Rim sign in breast lesions on diffusion-weighted magnetic resonance imaging: diagnostic accuracy and clinical usefulness

Exploring habitats-based spatial distributions: improving predictions of lymphovascular invasion in invasive breast cancer

RATIONALE AND OBJECTIVES

Accurate assessment of lymphovascular invasion (LVI) in invasive breast cancer (IBC) plays a pivotal role in tailoring personalized treatment plans. This study aimed to investigate habitats-based spatial distributions to quantitatively measure tumor heterogeneity on multiparametric magnetic resonance imaging (MRI) scans and assess their predictive capability for LVI in patients with IBC.

MATERIALS AND METHODS

In this retrospective cohort study, we consecutively enrolled 241 women diagnosed with IBC between July 2020 and July 2023 and who had 1.5 T/T1-weighted images, fat-suppressed T2-weighted images, and dynamic contrast-enhanced MRI. Habitats-based spatial distributions were derived from the gross tumor volume (GTV) and gross tumor volume plus peritumoral volume (GPTV). GTV_habitats and GPTV_habitats were generated through sub-region segmentation, and their performances were compared. Subsequently, a combined nomogram was developed by integrating relevant spatial distributions with the identified MR morphological characteristics. Diagnostic performance was compared using receiver operating characteristic curve analysis and decision curve analysis. Statistical significance was set at p < 0.05.

RESULTS

GPTV_habitats exhibited superior performance compared to GTV_habitats. Consequently, the GPTV_habitats, diffusion-weighted imaging rim signs, and peritumoral edema were integrated to formulate the combined nomogram. This combined nomogram outperformed individual MR morphological characteristics and the GPTV_habitats index, achieving area under the curve values of 0.903 (0.847 -0.959), 0.770 (0.689 -0.852), and 0.843 (0.776 -0.910) in the training set and 0.931 (0.863 -0.999), 0.747 (0.613 -0.880), and 0.849 (0.759 -0.938) in the validation set.

CONCLUSION

The combined nomogram incorporating the GPTV_habitats and identified MR morphological characteristics can effectively predict LVI in patients with IBC.

Radiomics-based analysis of dynamic contrast-enhanced magnetic resonance image: A prediction nomogram for lymphovascular invasion in breast cancer

OBJECTIVE

To develop and validate a nomogram for quantitively predicting lymphovascular invasion (LVI) of breast cancer (BC) based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and morphological features.

METHODS

We retrospectively divided 238 patients with BC into training and validation cohorts. Radiomic features from DCE-MRI were subdivided into A1 and A2, representing the first and second post-contrast images respectively. We utilized the minimal redundancy maximal relevance filter to extract radiomic features, then we employed the least absolute shrinkage and selection operator regression to screen these features and calculate individualized radiomics score (Rad score). Through the application of multivariate logistic regression, we built a prediction nomogram that integrated DCE-MRI radiomics and MR morphological features (MR-MF). The diagnostic capabilities were evaluated by comparing C-indices and calibration curves.

RESULTS

The diagnostic efficiency of the A1/A2 radiomics model surpassed that of the A1 and A2 alone. Furthermore, we incorporated the MR-MF (diffusion-weighted imaging rim sign, peritumoral edema) and optimized Radiomics into a hybrid nomogram. The C-indices for the training and validation cohorts were 0.868 (95% CI: 0.839-0.898) and 0.847 (95% CI: 0.787-0.907), respectively, indicating a good level of discrimination. Moreover, the calibration plots demonstrated excellent agreement in the training and validation cohorts, confirming the effectiveness of the calibration.

CONCLUSION

This nomogram combined MR-MF and A1/A2 Radiomics has the potential to preoperatively predict LVI in patients with BC.

Assessment of Lymphovascular Invasion in Breast Cancer Using a Combined MRI Morphological Features, Radiomics, and Deep Learning Approach Based on Dynamic Contrast-Enhanced MRI

BACKGROUND

Assessment of lymphovascular invasion (LVI) in breast cancer (BC) primarily relies on preoperative needle biopsy. There is an urgent need to develop a non-invasive assessment method.

PURPOSE

To develop an effective model to assess the LVI status in patients with BC using magnetic resonance imaging morphological features (MRI-MF), Radiomics, and deep learning (DL) approaches based on dynamic contrast-enhanced MRI (DCE-MRI).

STUDY TYPE

Cross-sectional retrospective cohort study.

POPULATION

The study included 206 BC patients, with 136 in the training set [97 LVI(-) and 39 LVI(+) cases; median age: 51.5 years] and 70 in the test set [52 LVI(-) and 18 LVI(+) cases; median age: 48 years].

FIELD STRENGTH/SEQUENCE

1.5 T/T1-weighted images, fat-suppressed T2-weighted images, diffusion-weighted imaging (DWI), and DCE-MRI.

ASSESSMENT

The MRI-MF model was developed with conventional MR features using logistic analyses. The Radiomic feature extraction process involved collecting data from categorized DCE-MRI datasets, specifically the first and second post-contrast images (A1 and A2). Next, a DL model was implemented to determine LVI. Finally, we established a joint diagnosis model by combining the MRI-MF, Radiomics, and DL approaches.

STATISTICAL TESTS

Diagnostic performance was compared using receiver operating characteristic curve analysis, confusion matrix, and decision curve analysis.

RESULTS

Rim sign and peritumoral edema features were used to develop the MRI-MF model, while six Radiomics signature from the A1 and A2 images were used for the Radiomics model. The joint model (MRI-MF + Radiomics + DL models) achieved the highest accuracy (area under the curve [AUC] = 0.857), being significantly superior to the MRI-MF (AUC = 0.724), Radiomics (AUC = 0.736), or DL (AUC = 0.740) model. Furthermore, it also outperformed the pairwise combination models: Radiomics + MRI-MF (AUC = 0.796), DL + MRI-MF (AUC = 0.796), or DL + Radiomics (AUC = 0.826).

DATA CONCLUSION

The joint model incorporating MRI-MF, Radiomics, and DL approaches can effectively determine the LVI status in patients with BC before surgery.

LEVEL OF EVIDENCE

4 TECHNICAL EFFICACY: Stage 2.

Prior Clinico-Radiological Features Informed Multi-Modal MR Images Convolution Neural Network: A novel deep learning framework for prediction of lymphovascular invasion in breast cancer

BACKGROUND

Current methods utilizing preoperative magnetic resonance imaging (MRI)-based radiomics for assessing lymphovascular invasion (LVI) in patients with early-stage breast cancer lack precision, limiting the options for surgical planning.

PURPOSE

This study aimed to develop a sophisticated deep learning framework called "Prior Clinico-Radiological Features Informed Multi-Modal MR Images Convolutional Neural Network (PCMM-Net)" to improve the accuracy of LVI prediction in breast cancer. By incorporating multiparameter MRI and prior clinical knowledge, PCMM-Net should enhance the precision of LVI assessment.

METHODS

A total of 341 patients with breast cancer were randomly divided into training and validation groups at a ratio of 7:3. Imaging features were extracted from T1-weighted, T2-weighted, and contrast-enhanced T1-weighted MRI sequences. Stepwise univariate and multivariate logistic regression were employed to establish a clinico-radiological model for LVI prediction. The radiomics model was built using redundancy and the least absolute shrinkage and selection operator. Then, two deep learning frameworks were developed: the Multi-Modal MR Images Convolutional Neural Network (MM-Net), which does not consider prior radiological features, and PCMM-Net, which incorporates multiparameter MRI and prior clinical knowledge. Receiver operating characteristic curves were used, and the corresponding areas under the curves (AUCs) were calculated for evaluation.

RESULTS

PCMM-Net achieved the highest AUC of 0.843. The clinico-radiological features displayed the lowest AUC value of 0.743, followed by MM-Net with an AUC of 0.774, and radiomics with an AUC of 0.795.

CONCLUSIONS

This study introduces PCMM-Net, an innovative deep learning framework that integrates prior clinico-radiological features for accurate LVI prediction in breast cancer. PCMM-Net demonstrates excellent diagnostic performance and facilitates the application of precision medicine.

Leveraging multimodal MRI-based radiomics analysis with diverse machine learning models to evaluate lymphovascular invasion in clinically node-negative breast cancer

Objective

This study aimed to investigate and validate the effectiveness of diverse radiomics models for preoperatively differentiating lymphovascular invasion (LVI) in clinically node-negative breast cancer (BC).

Methods

This study included 198 patients diagnosed with clinically node-negative bc and pathologically confirmed LVI status from January 2018-July 2023. The training dataset consisted of 138 patients, while the validation dataset included 60. Radiomics features were extracted from multimodal magnetic resonance imaging obtained from T1WI, T2WI, DCE, DWI, and ADC sequences. Dimensionality reduction and feature selection techniques were applied to the extracted features. Subsequently, machine learning approaches, including logistic regression, support vector machine, classification and regression trees, k-nearest neighbors, and gradient boosting machine models (GBM), were constructed using the radiomics features. The best-performing radiomic model was selected based on its performance using the confusion matrix. Univariate and multivariable logistic regression analyses were conducted to identify variables for developing a clinical-radiological (Clin-Rad) model. Finally, a combined model incorporating both radiomics and clinical-radiological model features was created.

Results

A total of 6195 radiomic features were extracted from multimodal magnetic resonance imaging. After applying dimensionality reduction and feature selection, seven valuable radiomics features were identified. Among the radiomics models, the GBM model demonstrated superior predictive efficiency and robustness, achieving area under the curve values (AUC) of 0.881 (0.823,0.940) and 0.820 (0.693,0.947) in the training and validation datasets, respectively. The Clin-Rad model was developed based on the peritumoral edema and DWI rim sign. In the training dataset, it achieved an AUC of 0.767 (0.681, 0.854), while in the validation dataset, it achieved an AUC of 0.734 (0.555-0.913). The combined model, which incorporated radiomics and the Clin-Rad model, showed the highest discriminatory capability. In the training dataset, it had an AUC value of 0.936 (0.892, 0.981), and in the validation dataset, it had an AUC value of 0.876 (0.757, 0.995). Additionally, decision curve analysis of the combined model revealed its optimal clinical efficacy.

Conclusion

The combined model, integrating radiomics and clinical-radiological features, exhibited excellent performance in distinguishing LVI status. This non-invasive and efficient approach holds promise for aiding clinical decision-making in the context of clinically node-negative BC.

Radiomic Machine Learning in Invasive Ductal Breast Cancer: Prediction of Ki-67 Expression Level Based on Radiomics of DCE-MRI

PURPOSE

Our study aimed to investigate the potential of radiomics with DCE-MRI for predicting Ki-67 expression in invasive ductal breast cancer.

METHOD

We conducted a retrospective study including 223 patients diagnosed with invasive ductal breast cancer. Radiomics features were extracted from DCE-MRI using 3D-Slicer software. Two Ki-67 expression cutoff values (20% and 29%) were examined. Patients were divided into training (70%) and test (30%) sets. The Elastic Net method selected relevant features, and five machine-learning models were established. Radiomics models were created from intratumoral, peritumoral, and combined regions. Performance was assessed using ROC curves, accuracy, sensitivity, and specificity.

RESULT

For a Ki-67 cutoff value of 20%, the combined model exhibited the highest performance, with area under the curve (AUC) values of 0.838 (95% confidence interval (CI): 0.774-0.897) for the training set and 0.863 (95% CI: 0.764-0.949) for the test set. The AUC values for the tumor model were 0.816 (95% CI: 0.745-0.880) and 0.830 (95% CI: 0.724-0.916), and for the peritumor model were 0.790 (95% CI: 0.711-0.857) and 0.808 (95% CI: 0.682-0.910). When the Ki-67 cutoff value was set at 29%, the combined model also demonstrated superior predictive ability in both training set (AUC: 0.796; 95% CI: 0.724-0.862) and the test set (AUC: 0.823; 95% CI: 0.723-0.911). The AUC values for the tumor model were 0.785 (95% CI: 0.708-0.861) and 0.784 (95% CI: 0.663-0.882), and for the peritumor model were 0.773 (95% CI: 0.690-0.844) and 0.729 (95% CI: 0.603-0.847).

CONCLUSION

Radiomics with DCE-MRI can predict Ki-67 expression in invasive ductal breast cancer. Integrating radiomics features from intratumoral and peritumoral regions yields a dependable prognostic model, facilitating pre-surgical detection and treatment decisions. This holds potential for commercial diagnostic tools.

Evaluation of breast lesions based on modified BI-RADS using high-resolution readout-segmented diffusion-weighted echo-planar imaging and T2/T1-weighted image

PURPOSE

To evaluate the diagnostic performance of a non-contrast magnetic resonance imaging (MRI) protocol combining high-resolution diffusion-weighted images (HR-DWI) using readout-segmented echo planar imaging, T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI), using our modified Breast Imaging-Reporting and Data System (modified BI-RADS).

METHODS

Two experienced radiologists, blinded to the final pathological diagnosis, categorized a total of 108 breast lesions (61 malignant and 47 benign) acquired with the above protocol using the modified BI-RADS with a diagnostic decision tree. The decision tree included subcategories of category 4, as in mammography (categories 2, 3, 4A, 4B, 4C, and 5). These results were compared with the pathological diagnoses.

RESULTS

The area under the ROC curve (AUC) was 0.89 (95% confidence interval [CI]: 0.83-0.95) for reader 1, and 0.89 (95% CI: 0.82-0.96) for reader 2. When categories 4C and above were classified as malignant, the sensitivity, specificity, and accuracy were 73.8%, 93.6%, and 82.4%, for reader 1; and 82.0%, 89.4%, and 85.2% for reader 2, respectively.

CONCLUSION

Our results suggest that using HR-DWI, T1WI/T2WI analyzed with a modified BI-RADS and a decision tree showed promising diagnostic performance in breast lesions, and is worthy of further study.

High-resolution diffusion-weighted imaging compared with conventional diffusion-weighted imaging and dynamic contrast-enhanced magnetic resonance imaging with regard to image quality and assessment of breast cancer morphology

PURPOSE

To evaluate the image quality and tumor morphology depiction ability of high resolution (HR) diffusion- weighted imaging (f-DWI) in comparison to conventional DWI (c-DWI) and dynamic contrast- enhanced magnetic resonance imaging (DCE-MRI) in the primary breast cancer setting.

METHODS

The f-DWI, c-DWI, and DCE-MRIs of 160 malignant breast masses were evaluated retrospectively by two independent radiologists. Data on image quality [sharpness, distortion, and perceived signalto- noise ratio (SNR)], apparent diffusion coefficient (ADC) value, lesion size, and tumor morphology (shape, margin, and internal pattern) obtained on f-DWI, c-DWI, and DCE-MRI were compared. Consistency between the readers and imaging methods for morphological parameters was analyzed.

RESULTS

The ADC values measured on f-DWI were significantly lower than those measured on c-DWI for both readers (P < 0.001 for each), whereas mean lesion size was significantly larger in c-DWI than in f-DWI and DCE-MRI for both readers (P < 0.001 for each). Higher consistency values were obtained for f-DWI compared with c-DWI when correlated with DCE-MRI for each morphological parameter. The least distorted images were obtained using DCE-MRI compared with c-DWI and f-DWI for both readers, whereas the highest distortion scores were obtained using c-DWI. Sharpness and perceived SNR scores were rated as significantly higher for f-DWI and DCE-MRI images compared with c-DWI by both readers (P < 0.001 for all). The concordance between c-DWI and DCE-MRI was fair to slight (κ = 0.15 to 0.41), whereas concordance between f-DWI and DCE-MRI was significantly better (κ = 0.68 to 0.87) for each reader and for all morphological parameters (P < 0.001). The highest concordance between the readers was achieved in margin assessment (κ = 0.87 to 0.89) regardless of the MRI method, followed by shape and internal pattern parameters (κ = 0.63 to 0.79).

CONCLUSION

The results demonstrated that f-DWI produces higher-quality images than c-DWI, enabling the morphological features to be identified in similar detail to that offered by HR DCE-MRI. Accordingly, f-DWI, as a method that highly correlates with DCE in determining the morphological characteristics of breast cancers, seems to have potential in the evaluation of breast tumors in patients for whom the use of contrast media is contraindicated.

Diagnostic Usefulness of Diffusion-Weighted MRI for Axillary Lymph Node Evaluation in Patients with Breast Cancer

This study aimed to determine whether apparent diffusion coefficient (ADC) and morphological features on diffusion-weighted MRI (DW-MRI) can discriminate metastatic axillary lymph nodes (ALNs) from benign in patients with breast cancer. Two radiologists measured ADC, long and short diameters, long-to-short diameter ratio, and cortical thickness and assessed eccentric cortical thickening, loss of fatty hilum, irregular margin, asymmetry in shape or number, and rim sign of ALNs on DW-MRI and categorized them into benign or suspicious ALNs. Pathologic reports were used as a reference standard. Statistical analysis was performed using the Mann-Whitney U test and chi-square test. Overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and diagnostic accuracy of DW-MRI were calculated. The ADC of metastatic ALNs was 0.905 × 10^-3 mm²/s, and that of benign ALNs was 0.991 × 10^-3 mm²/s (p = 0.243). All morphologic features showed significant difference between the two groups. The sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the final categorization on DW-MRI were 77.1%, 93.3%, 79.4%, 92.5%, and 86.2%, respectively. Our results suggest that morphologic evaluation of ALNs on DWI can discriminate metastatic ALNs from benign. The ADC value of metastatic ALNs was lower than that of benign nodes, but the difference was not statistically significant.

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.

Accuracy of multiparametric MRI in distinguishing the breast malignant lesions from benign lesions: a meta-analysis

BACKGROUND

The sensitivity of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for detecting breast cancer was high and the specificity was relatively low. However, diffusion-weighted imaging (DWI) has a high specificity in the diagnosis of malignant lesions.

PURPOSE

To evaluate the accuracy of the multiparametric MRI (mp-MRI) in distinguishing the breast malignant lesions from the benign lesions.

MATERIAL AND METHODS

A comprehensive search of the PubMed, Embase, and Cochrane Library electronic databases was conducted up to March 2020. Data were analyzed for the following indexes: pooled sensitivity and specificity; positive likelihood ratio; negative likelihood ratio; diagnostic odds ratio; and the area under the curve.

RESULTS

A total of 2356 patients with 1604 malignant and 967 benign breast lesions were included from 22 studies. The pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the curve for mp-MRI were 0.93, 0.85, 6.3, 0.08, 81, and 0.96, respectively. The pooled sensitivity, specificity, and area under the curve for DCE-MRI alone were 0.95, 0.71, and 0.92, respectively. The pooled sensitivity, specificity, and area under the curve for DWI alone were 0.88, 0.84, and 0.93, respectively.

CONCLUSION

The mp-MRI did not improve the sensitivity but increased the specificity for the diagnosis of breast malignant lesions.

Effectiveness of ADC Difference Value on Pre-neoadjuvant Chemotherapy MRI for Response Evaluation of Breast Cancer

Background: Neoadjuvant chemotherapy (NAC) is known to be a suitable treatment and first-line defense for locally advanced breast cancer. However, the NAC response may include unexpected outcomes, and it is not easy to predict the NAC response precisely. Especially, early detection of those patients who do not benefit from NAC is needed to reduce unnecessary therapy and side effects. Objective: The purpose of this study was to determine whether the pretreatment apparent diffusion coefficient (ADC) value is effective for predicting the response of breast cancer to NAC. Method: Forty-nine breast cancer cases with pre- and post-NAC breast MRI were enrolled. MRI was performed using a 1.5-T scanner with the basic protocol including diffusion-weighted imaging. ADC difference value (ADC-diff) was calculated in all cases. Results: ADC-diff was high in complete response and partial response cases (p < .05). ADC-diff correlated with the DWI rim sign, with a positive DWI rim sign being associated with a higher ADC-diff (p < .05). Conclusion: High-ADC difference value on the pretreatment MRI can provide information for a better response of NAC on breast cancer.

Preoperative Rim Enhancement on Magnetic Resonance Imaging Indicates Larger Tumor Size and Poor Prognosis in Chinese Basal-Like Breast Cancer Patients

Background: This study was to investigate the prevalence of preoperative rim enhancement, and its association with clinicopathological features, relapse, and survival profiles in Chinese basal-like breast cancer (BC) patients. Materials and Methods: The preoperative breast magnetic resonance imaging images of 145 basal-like BC patients who underwent surgical excision were obtained to determine rim enhancement. Besides, based on disease status and survival status during follow-up, the 1-year relapse rate/mortality, 3-year relapse rate/mortality, 5-year relapse rate/mortality were calculated; disease-free survival (DFS) and overall survival (OS) were determined. Results: There were 51 (35.2%) patients with rim enhancement and 94 (64.8%) patients without rim enhancement. Furthermore, rim enhancement was associated with larger tumor size and advanced T stage, whereas it did not associate with age, pathological differentiation, N stage, or TNM stage. In addition, rim enhancement was associated with higher 1-, 3-, and 5-year relapse rate and shorter DFS; meanwhile, rim enhancement was associated with increased 1-, 3-, and 5-year mortality rate and decreased OS. By multivariate Cox's regression analyses, rim enhancement, pathological differentiation, and N stage independently predicted reduced DFS; T stage independently predicted declined OS. Conclusion: Preoperative rim enhancement on MRI might be a possible noninvasive indicator for guiding personalized treatment strategies and improving prognosis in Chinese basal-like BC patients.

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.

Value of intravoxel incoherent motion and diffusion kurtosis imaging in predicting peritumoural infiltration of soft-tissue sarcoma: a prospective study based on MRI-histopathology comparisons

AIM

To investigate the performance of intravoxel incoherent motion (IVIM) and diffusion kurtosis imaging (DKI) in the identification of peritumoural infiltration of soft-tissue sarcoma (STS).

MATERIALS AND METHODS

From July 2018 to January 2020, 34 STS patients who underwent 3-T magnetic resonance imaging (MRI), including IVIM and DKI, were reviewed. The standard apparent diffusion coefficient (ADC), true diffusion (D), pseudo-diffusion coefficient (D∗), perfusion fraction (f), mean kurtosis (MK), and mean diffusion (MD) of each lesion were analysed independently by two observers. An MRI-histopathology control method was used to ensure the correspondence of MRI sections with histopathological sections. Differences in STS with and without infiltration were evaluated. The area under the curve (AUC) was used to determine the best cut-off point for different parameters. Interobserver agreement was assessed using the intraclass correlation coefficient.

RESULTS

Standard ADC, D, MK, and MD values reliably distinguished STS that had positive and negative infiltration. The MD value had the best diagnostic performance. Use of an MD cut-off value of 2.35 × 10^-3 mm²/s to distinguish positive and negative infiltration had an AUC of 0.85, accuracy of 88.2%, sensitivity of 94.4%, and specificity of 81.3%. The two independent observers had nearly perfect agreement for all parameters.

CONCLUSION

The standard ADC and D value of IVIM, and the MK and MD values of DKI reliably identify the presence of peritumoural infiltration of STS.

Dynamic contrast enhanced-MRI and diffusion-weighted image as predictors of lymphovascular invasion in node-negative invasive breast cancer

BACKGROUND

Lymphovascular invasion (LVI) is an important risk factor for prognosis of breast cancer and an unfavorable prognostic factor in node-negative invasive breast cancer patients. The purpose of this study was to evaluate the association between LVI and pre-operative features of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted imaging (DWI) in node-negative invasive breast cancer.

METHODS

Data were collected retrospectively from 132 cases who had undergone pre-operative MRI and had invasive breast carcinoma confirmed on the last surgical pathology report. MRI and DWI data were analyzed for the size of tumor, mass shape, margin, internal enhancement pattern, kinetic enhancement curve, high intratumoral T2-weighted signal intensity, peritumoral edema, DWI rim sign, and apparent diffusion coefficient (ADC) values. We calculated the relationship between presence of LVI and various prognostic factors and MRI features.

RESULTS

Pathologic tumor size, mass margin, internal enhancement pattern, kinetic enhancement curve, DWI rim sign, and the difference between maximum and minimum ADC were significantly correlated with LVI (p < 0.05).

CONCLUSIONS

We suggest that DCE-MRI with DWI would assist in predicting LVI status in node-negative invasive breast cancer patients.

Qualitative characterization of breast tumors with diffusion-weighted imaging has comparable accuracy to quantitative analysis

PURPOSE

To evaluate the applicability and accuracy of a new qualitative diffusion-weighted imaging (DWI) assessment method in the characterization of breast tumors compared to quantitative ADC measurement and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI).

MATERIALS AND METHODS

After review board approval, MRIs of 216 consecutive women with final diagnoses (131 malignant, 85 benign) were retrospectively analyzed. Two radiologists independently scored DWI and dynamic contrast-enhanced MRI (DCE-MRI) according to malignancy probability. Qualitative assessments were performed by combined analysis of tumor morphology and diffusion signal. Quantitative data was obtained from apparent diffusion coefficient (ADC) measurements. Lastly, descriptive DWI features were evaluated and recorded. Cohen's kappa, receiver operating characteristic and multivariate analyzes were applied.

RESULTS

Of malignant tumors, 97% were visible on DWI. Qualitative and quantitative DWI assessments provided comparable sensitivities of 89-94% and 88-92% and specificities of 51-61% and 59-67%, respectively. There was no statistical difference between the accuracies of qualitative and quantitative DWI (p ≥ 0.105). Best diagnostic values were obtained with DCE-MRI (sensitivity, 99-100%; specificity, 69-71%). Inter-reader agreement was moderate (kappa = 0.597) for qualitative DWI and substantial (kappa = 0.689) for DCE-MRI (p < 0.001). Agreement between qualitative DWI and DCE-MRI scores was moderate (kappa = 0.536 and 0.442). Visual diffusion signal, mass margin and shape were the most predictive features of malignancy on multivariate analysis of qualitative assessment.

CONCLUSION

Qualitative characterization of breast tumors on DWI has comparable accuracy to quantitative ADC analysis. This method might be used to make DWI more widely available with eliminating the need to a predetermined ADC threshold in tumor characterization. However, lower accuracy and inter-reader agreement of it compared to DCE-MRI should be considered.

A Comparison of Methods for High-Spatial-Resolution Diffusion-weighted Imaging in Breast MRI

Background Diffusion-weighted imaging (DWI) shows promise in detecting and monitoring breast cancer, but standard spin-echo (SE) echo-planar DWI methods often have poor image quality and low spatial resolution. Proposed alternatives include readout-segmented (RS) echo-planar imaging and axially reformatted (AR)-simultaneous multislice (SMS) imaging. Purpose To compare the resolution and image quality of standard SE echo-planar imaging DWI with two high-spatial-resolution alternatives, RS echo-planar and AR-SMS imaging, for breast imaging. Materials and Methods In a prospective study (2016-2018), three 5-minute DWI protocols were acquired at 3.0 T, including standard SE echo-planar imaging, RS echo-planar imaging with five segments, and AR-SMS imaging with four times slice acceleration. Participants were women undergoing breast MRI either as part of a treatment response clinical trial or undergoing breast MRI for screening or suspected cancer. A commercial breast phantom was imaged for resolution comparison. Three breast radiologists reviewed images in random order, including clinical images indicating the lesion, images with b value of 800 sec/mm², and apparent diffusion coefficient (ADC) maps from the three randomly labeled DWI methods. Readers measured the longest dimension and lesion-average ADC on three DWI methods, reported measurement confidence, and rated or ranked the quality of each image. The scores were fit to a linear mixed-effects model with intercepts for reader and subject. Results The smallest feature (1 mm) was only detectible in a phantom on images from AR-SMS DWI. Thirty lesions from 28 women (mean age, 50 years ± 13 [standard deviation]) were evaluated. On the five-point Likert scale for image quality, AR-SMS imaging scored 1.31 points higher than SE echo-planar imaging and 0.74 points higher than RS echo-planar imaging, whereas RS echo-planar imaging scored 0.57 points higher than SE echo-planar imaging (all P < .001). Conclusion The axially reformatted simultaneous multislice protocol was rated highest for image quality, followed by the readout-segmented echo-planar imaging protocol. Both were rated higher than the standard spin-echo echo-planar imaging. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Partridge in this issue.

The comparison of high-resolution diffusion weighted imaging (DWI) with high-resolution contrast-enhanced MRI in the evaluation of breast cancers

PURPOSE

We sought to investigate the performance of high resolution (HR) diffusion-weighted imaging (DWI) using readout-segmented echo-planar imaging (rs-EPI), compared with high-resolution contrast-enhanced MRI (HR CE-MRI) in terms of morphological accuracy, on the basis of the Breast Imaging and Reporting and Data System (BI-RADS) MRI descriptors and lesion size.

METHODS

This retrospective study included the image data of 94 patients with surgically confirmed malignant breast lesions who had undergone high resolution diffusion-weighted imaging (HR-DWI) and HR CE-MRI. Two radiologists blinded to the final diagnosis independently identified the lesions on HR-DWI, described the morphology of the lesions according to BI-RADS descriptors, and measured lesion size. HR CE-MRI was subsequently evaluated using the same procedure. The inter-method agreement of the morphology was assessed using kappa statistics. Correlation on size was also assessed.

RESULTS

Reader A detected 79 mass lesions and 37 non-mass lesions on HR-DWI and HR CE-MRI. Reader B detected 81 mass lesions and 33 non-mass lesions on HR-DWI and HR CE-MRI. Very high agreement (kappa = 0.81-0.89, p < .05) was observed in the shape and margin assessment of mass lesions, where agreement on internal enhancement/signals was moderate to substantial (kappa = 0.43-0.61, p < .05). Disagreement was mostly seen in the evaluation of rim enhancement. High agreement was observed for non-mass lesion distribution (kappa = 0.76-0.84, p < .05), and agreement on internal enhancement/signals was moderate to fair (kappa = 0.34-0.49, p < .05). Agreement among heterogeneous, clumped, and clustered-ring patterns was variable. Size assessment showed very strong correlation both in mass (Spearman's rho = 0.90-0.96, p < .0001) and non-mass lesions (Spearman's rho = 0.86, p < .0001).

CONCLUSIONS

The findings in morphology and lesion extent showed high agreement between HR-DWI and HR CE-MRI for malignant breast lesions. These results imply the potential of applying HR-DWI for evaluation of malignant breast lesions using BI-RADS MRI.

Differentiation between idiopathic granulomatous mastitis and invasive breast carcinoma, both presenting with non-mass enhancement without rim-enhanced masses: The value of whole-lesion histogram and texture analysis using apparent diffusion coefficient

PURPOSE

The aim of this study was to investigate whether whole-lesion histogram and texture analysis using apparent diffusion coefficient can discriminate between idiopathic granulomatous mastitis (IGM) and invasive breast carcinoma (IBC), both of which appeared as non-mass enhancement lesions without rim-enhanced masses.

METHOD

This retrospective study included 58 pathology-proven female patients at two independent study sites (27 IGM patients and 31 IBC patients). Diffusion-weighted imaging (3b values, 50, 400 or 500, and 800 s/mm²) was performed using 1.5 T or 3 T MR scanners from the same vendor. Whole-lesions were segmented and 11 features were extracted. Univariate analysis and multivariate logistic regression analysis were performed to identify significant variables for differentiating IGM from IBC. Receiver operating characteristic curve was assessed. The interobserver reliability between two observers for the histogram and texture measurement was also reported.

RESULTS

The 5th percentile, difference entropy and entropy of apparent diffusion coefficient showed significant differences between the two groups. An area under the curve of 0.778 (95 % CI: 0.648, 0.908), accuracy of 79.3 %, and sensitivity of 87.1 % was achieved using these three significant features. No significant feature was found with the multivariate analysis. For the interobserver reliability, all apparent diffusion coefficient parameters except skewness and kurtosis indicated good or excellent agreement, while these two features showed moderate agreement.

CONCLUSIONS

Whole-lesion histogram and texture analysis using apparent diffusion coefficient provide a non-invasive analytical approach to the differentiation between IGM and IBC, both presenting with non-mass enhancement without rim-enhanced masses.

Triple-negative breast cancer: Pretreatment magnetic resonance imaging features and clinicopathological factors associated with recurrence

PURPOSE

We aimed to investigate the magnetic resonance imaging (MRI) features and clinicopathologic factors with recurrence of triple-negative breast cancer (TNBC).

PATIENTS AND METHODS

We identified 281 patients with 288 surgically confirmed TNBC lesions who underwent pretreatment MRI between 2009 and 2015. The presence of intratumoral high signal on T2-weighted images, high-signal rim on diffusion-weighted images (DWI), and rim enhancement on the dynamic contrast-enhanced MRI and clinicopathological data were collected. Cox proportional analysis was performed.

RESULTS

Of the 288 lesions, 36 (12.5%) recurred after a median follow-up of 18 months (range, 3.6-68.3 months). Rim enhancement (hazard ratio [HR] = 3.15; 95% confidence interval [CI] = 1.01, 9.88; p = .048), and lymphovascular invasion (HR = 2.73, 95% CI = 1.20, 6.23; p = .016) were independently associated with disease recurrence. While fibroglandular volume, background parenchymal enhancement, intratumoral T2 high signal, and high-signal rim on DWI, were not found to be risk factors for recurrence.

CONCLUSION

Pretreatment MRI features may help predict a high risk of recurrence in patients with TNBC.

Associations Between Apparent Diffusion Coefficient Values and the Prognostic Factors of Breast Cancer

OBJECTIVES

Breast imaging can offer great information about breast cancer heterogeneity. The purpose of this study was to analyze the relationship between apparent diffusion coefficient (ADC) values and various prognostic factors and investigate whether ADC values are useful for breast cancer diagnosis, evaluation of treatment response, and determination of prognosis.

METHODS

A total of 111 cases of breast cancer were included in this study. Magnetic resonance findings were recorded according to the Breast Imaging Reporting and Data System magnetic resonance imaging lexicon. Diffusion-weighted imaging rim sign and minimum, maximum, and difference ADC values (ADCdiff) were also evaluated.

RESULTS

ADCdiff was related to all prognostic factors such as histological grade, Ki-67, tumor size, molecular subtype, axillary node metastasis, lymphvascular invasion, internal enhancement pattern, intratumoral high T2 signal, peritumoral edema, and diffusion-weighted imaging rim sign, whereas minimum and maximum ADC values showed variable associations.

CONCLUSIONS

Apparent diffusion coefficient values were shown to be correlated with many proven or possible prognostic factors of breast cancer. In particular, ADCdiff can reflect tumor heterogeneity and showed higher correlation.

Associations Between Magnetic Resonance Imaging Findings and Clincopathologic Factors in Triple-Negative Breast Cancer

OBJECTIVE

The aim of the study was to evaluate the magnetic resonance imaging findings associated with clinicopathologic factors in patients with triple-negative breast cancer.

METHODS

One hundred one patients with surgically confirmed triple-negative breast cancer who underwent preoperative breast magnetic resonance imaging with diffusion-weighted imaging (DWI) were included in this study. Presence of rim enhancement on contrast-enhanced T1-weighted imaging and hyperintense rim on DWI were visually assessed. Pathologic data about presence of recurrence and presence of lymphovascular invasion (LVI) were reviewed. Statistics for relative risk of recurrence carried out.

RESULTS

Of the 101, 13 cases (12.9%) were recurred after a median follow-up of 18.5 months. Rim enhancement was more frequently seen in the LVI-positive group (P = 0.046). Hyperintense rim on DWI and apparent diffusion coefficient values showed no significant relationship with clinical-pathologic factors.

CONCLUSIONS

Rim enhancement was significantly associated with positive LVI status in patients with triple-negative breast cancer. Our study suggests that rim enhancement may be useful to predict the prognosis.

Evaluation of Breast Cancer Morphology Using Diffusion-Weighted and Dynamic Contrast-Enhanced MRI: Intermethod and Interobserver Agreement

BACKGROUND

The capability of diffusion-weighted imaging (DWI) for morphological analysis of breast lesions is underexplored.

PURPOSE

To evaluate the utility of DWI for assessment of morphological features of breast cancer by comparing DWI and dynamic contrast-enhanced (DCE) MRI findings to determine intermethod and interobserver agreement.

STUDY TYPE

Retrospective.

POPULATION

Seventy-eight women with pathohistologically proven breast cancer.

FIELD STRENGTH/SEQUENCE

1.5T. DWI and DCE images.

ASSESSMENT

Diffusion-weighted and DCE images were placed in two separate case sets. Three radiologists, blinded to all other information, independently evaluated each case set on two separate occasions. Lesions were interpreted according to the fifth edition of the ACR BI-RADS lexicon.

STATISTICAL ANALYSIS

Kappa (κ) statistics were calculated in order to assess intermethod and interobserver agreement.

RESULTS

For values that attained statistical significance (P < 0.05), intermethod agreement ranged from fair (κ = 0.22) for nonmass internal patterns to significant (κ = 0.8) for lesion type. On DWI, interobserver agreement varied from fair (κ = 0.34) for mass shape to significant (κ = 0.75) for lesion type. On DCE MRI, interobserver agreement varied from fair (κ = 0.27) for irregular vs. spiculated mass margin to perfect (κ = 1) for circumscribed vs. noncircumscribed mass margin.

DATA CONCLUSION

On the whole, there was moderate intermethod agreement. The values of interobserver agreement were mostly similar between DWI and DCE MRI. This suggests that DWI is applicable for morphological assessment of breast cancer, notwithstanding substantially inferior spatial resolution compared to DCE MRI.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2019;49:1381-1390.

Assessment of breast mass morphology with diffusion-weighted MRI: Beyond apparent diffusion coefficient

BACKGROUND

Diffusion-weighted imaging (DWI) is a noncontrast-enhanced MRI technique. There are new promising studies on the use of DWI as a part of the enhanced or unenhanced abbreviated breast MRI protocols.

PURPOSE

To evaluate the ability of breast DWI in the assessment of mass morphology and determine the contribution of this morphologic evaluation in their characterization.

STUDY TYPE

Retrospective.

POPULATION

In all, 213 consecutive women were breast MR imaged and had a later confirmed diagnosis.

FIELD STRENGTH/SEQUENCE

Breast dynamic contrast-enhanced-MRI (DCE-MRI) and DWI at 1.5T.

ASSESSMENT

After Institutional Review Board approval, two radiologists first independently, and later in consensus, evaluated the visibility and morphology of the 143 malignant, 70 benign masses on DWI and DCE-MRI in separate sessions, blindly. Shape, margin, and internal pattern of the masses were evaluated according to BI-RADS lexicon. Apparent diffusion coefficient (ADC) and tumor size were measured by one radiologist.

STATISTICAL TESTS

Consistency between imaging methods and readers was evaluated with Cohen's kappa statistics. Multivariate analysis was applied to find the best predictors of malignancy.

RESULTS

Tumor visibility on DWI was high to moderate in at least 88% of cases. Consistency between DWI and DCE-MRI was substantial (kappa ≥0.757) for shape and margin and moderate (kappa = 0.505) for internal pattern. Interobserver agreement was substantial to moderate for all morphologic parameters (kappa ≥0.596). Morphology evaluated on DWI provided 83-84% accuracy in discriminating malignant from benign masses. ADC alone provided 90-91% accuracy. Both morphologic parameters and ADC were significantly associated with malignancy on multivariate analysis and provided 91-93% accuracy.

DATA CONCLUSION

DWI might be used not only for ADC evaluation but also for the morphological evaluation of breast masses to characterize them.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;48:1668-1677.

Rim sign and histogram analysis of apparent diffusion coefficient values on diffusion-weighted MRI in triple-negative breast cancer: Comparison with ER-positive subtype

Purpose

To investigate associations between the clinicopathologic features and MRI features of triple-negative breast cancer (TNBC) and ER-positive breast cancer (BC) via apparent diffusion coefficient (ADC) histogram analysis.

Materials and methods

In this study, 221 breast cancer patients with pre-operative MRI performed from August 2009 to March 2015 were included in a retrospective analysis. All patients had a pathologically confirmed diagnosis of invasive carcinoma and were grouped into ER-positive (149) or triple-negative (72) subtypes. DWI rim sign and various ADC parameters (mean; mode; 25, 50, and 75 percentiles; skewness; and kurtosis) between ER-positive and TNBC were compared using whole-lesion ADC histogram analysis. Univariate and multivariate regression analyses were used for statistical comparison.

Results

DWI rim signs were detected in 42.3% and 41.7% of ER-positive subtype and TNBC, respectively (P = 0.931). TNBC had poorer histologic grade (P<0.001) and higher Ki-67 expression (P <0.001) than ER-positive subtype BC. TNBC displayed higher ADC parameters (mean, mode, 50th & 75th percentiles, kurtosis on univariate analysis, all P<0.001; only kurtosis on multivariate anaylsis; P<0.001) than ER-positive subtype BC. TNBC had significantly more recurrence events than ER-positive subtype BC on univarate analysis (9.7% (7/72) vs. 2.7% (4/149), P = 0.035).

Conclusion

Poorer clinicopathologic outcomes were found in TNBC. Whole-lesion ADC histogram analysis revealed ADC kurtosis to be higher in TNBC than ER-positive subtype BC.

Differentiation of malignant and benign breast lesions: Added value of the qualitative analysis of breast lesions on diffusion-weighted imaging (DWI) using readout-segmented echo-planar imaging at 3.0 T

Objective

To determine the added value of qualitative analysis as an adjunct to quantitative analysis for the discrimination of benign and malignant lesions in patients with breast cancer using diffusion-weighted imaging (DWI) with readout-segmented echo-planar imaging (rs-EPI).

Methods

A total of 99 patients with 144 lesions were reviewed from our prospectively collected database. DWI data were obtained using rs-EPI acquired at 3.0 T. The diagnostic performances of DWI in the qualitative, quantitative, and combination analyses were compared with that of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). Additionally, the effect of lesion size on the diagnostic performance of the DWI combination analysis was evaluated.

Results

The strongest indicators of malignancy on DWI were a heterogeneous pattern (P = 0.005) and an apparent diffusion coefficient (ADC) value <1.0 × 10–3 mm2/sec (P = 0.002). The area under the curve (AUC) values for the qualitative analysis, quantitative analysis, and combination analysis on DWI were 0.732 (95% CI, 0.651–0.803), 0.780 (95% CI, 0.703–0.846), and 0.826 (95% CI, 0.754–0.885), respectively (P<0.0001). The AUC for the combination analysis on DWI was superior to that for DCE-MRI alone (0.651, P = 0.003) but inferior to that for DCE-MRI plus the ADC value (0.883, P = 0.03). For the DWI combination analysis, the sensitivity was significantly lower in the size ≤1 cm group than in the size >1 cm group (80% vs. 95.6%, P = 0.034).

Conclusions

Qualitative analysis of tumor morphology was diagnostically applicable on DWI using rs-EPI. This qualitative analysis adds value to quantitative analyses for lesion characterization in patients with breast cancer.

Is the necrosis/wall ADC ratio useful for the differentiation of benign and malignant breast lesions?

OBJECTIVE

To determine whether the necrosis/wall apparent diffusion coefficient (ADC) ratio is useful for the malignant-benign differentiation of necrotic breast lesions.

METHODS

Breast MRI was performed using a 3-T system. In this retrospective study, calculation of the necrosis/wall ADC ratio was based on ADC values measured from the necrosis and from the wall of malignant and benign breast lesions by diffusion-weighted imaging (DWI). By synchronizing post-contrast T₁ weighted images, the separate parts of wall and necrosis were maintained. All the diagnoses were pathologically confirmed. Statistical analyses were conducted using an independent sample t-test and receiver operating characteristic analysis. The intraclass and interclass correlations were evaluated.

RESULTS

A total of 66 female patients were enrolled, 38 of whom had necrotic breast carcinomas and 28 of whom had breast abscesses. The ADC values were obtained from both the wall and necrosis. The mean necrosis/wall ADC ratio (± standard deviation) was 1.61 ± 0.51 in carcinomas, and it was 0.65 ± 0.33 in abscesses. The area under the curve values for necrosis ADC, wall ADC and the necrosis/wall ADC ratio were 0.680, 0.068 and 0.942, respectively. A wall/necrosis ADC ratio cut-off value of 1.18 demonstrated a sensitivity of 97%, specificity of 93%, a positive-predictive value of 95%, a negative-predictive value of 96% and an accuracy of 95% in determining the malignant nature of necrotic breast lesions. There was a good intra- and interclass reliability for the ADC values of both necrosis and wall.

CONCLUSION

The necrosis/wall ADC ratio appears to be a reliable and promising tool for discriminating breast carcinomas from abscesses using DWI. Advances in knowledge: ADC values of the necrosis obtained by DWI are valuable for malignant-benign differentiation in necrotic breast lesions. The necrosis/wall ADC ratio appears to be a reliable and promising tool in the breast imaging field.

Rim sign and histogram analysis of apparent diffusion coefficient values on diffusion-weighted MRI in triple-negative breast cancer: Comparison with ER-positive subtype

PURPOSE

To investigate associations between the clinicopathologic features and MRI features of triple-negative breast cancer (TNBC) and ER-positive breast cancer (BC) via apparent diffusion coefficient (ADC) histogram analysis.

MATERIALS AND METHODS

In this study, 221 breast cancer patients with pre-operative MRI performed from August 2009 to March 2015 were included in a retrospective analysis. All patients had a pathologically confirmed diagnosis of invasive carcinoma and were grouped into ER-positive (149) or triple-negative (72) subtypes. DWI rim sign and various ADC parameters (mean; mode; 25, 50, and 75 percentiles; skewness; and kurtosis) between ER-positive and TNBC were compared using whole-lesion ADC histogram analysis. Univariate and multivariate regression analyses were used for statistical comparison.

RESULTS

DWI rim signs were detected in 42.3% and 41.7% of ER-positive subtype and TNBC, respectively (P = 0.931). TNBC had poorer histologic grade (P<0.001) and higher Ki-67 expression (P <0.001) than ER-positive subtype BC. TNBC displayed higher ADC parameters (mean, mode, 50th & 75th percentiles, kurtosis on univariate analysis, all P<0.001; only kurtosis on multivariate anaylsis; P<0.001) than ER-positive subtype BC. TNBC had significantly more recurrence events than ER-positive subtype BC on univarate analysis (9.7% (7/72) vs. 2.7% (4/149), P = 0.035).

CONCLUSION

Poorer clinicopathologic outcomes were found in TNBC. Whole-lesion ADC histogram analysis revealed ADC kurtosis to be higher in TNBC than ER-positive subtype BC.

Assessment of tumor morphology on diffusion-weighted (DWI) breast MRI: Diagnostic value of reduced field of view DWI

PURPOSE

To compare the diagnostic value of conventional, bilateral diffusion-weighted imaging (DWI) and high-resolution targeted DWI of known breast lesions.

MATERIALS AND METHODS

Twenty-one consecutive patients with known breast cancer or suspicious breast lesions were scanned with the conventional bilateral DWI technique, a high-resolution, reduced field of view (rFOV) DWI technique, and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) (3.0 T). We compared bilateral DWI and rFOV DWI quantitatively by measuring the lesions' apparent diffusion coefficient (ADC) values. For qualitative comparison, three dedicated breast radiologists scored image quality and performed lesion interpretation.

RESULTS

In a phantom, ADC values were in good agreement with the reference values. Twenty-one patients (30 lesions: 14 invasive carcinomas, 10 benign lesions [of which 5 cysts], 3 high-risk, and 3 in situ carcinomas) were included. Cysts and high-risk lesions were excluded from the quantitative analysis. Quantitatively, both bilateral and rFOV DWI measured lower ADC values in invasive tumors than other lesions. In vivo, rFOV DWI gave lower ADC values than bilateral DWI (1.11 × 10(-3) mm(2) /s vs. 1.24 × 10(-3) mm(2) /s, P = 0.002). Regions of interest (ROIs) were comparable in size between the two techniques (2.90 vs. 2.13 cm(2) , P = 0.721). Qualitatively, all three radiologists scored sharpness of rFOV DWI images as significantly higher than bilateral DWI (P ≤ 0.002). Receiver operating characteristic (ROC) curve analysis showed a higher area under the curve (AUC) in BI-RADS classification for rFOV DWI compared to bilateral DWI (0.71 to 0.93 vs. 0.61 to 0.76, respectively).

CONCLUSION

Tumor morphology can be assessed in more detail with high-resolution DWI (rFOV) than with standard bilateral DWI by providing significantly sharper images.