Recognition of Early Parkinson's Disease by Machine Learning Model Based on Cortical Morphology Features
10.3969/j.issn.1005-5185.2024.10.004
- VernacularTitle:基于皮层形态特征的机器学习模型识别早期帕金森病
- Author:
Dingcai RAO
1
;
Cailing SHI
;
Wenjun YUE
Author Information
1. 川北医学院附属医院放射科,四川 南充 637000
- Keywords:
Parkinson's disease;
Machine learning;
Magnetic resonance imaging;
Cerebral cortex;
Diagnosis;
Surface-based morphometry
- From:
Chinese Journal of Medical Imaging
2024;32(10):994-999
- CountryChina
- Language:Chinese
-
Abstract:
Purpose To explore the application value of machine learning models based on cortical morphological features in the diagnosis of early Parkinson's disease(PD).Materials and Methods MRI and clinical data of 170 subjects from January 2014 to December 2017,including 100 early PD patients and 70 healthy controls,were selected from the Parkinson's Progression Markers Initiative database.Firstly,computational anatomy toolbox was used to preprocess the images to extract the fractal dimension(FD)and gyrification index(GI)of the cerebral cortex,and the differences of two indexes between early PD and healthy controls were compared.Then,all subjects were randomly divided into the train set and the test set with a 7∶3 ratio,and the optimal features were selected by t-test and recursive feature elimination.The classification model was constructed by random forest and evaluated by the receiver operating characteristic curve,and the decision curve analysis was used to evaluate the clinical value of the model.Results Compared to healthy controls,early PD patients had reduced GI in the bilaterally precentral gyrus,bilaterally rostral middle frontal cortex,bilaterally caudal middle frontal cortex,bilaterally triangular part of inferior frontal gyrus,bilaterally opercular part of inferior frontal gyrus,bilaterally orbital part of inferior frontal gyrus,the right superior frontal gyrus,the right lateral orbitofrontal cortex and the right insula(all P<0.05),but there was no significant difference in the FD(all P>0.05).The results of model evaluation showed that the area under curve values of the FD,the GI and the combined model in the train set were 0.860,0.895 and 0.939,respectively,and those in the test set were 0.762,0.821 and 0.868,respectively.The Hosmer-Lemeshow test showed that there was no statistically significant difference in the goodness of fit between the train and test set(all P>0.05).The decision curve analysis curve showed that clinical net benefit of the combined model was optimal when the probability threshold was in the range of 0.10 to 0.88.Conclusion In the early stages of the disease,cortical morphology of PD patients have changed.Machine learning model based on cortical morphology features has good diagnostic performance,and may be of important value in assisting clinical early diagnosis of PD.
