Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University graduate school of medicine, Kyoto, Japan; Department of Radiology, Tenri Hospital, Nara, Japan.
Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University graduate school of medicine, Kyoto, Japan.
Magn Reson Imaging. 2023 May;98:132-139. doi: 10.1016/j.mri.2022.12.024. Epub 2023 Jan 3.
To evaluate the diagnostic performance of a non-contrast magnetic resonance imaging (MRI) protocol combining high-resolution diffusion-weighted images (HR-DWI) using readout-segmented echo planar imaging, T1-weighted imaging (T1WI), and T2-weighted imaging (T2WI), using our modified Breast Imaging-Reporting and Data System (modified BI-RADS).
Two experienced radiologists, blinded to the final pathological diagnosis, categorized a total of 108 breast lesions (61 malignant and 47 benign) acquired with the above protocol using the modified BI-RADS with a diagnostic decision tree. The decision tree included subcategories of category 4, as in mammography (categories 2, 3, 4A, 4B, 4C, and 5). These results were compared with the pathological diagnoses.
The area under the ROC curve (AUC) was 0.89 (95% confidence interval [CI]: 0.83-0.95) for reader 1, and 0.89 (95% CI: 0.82-0.96) for reader 2. When categories 4C and above were classified as malignant, the sensitivity, specificity, and accuracy were 73.8%, 93.6%, and 82.4%, for reader 1; and 82.0%, 89.4%, and 85.2% for reader 2, respectively.
Our results suggest that using HR-DWI, T1WI/T2WI analyzed with a modified BI-RADS and a decision tree showed promising diagnostic performance in breast lesions, and is worthy of further study.
评估一种非对比磁共振成像(MRI)方案的诊断性能,该方案结合了高分辨率弥散加权成像(HR-DWI)、T1 加权成像(T1WI)和 T2 加权成像(T2WI),使用我们改良的乳腺影像报告和数据系统(改良 BI-RADS)。
两名经验丰富的放射科医生,对最终病理诊断不知情,使用改良 BI-RADS 诊断决策树对总共 108 个乳腺病变(61 个恶性和 47 个良性)进行分类。决策树包括乳腺 X 线摄影(类别 2、3、4A、4B、4C 和 5)中类别 4 的子类别。将这些结果与病理诊断进行比较。
读者 1 的 ROC 曲线下面积(AUC)为 0.89(95%置信区间 [CI]:0.83-0.95),读者 2 的 AUC 为 0.89(95% CI:0.82-0.96)。当类别 4C 及以上被归类为恶性时,读者 1 的敏感性、特异性和准确性分别为 73.8%、93.6%和 82.4%;读者 2 的敏感性、特异性和准确性分别为 82.0%、89.4%和 85.2%。
我们的结果表明,使用 HR-DWI、T1WI/T2WI 结合改良 BI-RADS 和决策树分析在乳腺病变中具有有前景的诊断性能,值得进一步研究。