Differentiation of Breast Lesions and Distinguishing Their Histological Subtypes Using Diffusion-Weighted Imaging and ADC Values

Non-contrast Breast MR Imaging

Considering the high cost of dynamic contrast-enhanced MR imaging and various contraindications and health concerns related to administration of intravenous gadolinium-based contrast agents, there is emerging interest in non-contrast-enhanced breast MR imaging. Diffusion-weighted MR imaging (DWI) is a fast, unenhanced technique that has wide clinical applications in breast cancer detection, characterization, prognosis, and predicting treatment response. It also has the potential to serve as a non-contrast MR imaging screening method. Standardized protocols and interpretation strategies can help to enhance the clinical utility of breast DWI. A variety of other promising non-contrast MR imaging techniques are in development, but currently, DWI is closest to clinical integration, while others are still mostly used in the research setting.

Assessment of Prognostic Factors and Molecular Subtypes of Breast Cancer With a Continuous-Time Random-Walk MR Diffusion Model: Using Whole Tumor Histogram Analysis

BACKGROUND

The continuous-time random-walk (CTRW) diffusion model to evaluate breast cancer prognosis is rarely reported.

PURPOSE

To investigate the correlations between apparent diffusion coefficient (ADC) and CTRW-specific parameters with prognostic factors and molecular subtypes of breast cancer.

STUDY TYPE

Retrospective.

POPULATION

One hundred fifty-seven women (median age, 50 years; range, 26-81 years) with histopathology-confirmed breast cancer.

FIELD STRENGTH/SEQUENCE

Simultaneous multi-slice readout-segmented echo-planar imaging at 3.0T.

ASSESSMENT

The histogram metrics of ADC, anomalous diffusion coefficient (D), temporal diffusion heterogeneity (α), and spatial diffusion heterogeneity (β) were calculated for whole-tumor volume. Associations between histogram metrics and prognostic factors (estrogen receptor [ER], progesterone receptor [PR], human epidermal growth factor receptor 2 [HER2], and Ki-67 proliferation index), axillary lymph node metastasis (ALNM), and tumor grade were assessed. The performance of histogram metrics, both alone and in combination, for differentiating molecular subtypes (HER2-positive, Luminal or triple negative) was also assessed.

STATISTICAL TESTS

Comparisons were made using Mann-Whitney test between different prognostic factor statuses and molecular subtypes. Receiver operating characteristic curve analysis was used to assess the performance of mean and median histogram metrics in differentiating the molecular subtypes. A P value <0.05 was considered statistically significant.

RESULTS

The histogram metrics of ADC, D, and α differed significantly between ER-positive and ER-negative status, and between PR-positive and PR-negative status. The histogram metrics of ADC, D, α, and β were also significantly different between the HER2-positive and HER2-negative subgroups, and between ALNM-positive and ALNM-negative subgroups. The histogram metrics of α and β significantly differed between high and low Ki-67 proliferation subgroups, and between histological grade subgroups. The combination of αmean and βmean achieved the highest performance (AUC = 0.702) to discriminate the Luminal and HER2-positive subtypes.

DATA CONCLUSION

Whole-tumor histogram analysis of the CTRW model has potential to provide additional information on the prognosis and intrinsic subtyping classification of breast cancer.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Histogram analysis of multi-model high-resolution diffusion-weighted MRI in breast cancer: correlations with molecular prognostic factors and subtypes

Objective

To investigate the correlations between quantitative diffusion parameters and prognostic factors and molecular subtypes of breast cancer, based on a single fast high-resolution diffusion-weighted imaging (DWI) sequence with mono-exponential (Mono), intravoxel incoherent motion (IVIM), diffusion kurtosis imaging (DKI) models.

Materials and Methods

A total of 143 patients with histopathologically verified breast cancer were included in this retrospective study. The multi-model DWI-derived parameters were quantitatively measured, including Mono-ADC, IVIM-D, IVIM-D*, IVIM-f, DKI-Dapp, and DKI-Kapp. In addition, the morphologic characteristics of the lesions (shape, margin, and internal signal characteristics) were visually assessed on DWI images. Next, Kolmogorov-Smirnov test, Mann-Whitney U test, Spearman's rank correlation, logistic regression, receiver operating characteristic (ROC) curve, and Chi-squared test were utilized for statistical evaluations.

Results

The histogram metrics of Mono-ADC, IVIM-D, DKI-Dapp, and DKI-Kapp were significantly different between estrogen receptor (ER)-positive vs. ER-negative groups, progesterone receptor (PR)-positive vs. PR-negative groups, Luminal vs. non-Luminal subtypes, and human epidermal receptor factor-2 (HER2)-positive vs. non-HER2-positive subtypes. The histogram metrics of Mono-ADC, DKI-Dapp, and DKI-Kapp were also significantly different between triple-negative (TN) vs. non-TN subtypes. The ROC analysis revealed that the area under the curve considerably improved when the three diffusion models were combined compared with every single model, except for distinguishing lymph node metastasis (LNM) status. For the morphologic characteristics of the tumor, the margin showed substantial differences between ER-positive and ER-negative groups.

Conclusions

Quantitative multi-model analysis of DWI showed improved diagnostic performance for determining the prognostic factors and molecular subtypes of breast lesions. The morphologic characteristics obtained from high-resolution DWI can be identifying ER statuses of breast cancer.

The utility of diffusion-weighted imaging for differentiation of phyllodes tumor from fibroadenoma and breast cancer

Objective

To evaluate the utility of apparent diffusion coefficient (ADC) values for differentiating breast tumors.

Methods

The medical records of 17 patients with phyllodes tumor [PT; circular regions of interest (ROI-cs) n = 171], 74 patients with fibroadenomas (FAs; ROI-cs, n = 94), and 57 patients with breast cancers (BCs; ROI-cs, n = 104) confirmed by surgical pathology were retrospectively reviewed.

Results

There were significant differences between PTs, FAs, and BCs in ADCmean, ADCmax, and ADCmin values. The cutoff ADCmean for differentiating PTs from FAs was 1.435 × 10^-3 mm²/s, PTs from BCs was 1.100 × 10^-3 mm²/s, and FAs from BCs was 0.925 × 10^-3 mm²/s. There were significant differences between benign PTs, borderline PTs, and malignant PTs in ADCmean, ADCmax, and ADCmin values. The cutoff ADCmean for differentiating benign PTs from borderline PTs was 1.215 × 10^-3 mm²/s, and borderline PTs from malignant PTs was 1.665 × 10^-3 mm²/s.

Conclusion

DWI provides quantitative information that can help distinguish breast tumors.

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.

Contribution of Diffusion-Weighted Imaging and ADC Values to Papillary Breast Lesions

This study aimed to evaluate the role of apparent diffusion coefficient (ADC) values obtained from diffusion-weighted imaging (DWI) in the differentiation of malignant from benign papillary breast lesions. The magnetic resonance imaging (MRI) data of 94 breast papillary lesions confirmed by pathology were retrospectively analyzed. The differences in ADC values of papillary lesions under different enhancements in MRI and different pathological types were investigated, and the ADC threshold was determined by the receiver operating characteristic curve for its potential diagnostic value. The mean ADC values in borderline and malignant lesions (1.01 ± 0.20 × 10-3 mm2/s) were significantly lower compared to benign lesions (1.21 ± 0.27 × 10-3 mm2/s) (P &lt; 0.05). The optimal threshold of the ADC value could be 1.00 × 10-3 mm2/s. The ADC values were statistically significant in differentiating between benign and malignant papillary lesions whether in mass or non-mass enhancement (P &lt; 0.05). However, there were no statistical differences in the ADC values among borderline or any other histological subtypes of malignant lesions (P &gt; 0.05). Measuring ADC values from DWI can be used to identify benign and malignant breast papillary lesions. The diagnostic performance of the ADC value in identifying benign and malignant breast lesions is not affected by the way of lesion enhancement. However, it shows no use for differential diagnosis among malignant lesion subtypes for now. The ADC value of 1.00 × 10-3 mm2/s can be used as the most appropriate threshold for distinguishing between benign and malignant breast papillary lesions.

Value of magnetic resonance diffusion combined with perfusion imaging techniques for diagnosing potentially malignant breast lesions

BACKGROUND

Lesions of breast imaging reporting and data system (BI-RADS) 4 at mammography vary from benign to malignant, leading to difficulties for clinicians to distinguish between them. The specificity of magnetic resonance imaging (MRI) in detecting breast is relatively low, leading to many false-positive results and high rates of re-examination or biopsy. Diffusion-weighted imaging (DWI), combined with perfusion-weighted imaging (PWI), might help to distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.

AIM

To evaluate the value of DWI and PWI in diagnosing BI-RADS 4 breast lesions.

METHODS

This is a retrospective study which included patients who underwent breast MRI between May 2017 and May 2019 in the hospital. The lesions were divided into benign and malignant groups according to the classification of histopathological results. The diagnostic efficacy of DWI and PWI were analyzed respectively and combinedly. The 95 lesions were divided according to histopathological diagnosis, with 46 benign and 49 malignant. The main statistical methods used included the Student t-test, the Mann-Whitney U-test, the chi-square test or Fisher’s exact test.

RESULTS

The mean apparent diffusion coefficient (ADC) values in the parenchyma and lesion area of the normal mammary gland were 1.82 ± 0.22 × 10^-3 mm²/s and 1.24 ± 0.16 × 10^-3 mm²/s, respectively (P = 0.021). The mean ADC value of the malignant group was 1.09 ± 0.23 × 10^-3 mm²/s, which was lower than that of the benign group (1.42 ± 0.68 × 10^-3 mm²/s) (P = 0.016). The volume transfer constant (Ktrans) and rate constant (Kep) values were higher in malignant lesions than in benign ones (all P < 0.001), but there were no significant statistical differences regarding volume fraction (V_e) (P = 0.866). The sensitivity and specificity of PWI combined with DWI (91.7% and 89.3%, respectively) were higher than that of PWI or DWI alone. The accuracy of PWI combined with DWI in predicting pathological results was significantly higher than that predicted by PWI or DWI alone.

CONCLUSION

DWI, combined with PWI, might possibly distinguish between benign and malignant BI-RADS 4 breast lesions at mammography.

Apparent Diffusion Coefficient as a Noninvasive Biomarker for the Early Response in Hepatocellular Carcinoma after Transcatheter Arterial Chemoembolization using Drug-Eluting Beads

BACKGROUND

Prognostic evaluation for hepatocellular carcinoma (HCC) after transcatheter arterial chemoembolization (TACE) using drug-eluting beads (DEBs) is essential for guiding the personalized treatment and follow-up strategy. Apparent diffusion coefficient (ADC) has been reported as a biomarker in conventional TACE.

OBJECTIVE

To evaluate the diagnostic value of ADCbaseline, ADC change, and ADCratio in predicting the early objective response for HCC after DEB-TACE.

METHODS

This prospective single-center study included 32 consecutive patients undergoing dynamic contrast-enhanced magnetic resonance imaging (MRI) and diffusion-weighted imaging before and 1 month after DEB-TACE. After DEB-TACE, patients were grouped based on the modified Response Evaluation Criteria in Solid Tumors (mRECIST) criteria into responders (complete response [CR], partial response [PR] ) and nonresponders (stable disease [SD], progressive disease [PD]). The Mann-Whitney U test and receiver operating characteristic (ROC) curves were performed to assess the statistical differences in ADCbaseline, ADC change, and ADCratio between responders and nonresponders.

RESULTS

At post-DEB-TACE follow-up MRI, 62.5% (n = 20, 11 CRs, and 9 PRs) of patients showed objective response, and 37.5% (n = 12, 7 SDs, and 5 PDs) did not respond to chemoembolization. Nonresponders had a significantly higher ADCbaseline value than responders (p < 0.001). The ROC for identifying the response to chemoembolization demonstrated that the threshold ADCbaseline value of 0.920 × 10-3 mm2/s had 100% sensitivity and 70% specificity. The ADC change and ADCratio of responders were higher than that of nonresponders (p < 0.001).

CONCLUSION

ADCbaseline, ADC change, and ADCratio may be utilized as a noninvasive biomarker for predicting the early response of HCC to DEB-TACE.

Papillary Lesions of the Breast: Addition of DWI and TIRM Sequences to Routine Breast MRI Could Help in Differentiation Benign from Malignant

AIM

We aimed to investigate the magnetic resonance imaging (MRI) features of benign, atypical or malignant papillary breast lesions and to assess additional value of diffusion-weighted imaging (DWI) and turbo inversion recovery magnitude (TIRM) sequences to routine breast MRI.

BACKGROUND

Differentiation between benign and malignant papillary breast lesions is essential for patient management. However, morphologic features and enhancement patterns of malignant papillary lesions may overlap with those of benign papilloma.

METHODS

Seventy two papillary breast lesions (50 benign, 22 atypical or malignant) were included in the current study, retrospectively. We divided the patients into two groups as benign papillary breast lesions and atypical or malignant papillary breast lesions. Morphologic, dynamic, turbo inversion recovery magnitude (TIRM) values and diffusion features of the papillary lesions were compared between two groups.

RESULTS

Benign papillary lesions were smaller in size (p=0.006 and p=0.005, for radiologist 1 and 2 respectively), closer to areola (p=0.045 and 0.049 for radiologist 1 and 2 respectively) and had higher ADC values (p=0.001 for two radiologists) than atypical or malignant group. ROC curves showed diagnostic accuracy for ADC (AUC=0.770 and 0.762, p<0.0001 for two radiologists) and showed a cut-off value of ≤957 x 10-6 mm2/s (radiologist 1) and ≤ 910 x 10-6 mm2/s (radiologist 2).

CONCLUSION

MRI is a useful method for differentiation between benign and malignant papillary breast lesions. Centrally located, lesser in size and higher ADC values should be considered benign, whereas peripherally located, larger in size and lower ADC values should be considered malignant.

Differentiation of Benign and Malignant Breast Lesions Using ADC Values and ADC Ratio in Breast MRI

Magnetic resonance imaging (MRI) of the breast has been increasingly used for the detailed evaluation of breast lesions. Diffusion-weighted imaging (DWI) gives additional information for the lesions based on tissue cellularity. The aim of our study was to evaluate the possibilities of DWI, apparent diffusion coefficient (ADC) value and ADC ratio (the ratio between the ADC of the lesion and the ADC of normal glandular tissue) to differentiate benign from malignant breast lesions. Materials and methods: Eighty-seven patients with solid breast lesions (52 malignant and 35 benign) were examined on a 1.5 T MR scanner before histopathological evaluation. ADC values and ADC ratios were calculated. Results: The ADC values in the group with malignant tumors were significantly lower (mean 0.88 ± 0.15 × 10−3 mm2/s) in comparison with the group with benign lesions (mean 1.52 ± 0.23 × 10−3 mm2/s). A significantly lower ADC ratio was observed in the patients with malignant tumors (mean 0.66 ± 0.13) versus the patients with benign lesions (mean 1.12 ± 0.23). The cut-off point of the ADC value for differentiating malignant from benign breast tumors was 1.11 × 10−3 mm2/s with a sensitivity of 94.23%, specificity of 94.29%, and diagnostic accuracy of 98%, and an ADC ratio of ≤0.87 with a sensitivity of 94.23%, specificity of 91.43%, and a diagnostic accuracy of 95%. Conclusion: According to the results from our study DWI, ADC values and ADC ratio proved to be valuable additional techniques with high sensitivity and specificity for distinguishing benign from malignant breast lesions.

Application of Diffusion Weighted Imaging Techniques for Differentiating Benign and Malignant Breast Lesions

To explore the value of apparent diffusion coefficient (ADC), intravoxel incoherent motion (IVIM), and diffusional kurtosis imaging (DKI) based on diffusion weighted magnetic resonance imaging (DW-MRI) in differentiating benign and malignant breast lesions. A total of 215 patients with breast lesions were prospectively collected for breast MR examination. Single exponential, IVIM, and DKI models were calculated using a series of b values. Parameters including ADC, perfusion fraction (f), tissue diffusion coefficient (D), perfusion-related incoherent microcirculation (D*), average kurtosis (MK), and average diffusivity (MD) were compared between benign and malignant lesions. ROC curves were used to analyze the optimal diagnostic threshold of each parameter, and to evaluate the diagnostic efficacy of single and combined parameters. ADC, D, MK, and MD values were significantly different between benign and malignant breast lesions (P<0.001). Among the single parameters, ADC had the highest diagnostic efficiency (sensitivity 91.45%, specificity 82.54%, accuracy 88.84%, AUC 0.915) and the best diagnostic threshold (0.983 μm²/ms). The combination of ADC and MK offered high diagnostic performance (sensitivity 90.79%, specificity 85.71%, accuracy 89.30%, AUC 0.923), but no statistically significant difference in diagnostic performance as compared with single-parameter ADC (P=0.268). The ADC, D, MK, and MD parameters have high diagnostic value in differentiating benign and malignant breast lesions, and of these individual parameters the ADC has the best diagnostic performance. Therefore, our study revealed that the use of ADC alone should be useful for differentiating between benign and malignant breast lesions, whereas the combination of MK and ADC might improve the diagnostic performance to some extent.