Scholarlink

Background <p>Accurate differential diagnosis of neurodegenerative parkinsonisms is challenging due to overlapping early symptoms and high rates of misdiagnosis. To improve the diagnostic accuracy, we developed an integrated classification algorithm using multinomial logistic regression and Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) applied to <sup>18</sup>F-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images. In this novel classification approach, SSM/PCA is applied to FDG-PET brain images of patients with various parkinsonisms, which are compared against the constructed undetermined images. This process involves spatial normalization of the images and dimensionality reduction via PCA. The resulting principal components are then used in a multinomial logistic regression model, which generates disease-specific topographies that can be used to classify new patients. The algorithm was trained and optimized on a cohort of patients with neurodegenerative parkinsonisms and subsequently validated on a separate cohort of patients with parkinsonisms.</p> Results <p>The Area Under the Curve (AUC) values were the highest for progressive supranuclear palsy (PSP) (AUC = 0.95), followed by Parkinson’s disease (PD) (AUC = 0.93) and multiple system atrophy (MSA) (AUC = 0.90). When classifying the patients based on their calculated probability for each group, the desired tradeoff between sensitivity and specificity had to be selected. With a 99% probability threshold for classification into a disease group, 82% of PD patients, 29% of MSA patients, and 77% of PSP patients were correctly identified. Only 5% of PD, 6% of MSA and 6% of PSP patients were misclassified, whereas the remaining patients (13% of PD, 65% of MSA and 18% of PSP) are undetermined by our classification algorithm.</p> Conclusions <p>Compared to existing algorithms, this approach offers comparable accuracy and reliability in diagnosing PD, MSA, and PSP with no need of healthy control images. It can also distinguish between multiple types of parkinsonisms simultaneously and offers the flexibility to easily accommodate new groups.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Multinomial logistic regression algorithm for the classification of patients with parkinsonisms

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月16日

查看全文
  • Eva Štokelj,
  • Tomaž Rus,
  • Jan Jamšek,
  • Maja Trošt,
  • Urban Simončič

摘要

Background

Accurate differential diagnosis of neurodegenerative parkinsonisms is challenging due to overlapping early symptoms and high rates of misdiagnosis. To improve the diagnostic accuracy, we developed an integrated classification algorithm using multinomial logistic regression and Scaled Subprofile Model/Principal Component Analysis (SSM/PCA) applied to 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) brain images. In this novel classification approach, SSM/PCA is applied to FDG-PET brain images of patients with various parkinsonisms, which are compared against the constructed undetermined images. This process involves spatial normalization of the images and dimensionality reduction via PCA. The resulting principal components are then used in a multinomial logistic regression model, which generates disease-specific topographies that can be used to classify new patients. The algorithm was trained and optimized on a cohort of patients with neurodegenerative parkinsonisms and subsequently validated on a separate cohort of patients with parkinsonisms.

Results

The Area Under the Curve (AUC) values were the highest for progressive supranuclear palsy (PSP) (AUC = 0.95), followed by Parkinson’s disease (PD) (AUC = 0.93) and multiple system atrophy (MSA) (AUC = 0.90). When classifying the patients based on their calculated probability for each group, the desired tradeoff between sensitivity and specificity had to be selected. With a 99% probability threshold for classification into a disease group, 82% of PD patients, 29% of MSA patients, and 77% of PSP patients were correctly identified. Only 5% of PD, 6% of MSA and 6% of PSP patients were misclassified, whereas the remaining patients (13% of PD, 65% of MSA and 18% of PSP) are undetermined by our classification algorithm.

Conclusions

Compared to existing algorithms, this approach offers comparable accuracy and reliability in diagnosing PD, MSA, and PSP with no need of healthy control images. It can also distinguish between multiple types of parkinsonisms simultaneously and offers the flexibility to easily accommodate new groups.