摘要
目的 分析多模态磁共振成像(MRI)图像中定量特征构建影像组学模型对乳腺癌诊断价值。方法选择2020年1月至2021年1月乳腺相关疾病患者95例,以病理诊断结果分为良性组(57例)及恶性组(38例);病例纳入时间分为训练组(66例)及验证组(29例)。均经T_1加权成像(T_1WI)、T_2加权成像(T_2WI)、扩散加权成像(DWI)、动态对比增强(DCE)、表观弥散系数(ADC)多模态MRI检查,比较2组患者临床病理资料,MRI检查结果与病理结果进行对照分析,提取多模态病灶相关影像组学参数,采用支持向量机(SVM)分类器基于训练组患者单一、多影像组学指标构建诊断乳腺癌影像组学模型,通过验证组分析模型诊断效能,诊断效能通过构建受试者工作特征(ROC)曲线进行分析。结果良性乳腺疾病中纤维腺瘤占比49%;恶性乳腺疾病中浸润性导管癌占比74%。恶性组患者年龄、肿块大小高于良性组(P<0.05)。采用四格表法分析发现,T_1WI、T_2WI、ADC、DCE、DWI诊断乳腺癌的灵敏度为61%、67%、71%、79%、86%。从训练组患者病灶感兴趣区(ROI)提取平扫、扩散、增强等MRI检查提取形态特征、一阶特征、纹理特征3种特征。ROC曲线分析发现,T_1WI、T_2WI、DWI、ADC、DCE联合诊断乳腺癌的曲线下面积(AUC)分别为0.715、0.769、0.785、0.835、0.792。平扫、扩散、增强、平扫+扩散、平扫+增强、增强+扩散、平扫+增强+扩散等多影像组学模型诊断乳腺癌的AUC分别为0.746、0.798、0.816、0.839、0.890、0.906、0.927。结论通过多模态MRI图像中定量特征构建影像组学模型在诊断乳腺癌方面具有一定价值,其中平扫+增强+扩散等多影像组学模型诊断乳腺癌的价值更高,可在临床广泛应用。
Objective To analyze the value of quantitative features in multimodal magnetic resonance imaging(MRI)images to construct imaging histological models for the diagnosis of breast cancer.Methods Ninety-five patients with breast-related diseases from January 2020 to January 2021 were selected,and the pathological findings were divided into a benign group(n=57)and a malignant group(n=38),and the cases were divided into a training group(n=66)and validation group(n=29)at the time of inclusion.All the cases were examined by T1-weighted imaging(T1WI),T2-weighted imaging(T2WI),diffusion-weighted imaging(DWI),dynamic contrast enhancement(DCE),and apparent diffusion coefficient(ADC)multimodality MRI.The clinicopathological data of the two groups were compared.The MRI findings were analyzed based on the pathological findings.The support vector machine(SVM)classifier was used construct an imageomics model for diagnosis of breast cancer based on multiple imageomics indicators of patients in the training group.Breast cancer imaging histological model based on multiple imageomics indicators of patients in the training group.The validation group analyzed the diagnostic efficacy of the model,and the diagnostic efficacy was analyzed by constructing receiver operator characteristic(ROC)curves.Results Fibroadenoma accounted for 49%of benign breast diseases in this experiment,and invasive ductal carcinoma accounted for 74%of malignant breast diseases.The age and mass size of patients in the malignant group were higher than those in the benign group(P<0.05).The sensitivity of T1WI, T2WI, ADC, DCE and DWI, in diagnosing breast cancer was found to be 61%, 67%, 71%, 79%, and 86% using the four-grid table method. Three kinds of features were extracted from the ROI area of patients in the training group for MRI examinations of plain, diffusion, and enhancement, including morphological features, first-order features, and textural features. ROC curve analysis found that the AUC of T1WI, T2WI, DWI, ADC, and DCE for the diagnosis of breast cance
作者
刘琳
魏春燕
张桂芳
夏辉
王丙聚
郭爱红
Liu Lin;Wei Chunyan;Zhang Guifang;Xia Hui;Wang Bingju;Guo Aihong(Department of Radiology,417 Hospital of Nuclear Industry,Shaanxi,710600,China;不详)
出处
《山西医药杂志》
CAS
2024年第3期163-168,共6页
Shanxi Medical Journal
基金
陕西省重点研发计划项目(2020SF-146,2020SF-150)。
