基于不同扩散模型参数图的影像组学分析磁共振早期诊断临床显著性前列腺癌的价值  

To analyze the value of radiomics based on different diffusion model parameter maps in the early diagnosis of clinically significant prostate cancer by magnetic resonance imaging

作　　者：杜兵 戚轩 杨宏楷[1] 齐东 何永胜[1] DU Bing;QI Xuan;YANG Hongkai;QI Dong;HE Yongsheng(Department of Radiology,Ma'anshan People's Hospital,Ma'anshan 243000,China;Wannan Medical College,Wuhu 241002,China;Anhui Medical University,Hefei 230032,China)

机构地区：[1]马鞍山市人民医院影像科,马鞍山243000 [2]皖南医学院,芜湖241002 [3]安徽医科大学,合肥230032

出　　处：《磁共振成像》2024年第2期83-89,共7页Chinese Journal of Magnetic Resonance Imaging

基　　金：安徽省重点研究与开发计划项目(编号:2022e07020065)。

摘　　要：目的旨在探讨基于磁共振单指数和扩散峰度模型功能参数图的影像组学分析早期诊断临床显著性前列腺癌(clinically significant prostate cancer,csPCa)的价值。材料与方法回顾性地分析2022年4月至2023年7月就诊于马鞍山市人民医院的前列腺疾患病例238例,经超声下引导穿刺或手术病理证实,其中csPCa 96例、非临床显著性前列腺癌(non-clinically significant prostate cancer,ncsPca)142例,年龄56~84(62.34±7.62)岁。将238例患者按照7∶3的比例进行随机分组为训练集和测试集。所有患者均行MRI多参数扫描,通过后处理生成表观扩散系数(apparent diffusion coefficient,ADC)伪彩图,并得到扩散峰度模型中的平均扩散峰度(mean kurtosis,MK)和平均扩散系数(mean diffusivty,MD)伪彩图,图像预处理后,提取各个功能参数图的共计1056个组学特征,对ADC、MD和MK模型的数据采用最大相关最小冗余(maximum relevance minimum redundancy,MRMR)算法和最小绝对收缩和选择算子(least absolute shrinkage and selection operator,LASSO)消除冗余、进行特征降维,保留与标签高相关的特征,应用10倍交叉验证后得到特征子集。最终ADC模型筛选出5个组学特征,MD模型筛选出6个组学特征,MK模型筛选出6个组学特征,建立逻辑回归模型,分别计算临床模型、影像学模型和临床-影像学联合模型的阈值、准确度、敏感度、特异度,绘制受试者工作特征(receiver operating characteristic,ROC)曲线并计算曲线下面积(area under the curve,AUC)及95%置信区间(confidence interval,CI),利用DeLong检验对各个模型进行两两组合,比较两组间的AUC值是否具有统计学意义,进一步使用决策曲线分析(decision curve analysis,DCA)评估模型的净获益。结果临床模型在训练集中的AUC、特异度和敏感度分别为0.840(95%CI:0.778~0.901)、78.7%、76.8%,在测试集中分别为0.675(95%CI:0.539~0.812)、79.0%、59.2%。影像学模型中ADC模型在训练集中Objective:To explore the predictive value of radiomics analysis basedon magnetic resonance single-index and diffusion kurtosis model functional parameter maps for clinically significant prostate cancer(csPCa).Materials and Methods:A retrospective analysis was conducted on 238 prostate patients who visited Ma'anshan People's Hospital from April 2022 to July 2023.They were confirmed by ultrasound-guided puncture or surgical pathology,including 96 csPCa patients and 142 non-csPCa patients.The age of the patients 56-84(62.34±7.62)years old.The Clinical data within and between the groups were compared.All patients underwent magnetic resonance multi-parameter scanning,after post-processing,the apparent diffusion coefficient(ADC)pseudo-color plots were generated,and the mean kurtosis(MK)and mean diffusivty(MD)pseudo-color plots in the diffusion kurtosis model were obtained.After image preprocessing,the image features of eachfunctional parameter map are extracted.There are a total of 1056 radiomics features.The maximum correlation minimum redundancy(MRMR)algorithm and least absolute shrinkage and selection operator(LASSO)are used to eliminateredundancy,perform feature dimensionality reduction,and retain high-quality labels for the data of ADC,MD,and MK models.For relevant features,10-foldcross-validation was applied to obtain a feature subset,and 238 patients were randomly divided into groups in a ratio of 7∶3.Finally,the ADC model screened out 5 omics features,and the MD model screened out 6 omics features.The MK model screened out 6 omics features,established alogistic regression model,calculated the threshold,accuracy,sensitivity,and specificity of the clinical models,radiology,and clinical-radiology models,and drew the receiver operating characteristic(ROC)curve.Calculate the area under the curve(AUC)and 95%confidence interval(CI),use the DeLong test to combine each model in pairs,compare whether the AUC values between the two groups are statistically significant,and further use decision curve analysis(DCA)to eval

关 键 词：前列腺癌 磁共振成像 影像组学 扩散加权成像 诊断效能 

分 类 号：R445.2[医药卫生—影像医学与核医学] R737.25[医药卫生—诊断学]