Akane Ohashi1, Masako Kataoka2, Mami Iima3, Shotaro Kanao4, Maya Honda5, Yuta Urushibata6, Marcel Dominik Nickel7, Ayami Ohno Kishimoto8, Rie Ota9, Masakazu Toi10, Kaori Togashi11. 1. Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho Shogoin Sakyo-ku, Kyoto, Japan. Electronic address: amaoh135@gmail.com. 2. Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho Shogoin Sakyo-ku, Kyoto, Japan. Electronic address: makok@kuhp.kyoto-u.ac.jp. 3. Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho Shogoin Sakyo-ku, Kyoto, Japan; Institute for Advancement of Clinical and Translational Science (iACT), Kyoto University Hospital, 54 Kawahara-cho Shogoin Sakyo-ku, Kyoto, Japan. Electronic address: mamiiima@kuhp.kyoto-u.ac.jp. 4. Kobe City Medical Center General Hospital, 2-1-1 Minatojimaminami-cho, Chuo-ku, Kobe, Japan. 5. Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho Shogoin Sakyo-ku, Kyoto, Japan. Electronic address: mayah217@kuhp.kyoto-u.ac.jp. 6. Siemens Healthcare K.K., Shinagawa-ku, Tokyo, Japan. Electronic address: yuta.urushibata@siemens-healthineers.com. 7. Siemens Healthcare GmbH, Erlangen, Germany. Electronic address: marcel.nickel@siemens-healthineers.com. 8. Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho Shogoin Sakyo-ku, Kyoto, Japan. Electronic address: akohno@kuhp.kyoto-u.ac.jp. 9. Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho Shogoin Sakyo-ku, Kyoto, Japan. Electronic address: ota_rie0624@kuhp.kyoto-u.ac.jp. 10. Department of Breast Surgery, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, Japan. Electronic address: toi@kuhp.kyoto-u.ac.jp. 11. Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho Shogoin Sakyo-ku, Kyoto, Japan. Electronic address: ktogashi@kuhp.kyoto-u.ac.jp.
Abstract
PURPOSE: To evaluate the diagnostic performance of a multiparametric approach to breast lesions including apparent diffusion coefficient (ADC) from diffusion-weighted images (DWI), maximum slope (MS) from ultrafast dynamic contrast enhanced (UF-DCE) MRI, lesion size, and patient's age. MATERIALS AND METHODS: In total, 96 lesions (73 malignant, 23 benign) were evaluated. UF-DCE MRI was acquired using a prototype 3D-gradient-echo volumetric interpolated breath-hold examination (VIBE) with compressed sensing. Images were obtained up to 1 min after gadolinium injection. MS was calculated as the percentage relative enhancement/s. An ADC map was automatically generated from DWI at b = 0 and b = 1000 s/mm2. MS and ADC values were measured by two radiologists independently. Interrater agreement was evaluated using intraclass correlation coefficients. Univariate and multivariate logistic regression analyses were performed using MS, ADC, lesion size, and the patient's age. The parameters of the prediction model were generated from the results of the multivariate logistic regression analysis. Area under the curve (AUC) was used to compare diagnostic performance of the prediction model and each parameter. RESULTS: Interrater agreements on MS and ADC were excellent (ICC 0.99 and 0.88, respectively). MS, ADC, and patient's age remained as significant parameters after univariate and multivariate logistic regression analysis. The prediction model using these significant parameters yielded an AUC of 0.90, significantly higher than that of MS (AUC 0.74, p = 0.01). The AUCs of ADC, MS, patient's age were 0.87, 0.74 and 0.73, respectively. CONCLUSIONS: A multiparametric model using ADC from DWI, MS from UF-DCE MRI, and patient's age showed excellent diagnostic performance, with greater contribution of ADC. Combining DWI and UF-DCE MRI might reduce scanning time while preserving diagnostic performance.
PURPOSE: To evaluate the diagnostic performance of a multiparametric approach to breast lesions including apparent diffusion coefficient (ADC) from diffusion-weighted images (DWI), maximum slope (MS) from ultrafast dynamic contrast enhanced (UF-DCE) MRI, lesion size, and patient's age. MATERIALS AND METHODS: In total, 96 lesions (73 malignant, 23 benign) were evaluated. UF-DCE MRI was acquired using a prototype 3D-gradient-echo volumetric interpolated breath-hold examination (VIBE) with compressed sensing. Images were obtained up to 1 min after gadolinium injection. MS was calculated as the percentage relative enhancement/s. An ADC map was automatically generated from DWI at b = 0 and b = 1000 s/mm2. MS and ADC values were measured by two radiologists independently. Interrater agreement was evaluated using intraclass correlation coefficients. Univariate and multivariate logistic regression analyses were performed using MS, ADC, lesion size, and the patient's age. The parameters of the prediction model were generated from the results of the multivariate logistic regression analysis. Area under the curve (AUC) was used to compare diagnostic performance of the prediction model and each parameter. RESULTS: Interrater agreements on MS and ADC were excellent (ICC 0.99 and 0.88, respectively). MS, ADC, and patient's age remained as significant parameters after univariate and multivariate logistic regression analysis. The prediction model using these significant parameters yielded an AUC of 0.90, significantly higher than that of MS (AUC 0.74, p = 0.01). The AUCs of ADC, MS, patient's age were 0.87, 0.74 and 0.73, respectively. CONCLUSIONS: A multiparametric model using ADC from DWI, MS from UF-DCE MRI, and patient's age showed excellent diagnostic performance, with greater contribution of ADC. Combining DWI and UF-DCE MRI might reduce scanning time while preserving diagnostic performance.