imaging (b-values of 0, 200, 800, and 1000 sec/mm2).

ASSESSMENT

For DWI-vMRE assessment, lesions were manually segmented using apparent diffusion coefficient (ADC0-1000) map, then the region of interests were copied to the map of shifted-ADC (sADC200-800, sADC 200-1500). For SWE assessment, the shear modulus of the lesions was measured by US elastic modulus (μUSE). Intraclass/interclass kappa coefficients were calculated to measure the consistency.

STATISTICAL TESTS

Pearson's correlation was used to assess the relationship between sADC and μUSE. A receiver operating characteristic analysis with the area under the curve (AUC) was performed to compare the diagnostic accuracy between benign and malignant breast lesions of sADC and μUSE. A P value <0.05 was considered statistically significant.

RESULTS

There were significant differences between benign and malignant breast lesions of μUSE (24.17 ± 10.64 vs. 37.20 ± 12.61), sADC200-800 (1.38 ± 0.31 vs. 0.97 ± 0.18 × 10-3 mm2/sec), and sADC200-1500 (1.14 ± 0.30 vs. 0.78 ± 0.13 × 10-3 mm2/sec). In all breast lesions, a moderate but significant correlation was observed between μUSE and sADC200-800/sADC200-1500 (r = -0.49/-0.44). AUC values to differentiate benign from malignant lesions were as follows: μUSE, 0.78; sADC200-800, 0.89; sADC200-1500, 0.89.

DATA CONCLUSIONS

Both SWE and DWI-vMRE could be used for the differentiation of benign versus malignant breast lesions. Furthermore, DWI-vMRE with the use of sADC show relatively higher AUC values than SWE.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Diffusion Breast MRI: Current Standard and Emerging Techniques

The role of diffusion weighted imaging (DWI) as a biomarker has been the subject of active investigation in the field of breast radiology. By quantifying the random motion of water within a voxel of tissue, DWI provides indirect metrics that reveal cellularity and architectural features. Studies show that data obtained from DWI may provide information related to the characterization, prognosis, and treatment response of breast cancer. The incorporation of DWI in breast imaging demonstrates its potential to serve as a non-invasive tool to help guide diagnosis and treatment. In this review, current technical literature of diffusion-weighted breast imaging will be discussed, in addition to clinical applications, advanced techniques, and emerging use in the field of radiomics.

Radiomics-based machine learning analysis and characterization of breast lesions with multiparametric diffusion-weighted MR

Background

This study aimed to evaluate the utility of radiomics-based machine learning analysis with multiparametric DWI and to compare the diagnostic performance of radiomics features and mean diffusion metrics in the characterization of breast lesions.

Methods

This retrospective study included 542 lesions from February 2018 to November 2018. One hundred radiomics features were computed from mono-exponential (ME), biexponential (BE), stretched exponential (SE), and diffusion-kurtosis imaging (DKI). Radiomics-based analysis was performed by comparing four classifiers, including random forest (RF), principal component analysis (PCA), L1 regularization (L1R), and support vector machine (SVM). These four classifiers were trained on a training set with 271 patients via ten-fold cross-validation and tested on an independent testing set with 271 patients. The diagnostic performance of the mean diffusion metrics of ME (mADC_{all b}, mADC_0–1000), BE (mD, mD^*, mf), SE (mDDC, mα), and DKI (mK, mD) were also calculated for comparison. The area under the receiver operating characteristic curve (AUC) was used to compare the diagnostic performance.

Results

RF attained higher AUCs than L1R, PCA and SVM. The AUCs of radiomics features for the differential diagnosis of breast lesions ranged from 0.80 (BE_D*) to 0.85 (BE_D). The AUCs of the mean diffusion metrics ranged from 0.54 (BE_mf) to 0.79 (ME_mADC_0–1000). There were significant differences in the AUCs between the mean values of all diffusion metrics and radiomics features of AUCs (all P < 0.001) for the differentiation of benign and malignant breast lesions. Of the radiomics features computed, the most important sequence was BE_D (AUC: 0.85), and the most important feature was FO-10 percentile (Feature Importance: 0.04).

Conclusions

The radiomics-based analysis of multiparametric DWI by RF enables better differentiation of benign and malignant breast lesions than the mean diffusion metrics.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03117-5.

Diagnostic performance of breast tumor tissue selection in diffusion weighted imaging: A systematic review and meta-analysis

BACKGROUND

Several methods for tumor delineation are used in literature on breast diffusion weighted imaging (DWI) to measure the apparent diffusion coefficient (ADC). However, in the process of reaching consensus on breast DWI scanning protocol, image analysis and interpretation, still no standardized optimal breast tumor tissue selection (BTTS) method exists. Therefore, the purpose of this study is to assess the impact of BTTS methods on ADC in the discrimination of benign from malignant breast lesions in DWI in terms of sensitivity, specificity and area under the curve (AUC).

METHODS AND FINDINGS

In this systematic review and meta-analysis, adhering to the PRISMA statement, 61 studies, with 65 study subsets, in females with benign or malignant primary breast lesions (6291 lesions) were assessed. Studies on DWI, quantified by ADC, scanned on 1.5 and 3.0 Tesla and using b-values 0/50 and ≥ 800 s/mm2 were included. PubMed and EMBASE were searched for studies up to 23-10-2019 (n = 2897). Data were pooled based on four BTTS methods (by definition of measured region of interest, ROI): BTTS1: whole breast tumor tissue selection, BTTS2: subtracted whole breast tumor tissue selection, BTTS3: circular breast tumor tissue selection and BTTS4: lowest diffusion breast tumor tissue selection. BTTS methods 2 and 3 excluded necrotic, cystic and hemorrhagic areas. Pooled sensitivity, specificity and AUC of the BTTS methods were calculated. Heterogeneity was explored using the inconsistency index (I2) and considering covariables: field strength, lowest b-value, image of BTTS selection, pre-or post-contrast DWI, slice thickness and ADC threshold. Pooled sensitivity, specificity and AUC were: 0.82 (0.72-0.89), 0.79 (0.65-0.89), 0.88 (0.85-0.90) for BTTS1; 0.91 (0.89-0.93), 0.84 (0.80-0.87), 0.94 (0.91-0.96) for BTTS2; 0.89 (0.86-0.92), 0.90 (0.85-0.93), 0.95 (0.93-0.96) for BTTS3 and 0.90 (0.86-0.93), 0.84 (0.81-0.87), 0.86 (0.82-0.88) for BTTS4, respectively. Significant heterogeneity was found between studies (I2 = 95).

CONCLUSIONS

None of the breast tissue selection (BTTS) methodologies outperformed in differentiating benign from malignant breast lesions. The high heterogeneity of ADC data acquisition demands further standardization, such as DWI acquisition parameters and tumor tissue selection to substantially increase the reliability of DWI of the breast.

Role of Breast MRI in the Evaluation and Detection of DCIS: Opportunities and Challenges

Historically, breast magnetic resonance imaging (MRI) was not considered an effective modality in the evaluation of ductal carcinoma in situ (DCIS). Over the past decade this has changed, with studies demonstrating that MRI is the most sensitive imaging tool for detection of all grades of DCIS. It has been suggested that not only is breast MRI the most sensitive imaging tool for detection but it may also detect the most clinically relevant DCIS lesions. The role and outcomes of MRI in the preoperative setting for patients with DCIS remains controversial; however, several studies have shown benefit in the preoperative evaluation of extent of disease as well as predicting an underlying invasive component. The most common presentation of DCIS on MRI is nonmass enhancement (NME) in a linear or segmental distribution pattern. Maximizing breast MRI spatial resolution is therefore beneficial, given the frequent presentation of DCIS as NME on MRI. Emerging MRI techniques, such as diffusion-weighted imaging (DWI), have shown promising potential to discriminate DCIS from benign and invasive lesions. Future opportunities including advanced imaging visual techniques, radiomics/radiogenomics, and machine learning / artificial intelligence may also be applicable to the detection and treatment of DCIS. Level of Evidence: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2020;52:697-709.

7T CEST MRI: A potential imaging tool for the assessment of tumor grade and cell proliferation in breast cancer

OBJECTIVES

To investigate the feasibility of chemical exchange saturation transfer (CEST) MRI in patients with breast carcinomas and possible correlations between magnetization transfer asymmetry (MTR_asym) values and histological features, such as tumor grade and the Ki-67 proliferation index.

MATERIALS AND METHODS

Nine healthy subjects and 18 female patients were enrolled for this study. The imaging protocol for the patients consisted of diffusion-weighted imaging (DWI), CEST imaging, and T1-weighted, contrast-enhanced (CE)-MRI. CEST was performed using a 3D gradient echo (GRE) sequence, employing eight pre-saturation pulses of a duration of 50 ms and a duty cycle (DC) of 80%, with a mean amplitude of the saturation pulse train of 1 μT. The Z-spectrum was plotted and MTR_asym values calculated for the frequency of the maximum of MTR_asym curve, were correlated with the Ki-67 proliferation index and apparent diffusion coefficient (ADC). Patient data were statistically assessed using the Games-Howell post-hoc and Pearson's correlation test.

RESULTS

Different tumor types had asymmetry peaks at different positions of Z-spectrum. MTR_asym (mean ± SD) (%) calculated for G1 (3.0 ± 0.3; range: 2.70-3.50) was not significantly lower than for G2 (4.50 ± 1.30; range: 3.20-6.50; p = 0.066). In contrast, the increase in MTR_asym between G1 and G3 (6.40 ± 1.70; range: 4.80-9.80) lesions was significant (p = 0.007). No significant difference was observed between G2 and G3 with regard to MTR_asym (p = 0.089). There was a strong positive correlation between the MTR_asym, and Ki-67 proliferation index (r = 0.890; p = 0.001), while there was a moderate negative correlation between MTR_asym and ADC values (r = -0.506; p = 0.027).

CONCLUSIONS

Calculated MTR_asym demonstrates a strong positive correlation with tumor proliferation and has the potential to become a valuable biomarker for breast tumor characterization.

The potential of multiparametric MRI of the breast

MRI is an essential tool in breast imaging, with multiple established indications. Dynamic contrast-enhanced MRI (DCE-MRI) is the backbone of any breast MRI protocol and has an excellent sensitivity and good specificity for breast cancer diagnosis. DCE-MRI provides high-resolution morphological information, as well as some functional information about neoangiogenesis as a tumour-specific feature. To overcome limitations in specificity, several other functional MRI parameters have been investigated and the application of these combined parameters is defined as multiparametric MRI (mpMRI) of the breast. MpMRI of the breast can be performed at different field strengths (1.5-7 T) and includes both established (diffusion-weighted imaging, MR spectroscopic imaging) and novel MRI parameters (sodium imaging, chemical exchange saturation transfer imaging, blood oxygen level-dependent MRI), as well as hybrid imaging with positron emission tomography (PET)/MRI and different radiotracers. Available data suggest that multiparametric imaging using different functional MRI and PET parameters can provide detailed information about the underlying oncogenic processes of cancer development and progression and can provide additional specificity. This article will review the current and emerging functional parameters for mpMRI of the breast for improved diagnostic accuracy in breast cancer.

Diffusion-weighted MRI of breast lesions: a prospective clinical investigation of the quantitative imaging biomarker characteristics of reproducibility, repeatability, and diagnostic accuracy

Diffusion-weighted MRI (DWI) provides insights into tissue microstructure by visualization and quantification of water diffusivity. Quantitative evaluation of the apparent diffusion coefficient (ADC) obtained from DWI has been proven helpful for differentiating between malignant and benign breast lesions, for cancer subtyping in breast cancer patients, and for prediction of response to neoadjuvant chemotherapy. However, to further establish DWI of breast lesions it is important to evaluate the quantitative imaging biomarker (QIB) characteristics of reproducibility, repeatability, and diagnostic accuracy. In this intra-individual prospective clinical study 40 consecutive patients with suspicious findings, scheduled for biopsy, underwent an identical 3T breast MRI protocol of the breast on two consecutive days (>24 h). Mean ADC of target lesions was assessed (two independent readers) in four separate sessions. Reproducibility, repeatability, and diagnostic accuracy between examinations (E1, E2), readers (R1, R2), and measurements (M1, M2) were assessed with intraclass correlation coefficients (ICCs), coefficients of variation (CVs), Bland-Altman plots, and receiver operating characteristic (ROC) analysis with calculation of the area under the ROC curve (AUC). The standard of reference was either histopathology (n = 38) or imaging follow-up of up to 24 months (n = 2). Eighty breast MRI examinations (median E1-E2, 2 ± 1.7 days, 95% confidence interval (CI) 1-2 days, range 1-11 days) in 40 patients (mean age 56, standard deviation (SD) ±14) were evaluated. In 55 target lesions (mean size 25.2 ± 20.8 (SD) mm, range 6-106 mm), mean ADC values were significantly (P < 0.0001) higher in benign (1.38, 95% CI 1.27-1.49 × 10(-3)  mm(2) /s) compared with malignant (0.86, 95% CI 0.81-0.91 × 10(-) (3)  mm(2) /s) lesions. Reproducibility and repeatability showed high agreement for repeated examinations, readers, and measurements (all ICCs >0.9, CVs 3.2-8%), indicating little variation. Bland-Altman plots demonstrated no systematic differences, and diagnostic accuracy was not significantly different in the two repeated examinations (all ROC curves >0.91, P > 0.05). High reproducibility, repeatability, and diagnostic accuracy of DWI provide reliable characteristics for its use as a potential QIB, to further improve breast lesion detection, characterization, and treatment monitoring of breast lesions.

Diffusion-weighted breast MRI: Clinical applications and emerging techniques

Diffusion-weighted MRI (DWI) holds potential to improve the detection and biological characterization of breast cancer. DWI is increasingly being incorporated into breast MRI protocols to address some of the shortcomings of routine clinical breast MRI. Potential benefits include improved differentiation of benign and malignant breast lesions, assessment and prediction of therapeutic efficacy, and noncontrast detection of breast cancer. The breast presents a unique imaging environment with significant physiologic and inter-subject variations, as well as specific challenges to achieving reliable high quality diffusion-weighted MR images. Technical innovations are helping to overcome many of the image quality issues that have limited widespread use of DWI for breast imaging. Advanced modeling approaches to further characterize tissue perfusion, complexity, and glandular organization may expand knowledge and yield improved diagnostic tools.

LEVEL OF EVIDENCE

5 J. Magn. Reson. Imaging 2016 J. Magn. Reson. Imaging 2017;45:337-355.