Diffusion-Weighted Magnetic Resonance Imaging of Breast Lesions: First Experiences at 3 T

Enhancing the Kaiser score for lesion characterization in unenhanced breast MRI

PURPOSE

To adapt the methodology of the Kaiser score, a clinical decision rule for lesion characterization in breast MRI, for unenhanced protocols.

METHOD

In this retrospective IRB-approved cross-sectional study, we included 93 consecutive patients who underwent breast MRI between 2021 and 2023 for further work-up of BI-RADS 0, 3-5 in conventional imaging or for staging purposes (BI-RADS 6). All patients underwent biopsy for histologic verification or were followed for a minimum of 12 months. MRI scans were conducted using 1.5 T or 3 T scanners using dedicated breast coils and a protocol in line with international recommendations including DWI and ADC. Lesion characterization relied solely on T2w and DWI/ADC-derived features (such as lesion type, margins, shape, internal signal, surrounding tissue findings, ADC value). Statistical analysis was done using decision tree analysis aiming to distinguish benign (histology/follow-up) from malignant outcomes.

RESULTS

We analyzed a total of 161 lesions (81 of them non-mass) with a malignancy rate of 40%. Lesion margins (spiculated, irregular, or circumscribed) were identified as the most important criterion within the decision tree, followed by the ADC value as second most important criterion. The resulting score demonstrated a strong diagnostic performance with an AUC of 0.840, providing both rule-in and rule-out criteria. In an independent test set of 65 lesions the diagnostic performance was verified by two readers (AUC 0.77 and 0.87, kappa: 0.62).

CONCLUSIONS

We developed a clinical decision rule for unenhanced breast MRI including lesion margins and ADC value as the most important criteria, achieving high diagnostic accuracy.

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.

Metaplastic Carcinoma of the Breast: MRI Features with Clinical and Histopathologic Correlation

RATIONALE AND OBJECTIVES

Metaplastic carcinoma of the breast (MCB) is a rare type of breast carcinoma, and there are only limited data about the MRI findings of MCB. This article aimed to review the MRI features of MCB and correlate the MRI findings with clinical and histopathologic characteristics.

MATERIALS AND METHODS

A total of 23 patients were enrolled from a signal institution. The clinical data, histological diagnosis and MRI findings were retrospectively reviewed.

RESULTS

The average age of the 23 patients was 51.6 years. Twenty-one (91.3%) patients presented with a palpable mass. Squamous cell carcinoma was the most common subtype (10/23, 43.5%). Axillary lymph node metastasis was detected in 4 (17.4%) patients. The immunohistochemical profile showed the estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor-2 (Her-2) expression were negative in 87.0%, 95.7%, and 91.3% of the patients respectively. On MRI, most cases exhibited an irregular mass (18/23, 78.3%) with high signal intensity on T2-weighted images (22/23,95.7%), non-circumscribed margin (19/23, 82.6%), heterogeneous enhancement (13/23, 56.5%), Type III (12/23, 52.2%) time intensity curve, and diffusion restriction (20/23, 87.0%) on diffusion weighted images (DWI).

CONCLUSION

MCB most commonly demonstrated definitely malignant MRI features, which may reflect the histopathologic characteristic of this rare and aggressive breast carcinoma.

Characterization of breast lesions using multi-parametric diffusion MRI and machine learning

Objective. To investigate quantitative imaging markers based on parameters from two diffusion-weighted imaging (DWI) models, continuous-time random-walk (CTRW) and intravoxel incoherent motion (IVIM) models, for characterizing malignant and benign breast lesions by using a machine learning algorithm.Approach. With IRB approval, 40 women with histologically confirmed breast lesions (16 benign, 24 malignant) underwent DWI with 11b-values (50 to 3000 s/mm2) at 3T. Three CTRW parameters,Dm,α, andβand three IVIM parametersDdiff,Dperf, andfwere estimated from the lesions. A histogram was generated and histogram features of skewness, variance, mean, median, interquartile range; and the value of the 10%, 25% and 75% quantiles were extracted for each parameter from the regions-of-interest. Iterative feature selection was performed using the Boruta algorithm that uses the Benjamin Hochberg False Discover Rate to first determine significant features and then to apply the Bonferroni correction to further control for false positives across multiple comparisons during the iterative procedure. Predictive performance of the significant features was evaluated using Support Vector Machine, Random Forest, Naïve Bayes, Gradient Boosted Classifier (GB), Decision Trees, AdaBoost and Gaussian Process machine learning classifiers.Main Results. The 75% quantile, and median ofDm; 75% quantile off;mean, median, and skewness ofβ;kurtosis ofDperf; and 75% quantile ofDdiffwere the most significant features. The GB differentiated malignant and benign lesions with an accuracy of 0.833, an area-under-the-curve of 0.942, and an F1 score of 0.87 providing the best statistical performance (p-value < 0.05) compared to the other classifiers.Significance. Our study has demonstrated that GB with a set of histogram features from the CTRW and IVIM model parameters can effectively differentiate malignant and benign breast lesions.

Raafat B, Alamri S, Alghamdi AJ, Osman H, Ahmed RM, Almahwasi A, S. Alamri A, Ansari M. Distinction of Breast Masses from Benign to Malignant using Magnetic Resonance Imaging and Dynamic Contrast-Enhanced in Tertiary Care Hospitals of Taif, Saudi Arabia: A Retrospective Study

Background: Breast cancer is the most frequent cancer among women throughout the world. A range of noninvasive techniques have been employed for early prevention. In health-care practice, however, quality and sensitivity remained critical. Objective: The aim of this study is to see how well Breast Magnetic Resonance Imaging (MRIs) and Dynamic Contrast-Enhanced MRI (DCE-MRI) techniques can detect breast cancer and distinguish between malignant and benign lesions. Methods: A retrospective study was conducted at the Taif Hospitals, Saudi Arabia. The Picture Archiving and Communication System was used to acquire medical records and data from 50 individuals with probable breast cancer, and breast MRI pictures were analyzed. Breast Imaging Reporting and Data System (BI-RADS) radiologist reports and DCE-MRI kinetic curves were evaluated. Excel was also used to test the sensitivity and specificity of breast MRI. Results: According to the BI-RADS results, biopsies, and breast MRI data, 52 percent of 50 patients were categorized as benign, 24 percent as malignant, and 24 percent had no abnormalities. Biopsy revealed that 61.5 percent of the malignant lesions were benign, whereas 38.5 percent were appropriately identified as cancerous. The majority of malignant tumors were discovered in patients over the age of 50. The washout curve correctly identified 60% of the malignant lesions and 40% of the benign lesions. Our data demonstrated the usefulness of MRI in detecting breast cancers by analyzing BI-RADS and utilizing DCE-MRI. False-positive, on the other hand, can lead to unnecessary biopsies. Conclusion: Breast cancer is more common among women of their fifties and beyond. Biopsies, breast MRIs, and kinetic curve analysis can all be utilized to differentiate between benign and malignant breast masses with high sensitivity and specificity.

Assessment of Suspected Breast Lesions in Early-Stage Triple-Negative Breast Cancer during Follow-Up after Breast-Conserving Surgery Using Multiparametric MRI

Background

The local recurrence rate of triple-negative breast cancer (TNBC) can be as high as 12%.The standard treatment for early-stage TNBC is breast-conserving surgery (BCS), followed by postoperative radiotherapy with or without chemotherapy. However, detection of the local recurrence of the disease after radiotherapy is a major issue.

Objective

The aim of this study was at investigating the role of dynamic and functional magnetic resonance imaging (MRI) during follow-up after BCS and radiotherapy with/without chemotherapy to differentiate between locoregional recurrence and postoperative fibrosis. Patients and Methods. This prospective study was conducted at the oncology, radiology, and pathology departments, Tanta University. It involved 50 patients with early-stage TNBC who were treated with BCS, followed by radiotherapy with/without chemotherapy. The suspected lesions were evaluated during the follow-up period by sonomammography. All patients were subjected to MRI, including conventional sequences, diffusion-weighted imaging (DWI), and dynamic postcontrast study.

Results

Ten cases were confirmed as recurrent malignant lesions. After contrast administration, they all exhibited irregular T1 hypodense lesions of variable morphology with diffusion restriction and positive enhancement. Eight cases displayed a type III curve, while two showed a type II curve. Histopathological assessment was consistent with the MRI findings in all eight cases. The combination of the data produced by DWI-MRI and dynamic contrast-enhanced (DCE) MRI resulted in 100%sensitivity, 92.5% specificity, 90.9% positive predictive value, 100% negative predictive value, and 98% accuracy.

Conclusion

Combination of DWI-MRI and DCE-MRI could have high diagnostic value for evaluating postoperative changes in patients with TNBC after BCS, followed by radiotherapy with/without chemotherapy. Trial Registrations. No trial to be registered.

Distinguishing between benign and malignant breast lesions using diffusion weighted imaging and intravoxel incoherent motion: A systematic review and meta-analysis

PURPOSE

We sought to evaluate the diagnostic performance of diffusion weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for distinguishing between benign and malignant breast tumors by performing a meta-analysis.

METHODS

We comprehensively searched the electronic databases PubMed and Embase from January 2000 to April 2020 for studies in English. Studies were included if they reported the sensitivity and specificity for identifying benign and malignant breast lesions using DWI or IVIM. Studies were reviewed according to QUADAS-2. The data inhomogeneity and publication bias were also assessed. In order to explore the influence of different field strengths and different b values on diagnostic efficiency, we conducted subgroup analysis.

RESULTS

We analyzed 79 studies, which included a total of 6294 patients with 4091 malignant lesions and 2793 benign lesions. Overall, the pooled sensitivity and specificity of ADC for detecting malignant breast tumors were 0.87 (0.86-0.88) and 0.80 (0.78-0.81), respectively. The PLR was 5.09 (4.16-6.24); the NLR was 0.15 (0.13-0.18); and the DOR was 38.95 (28.87-52.54). The AUC value was 0.9297. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity and specificity was 0.85 (0.82-0.88) and 0.87(0.83-0.90), respectively; the PLR was 5.65 (3.91-8.18); the NLR was 0.17 (0.12-0.26); and the DOR was 38.44 (23.57-62.69). The AUC value was 0.9265. Most of parameters demonstrated considerable statistically significant heterogeneity (P < 0.05, I²>50 %) except the pooled DOR, PLR of D and the pooled DOR and NLR of D*.

CONCLUSIONS

Our meta-analysis indicated that DWI and IVIM had high sensitivity and specificity in the differential diagnosis of breast lesions; and compared with DWI, IVIM could not further increase the diagnostic performance. There was no significant difference in diagnostic accuracy.

The diagnostic value of MRI for architectural distortion categorized as BI-RADS category 3-4 by mammography

BACKGROUND

Architectural distortion is a common mammographic sign that can be benign or malignant. This study investigated the diagnostic value of magnetic resonance imaging (MRI) for architectural distortions that were category 3-4 under the breast imaging reporting and data system (BI-RADS) by mammography.

METHODS

We retrospectively analyzed 219 pathologically confirmed lesions in 208 patients who had BI-RADS category 3-4 architectural distortion in mammography images. Two radiologists described and categorized the architectural distortion and assigned the BI-RADS categories to the corresponding lesions on MRI images. Using the postoperative pathological diagnosis as the gold standard, we performed receiver operating characteristic (ROC) analysis for the efficacy of mammography and MRI in differentiating patients with benign or malignant lesions.

RESULTS

Totally 151 benign lesions and 68 malignant lesions were confirmed. According to the full-field digital mammography (FFDM), 82 lesions were in BI-RADS category 3, 104 lesions in 4A, 29 lesions in 4B, and 4 lesions in 4C. The positive predictive values of FFDM for BI-RADS categories 3, 4A, 4B, and 4C were 13.4% (11/82), 27.9% (29/104), 82.8% (24/29), and 100.0% (4/4), respectively. According to MRI, 59 lesions were in BI-RADS categories 1-2, 87 lesions in 3, 39 lesions in 4, and 34 lesions in 5, with their positive predictive values being 0.0% (0/58), 2.3% (2/87), 89.7% (35/39), and 100.0% (34/34), respectively. The area under the ROC curve (AUC) of breast benign and malignant lesions differentiated by FFDM was 0.647, and the diagnostic sensitivity, specificity, and Youden index were 86.3%, 41.7%, and 0.280, respectively. The AUC of FFDM combined with dynamic contrast-enhanced MRI (DCE-MRI) in differentiating breast benign vs. malignant lesions was 0.851, and the diagnostic sensitivity, specificity, and Youden index were 89.2%, 80.7%, and 0.699, respectively. The AUC of FFDM combined with DCE-MRI and the apparent diffusion coefficient (ADC) in differentiating benign vs. malignant lesions was 0.983, and the diagnostic sensitivity, specificity, and Youden index were 98.1%, 97.5%, and 0.956, respectively.

CONCLUSIONS

MRI can improve the diagnostic efficiency of mammography in diagnosing BI-RADS category 3-4 architectural distortions and can help in the qualitative diagnosis of architectural distortion lesions.

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.

Positron emission tomography/computed tomography using 2-deoxy-2-fluoro-18-fluoro-D-glucose peri-tumoral halo uptake layer method outperforms magnetic resonance imaging and ultrasound methods in tumor size measurement of breast cancer

As a non-invasive method, positron emission tomography (PET)/computed tomography (CT) using 2-deoxy-2-fluoro-18-fluoro-D-glucose (18F-FDG) is applied as a useful modality in the diagnosis of breast cancer. By evaluating glucose metabolism, this method can also be used in staging, restaging and post-therapeutic response evaluation. To evaluate the reliability of the ¹⁸F-FDG PET/CT-based peri-tumoral halo uptake layer (PHL) method for assessing tumor size, a total of 79 female patients with breast cancer who underwent ¹⁸F-FDG PET/CT, breast ultrasound and magnetic resonance imaging (MRI) evaluations were included in the present study. Upon examination by two independent nuclear medicine radiologists, tumor sizes were estimated by ¹⁸F-FDG PET/CT using margins defined as the inner line of the PHL. Pathological tumor sizes were evaluated on the direction of largest diameter indicated by previous imaging examination, which were also utilized as final standards. Statistical analysis of the results suggested that ¹⁸F-FDG PET/CT had a more linear correlation with pathology compared with breast ultrasound (r²=0.89 vs. 0.73) and MRI (r²=0.89 vs. 0.69) in terms of tumor size estimation, including a significantly lower bias in size difference relative to pathology. ¹⁸F-FDG PET/CT also exhibited improved performance compared with breast ultrasound and MRI in T stage assessment. These results indicated that the ¹⁸F-FDG PET/CT-based PHL method was superior to breast ultrasound and MRI, and that it provides sufficient reliability and high accuracy for measuring tumor size in patients with breast cancer.

Can apparent diffusion coefficient (ADC) distinguish breast cancer from benign breast findings? A meta-analysis based on 13 847 lesions

BACKGROUND

The purpose of the present meta-analysis was to provide evident data about use of Apparent Diffusion Coefficient (ADC) values for distinguishing malignant and benign breast lesions.

METHODS

MEDLINE library and SCOPUS database were screened for associations between ADC and malignancy/benignancy of breast lesions up to December 2018. Overall, 123 items were identified. The following data were extracted from the literature: authors, year of publication, study design, number of patients/lesions, lesion type, mean value and standard deviation of ADC, measure method, b values, and Tesla strength. The methodological quality of the 123 studies was checked according to the QUADAS-2 instrument. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian and Laird random-effects models with inverse-variance weights were used without any further correction to account for the heterogeneity between the studies. Mean ADC values including 95% confidence intervals were calculated separately for benign and malign lesions.

RESULTS

The acquired 123 studies comprised 13,847 breast lesions. Malignant lesions were diagnosed in 10,622 cases (76.7%) and benign lesions in 3225 cases (23.3%). The mean ADC value of the malignant lesions was 1.03 × 10^- 3 mm²/s and the mean value of the benign lesions was 1.5 × 10^- 3 mm²/s. The calculated ADC values of benign lesions were over the value of 1.00 × 10^- 3 mm²/s. This result was independent on Tesla strength, choice of b values, and measure methods (whole lesion measure vs estimation of ADC in a single area).

CONCLUSION

An ADC threshold of 1.00 × 10^- 3 mm²/s can be recommended for distinguishing breast cancers from benign lesions.

A Meta-analysis of the Diagnostic Performance of Diffusion MRI for Breast Lesion Characterization

Background Various techniques are available to assess diffusion properties of breast lesions as a marker of malignancy at MRI. The diagnostic performance of these diffusion markers has not been comprehensively assessed. Purpose To compare by meta-analysis the diagnostic performance of parameters from diffusion-weighted imaging (DWI), diffusion-tensor imaging (DTI), and intravoxel incoherent motion (IVIM) in the differential diagnosis of malignant and benign breast lesions. Materials and Methods PubMed and Embase databases were searched from January to March 2018 for studies in English that assessed the diagnostic performance of DWI, DTI, and IVIM in the breast. Studies were reviewed according to eligibility and exclusion criteria. Publication bias and heterogeneity between studies were assessed. Pooled summary estimates for sensitivity, specificity, and area under the curve were obtained for each parameter by using a bivariate model. A subanalysis investigated the effect of MRI parameters on diagnostic performance by using a Student t test or a one-way analysis of variance. Results From 73 eligible studies, 6791 lesions (3930 malignant and 2861 benign) were included. Publication bias was evident for studies that evaluated apparent diffusion coefficient (ADC). Significant heterogeneity (P < .05) was present for all parameters except the perfusion fraction (f). The pooled sensitivity, specificity, and area under the curve for ADC was 89%, 82%, and 0.92, respectively. The highest performing parameter for DTI was the prime diffusion coefficient (λ1), and pooled sensitivity, specificity, and area under the curve was 93%, 90%, and 0.94, respectively. The highest performing parameter for IVIM was tissue diffusivity (D), and the pooled sensitivity, specificity, and area under the curve was 88%, 79%, and 0.90. Choice of MRI parameters had no significant effect on diagnostic performance. Conclusion Diffusion-weighted imaging, diffusion-tensor imaging, and intravoxel incoherent motion have comparable diagnostic accuracy with high sensitivity and specificity. Intravoxel incoherent motion is comparable to apparent diffusion coefficient. Diffusion-tensor imaging is potentially promising but to date the number of studies is limited. © RSNA, 2019 Online supplemental material is available for this article.

Threshold Isocontouring on High b-Value Diffusion-Weighted Images in Magnetic Resonance Mammography

OBJECTIVES

Motivated by the similar appearance of malignant breast lesions in high b-value diffusion-weighted imaging (DWI) and positron emission tomography, the purpose of this work was to evaluate the applicability of a threshold isocontouring approach commonly used in positron emission tomography to analyze DWI data acquired from female human breasts with minimal interobserver variability.

METHODS

Twenty-three female participants (59.4 ± 10.0 years) with 23 lesions initially classified as suggestive of cancers in x-ray mammography screening were subsequently imaged on a 1.5-T magnetic resonance imaging scanner. Diffusion-weighted imaging was performed prior to biopsy with b values of 0, 100, 750, and 1500 s/mm. Isocontouring with different threshold levels was performed on the highest b-value image to determine the voxels used for subsequent evaluation of diffusion metrics. The coefficient of variation was computed by specifying 4 different regions of interest drawn around the lesion. Additionally, a receiver operating statistical analysis was performed.

RESULTS

Using a relative threshold level greater than or equal to 0.85 almost completely suppresses the intra-individual and inter-individual variability. Among 4 studied diffusion metrics, the diffusion coefficients from the intravoxel incoherent motion model returned the highest area under curve value of 0.9. The optimal cut-off diffusivity was found to be 0.85 μm/ms with a sensitivity of 87.5% and specificity of 90.9%.

CONCLUSION

Threshold isocontouring on high b-value maps is a viable approach to reliably evaluate DWI data of suspicious focal lesions in magnetic resonance mammography.

Short tau inversion recovery in breast diffusion-weighted imaging: signal-to-noise ratio and apparent diffusion coefficients using a breast phantom in comparison with spectral attenuated inversion recovery

OBJECTIVE

This study aimed to compare the signal-to-noise ratios (SNRs) and apparent diffusion coefficients (ADCs) obtained using two fat suppression techniques in breast diffusion-weighted imaging (DWI) of a phantom.

MATERIALS AND METHODS

The breast phantom comprised agar gels with four different concentrations of granulated sugar (samples 1, 2, 3, and 4). DWI with short tau inversion recovery (STIR-DWI) and that with spectral attenuated inversion recovery (SPAIR-DWI) were performed using 3.0-T magnetic resonance imaging, and the obtained SNRs and ADCs were compared. ADCs were also compared between the right and left breast phantoms.

RESULTS

For samples 3 and 4, SNRs obtained using STIR-DWI were lower than those obtained using SPAIR-DWI. For samples 2, 3, and 4, overall ADCs obtained using STIR-DWI were significantly higher than those obtained using SPAIR-DWI (p < 0.001 for all), although no significant difference was observed for sample 1 (p = 0.62). STIR-DWI shows a positive bias and wide limits of agreement in Bland-Altman plot. The coefficients of variance of overall ADCs were good in STIR-DWI and SPAIR-DWI. For all samples, STIR-DWI demonstrated slightly larger percentage differences in ADCs between the right and left phantoms than SPAIR-DWI.

CONCLUSION

SNRs and ADCs obtained using STIR-DWI are influenced by the T ₁ value; a shorter T ₁ value decreases SNRs, overestimates ADCs, and induces the measurement error in ADCs. STIR-DWI showed a larger difference in ADCs between the right and left phantoms than SPAIR-DWI.

Accuracy of diffusion kurtosis imaging in characterization of breast lesions

OBJECTIVE

The aim of this study was to evaluate the accuracy of diffusion kurtosis in the characterization and differentiation of breast lesions.

METHODS

49 females with 53 breast lesions underwent breast MRI. The MRI magnetic field is 1.5 T, and the protocol is standard MRI sequences, dynamic sequences pre- and post-contrast agent administration and diffusion images. Diffusion kurtosis imaging (DKI) was applied as part of our standard breast MRΙ protocol. Two experienced radiologists on breast MRI, blinded to the final diagnosis, reviewed the parametric maps and placed a volume of interest on all slices including each lesion. Kurtosis [K apparent (K_app)] and corrected apparent diffusion coefficient [D apparent (D_app)] median values were then calculated from the whole-lesion histogram analysis. Receiver-operating characteristic analysis was used to determine the most effective cut-off values for the differentiation between benign and malignant pathologies. Histological analysis of the breast lesions was performed, and further comparative analysis of the results was performed to investigate the accuracy of the method.

RESULTS

Benign (n = 19) and malignant lesions (n = 34) had mean diameters of 20.8 mm (10.1-31.5 mm) and 26.4 mm (10.5-42.3 mm), respectively. The lowest and the highest kurtosis values (K_app) of malignant lesions were significantly higher than those of benign lesions. A cut-off of 0.71 provided specificity of 93.7% and sensitivity 97.1%, and the area under the curve (AUC) was 0.976 (p < 0.0001). The lowest and the highest D_app values of malignant lesions were lower than those of benign lesions. A cut-off value of 1.57 × 10^-3 mm² s^-1 provided specificity of 93.7% and sensitivity of 91.2% with AUC of 0.949 (p < 0.0001).

CONCLUSION

DKI is an accurate additional tool for the characterization and differentiation of breast lesions with high K_app and D_app sensitivity and specificity rates. Advances in knowledge: DKI is able to distinguish benign from malignant breast pathologies. DKI increases the specificity of breast MRI.

Whole-lesion histogram analysis metrics of the apparent diffusion coefficient as a marker of breast lesions characterization at 1.5 T

INTRODUCTION

To retrospectively assess the role of whole-lesion apparent diffusion coefficient (ADC) in the characterization of breast tumors by comparing different histogram metrics.

METHODS

49 patients with 53 breast lesions underwent magnetic resonance imaging (MRI). ADC histogram parameters, including the mean, mode, 10th/50th/90th percentile, skewness, kurtosis, and entropy ADCs, were derived for the whole-lesion volume in each patient. Mann-Whitney U-test, area under the receiver-operating characteristic curve (AUC) were used for statistical analysis.

RESULTS

The mean, mode and 10th/50th/90th percentile ADC values were significantly lower in malignant lesions compared with benign ones (all P < 0.0001), while skewness was significantly higher in malignant lesions P = 0.02. However, no significant difference was found between entropy and kurtosis values in malignant lesions compared with benign ones (P = 0.06 and P = 1.00, respectively). Univariate logistic regression showed that 10th and 50th percentile ADC yielded the highest AUC (0.985; 95% confidence interval [CI]: 0.902, 1.000 and 0.982; 95% confidence interval [CI]: 0.896, 1.000 respectively), whereas kurtosis value yielded the lowest AUC (0.500; 95% CI: 0.355, 0.645), indicating that 10th and 50th percentile ADC values may be more accurate for lesion discrimination.

CONCLUSION

Whole-lesion ADC histogram analysis could be a helpful index in the characterization and differentiation between benign and malignant breast lesions with the 10th and 50th percentile ADC be the most accurate discriminators.

MRI in the differential diagnosis of primary architectural distortion detected by mammography

PURPOSE

We aimed to evaluate the diagnostic accuracy of a combination of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and apparent diffusion coefficient (ADC) values in lesions that manifest with architectural distortion (AD) on mammography.

METHODS

All full-field digital mammography (FFDM) images obtained between August 2010 and January 2013 were reviewed retrospectively, and 57 lesions showing AD were included in the study. Two independent radiologists reviewed all mammograms and MRI data and recorded lesion characteristics according to the BI-RADS lexicon. The gold standard was histopathologic results from biopsies or surgical excisions and results of the two-year follow-up. Receiver operating characteristic curve analysis was carried out to define the most effective threshold ADC value to differentiate malignant from benign breast lesions. We investigated the sensitivity and specificity of FFDM, DCE-MRI, FFDM+DCE-MRI, and DCE-MRI+ADC.

RESULTS

Of the 57 lesions analyzed, 28 were malignant and 29 were benign. The most effective threshold for the normalized ADC (nADC) was 0.61 with 93.1% sensitivity and 75.0% specificity. The sensitivity and specificity of DCE-MRI combined with nADC was 92.9% and 79.3%, respectively. DCE-MRI combined with nADC showed the highest specificity and equal sensitivity compared with other modalities, independent of the presentation of calcification.

CONCLUSION

DCE-MRI combined with nADC values was more reliable than mammography in differentiating the nature of disease manifesting as primary AD on mammography.

Whole-lesion apparent diffusion coefficient (ADC) metrics as a marker of breast tumour characterization-comparison between ADC value and ADC entropy

OBJECTIVE

To prospectively assess the role of whole-lesion apparent diffusion coefficient (ADC) metrics in the characterization of breast tumours by comparing ADC value with ADC entropy.

METHODS

49 patients with 53 breast lesions underwent phased-array breast coil 1.5-T MRI. Two radiologists experienced in breast MRI, blinded to the final diagnosis, reviewed the ADC maps and placed a volume of interest on all slices including each lesion on the ADC map to obtain whole-lesion mean ADC value and ADC entropy. The mean ADC value and ADC entropy in benign and malignant lesions were compared by the Mann-Whitney U-test. Receiver-operating characteristic analysis was performed to assess the sensitivity and specificity of the two variables in the characterization of the breast lesions.

RESULTS

The benign (n = 19) and malignant lesions (n = 34) had mean diameters of 20.8 mm (10.1-31.5 mm) and 26.4 mm (10.5-42.3 mm), respectively. The mean ADC value of the malignant lesions was significantly lower than that of the benign ones (0.87 × 10^-3 vs 1.49 × 10^-3 mm² s^-1; p < 0.0001). Malignant ADC entropy was higher than benign entropy, without reaching levels of statistical significance (5.4 vs 5.0; p = 0.064). At a mean ADC cut-off value of 1.16 × 10^-3 mm² s^-1, the sensitivity and specificity for diagnosing malignancy became optimal (97.1% and 93.7, respectively) with an area under the curve (AUC) of 0.975. With regard to ADC entropy, the sensitivity and specificity at a cut-off of 5.18 were 67.6 and 68.7%, respectively, with an AUC of 0.664.

CONCLUSION

Whole-lesion mean ADC could be a helpful index in the characterization of suspicious breast lesions, with higher sensitivity and specificity than ADC entropy. Advances in knowledge: Two separate parameters of the whole-lesion histogram were compared for their diagnostic accuracy in characterizing breast lesions. Mean ADC was found to be able to characterize breast lesions, whereas entropy proved to be unable to differentiate benign from malignant breast lesions. It is, however, likely that entropy may distinguish these two groups if a larger cohort were used, or the fact that this may be influenced by the molecular subtypes of breast cancers included.

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.

Significance of the ADC ratio in the differential diagnosis of breast lesions

BACKGROUND

Magnetic resonance imaging (MRI) has high sensitivity but low specificity for breast cancer, and consequently, new techniques to improve the specificity of breast MRI in diagnosing breast cancer are under development.

PURPOSE

To assess the ability of the apparent diffusion coefficient (ADC) compared with the ADC ratio (ADCr) to differentially diagnose benign compared with malignant breast lesions.

MATERIAL AND METHODS

Forty-eight women with breast lesions (average age, 45 years) underwent MRI scanning including T1-weighted dynamic contrast-enhanced (DCE) scanning and diffusion-weighted imaging (DWI). The average ADC and ADCr values for both lesions and pectoralis major muscles (ADCrmuscle and ADCrmuscle) were measured in patients with malignant (n = 25) and benign (n = 23) breast lesions. The ADCr of the contralateral breast (ADCr contralateral) was also evaluated. All histology was confirmed by pathological analysis of biopsied tissue. ADC and ADCr values were analyzed using receiver-operating characteristic (ROC) curves.

RESULTS

For benign lesions compared with malignant lesions, lesion-side ADC was 1.45 vs. 1.05, respectively (P < 0.001), normal-side ADC was 1.82 vs.1.64 (P = 0.002), ADCrmuscle was 1.35 vs. 0.9 (P < 0.001), and ADCrcontralateral was 0.79 vs. 0.64 (P = 0.001). ADCrmuscle showed higher sensitivity (82.61%) and specificity (96.00%) than ADCrcontralateral (60.87% and 92.00%, respectively) and ADC (69.57% and 96.00%) for discriminating malignant from benign lesions. The AUC using ADCrmuscle had higher discriminatory power (0.92, P < 0.001) for malignant versus benign breast lesions compared with either ADC (0.82, P < 0.001) or ADCrcontralateral (0.78, P = 0.001).

CONCLUSION

The ADCrmuscle value showed higher sensitivity and specificity and improved diagnostic accuracy compared with either ADC or ADCrcontralateral in differentiating benign from malignant breast lesions.

Qualitative and quantitative diffusion-weighted imaging of the breast at 3T - A useful adjunct to contrast-enhanced MRI in characterization of breast lesions

Objective:

To distinguish between benign and malignant breast lesions on the basis of their signal intensity on diffusion-weighted imaging and their apparent diffusion coefficient (ADC) values at 3 T MRI, along with histopathological correlation.

Materials and Methods:

A retrospective analysis of 500 patients who underwent 3 T MRI between August 2011 and May 2013 was done. Of these, 226 patients with 232 lesions that were proved by histopathology were included in the study. ADC values were calculated at b values of 0, 1000, and 1500 s/mm² after identification on contrast-enhanced images and appropriate ROI(Region of interest) placement. ADC value and histopathology correlation was analyzed.

Results:

Out of 232 lesions, 168 lesions were histologically malignant and 64 were histologically benign. With an ADC cut-off value of 1.1 ×10⁻³ mm²/s for malignant lesions, a sensitivity of 92.80% and specificity of 80.23% was obtained. Out of 12/232 false-negative lesions, 6 were mucinous carcinoma in which a high ADC value of 1.8-1.9 ×10⁻³ mm²/s was obtained. Purely DCIS (Ductal carcinoma in situ) lesions presenting as non-mass-like enhancement had a high ADC value of 1.2-1.5 ×10⁻³ mm²/s, thereby reducing specificity.

Conclusion:

Diffusion-weighted Imaging and quantitative assessment by ADC values may act as an effective parameter in increasing the diagnostic accuracy and specificity of contrast-enhanced breast MRI in characterization of breast lesions.

Diffusion weighted imaging and apparent diffusion coefficient in 3 tesla magnetic resonance imaging of breast lesions

OBJECTIVE

To evaluate the utility of diffusion-weighted-imaging (DWI) and apparent-diffusion-coefficient (ADC) in a 3T magnetic-resonance-imaging (MRI) study of breast cancer. In particular, the study aims to classify ADC-values according to histology either for benign or malignant lesions.

METHODS

110 Breast MRI with MRI-DWI sequences and quantitative evaluation of the ADC were retrospectively reviewed. Results obtained with MRI-DWI and with biopsy were analyzed and ADC values were compared to histological results.

RESULTS

MRI showed a 95.5% sensitivity and a 83.7% specificity. The mean ADC values of benign and malignant lesions were 2.06 ± 0.19 and 1.03 ± 0.07 mm(2)/s, respectively (p < .05).

CONCLUSIONS

DWI and ADC-values could help distinguishing malignant and benign breast masses.

Fat suppression techniques (STIR vs. SPAIR) on diffusion-weighted imaging of breast lesions at 3.0 T: preliminary experience

PURPOSE

The aim of this work was to perform a qualitative and quantitative comparison of the performance of two fat suppression techniques on breast diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

Fifty-one women underwent clinical breast magnetic resonance imaging, including DWI with short TI inversion recovery (STIR) and spectral attenuated inversion recovery (SPAIR). Four were excluded from the analysis due to image artefacts. Rating of fat suppression uniformity and lesion visibility were performed. Agreement between the two sequences was evaluated. Additionally, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC) values for normal gland, benign and malignant lesions were compared. Receiver operating characteristic analysis was also performed.

RESULTS

From the 52 lesions found, 47 were detected by both sequences. DWI-STIR evidenced more homogeneous fat suppression (p = 0.03). Although these lesions were seen with both techniques, DWI-SPAIR evidenced higher score for lesion visibility in nine of them. SNR and CNR were comparable, except for SNR in benign lesions (p < 0.01), which was higher for DWI-SPAIR. Mean ADC values for lesions were similar. ADC for normal fibroglandular tissue was higher when using DWI-STIR (p = 0.006). Sensitivity, specificity, accuracy and area under the curve values were alike: 84.0 % for both; 77.3, 71.4 %; 80.9, 78.3 %; 82.5, 81.3 % for DWI-SPAIR and DWI-STIR, respectively.

CONCLUSION

DWI-STIR showed superior fat suppression homogeneity. No differences were found for SNR and CNR, except for SNR in benign lesions. ADCs for lesions were comparable. Findings in this study are consistent with previous studies at 1.5 T, meaning that both fat suppression techniques are appropriate for breast DWI at 3.0 T.

Quantitative Analysis of Diffusion-Weighted Imaging for Diagnosis of Puerperal Breast Abscess After Polyacrylamide Hydrogel Augmentation Mammoplasty: Compared with Other Conventional Modalities

PURPOSE

Puerperal breast abscess after polyacrylamide hydrogel (PAAG) augmentation mammoplasty can induce breast auto-inflation resulting in serious consequences. Mammography, ultrasound, and conventional MRI are poor at detecting related PAAG abnormality histologically. We evaluated the value of diffusion-weighted imaging (DWI) in the quantitative analysis of puerperal PAAG abscess after augmentation mammoplasty.

MATERIALS AND METHODS

This was a retrospective study, and a waiver for informed consent was granted. Sixteen puerperal women with breast discomfort underwent conventional breast non-enhanced MRI and axial DWI using a 3T MR scanner. Qualitative analysis of the signal intensity on DWI and conventional sequences was performed. The apparent diffusion coefficient (ADC) values of the affected and contralateral normal PAAG cysts were measured quantitatively. Paired t test was used to evaluate whether there was significant difference.

RESULTS

Both affected and normal PAAG cysts showed equal signal intensity on conventional T1WI and fat saturation T2WI, which were not helpful in detecting puerperal PAAG abscess. However, the affected PAAG cysts had a significantly decreased ADC value of 1.477 ± 0.332 × 10(-3)mm(2)/s and showed obvious hypo-intensity on the ADC map and increased signal intensity on DWI compared with the ADC value of 2.775 ± 0.233 × 10(-3)mm(2)/s of the contralateral normal PAAG cysts.

CONCLUSION

DWI and quantitative measurement of ADC values are of great value for the diagnosis of puerperal PAAG abscess. Standardized MRI should be suggested to these puerperal women with breast discomfort or just for the purpose of check up. DWI should be selected as the essential MRI sequence.

Breast DWI at 3 T: influence of the fat-suppression technique on image quality and diagnostic performance

AIM

To evaluate two fat-suppression techniques: short tau inversion recovery (STIR) and spectral adiabatic inversion recovery (SPAIR) regarding image quality and diagnostic performance in diffusion-weighted imaging (DWI) of breast lesions at 3 T.

MATERIALS AND METHODS

Ninety-two women (mean age 48 ± 12.1 years; range 21-78 years) underwent breast MRI. Two DWI pulse sequences, with b-values (50 and 1000 s/mm(2)) were performed with STIR and SPAIR. The signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), suppression homogeneity, and apparent diffusion coefficient (ADC) values were quantitatively assessed for each technique. Values were compared between techniques and lesion type. Receiver operating characteristics (ROC) analysis was used to evaluate lesion discrimination.

RESULTS

One hundred and fourteen lesions were analysed (40 benign and 74 malignant). SNR and CNR were significantly higher for DWI-SPAIR; fat-suppression uniformity was better for DWI-STIR (p < 1 × 10(-4)). ADC values for benign and malignant lesions and normal tissue were 1.92 × 10(-3), 1.18 × 10(-3), 1.86 × 10(-3) s/mm(2) for DWI-STIR and 1.80 × 10(-3), 1.11 × 10(-3), 1.79 × 10(-3) s/mm(2) for SPAIR, respectively. Comparison between fat-suppression techniques showed significant differences in mean ADC values for benign (p = 0.013) and malignant lesions (p = 0.001). DWI-STIR and -SPAIR ADC cut-offs were 1.42 × 10(-3) and 1.46 × 10(-3) s/mm(2), respectively. Diagnostic performance for DWI-STIR versus SPAIR was: accuracy (81.6 versus 83.3%), area under curve (87.7 versus 89.2%), sensitivity (79.7 versus 85.1%), and specificity (85 versus 80%). Positive predictive value was similar.

CONCLUSION

The fat-saturation technique used in the present study may influence image quality and ADC quantification. Nevertheless, STIR and SPAIR techniques showed similar diagnostic performances, and therefore, both are suitable for use in clinical practice.

Characterization of breast tumors using diffusion kurtosis imaging (DKI)

Aim

The aim of this study was to investigate and evaluate the role of magnetic resonance (MR) diffusion kurtosis imaging (DKI) in characterizing breast lesions.

Materials and Methods

One hundred and twenty-four lesions in 103 patients (mean age: 57±14 years) were evaluated by MR DKI performed with 7 b-values of 0, 250, 500, 750, 1,000, 1,500, 2,000 s/mm² and dynamic contrast-enhanced (DCE) MR imaging. Breast lesions were histologically characterized and DKI related parameters—mean diffusivity (MD) and mean kurtosis (MK)—were measured. The MD and MK in normal fibroglandular breast tissue, benign and malignant lesions were compared by One-way analysis of variance (ANOVA) with Tukey's multiple comparison test. Receiver operating characteristic (ROC) analysis was performed to assess the sensitivity and specificity of MD and MK in the diagnosis of breast lesions.

Results

The benign lesions (n = 42) and malignant lesions (n = 82) had mean diameters of 11.4±3.4 mm and 35.8±20.1 mm, respectively. The MK for malignant lesions (0.88±0.17) was significantly higher than that for benign lesions (0.47±0.14) (P<0.001), and, in contrast, MD for benign lesions (1.97±0.35 (10⁻³ mm²/s)) was higher than that for malignant lesions (1.20±0.31 (10⁻³ mm²/s)) (P<0.001). At a cutoff MD/MK 1.58 (10⁻³ mm²/s)/0.69, sensitivity and specificity of MD/MK for the diagnosis of malignant were 79.3%/84.2% and 92.9%/92.9%, respectively. The area under the curve (AUC) is 0.86/0.92 for MD/MK.

Conclusions

DKI could provide valuable information on the diffusion properties related to tumor microenvironment and increase diagnostic confidence of breast tumors.

[Diffusion-weighted breast imaging. Clinical implementation procedure]

Diffusion-weighted imaging (DWI) of the breast provides additional contrast information in breast magnetic resonance imaging (MRI). The DWI procedure can easily be implemented in the routine breast MRI protocol with little time expenditure regarding image acquisition and evaluation. Evaluation of the DW images can be performed with or without the routine breast MRI sequences (T2w and T1w with contrast material) but evaluation in combination with the routine program is highly recommended. Objective analysis of the tissue diffusion can be achieved by calculating the apparent diffusion coefficient (ADC) value with the scanner software. The choice of the DW sequence, evaluation and determination of the ADC threshold to differentiate between benign and malignant lesions should be scanner adapted. The use of DW imaging qualifies for routine use regarding the differentiation between malignant and benign breast lesions. Non-mass-like lesions and monitoring neoadjuvant chemotherapy can also be evaluated with DW sequences. The benefit of the additional information from DW-MR mammography to characterize non-mass-like lesions and in the course of neoadjuvant chemotherapy remains unclear to date.

Application of the diffusion kurtosis model for the study of breast lesions

OBJECTIVES

To evaluate diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) in the differentiation and characterisation of breast lesions.

METHODS

Thirty-six women underwent breast magnetic resonance imaging (MRI) including a DWI sequence with multiple b-values (50-3,000 s/mm(2)). Mean values for apparent diffusion coefficient (ADC), mean diffusivity (MD) and mean kurtosis (MK) were calculated by lesion type and histological subtype. Differences and correlation between parameters were determined.

RESULTS

Forty-four lesions were found. There were significant differences between benign and malignant lesions for all parameters (ADC, p = 0.017; MD, p = 0.028; MK, p = 0.017). ADC and MD were higher for benign (1.96 ± 0.41 × 10(-3) and 2.17 ± 0.42 × 10(-3) mm(2)/s, respectively) than for malignant lesions (1.33 ± 0.18 × 10(-3) and 1.52 ± 0.50 × 10(-3) mm(2)/s). MK was higher for malignant (0.61 ± 0.27) than benign lesions (0.37 ± 0.18). We found differences between invasive ductal carcinoma (IDC) and fibroadenoma (FA) for all parameters (ADC, MD and MK): p = 0.016, 0.022 and 0.016, respectively. FA and fibrocystic change (FC) showed differences only in MK (p = 0.016).

CONCLUSIONS

Diffusion in breast lesions follows a non-Gaussian distribution. MK enables differentiation and characterisation of breast lesions, providing new insights into microstructural complexity. To confirm these results, further investigation in a broader sample should be performed.

Intravoxel incoherent motion diffusion-weighted MRI at 3.0 T differentiates malignant breast lesions from benign lesions and breast parenchyma

PURPOSE

To study the differentiation of malignant breast lesions from benign lesions and fibroglandular tissue (FGT) using apparent diffusion coefficient (ADC) and intravoxel incoherent motion (IVIM) parameters.

MATERIALS AND METHODS

This retrospective study included 26 malignant and 14 benign breast lesions in 35 patients who underwent diffusion-weighted MRI at 3.0T and nine b-values (0-1000 s/mm(2) ). ADC and IVIM parameters (perfusion fraction fp , pseudodiffusion coefficient Dp , and true diffusion coefficient Dd ) were determined in lesions and FGT. For comparison, IVIM was also measured in 16 high-risk normal patients. A predictive model was constructed using linear discriminant analysis. Lesion discrimination based on ADC and IVIM parameters was assessed using receiver operating characteristic (ROC) and area under the ROC curve (AUC).

RESULTS

In FGT of normal subjects, fp was 1.1 ± 1.1%. In malignant lesions, fp (6.4 ± 3.1%) was significantly higher than in benign lesions (3.1 ± 3.3%, P = 0.0025) or FGT (1.5 ± 1.2%, P < 0.001), and Dd ((1.29 ± 0.28) × 10(-3) mm(2) /s) was lower than in benign lesions ((1.56 ± 0.28) × 10(-3) mm(2) /s, P = 0.011) or FGT ((1.86 ± 0.34) × 10(-3) mm(2) /s, P < 0.001). A combination of Dd and fp provided higher AUC for discrimination between malignant and benign lesions (0.84) or FGT (0.97) than ADC (0.72 and 0.86, respectively).

CONCLUSION

The IVIM parameters provide accurate identification of malignant lesions.

Role of diffusion-weighted imaging as an adjunct to contrast-enhanced breast MRI in evaluating residual breast cancer following neoadjuvant chemotherapy

OBJECTIVE

To investigate whether the addition of diffusion-weighted imaging (DWI) to dynamic contrast-enhanced MRI (DCE-MRI) improves diagnostic performance in predicting pathologic response and residual breast cancer size following neoadjuvant chemotherapy.

MATERIALS AND METHODS

A total of 78 consecutive patients who underwent preoperative breast MRI with DWI following neoadjuvant chemotherapy were enrolled. DWI was performed on a 1.5 T system with b values of 0 and 750 s/mm. or on a 3T system with b values of 0 and 800 or 0 and 1,000 s/mm. The images on DCE-MRI alone, DWI alone, and DCE-MRI plus DWI were retrospectively reviewed. We evaluated the diagnostic performances of the three MRI protocols for the detection of residual cancer. The tumor size as predicted by MRI was compared with histopathologic findings. Apparent diffusion coefficient (ADC) values were also compared between the groups with and without residual cancer.

RESULTS

Of the 78 patients, 59 (75.6%) had residual cancer. For detection of residual cancer, DCE-MRI plus DWI had higher specificity (80.0%), accuracy (91.0%), and PPV (93.2%) than DCE-MRI or DWI alone (P=0.004, P=0.007, and P=0.034, respectively). The ICC values for residual cancer size between MRI and histopathology were 0.891 for DCE-MRI plus DWI, 0.792 for DCE-MRI, and 0.773 for DWI. ADC values showed no significant differences between residual cancer and chemotherapeutic changes (P=0.130).

CONCLUSIONS

The addition of DWI to DCE-MRI significantly improved diagnostic performance in predicting pathologic response and residual breast cancer size after neoadjuvant chemotherapy.

Clinical and technical considerations for high quality breast MRI at 3 Tesla

The use of breast MRI at 3 tesla (T) has increased in use substantially in recent years. Potential benefits of moving to higher field strength MRI include improved morphologic and kinetic assessment of breast lesions through higher spatial and temporal resolution dynamic contrast-enhanced MR examinations. Furthermore, higher field strength holds promise for the development of superior advanced breast MRI techniques, such as diffusion weighted imaging and MR spectroscopy. To fully realize the benefits of moving to 3T, a thorough understanding of the technical and safety challenges of higher field strength imaging specific to breast MRI is paramount. Through the use of advanced coil technology, parallel imaging, dual-source parallel radiofrequency excitation, and image-based shimming techniques, many of these limiting technical factors can be overcome to achieve high quality breast MRI at 3T.

Comparison of the diagnostic performances of diffusion parameters in diffusion weighted imaging and diffusion tensor imaging of breast lesions

PURPOSE

To evaluate the diagnostic efficiency of the diffusion parameters measured by conventional diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) for discrimination of malignant breast lesions from benign lesions and the normal breast.

MATERIALS AND METHODS

The study included 52 women with 55 breast lesions (30 malignant, 25 benign). DTI and DWI were performed complementary to dynamic contrast MRI at 3T. Apparent diffusion coefficient (ADC) of DWI, mean diffusivity (MD) and fractional anisotropy (FA) values of DTI were measured for lesions and contralateral breast parenchyma in each patient. We used b factors of 0, 50, 850, 1000 and 1500 s/mm(2) for DWI and b 0 and 1000 s/mm(2) for DTI. ADC, MD and FA values were compared between malignant and benign lesions, and the normal parenchyma by univariate and multivariate analyses.

RESULTS

Diffusion parameters showed no difference according to menopausal status in the normal breast. ADC and MD values of the malignant lesions were significantly lower than benign lesions and normal parenchyma (p=0.001). The FA showed no statistical significance. With the cut-off values of ≤ 1.23 × 10(-3)mm(2)/s (b 0-1000 s/mm(2)) and ≤ 1.12 × 10(-3)mm(2)/s (b 0-1500 s/mm(2)), ADC showed 92.85% and 96.15% sensitivity; 72.22% and 73.52% PPV, respectively. With a cut-off value of ≤ 1.27 × 10(-3)mm(2)/s (b 1000 s/mm(2)), MD was 100% sensitive with a PPV of 65.90%. Comparing the diagnostic performance of the parameters in DTI with DWI, we obtained similar efficiency of ADC with b values of 0,1000 and 0,1500 s/mm(2) and MD with a b value of 0, 1000 s/mm(2) (AUC = 0.82 ± 0.07).

CONCLUSION

ADC of DWI and MD of DTI values provide significant discriminative factors for benign and malignant breast lesions. FA measurement was not discriminative. Supported with clinical and dynamic contrast MRI findings, DWI and DTI findings provide significant contribution to the final radiologic decision.

Magnetic resonance in the detection of breast cancers of different histological types

Breast cancer incidence is increasing worldwide. Early detection is critical for long-term patient survival, as is monitoring responses to chemotherapy for management of the disease. Magnetic resonance imaging and spectroscopy (MRI/MRS) has gained in importance in the last decade for the diagnosis and monitoring of breast cancer therapy. The sensitivity of MRI/MRS for anatomical delineation is very high and the consensus is that MRI is more sensitive in detection than x-ray mammography. Advantages of MRS include delivery of biochemical information about tumor metabolism, which can potentially assist in the staging of cancers and monitoring responses to treatment. The roles of MRS and MRI in screening and monitoring responses to treatment of breast cancer are reviewed here. We rationalize how it is that different histological types of breast cancer are differentially detected and characterized by MR methods.

Nonmalignant breast lesions: ADCs of benign and high-risk subtypes assessed as false-positive at dynamic enhanced MR imaging

PURPOSE

To evaluate the diffusion-weighted (DW) imaging characteristics of nonmalignant lesion subtypes assessed as false-positive findings at conventional breast magnetic resonance (MR) imaging.

MATERIALS AND METHODS

This HIPAA-compliant retrospective study had institutional review board approval, and the need for informed patient consent was waived. Lesions assessed as Breast Imaging Reporting and Data System category 4 or 5 at clinical dynamic contrast material-enhanced MR imaging that subsequently proved nonmalignant at biopsy were retrospectively reviewed. One hundred seventy-five nonmalignant breast lesions in 165 women were evaluated. Apparent diffusion coefficients (ADCs) from DW imaging (b = 0, 600 sec/mm(2)) were calculated for each lesion and were compared between subtypes and with an ADC threshold of 1.81 × 10(-3) mm(2)/sec (determined in a prior study to achieve 100% sensitivity).

RESULTS

Eighty-one (46%) lesions exhibited ADCs greater than the predetermined threshold. The most prevalent lesion subtypes with mean ADCs above the threshold were fibroadenoma ([1.94 ± 0.38 {standard deviation}] × 10(-3) mm(2)/sec; n = 30), focal fibrosis ([1.84 ± 0.48] × 10(-3) mm(2)/sec; n = 19), normal tissue ([1.81 ± 0.47] × 10(-3) mm(2)/sec; n = 13), apocrine metaplasia ([2.01 ± 0.38] × 10(-3) mm(2)/sec; n = 13), usual ductal hyperplasia ([1.83 ± 0.49] × 10(-3) mm(2)/sec; n = 12), and inflammation ([1.95 ± 0.46] × 10(-3) mm(2)/sec; n = 10). Atypical ductal hyperplasia ([1.48 ± 0.36] × 10(-3) mm(2)/sec; n = 23) was the most common lesion subtype with ADC below the threshold. Lymph nodes exhibited the lowest mean ADC of all nonmalignant lesions ([1.28 ± 0.23] × 10(-3) mm(2)/sec; n = 4). High-risk lesions (atypical ductal hyperplasia and lobular neoplasia) showed significantly lower ADCs than other benign lesions (P < .0001) and were the most common lesions with ADCs below the threshold.

CONCLUSION

Assessing ADC along with dynamic contrast-enhanced MR imaging features may decrease the number of avoidable false-positive findings at breast MR imaging and reduce the number of preventable biopsies. The ability of DW imaging to help differentiate high-risk lesions requiring additional work-up from other nonmalignant subtypes may further improve patient care.

SUPPLEMENTAL MATERIAL

http://radiology.rsna.org/lookup/suppl/doi:10.1148/radiol.12112672/-/DC1.

[Influence of b value on the measurement of contrast and apparent diffusion coefficient in 3.0 Tesla breast magnetic resonance imaging]

Diffusion-weighted imaging (DWI) has been used to characterize not only the brain, but also the breast by implementation of faster imaging techniques and higher magnetic field strengths. However, the optimum b value, which is an important scan parameter for DW images contrast on 3 T breast magnetic resonance imaging (MRI) has not been established. The purpose of this study was to investigate the influence of different b value combinations on the image contrast and apparent diffusion coefficient (ADC) in patients with known invasive carcinoma, ductal carcinoma in situ (DCIS), and normal mammary gland in breast DWI. The analysis procedure consisted of the following methods: 1) T(2) correction of DW images with echo-planar imaging (EPI) T(2)-weighted images; 2) contrast measurement between normal mammary gland and tumor tissues; 3) ADC measurement of normal mammary gland and tumor tissues. In many cases, the highest contrast between normal mammary gland and tumor tissues was obtained using a b value of 1500 s/mm(2). Our results indicated that when only one b value is used, the b value in which signal intensities of normal mammary gland decreases down to noise level, and the contrast between normal mammary gland and tumor tissues is recommended. ADC value decreased with increasing b value. Therefore, when determining the ADC threshold level, it is important to perform the evaluation using ADC values calculated from DW images with the same b value in clinical studies.

Apparent diffusion coefficient measurements in the differentiation between benign and malignant lesions: a systematic review

Objectives

To systematically review the value of apparent diffusion coefficient (ADC) measurement in the differentiation between benign and malignant lesions.

Methods

A systematic search of the Medline/Pubmed and Embase databases revealed 109 relevant studies. Quality of these articles was assessed using the Quality Assessment of the Studies of Diagnostic Accuracy Included in Systematic Reviews (QUADAS) criteria. Reported ADC values of benign and malignant lesions were compared per organ.

Results

The mean quality score of the reviewed articles was 50%. Comparison of ADC values showed marked variation among studies and between benign and malignant lesions in various organs. In several organs, such as breast, liver, and uterus, ADC values discriminated well between benign and malignant lesions. In other organs, such as the salivary glands, thyroid, and pancreas, ADCs were not significantly different between benign and malignant lesions.

Conclusion

The potential utility of ADC measurement for the characterisation of tumours differs per organ. Future well-designed studies are required before ADC measurements can be recommended for the differentiation of benign and malignant lesions. These future studies should use standardised acquisition protocols and provide complete reporting of study methods, to facilitate comparison of results and clinical implementation of ADC measurement for tumour characterisation.

Electronic supplementary material

The online version of this article (doi:10.1007/s13244-012-0175-y) contains supplementary material, which is available to authorized users.

Diffusion-weighted and dynamic contrast-enhanced MRI in evaluation of early treatment effects during neoadjuvant chemotherapy in breast cancer patients

PURPOSE

To use dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) MRI at 3 Tesla (T) for early evaluation of treatment effects in breast cancer patients undergoing neoadjuvant chemotherapy (NAC), and assess the reliability of DW-MRI.

MATERIALS AND METHODS

DW- and DCE-MRI acquisitions of 15 breast cancer patients were performed before and after one cycle of NAC. MRI tumor diameter and volume, apparent diffusion coefficient (ADC) and kinetic parameters (K(trans), v(e)) were derived. The reliability of ADC before NAC was assessed. Changes in MRI parameters after NAC were analyzed, and logistic regression analysis was used to find the best predictors for pathologic response.

RESULTS

The reliability for ADC values was high, with intraclass correlation coefficient of 0.84 (P = 0.001). After one cycle of NAC, MRI tumor diameter (8%, P = 0.005) and tumor volume (30%, P = 0.008) was reduced for all patients, while ADC mean values increased (0.12 mm(2)/s, P = 0.008). The best predictor for treatment response was a change in MRI tumor diameter with mean error rate of 0.167 (13% for responders, 5% for nonresponders, P = 0.291).

CONCLUSION

Changes in MRI derived tumor diameter and ADC after only one cycle of NAC could provide a valuable tool for early evaluation of treatment effects in breast cancer patients.

Functional magnetic resonance: biomarkers of response in breast cancer

Functional magnetic resonance (MR) encompasses a spectrum of techniques that depict physiological and molecular processes before morphological changes are visible on conventional imaging. As understanding of the pathophysiological and biomolecular processes involved in breast malignancies evolves, newer functional MR techniques can be employed that define early predictive and surrogate biomarkers for monitoring response to chemotherapy. Neoadjuvant chemotherapy is increasingly used in women with primary breast malignancies to down-stage the tumour and enable successful breast conservation surgery. It also plays a role in the treatment of undetected micrometastases. Cardinal physiological features of tumours that occur as a result of interactions between cancer cells, stromal cells and secreted factors and cytokines and how they change with treatment provide the opportunity to detect changes in the tumour microenvironment prior to any morphological change. Through sequential imaging, tumour response can be assessed and non-responders can be identified early to enable alternative therapies to be considered. This review summarises the functional magnetic resonance biomarkers of response in patients with breast cancer that are currently available and under development. We describe the current state of each biomarker and explore their potential clinical uses and limitations in assessing treatment response. With the aid of selected interesting cases, biomarkers related to dynamic contrast-enhanced MRI, diffusion-weighted MRI, T2*/BOLD and MR spectroscopy are described and illustrated. The potential of newer approaches, such as MR elastography, are also reviewed.