| 丁苗苗,赖朝奇,苏赟,陈新吟,张翔.4种非高斯分布弥散成像参数鉴别乳腺影像报告和数据系统MRI 4类乳腺良、恶性病变[J].中国医学影像技术,2025,41(9):1586~1590 |
| 4种非高斯分布弥散成像参数鉴别乳腺影像报告和数据系统MRI 4类乳腺良、恶性病变 |
| Four non-Gaussian distributed diffusion imaging parameters for differentiating breast imaging reporting and data system MRI category 4 benign and malignant breast tumors |
| 投稿时间：2025-02-13 修订日期：2025-08-13 |
| DOI：10.13929/j.issn.1003-3289.2025.09.028 |
| 中文关键词: 乳腺肿瘤|诊断,鉴别|弥散磁共振成像 |
| 英文关键词:breast neoplasms|diagnosis, differential|diffusion magnetic resonance imaging |
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| 中文摘要: |
| 目的 观察4种非高斯弥散模型参数单一及联合鉴别乳腺影像报告和数据系统(BI-RADS) MRI 4类乳腺良、恶性病变的效能。方法 回顾性分析159例患者共161个BI-RADS MRI 4类乳腺病变,根据病理结果分为恶性组(n=132)与良性组(n=29)。基于弥散加权成像(DWI)计算表观弥散系数(ADC)值,获取多b值弥散成像数据,以4种非高斯分布模型进行拟合,获取弥散峰度成像(DKI)、拉伸指数模型(SEM)、连续时间随机游走(CTRW)及分数阶微积分模型(FROC)。采用单因素和多因素logistic回归筛选鉴别BI-RADS MRI 4类良、恶性病变的弥散定量指标。绘制受试者工作特征(ROC)曲线,通过约登指数确定最佳阈值,以ROC曲线下面积(AUC)及最佳阈值下相应敏感度、特异度及准确率评估并比较ADC值、单一非高斯弥散模型参数及其联合的鉴别效能。结果 恶性组αCTRW、KDKI及μFROC值均高于,而ADC、DCTRW、DFROC、DDCSEM、αSEM及DDKI值均低于良性组(P均＜0.05)。多因素logistic回归分析显示,DCTRW和αCTRW值均为区分BI-RADS MRI 4类良、恶性病变的独立因素(P均＜0.05),其联合模型的AUC均高于单一ADC、DCTRW及αCTRW值(P均＜0.05)。结论 DCTRW与αCTRW值联合模型鉴别BI-RADS MRI 4类乳腺良、恶性病变的效能优于单一参数。 |
| 英文摘要: |
| Objective To explore the efficacy of single and combined parameters from 4 non-Gaussian diffusion models for differentiating breast imaging reporting and data system (BI-RADS) MRI category 4 benign and malignant breast tumors. Methods A total of 161 BI-RADS MRI category 4 breast lesions from 159 patients were retrospectively enrolled. Based on pathological results, the lesions were divided into malignant group (n=132) and benign group (n=29). The apparent diffusion coefficient (ADC) values were calculated from diffusion weighted imaging (DWI) sequences. Multi-b-value diffusion imaging data were acquired and fitted using 4 non-Gaussian models to obtain respective parameters, including diffusion kurtosis imaging (DKI), stretched exponential model (SEM), continuous-time random walk (CTRW) and fractional order calculus (FROC) model. Univariable and multivariable logistic regression analyses were employed to identify the diffusion quantitative indicators useful for differentiating benign and malignant BI-RADS MRI category 4 breast tumors. Receiver operating characteristic (ROC) curves were drawn, and the optimal threshold was determined using Youden index. The differentiating performance of ADC value, single parameter and their combination from non-Gaussian diffusion models were assessed and compared according to the area under the curve (AUC) of ROC curves, as well as the sensitivity, specificity and accuracy under the optimal thresholds. Results In malignant group, αCTRW, KDKI and μFROC values were higher, while ADC, DCTRW, DFROC, DDCSEM, αSEM and DDKI values were lower than those in benign group (all P＜0.05). Multivariable logistic regression analysis identified DCTRW and αCTRW values as independent factors for differentiating benign and malignant BI-RADS MRI category 4 breast tumors (both P＜0.05), and a combined model was then constructed. The AUC of the combined model was higher than that of each single parameter including ADC, DCTRW and αCTRW values (all P＜0.05). Conclusion The combined model of DCTRW and αCTRW had better efficacy than each single parameter for differentiating benign and malignant BI-RADS MRI category 4 breast tumors. |
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