Diffusion-weighted imaging of suspicious (BI-RADS 4) breast lesions: stratification based on histopathology

Understanding BI-RADS Category 3

The Breast Imaging Reporting and Data System (BI-RADS) category 3 assessment is used for breast imaging findings considered "probably benign," with less than a 2% likelihood of malignancy. It is used to increase specificity by decreasing the number of breast biopsies. It has been validated for mammography, breast US, and emerging indications for use in contrast-enhanced breast MRI. Despite the long-term use of category 3 and numerous published studies that evaluate characteristic imaging findings appropriate for this category, there is still misuse and confusion regarding its accurate use. Imaging findings classified as category 3 require short-term follow-up to assess stability and identify changes that may warrant a biopsy for early diagnosis of breast cancer. Category 3 should not be used in a screening study without a comprehensive diagnostic evaluation that may reveal suspicious features or downgrade a finding to benign. In mammography, category 3 findings are validated for grouped round calcifications, oval circumscribed masses, and nonpalpable asymmetries. In US, category 3 can be applied to oval circumscribed parallel solid masses and complicated cysts. Category 3 can be assigned to clustered microcysts when they are very small or deep in the breast. Recent studies have yielded characteristic findings appropriate for MRI category 3 that are expected to be included in the sixth edition of the BI-RADS atlas. These include oval circumscribed masses with associated T2-hyperintense signal, focal non-mass enhancement, and foci of enhancement with associated T 2-hyperintense signal. Surveillance with short-interval imaging enables radiologists to monitor findings and act early when a change is detected. ©RSNA, 2024 Supplemental material is available for this article. See the invited commentary by Cohen and Leung.

The diagnostic accuracy of diffusion-weighted magnetic resonance imaging and shear wave elastography in comparison to dynamic contrast-enhanced MRI for diagnosing BIRADS 3 and 4 lesions

Background

Breast cancer is one of the leading causes of female morbidity and mortality. Management options vary between lesions of BIRADS categories 3 and 4. Therefore, reliable differentiation would improve outcome. Although sonomammography and contrast-enhanced breast magnetic resonance imaging (CE-MRI) remain the cornerstone for assessment of breast disease, additional, non-invasive techniques can be used to increase the efficiency of evaluation such as shear wave elastography (SWE) and diffusion-weighted magnetic resonance imaging (DW-MRI). This prospective study included 66 breast lesions that were categorized as BIRADS 3 or 4 by ultrasound ± mammography. All lesions were evaluated by SWE, CE-MRI and DW-MRI. For SWE, lesions were evaluated by both qualitative and quantitative methods. For CE-MRI, both morphological and kinematic evaluations were done and for DW-MRI, both qualitative and quantitative assessments were studied. Results of all imaging modalities were correlated to histopathology.

Results

Thirty-seven out of the examined 66 lesions (56.06%) were categorised as BIRADS 3, out of which 1 (2.7%) turned out to be malignant on histopathology and 36 (97.29%) were proved benign. Twenty-nine (43.93%) were categorized as BIRADS 4, out of which 2 (6.89%) turned out to be benign on pathology and 27 (93.1%) were proved malignant. Morphological and kinematic evaluations of CE-MRI showed 92.59% and 92.86%sensitivity, 94.74% and 84.21% specificity, 92.59 and 81.25%PPV, 94.74 and 94.12% NPV, and 93.85% and 87.88% accuracy respectively. Color-coded scoring of SWE showed indices of 89.29%, 68.42%, 67.57%, 89.66%, and 77.27% respectively. The calculated cut-off value for Emax differentiating benign from malignant was 65.15 kpa, resulting in indices of 96.43%, 57.89%, 95.65%, 62.79%, and 74.24% respectively. For Eratio, the calculated cut-off value was 4.55, resulting in indices of 71.43%, 68.42%, 76.47%, 62.50% and 69.70% respectively. For qualitative evaluation of DW-MRI, indices were 78.57%, 65.79%, 62.86%, 80.65%, and 71.21% respectively. For ADC, the calculated cut-off value was 1.25 × 103 mm2/s, which resulted in indices of 75.00%, 84.21%, 82.05%, 77.78%, and 80.30% respectively.

Conclusion

CE-MRI showed the best diagnostic performance indices. While, SWE and DW-MRI present variable diagnostic performance, both techniques can be used as an adjunct to other imaging modalities to aid the clinical decision and increase its diagnostic confidence.

Multiparameter MRI Model With DCE-MRI, DWI, and Synthetic MRI Improves the Diagnostic Performance of BI-RADS 4 Lesions

Objectives

To evaluate the value of synthetic magnetic resonance imaging (syMRI), diffusion-weighted imaging (DWI), DCE-MRI, and clinical features in breast imaging–reporting and data system (BI-RADS) 4 lesions, and develop an efficient method to help patients avoid unnecessary biopsy.

Methods

A total of 75 patients with breast diseases classified as BI-RADS 4 (45 with malignant lesions and 30 with benign lesions) were prospectively enrolled in this study. T1-weighted imaging (T1WI), T2WI, DWI, and syMRI were performed at 3.0 T. Relaxation time (T1 and T2), apparent diffusion coefficient (ADC), conventional MRI features, and clinical features were assessed. “T” represents the relaxation time value of the region of interest pre-contrast scanning, and “T+” represents the value post-contrast scanning. The rate of change in the T value between pre- and post-contrast scanning was represented by ΔT%.

Results

ΔT1%, T2, ADC, age, body mass index (BMI), menopause, irregular margins, and heterogeneous internal enhancement pattern were significantly associated with a breast cancer diagnosis in the multivariable logistic regression analysis. Based on the above parameters, four models were established: model 1 (BI-RADS model, including all conventional MRI features recommended by BI-RADS lexicon), model 2 (relaxation time model, including ΔT1% and T2), model 3 [multi-parameter (mp)MRI model, including ΔT1%, T2, ADC, margin, and internal enhancement pattern], and model 4 (combined image and clinical model, including ΔT1%, T2, ADC, margin, internal enhancement pattern, age, BMI, and menopausal state). Among these, model 4 has the best diagnostic performance, followed by models 3, 2, and 1.

Conclusions

The mpMRI model with DCE-MRI, DWI, and syMRI is a robust tool for evaluating the malignancies in BI-RADS 4 lesions. The clinical features could further improve the diagnostic performance of the model.

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.

Impact of breast magnetic resonance imaging on the locoregional staging and management of breast cancer

Objective

To assess the impact of magnetic resonance imaging (MRI) on the locoregional staging of breast cancer.

Materials and Methods

We evaluated 61 patients with breast cancer who underwent pre-treatment breast MRI, between August 2015 and April 2016. An experienced breast surgeon determined the surgical treatment, on the basis of the findings of conventional imaging examinations, and made a subsequent treatment recommendation based on the MRI findings, then determining whether the MRI changed the approach, as well as whether it had a positive or negative impact on the treatment.

Results

The mean age was 50.8 years (standard deviation, 12.0 years). The most common histological type was invasive breast carcinoma of no special type (in 68.9%), and the most common molecular subtype was luminal B (in 45.9%). Breast MRI modified the therapeutic management in 23.0% of the cases evaluated, having a positive impact in 82.7%.

Conclusion

Breast MRI is an useful tool for the locoregional staging of breast cancer, because it provides useful information that can have a positive impact on patient treatment.

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.

DCE-MRI-Derived Volume Transfer Constant (Ktrans) and DWI Apparent Diffusion Coefficient as Predictive Markers of Short- and Long-Term Efficacy of Chemoradiotherapy in Patients With Esophageal Cancer

This study aimed to evaluate both the short- and long-term efficacies of chemoradiotherapy in relation to the treatment of esophageal cancer . This was achieved through the use of dynamic contrast-enhanced magnetic resonance imaging–derived volume transfer constant and diffusion weighted imaging–derived apparent diffusion coefficient . Patients with esophageal cancer were assigned into the sensitive and resistant groups based on respective efficacies in chemoradiotherapy. Dynamic contrast-enhanced magnetic resonance imaging and diffusion weighted imaging were used to measure volume transfer constant and apparent diffusion coefficient, while computed tomography was used to calculate tumor size reduction rate. Pearson correlation analyses were conducted to analyze correlation between volume transfer constant, apparent diffusion coefficient, and the tumor size reduction rate. Receiver operating characteristic curve was constructed to analyze the short-term efficacy of volume transfer constant and apparent diffusion coefficient, while Kaplan-Meier curve was employed for survival rate analysis. Cox proportional hazard model was used for the risk factors for prognosis of patients with esophageal cancer. Our results indicated reduced levels of volume transfer constant, while increased levels were observed in ADC_min, ADC_mean, and ADC_max following chemoradiotherapy. A negative correlation was determined between ADC_min, ADC_mean, and ADC_max, as well as in the tumor size reduction rate prior to chemoradiotherapy, whereas a positive correlation was uncovered postchemoradiotherapy. Volume transfer constant was positively correlated with tumor size reduction rate both before and after chemoradiotherapy. The 5-year survival rate of patients with esophageal cancer having high ADC_min, ADC_mean, and ADC_max and volume transfer constant before chemoradiotherapy was greater than those with respectively lower values. According to the Cox proportional hazard model, ADC_mean, clinical stage, degree of differentiation, and tumor stage were all confirmed as being independent risk factors in regard to the prognosis of patients with EC. The findings of this study provide evidence suggesting that volume transfer constant and apparent diffusion coefficient as being tools allowing for the evaluation of both the short- and long-term efficacies of chemoradiotherapy esophageal cancer treatment.