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 1 056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模型在训练集中的AUC、特异度和敏感度分别为0.927(95%CI:0.890～0.964)、81.9%、86.9%,在测试集中分别为0.909(95%CI:0.835-0.983)、90.6%、84.1%;MD模型在训练集中的AUC、特异度和敏感度分别为0.934(95%CI:0.899～0.969)、85.1%、84.0%,在测试集中分别为0.960(95%CI:0.910～1.000)、93.0%、85.1%;MK模型在训练集中的AUC、特异度和敏感度分别为0.935(95%CI:0.900～0.971)、90.4%、84.0%,在测试集中分别为0.856(95%CI:0.770～0.941)、81.3%、66.6%.临床-影像学联合模型在训练集中的AUC、特异度和敏感度分别为 0.946(95%CI:0.912～0.980)、88.2%、89.8%,在测试集中分别为 0.963(95%CI:0.925～1.000)、93.0%、85.1%.DeLong检验结果显示影像学模型和临床-影像学联合模型两两比较差异均无统计学意义(P>0.05),临床模型与其他两个模型的AUC值差异具有统计学意义(Z=2.836,P=0.004).DCA显示各个模型的阈值概率在0.1～1.0范围内,对临床有净获益,不同模型对csPCa的诊断均具有较高的诊断效能,以临床-影像学联合模型的诊断效能最高.结论 MRI单指数、扩散峰度模型功能参数图的影像组学分析技术是csPCa的有效检出方法,构建的临床-影像学联合模型对csPCa具有较高的诊断价值,能够为临床早期诊断和治疗提供相关技术支持.
Abstract
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 1 056 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 evaluate model performance.Results:The AUC,specificity and sensitivity of the clinical model in the training set were 0.840(95%CI:0.778-0.901),78.7%and 76.8%,and in the test set were 0.675(95%CI:0.539-0.812),79.0%and 59.2%,respectively.The AUC,specificity and sensitivity of the ADC model in the training set were 0.927(95%CI:0.890-0.964),81.9%,86.9%,and in the test set were 0.909(95%CI:0.835-0.983),90.6%,84.1%,respectively;the AUC,specificity and sensitivity of the MD model in the trainingset were 0.934(95%CI:0.899-0.969),85.1%,84.0%,and in the test set were 0.960(95%CI:0.910-1.000),93.0%,85.1%,respectively;the AUC,specificity and sensitivity of the MK model in the training set were 0.935(95%CI:0.900-0.971),90.4%,84.0%,and in the test set were 0.856(95%CI:0.770-0.941),81.3%,66.6%,respectively.The AUC,specificity and sensitivity of the clinical-radiology model in the training set were 0.946(95%CI:0.912-0.980),88.2%and 89.8%,and in the test set were 0.963(95%CI:0.925-1.000),93.0%and 85.1%,respectively.DeLong test results showed that there was no significant difference between the radiology model and the clinical-radiology combined model(P>0.05).There was a significant difference in AUC value between the clinical model and the other two models(Z= 2.836,P=0.004),and there was no significant difference between the other two groups of models(P>0.05).The decision curve shows that the threshold probability of each model is in the range of 0.1-1.0,which has a net benefit for clinical practice.Different models have a positive effect on the diagnosis of csPCa.The clinical-radiology model having the highest diagnostic performance.Conclusions:The radiomics analysis technology of MRI mono-exponential and diffusion kurtosis model functional parameter map is an effective method for the detection of csPCa.The clinical-radiology combined model has high diagnostic value for csPCa,which can provide relevant technical support for early clinical diagnosis and treatment.
维普
万方数据