背景:这项研究探讨了将分数阶微积分(FROC)扩散加权模型与同时多层(SMS)加速技术相结合在区分良性和恶性乳腺病变中的诊断价值。
方法:178个病变(73个良性,105恶性)使用多个b值(14b值,最高3000s/mm2)。独立样本t检验或Mann-WhitneyU检验比较图像质量得分,FROC模型参数(D,,),两组之间的ADC值。多变量逻辑回归分析确定了自变量并构建了列线图。用受试者工作特征(ROC)曲线和校准图评估模型辨别能力。Spearman相关分析和Bland-Altman图评估了参数相关性和一致性。
结果:恶性病变表现为较低的D,ADC值高于良性病变(P<0.05),值较高(P<0.05)。在SSEPI-DWI和SMS-SSEPI-DWI序列中,D值的AUC和诊断准确性最大,D值显示出最高的诊断灵敏度,而价值表现出最高的特异性。D和组合模子具有最高的AUC和精确性。D和ADC值显示序列之间的高度相关性,温和。Bland-Altman图显示了无偏的参数值。
结论:SMS-SSEPI-DWIFROC模型在可接受的时间内提供了良好的图像质量和病变特征值。与SSEPI-DWI相比,它显示出一致的诊断性能,特别是在D和数值中,并显著减少扫描时间。
BACKGROUND: This study explores the diagnostic value of combining fractional-order calculus (FROC) diffusion-weighted model with simultaneous multi-slice (SMS) acceleration technology in distinguishing benign and malignant breast lesions.
METHODS: 178 lesions (73 benign, 105 malignant) underwent magnetic resonance imaging with diffusion-weighted imaging using multiple b-values (14 b-values, highest 3000 s/mm2). Independent samples t-test or Mann-Whitney U test compared image quality scores, FROC model parameters (D,, ), and ADC values between two groups. Multivariate logistic regression analysis identified independent variables and constructed nomograms. Model discrimination ability was assessed with receiver operating characteristic (ROC) curve and calibration chart. Spearman correlation analysis and Bland-Altman plot evaluated parameter correlation and consistency.
RESULTS: Malignant lesions exhibited lower D, and ADC values than benign lesions (P < 0.05), with higher values (P < 0.05). In SSEPI-DWI and SMS-SSEPI-DWI sequences, the AUC and diagnostic accuracy of D value are maximal, with D value demonstrating the highest diagnostic sensitivity, while value exhibits the highest specificity. The D and combined model had the highest AUC and accuracy. D and ADC values showed high correlation between sequences, and moderate. Bland-Altman plot demonstrated unbiased parameter values.
CONCLUSIONS: SMS-SSEPI-DWI FROC model provides good image quality and lesion characteristic values within an acceptable time. It shows consistent diagnostic performance compared to SSEPI-DWI, particularly in D and values, and significantly reduces scanning time.