Computer-aided detection (CAD) for breast MRI: evaluation of efficacy at 3.0 T

OBJECTIVE: The purpose of the study was to evaluate the accuracy of 3.0-T breast MRI interpretation using manual and fully automated kinetic analyses. MATERIAL AND METHODS: Manual MRI interpretation was done on an Advantage Workstation. Retrospectively, all examinations were processed with a computer-aided detection (CAD) system. CAD data sets were interpreted by two experienced breast radiologists and two residents. For each lesion automated analysis of enhancement kinetics was evaluated at 50% and 100% thresholds. Forty-nine malignant and 22 benign lesions were evaluated. RESULTS: Using threshold enhancement alone, the sensitivity and specificity of CAD were 97.9% and 86.4%, respectively, for the 50% threshold, and 97.9% and 90%, respectively, for the 100% threshold. Manual interpretation by two breast radiologists showed a sensitivity of 84.6% and a specificity of 68.8%. For the same two radiologists the mean sensitivity and specificity for CAD-based interpretation was 90.4% (not significant) and 81.3% (significant at p < 0.05), respectively. With one-way ANOVA no significant differences were found between the two breast radiologists and the two residents together, or between any two readers separately. CONCLUSION: CAD-based analysis improved the specificity compared with manual analysis of enhancement. Automated analysis at 50% and 100% thresholds showed a high sensitivity and specificity for readers with varying levels of experience

Feasibility of MRI-guided large-core-needle biopsy of suspiscious breast lesions at 3 T

The feasibility of large-core-needle magnetic resonance imaging (MRI)-guided breast biopsy at 3 T was assessed. Thirty-one suspicious breast lesions shown only by MRI were detected in 30 patients. Biopsy procedures were performed in a closed-bore 3-T clinical MR system on a dedicated phased-array breast coil with a commercially available add-on stereotactic biopsy device. Tissue sampling was technically successful in 29/31 (94%) lesions. Median lesion size (n = 29) was 9 mm. Histopathological analysis showed 19 benign lesions (66%) and one inconclusive biopsy result (3%). At follow-up of these lesions, 15 lesions showed no malignancy, no information was available in three patients and two lesions turned out to be malignant (one lesion at surgical excision 1 month after biopsy and one lesion at a second biopsy because of a more malignant enhancement curve at 12-months follow-up MRI). Nine biopsy results showed a malignant lesion (31%) which were all surgically removed. No complications occurred. MRI-guided biopsy at 3 T is a safe and effective method for breast biopsy in lesions that are occult on mammography and ultrasound. Follow-up MRI at 6 months after the biopsy should be performed in case of a benign biopsy result

Optoacoustic imaging of the breast: correlation with histopathology and histopathologic biomarkers

Aim: This study was conducted in order to investigate the role of gray-scale ultrasound (US) and optoacoustic imaging combined with gray-scale ultrasound (OA/US) to better differentiate between breast cancer molecular subtypes. Materials and methods: All 67 malignant masses included in the Maestro trial were retrospectively reviewed to compare US and OA/US feature scores and histopathological findings. Kruskal–Wallis tests were used to analyze the relationship between US and OA/US features and molecular subtypes of breast cancer. If a significant relationship was found, additional Wilcoxon–Mann–Whitney tests were used to identify the differences between molecular subtype groups. Results: US sound transmission helped to differentiate between LUMA and LUMB, LUMB and TNBC, and LUMB and all other molecular subtypes combined (p values < 0.05). Regarding OA/US features, the sum of internal features helped to differentiate between TNBC and HER2-enriched subtypes (p = 0.049). Internal vessels (p = 0.025), sum of all internal features (p = 0.019), and sum of internal and external features (p = 0.028) helped to differentiate between LUMA and LUMB. All internal features, the sum of all internal features, the sum of all internal and external features, and the ratio of internal and external features helped to differentiate between LUMA and TNBC. The same features also helped to differentiate between LUMA and TNBC from other molecular subtypes (p values < 0.05). Conclusions: The use of OA/US might help radiologists to better differentiate between breast cancer molecular subtypes. Further studies need to be carried out in order to validate these results. Key Points: • The combination of functional and morphologic information provided by optoacoustic imaging (OA) combined with gray-scale US helped to differentiate between breast cancer molecular subtypes

Optoacoustic imaging of the breast : correlation with histopathology and histopathologic biomarkers

Aim: This study was conducted in order to investigate the role of gray-scale ultrasound (US) and optoacoustic imaging combined with gray-scale ultrasound (OA/US) to better differentiate between breast cancer molecular subtypes. Materials and methods: All 67 malignant masses included in the Maestro trial were retrospectively reviewed to compare US and OA/US feature scores and histopathological findings. Kruskal–Wallis tests were used to analyze the relationship between US and OA/US features and molecular subtypes of breast cancer. If a significant relationship was found, additional Wilcoxon–Mann–Whitney tests were used to identify the differences between molecular subtype groups. Results: US sound transmission helped to differentiate between LUMA and LUMB, LUMB and TNBC, and LUMB and all other molecular subtypes combined (p values < 0.05). Regarding OA/US features, the sum of internal features helped to differentiate between TNBC and HER2-enriched subtypes (p = 0.049). Internal vessels (p = 0.025), sum of all internal features (p = 0.019), and sum of internal and external features (p = 0.028) helped to differentiate between LUMA and LUMB. All internal features, the sum of all internal features, the sum of all internal and external features, and the ratio of internal and external features helped to differentiate between LUMA and TNBC. The same features also helped to differentiate between LUMA and TNBC from other molecular subtypes (p values < 0.05). Conclusions: The use of OA/US might help radiologists to better differentiate between breast cancer molecular subtypes. Further studies need to be carried out in order to validate these results. Key Points: • The combination of functional and morphologic information provided by optoacoustic imaging (OA) combined with gray-scale US helped to differentiate between breast cancer molecular subtypes

Downgrading of Breast Masses Suspicious for Cancer by Using Optoacoustic Breast Imaging

Purpose To assess the ability of optoacoustic (OA) ultrasonography (US) to help correctly downgrade benign masses classified as Breast Imaging Reporting and Data System (BI-RADS) 4a and 4b to BI-RADS 3 or 2. Materials and Methods OA/US technology uses laser light to detect relative amounts of oxygenated and deoxygenated hemoglobin in and around suspicious breast masses. In this prospective, multicenter study, results of 209 patients with 215 breast masses classified as BI-RADS 4a or 4b at US are reported. Patients were enrolled between 2015 and 2016. Masses were first evaluated with US with knowledge of previous clinical information and imaging results, and from this information a US imaging-based probability of malignancy (POM) and BI-RADS category were assigned to each mass. The same masses were then re-evaluated at OA/US. During the OA/US evaluation, radiologists scored five OA/US features, and then reassigned an OA/US-based POM and BI-RADS category for each mass. BI-RADS downgrade and upgrade percentages at OA/US were assessed by using a weighted sum of the five OA feature scores. Results At OA/US, 47.9% (57 of 119; 95% CI: 0.39, 0.57) of benign masses classified as BI-RADS 4a and 11.1% (three of 27; 95% CI: 0.03, 0.28) of masses classified as BI-RADS 4b were correctly downgraded to BI-RADS 3 or 2. Two of seven malignant masses classified as BI-RADS 4a at US were incorrectly downgraded, and one of 60 malignant masses classified as BI-RADS 4b at US was incorrectly downgraded for a total of 4.5% (three of 67; 95% CI: 0.01, 0.13) false-negative findings. Conclusion At OA/US, benign masses classified as BI-RADS 4a could be downgraded in BI-RADS category, which would potentially decrease biopsies negative for cancer and short-interval follow-up examinations, with the limitation that a few masses may be inappropriately downgraded