摘要
目的探讨基于多种结构MRI特征构建的机器学习模型诊断帕金森病(PD)的价值。方法回顾性分析2017年11月至2019年8月在苏州大学附属第二医院神经内科就诊的60例PD患者(PD组)和同期招募的56名社区健康老年人(NC组)的临床及影像资料。首先对所有受试者进行全脑MR扫描,然后基于不同的脑分区模板,从小脑、深部核团和皮层提取多种结构MRI特征,利用Mann-Whitney U检验和最小绝对值收缩与选择算子回归筛选一组最具诊断鉴别力的特征,最后运用逻辑回归(LR)和线性判别分析(LDA)两种分类器,结合5折交叉验证策略分别构建小脑、深部核团、皮层和基于所有特征的综合模型。采用受试者操作特征曲线的曲线下面积(AUC)和决策曲线分析(DCA)评价各模型的诊断效能和临床净收益。结果最终筛选出4个小脑特征(LobuleⅥ体积非对称指数、LobuleⅦB皮层厚度非对称指数、灰质体积非对称指数及右侧LobuleⅥ灰质体积)、3个深部核团(右侧伏隔核绝对体积、伏隔核绝对和相对体积)和3个皮层(左侧PFm局部脑回指数、右侧额上回局部分形维数和左侧枕上回沟深)特征为最具诊断鉴别力的特征,并构建模型。验证集中,基于LR分类器的小脑、深部核团、皮层和综合模型诊断PD的AUC值分别为0.692、0.641、0.747和0.816,基于LDA分类器的小脑、深部核团、皮层和综合模型诊断PD的AUC值分别为0.726、0.610、0.752和0.818。基于LR和LDA分类器的综合模型诊断PD的效能均优于其他模型(P<0.05)。DCA曲线显示验证集中基于LR和LDA分类器下的综合模型临床净收益最高。结论基于LR和LDA分类器的小脑、深部核团、皮层特征的综合模型诊断PD具有良好的效能和临床净收益。
Objective To explore the value of machine learning models based on multiple structural MRI features for diagnosis of Parkinson disease(PD).Methods The clinical and imaging data of 60 PD patients(PD group)diagnosed in the Neurology Department of the Second Affiliated Hospital of Soochow University from November 2017 to August 2019 and 56 normal elderly people(NC group)recruited from the community were retrospectively analyzed.All subjects underwent brain MR imaging.Multiple structural MRI features were extracted from cerebellum,deep nuclei and of brain cortex based on different partition templates.The Mann-Whitney U test,as well as least absolute shrinkage and selection operator regression were used to select the most discriminating features.Finally,logistic regression(LR)and linear discriminant analysis(LDA)classifier combined with the 5-fold cross-validation scheme were used to construct the models based on structural features of cerebellum,deep nuclei and cortex,and a combined model based on all features.The receiver operating characteristic curves were drawn,and the diagnostic performance and clinical net benefit of each model were evaluated by the area under curve(AUC)and the decision curve analysis(DCA).Results In total,four cerebellum(asymmetry index of LobuleⅥvolume,asymmetry index of LobuleⅦB cortical thickness,asymmetry index of total gray matter volume and absolute value of right LobuleⅥgray matter volume),3 deep nuclei(absolute value of right nucleus accumbens volume,absolute and relative value of total nucleus accumbens volume)and 3 cortex features(local gyration index of left PFm,local fractal dimension of right superior frontal gyrus and sulcal depth of left superior occipital gyrus)were selected as the most discriminating features,and the related models were constructed.In validation set,the AUC of cerebellum,deep nuclei,cortex and combined models for diagnosis of PD based on LR classifier were 0.692,0.641,0.747 and 0.816;the AUC of cerebellum,deep nuclei,cortex and combined models for diagno
作者
亚洋
王二磊
伋立荣
邹楠
鲍奕清
毛成洁
罗蔚峰
印宏坤
范国华
Ya Yang;Wang Erlei;Ji Lirong;Zou Nan;Bao Yiqing;Mao Chengjie;Luo Weifeng;Yin Hongkun;Fan Guohua(Department of Radiology,the Second Affiliated Hospital of Soochow University,Suzhou 215004,China;Department of Neurology,the Second Affiliated Hospital of Soochow University,Suzhou 215004,China;Institute of Advanced Research,Infervision Medical Technology Co.,Ltd,Beijing 100025,China)
出处
《中华放射学杂志》
CAS
CSCD
北大核心
2023年第4期370-377,共8页
Chinese Journal of Radiology
基金
苏州大学附属第二医院科研预研基金(SDFEYJLC2101)
苏州市科技发展计划(SKY2022011)
江苏省老年健康科研项目(LKZ2022009)
苏州市医学会“影像医星”科技立项项目(2022YX-M03)。
关键词
帕金森病
磁共振成像
机器学习
Parkinson disease
Magnetic resonance imaging
Machine learning
