Digital breast tomosynthesis (DBT) has become a standard screening tool for breast cancer. However, its diagnostic performance is still limited compared to contrast-enhanced MRI, especially in women with dense breasts. Ultrafast dynamic contrast-enhanced (UF DCE) MRI and diffusion-weighted imaging (DWI) offer a shorter scanning time than conventional MRI and have shown promise in previous studies. However, few reports have explored the added value of UF DCE-MRI and DWI on DBT in diagnosing breast lesions. This study aimed to assess the diagnostic performance of abbreviated MRI using UF DCE-MRI and DWI compared to DBT. The study included 53 lesions in 42 women who underwent the UF DCE-MRI protocol and DBT within three months. Two radiologists recorded the BI-RADS category and breast composition assessment of tomosynthesis, as well as the MR parameters of each lesion (MS and ADC). In addition to the inter-rater agreements, diagnostic performance was evaluated with an area under the receiver operating curve (AUC). The results showed good to excellent agreement in the inter-rater assessment of the BI-RADS category and each parameter. The AUC of the DBT BI-RADS and UF DCE-MRI BI-RADS categories were 0.84 and 0.90, respectively. The predictive model using UF DCE-MRI BI-RADS in combination with ADC of DWI demonstrated an AUC of 0.95. In conclusion, abbreviated MRI using UF DCE-MRI and DWI may have the potential to add value to DBT diagnosis in breast lesions. Further research is needed to determine the role of abbreviated MRI in the diagnosis of breast lesions in a prospective screening setting.
The purpose of this study is to investigate the prediction of Ki-67 expression of breast cancers using MRI parameters from ultrafast (UF) DCE-MRI, DWI, T2WI, and the lesion size. Breast MRI was performed with a 3T scanner using dedicated breast coils. UF DCE-MRI was obtained using Compressed Sensing-VIBE (prototype sequence). As a kinetic parameter of UF DCE-MRI, maximum slope (MS) was defined as percentage relative enhancement (%/s), and time to enhance (TTE) was defined as the time interval between the aorta and lesion enhancement. The apparent diffusion coefficient (ADC) was derived from DWI. Two radiologists measured each MR parameter, and inter-rater agreement was evaluated. Univariate and multivariate logistic regression analyses were perfomed to predict low Ki-67 (<; 14%) and high Ki-67 (≥ 14%) expression using MS, TTE, ADC, T2- signal intensity (SI), and lesion size. The significant parameters (p-values of < 0.05) were selected for the prediction model, and the diagnostic performance of the model was evaluated using ROC curve analysis. A total of 191 invasive carcinomas defined as mass lesions were included (72 low Ki-67/ 119 high Ki-67 lesions). The inter-rater agreements of all parameters were excellent. After univariate and multivariate logistic regression analysis, ADC and lesion size remained significant parameters. Using these significant parameters, the multi-parametric prediction model yielded an AUC of 0.77 (95%CI of 0.70-0.84) (sensitivity 72.3%, specificity 76.4%, and PPV 83.5%, and NPV 62.5%). DWI parameter (ADC) may be more valuable than UF DCE-MRI parameters (MS, TTE) to predict high Ki-67 in mass-shaped invasive breast carcinoma.
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