Foci on breast magnetic resonance imaging in high-risk women: cancer or not?

Contrast-enhanced mammography versus breast MRI in the assessment of multifocal and multicentric breast cancer: a retrospective study

BACKGROUND

Breast cancer multifocality and multicentricity diagnosis influences the surgeon's choice between applying breast conservative therapy or performing mastectomy.

PURPOSE

To assess the role of contrast enhanced mammography (CEM) and breast magnetic resonance imaging (MRI) in the assessment of preoperative breast cancer multifocality and multicentricity and to assess their accuracy, agreement and impact on the surgical management.

MATERIAL AND METHODS

The study retrospectively included cases over a 5-year period. After analysis and interpretation of suspicious breast lesions, a comparative evaluation of CEM and MRI was conducted with the assessment of diagnostic indices, including sensitivity, specificity and diagnostic accuracy. The kappa (κ) measure of agreement between both modalities was measured. The postoperative specimen pathology was the reference standard.

RESULTS

One hundred and twenty-two female cases with 126 breast lesions were evaluated. Specimen pathology, MRI and CEM showed a single neoplastic lesion in 67.5%, 35% and 48.5% of cases, respectively, and multiple neoplastic lesions in 32.5%, 65% and 51.6% of cases, respectively. The sensitivity, specificity and accuracy of MRI were 95.12%, 49.41%,and 64.29%, and the CEM values were 85.37%, 64.71% and 71.43%, respectively. The κ value was 0.592 with an intermediate agreement between both modalities. When comparing between both modalities, enhancing foci showed a statistically significant difference, although there were no statistically significant difference in terms of high breast density or molecular subtype.

CONCLUSION

In terms of breast cancer multifocality and multicentricity evaluation, MRI showed a higher sensitivity, while CEM showed a higher specificity, and there was moderate agreement between the two modalities.

'Earlier than Early' Detection of Breast Cancer in Israeli BRCA Mutation Carriers Applying AI-Based Analysis to Consecutive MRI Scans

Female BRCA1/BRCA2 (=BRCA) pathogenic variants (PVs) carriers are at a substantially higher risk for developing breast cancer (BC) compared with the average risk population. Detection of BC at an early stage significantly improves prognosis. To facilitate early BC detection, a surveillance scheme is offered to BRCA PV carriers from age 25-30 years that includes annual MRI based breast imaging. Indeed, adherence to the recommended scheme has been shown to be associated with earlier disease stages at BC diagnosis, more in-situ pathology, smaller tumors, and less axillary involvement. While MRI is the most sensitive modality for BC detection in BRCA PV carriers, there are a significant number of overlooked or misinterpreted radiological lesions (mostly enhancing foci), leading to a delayed BC diagnosis at a more advanced stage. In this study we developed an artificial intelligence (AI)-network, aimed at a more accurate classification of enhancing foci, in MRIs of BRCA PV carriers, thus reducing false-negative interpretations. Retrospectively identified foci in prior MRIs that were either diagnosed as BC or benign/normal in a subsequent MRI were manually segmented and served as input for a convolutional network architecture. The model was successful in classification of 65% of the cancerous foci, most of them triple-negative BC. If validated, applying this scheme routinely may facilitate 'earlier than early' BC diagnosis in BRCA PV carriers.

Machine learning for multi-parametric breast MRI: radiomics-based approaches for lesion classification

In the artificial intelligence era, machine learning (ML) techniques have gained more and more importance in the advanced analysis of medical images in several fields of modern medicine. Radiomics extracts a huge number of medical imaging features revealing key components of tumor phenotype that can be linked to genomic pathways. The multi-dimensional nature of radiomics requires highly accurate and reliable machine-learning methods to create predictive models for classification or therapy response assessment.

Multi-parametric breast magnetic resonance imaging (MRI) is routinely used for dense breast imaging as well for screening in high-risk patients and has shown its potential to improve clinical diagnosis of breast cancer. For this reason, the application of ML techniques to breast MRI, in particular to multi-parametric imaging, is rapidly expanding and enhancing both diagnostic and prognostic power. In this review we will focus on the recent literature related to the use of ML in multi-parametric breast MRI for tumor classification and differentiation of molecular subtypes. Indeed, at present, different models and approaches have been employed for this task, requiring a detailed description of the advantages and drawbacks of each technique and a general overview of their performances.

Enhanced Masses on Contrast-Enhanced Breast: Differentiation Using a Combination of Dynamic Contrast-Enhanced MRI and Quantitative Evaluation with Synthetic MRI

BACKGROUND

The addition of synthetic MRI might improve the diagnostic performance of dynamic contrast-enhanced MRI (DCE-MRI) in patients with breast cancer.

PURPOSE

To evaluate the diagnostic value of a combination of DCE-MRI and quantitative evaluation using synthetic MRI for differentiation between benign and malignant breast masses.

STUDY TYPE

Retrospective, observational.

POPULATION

In all, 121 patients with 131 breast masses who underwent DCE-MRI with additional synthetic MRI were enrolled.

FIELD STRENGTH/SEQUENCE

3.0 Tesla, T₁ -weighted DCE-MRI and synthetic MRI acquired by a multiple-dynamic, multiple-echo sequence.

ASSESSMENT

All lesions were differentiated as benign or malignant using the following three diagnostic methods: DCE-MRI type based on the Breast Imaging-Reporting and Data System; synthetic MRI type using quantitative evaluation values calculated by synthetic MRI; and a combination of the DCE-MRI + Synthetic MRI types. The diagnostic performance of the three methods were compared.

STATISTICAL TESTS

Univariate (Mann-Whitney U-test) and multivariate (binomial logistic regression) analyses were performed, followed by receiver-operating characteristic curve (AUC) analysis.

RESULTS

Univariate and multivariate analyses showed that the mean T₁ relaxation time in a breast mass obtained by synthetic MRI prior to injection of contrast agent (pre-T₁ ) was the only significant quantitative value acquired by synthetic MRI that could independently differentiate between malignant and benign breast masses. The AUC for all enrolled breast masses assessed by DCE-MRI + Synthetic MRI type (0.83) was significantly greater than that for the DCE-MRI type (0.70, P < 0.05) or synthetic MRI type (0.73, P < 0.05). The AUC for category 4 masses assessed by the DCE-MRI + Synthetic MRI type was significantly greater than that for those assessed by the DCE-MRI type (0.74 vs. 0.50, P < 0.05).

DATA CONCLUSION

A combination of synthetic MRI and DCE-MRI improves the accuracy of diagnosis of benign and malignant breast masses, especially category 4 masses. Level of Evidence 4 Technical Efficacy Stage 2 J. MAGN. RESON. IMAGING 2021;53:381-391.

Screening in patients with increased risk of breast cancer (part 1): pros and cons of MRI screening

Screening plays an important role in women with a high risk of breast cancer. Given this population's high incidence of breast cancer and younger age of onset compared to the general population, it is recommended that screening starts earlier. There is ample evidence that magnetic resonance imaging (MRI) is the most sensitive diagnostic tool, and American and the European guidelines both recommend annual MRI screening (with supplementary annual mammography) as the optimum screening modality. Nevertheless, the current guidelines do not totally agree about the recommendations for MRI screening in some subgroups of patients. The first part of this article on screening in women with increased risk of breast cancer reviews the literature to explain and evaluate the advantages of MRI screening compared to screening with mammography alone: increased detection of smaller cancers with less associated lymph node involvement and a reduction in the rate of interval cancers, which can have an impact on survival and mortality (with comparable effects to other preventative measures). At the same time, however, we would like to reflect on the drawbacks of MRI screening that affect its applicability.

A machine learning approach for differentiating malignant from benign enhancing foci on breast MRI

BACKGROUND

Differentiate malignant from benign enhancing foci on breast magnetic resonance imaging (MRI) through radiomic signature.

METHODS

Forty-five enhancing foci in 45 patients were included in this retrospective study, with needle biopsy or imaging follow-up serving as a reference standard. There were 12 malignant and 33 benign lesions. Eight benign lesions confirmed by over 5-year negative follow-up and 15 malignant histopathologically confirmed lesions were added to the dataset to provide reference cases to the machine learning analysis. All MRI examinations were performed with a 1.5-T scanner. One three-dimensional T1-weighted unenhanced sequence was acquired, followed by four dynamic sequences after intravenous injection of 0.1 mmol/kg of gadobenate dimeglumine. Enhancing foci were segmented by an expert breast radiologist, over 200 radiomic features were extracted, and an evolutionary machine learning method ("training with input selection and testing") was applied. For each classifier, sensitivity, specificity and accuracy were calculated as point estimates and 95% confidence intervals (CIs).

RESULTS

A k-nearest neighbour classifier based on 35 selected features was identified as the best performing machine learning approach. Considering both the 45 enhancing foci and the 23 additional cases, this classifier showed a sensitivity of 27/27 (100%, 95% CI 87-100%), a specificity of 37/41 (90%, 95% CI 77-97%), and an accuracy of 64/68 (94%, 95% CI 86-98%).

CONCLUSION

This preliminary study showed the feasibility of a radiomic approach for the characterisation of enhancing foci on breast MRI.

Motion artifacts, lesion type, and parenchymal enhancement in breast MRI: what does really influence diagnostic accuracy?

BACKGROUND

Motion artifacts can reduce image quality of breast magnetic resonance imaging (MRI). There is a lack of data regarding their effect on diagnostic estimates.

PURPOSE

To evaluate factors that potentially influence readers' diagnostic estimates in breast MRI: motion artifacts; amount of fibroglandular tissue; background parenchymal enhancement; lesion size; and lesion type.

MATERIAL AND METHODS

This Institutional Review Board-approved, retrospective, cross-sectional, single-center study included 320 patients (mean age = 55.1 years) with 334 histologically verified breast lesions (139 benign, 195 malignant) who underwent breast MRI. Two expert breast radiologists evaluated the images considering: motion artifacts (1 = minimal to 4 = marked); fibroglandular tissue (BI-RADS FGT); background parenchymal enhancement (BI-RADS BPE); lesion size; lesion type; and BI-RADS score. Univariate (Chi-square) and multivariate (Generalized Estimation Equations [GEE]) statistics were used to identify factors influencing sensitivity, specificity, and accuracy.

RESULTS

Lesions were: 230 mass (68.9%) and 59 non-mass (17.7%), no foci. Forty-five lesions (13.5%) did not enhance in MRI but were suspicious or unclear in conventional imaging. Sensitivity, specificity, and accuracy were 93.8%, 83.4%, and 89.8% for Reader 1 and 95.4%, 87.8%, and 91.9% for Reader 2. Lower sensitivity was observed in case of increased motion artifacts ( P = 0.007), non-mass lesions ( P < 0.001), and small lesions ≤ 10 mm ( P < 0.021). No further factors (e.g. BPE, FGT) significantly influenced diagnostic estimates. At multivariate analysis, lesion type and size were retained as independent factors influencing the diagnostic performance ( P < 0.033).

CONCLUSION

Motion artifacts can impair lesion characterization with breast MRI, but lesion type and small size have the strongest influence on diagnostic estimates.

Lesion characteristics, histopathologic results, and follow-up of breast lesions after MRI-guided biopsy

PURPOSE

We aimed to assess the effectiveness of magnetic resonance imaging (MRI)-guided vacuum-assisted breast biopsy (VABB), evaluate and compare the characteristics and histopathologic findings of lesions, and overview the follow-up results of benign lesions.

METHODS

MRI findings and histopathologic results of breast lesions biopsied by MRI-guided VABB between 2013 and 2016 were retrospectively analyzed. MRI findings closely related with malignancy were investigated in particular. Follow-up results of benign lesions were evaluated.

RESULTS

MRI-guided VABB was applied to 116 lesions of 112 women. Of the lesions, 75 (65%) were benign, while 41 (35%) were malignant. Segmental (94%), clustered (89%), and clustered ring (67%) non-mass-like enhancement patterns were found to be more related with malignancy. False-negative rate of MRI-guided VABB was 12%, underestimation rate was 21%. One of the 54 followed-up benign lesions had a malignant result.

CONCLUSION

MRI-guided VABB is a reliable method for the diagnosis of breast lesions that are positive only on MRI. Follow-up results show that cancer detection rate is low for radio-pathologically concordant lesions. Further multicenter studies with larger patient population are needed to elucidate these results.