Abstract:PurposeTo assess how frequently foci are identified on MRI in high-risk patients, and their association with malignancy, breast density, and background parenchymal enhancement (BPE).Materials and methodsIn this multicentric study, two readers, in consensus, retrospectively reviewed screening breast MRI of 245 high-risk women, performed between 2009 and 2014. Eligible patients had at least two consecutive screening MRI, and a follow-up of at least 1 year after a lesion was first detected; histology was availabl… Show more
“…Thus, the characterization of nonmass enhancement and foci is important. However, a standardized classification scheme for the interpretation of lesions showing nonmass‐like enhancement does not exist, and the best management of foci remains under discussion 5,38 . In the future, we would like to continue investigating the utility of synthetic MRI to differentiate benign from malignant lesions in patients with nonmass enhancement and foci.…”
BackgroundThe addition of synthetic MRI might improve the diagnostic performance of dynamic contrast‐enhanced MRI (DCE‐MRI) in patients with breast cancer.PurposeTo 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 TypeRetrospective, observational.PopulationIn all, 121 patients with 131 breast masses who underwent DCE‐MRI with additional synthetic MRI were enrolled.Field Strength/Sequence3.0 Tesla, T1‐weighted DCE‐MRI and synthetic MRI acquired by a multiple‐dynamic, multiple‐echo sequence.AssessmentAll 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 TestsUnivariate (Mann–Whitney U‐test) and multivariate (binomial logistic regression) analyses were performed, followed by receiver‐operating characteristic curve (AUC) analysis.ResultsUnivariate 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 ConclusionA 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 4Technical Efficacy Stage 2J. MAGN. RESON. IMAGING 2021;53:381–391.
“…Thus, the characterization of nonmass enhancement and foci is important. However, a standardized classification scheme for the interpretation of lesions showing nonmass‐like enhancement does not exist, and the best management of foci remains under discussion 5,38 . In the future, we would like to continue investigating the utility of synthetic MRI to differentiate benign from malignant lesions in patients with nonmass enhancement and foci.…”
BackgroundThe addition of synthetic MRI might improve the diagnostic performance of dynamic contrast‐enhanced MRI (DCE‐MRI) in patients with breast cancer.PurposeTo 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 TypeRetrospective, observational.PopulationIn all, 121 patients with 131 breast masses who underwent DCE‐MRI with additional synthetic MRI were enrolled.Field Strength/Sequence3.0 Tesla, T1‐weighted DCE‐MRI and synthetic MRI acquired by a multiple‐dynamic, multiple‐echo sequence.AssessmentAll 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 TestsUnivariate (Mann–Whitney U‐test) and multivariate (binomial logistic regression) analyses were performed, followed by receiver‐operating characteristic curve (AUC) analysis.ResultsUnivariate 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 ConclusionA 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 4Technical Efficacy Stage 2J. MAGN. RESON. IMAGING 2021;53:381–391.
“…Although none of the lesions was defined as a focus, and the mean lesion size was relatively large, we did analyze lesions in the range of 3-10 mm separately and we found that size can reduce the sensitivity of MRI. Foci were either classified as BPE, or as BI-RADS 3, and sent to control due to the low rate of malignancy for foci (28). As only histologically verified lesions were included in our analysis, no focus was included in the study.…”
Motion artifacts can impair lesion characterization with breast MRI, but lesion type and small size have the strongest influence on diagnostic estimates.
“…might be difficult to further characterise. These small findings were defined by the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) as enhancing foci [7]. Depending on the spatial resolution, it is difficult to evaluate their morphology and dynamic behaviour, while the small size makes difficult to perform MRI-guided needle biopsy, so that their changes are commonly longitudinally monitored with serial examinations to reach a conclusive diagnosis [8].…”
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.
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