Objectives

It is important to subdivide Parkinson's disease (PD) into subtypes, enabling potentially earlier disease recognition and tailored treatment strategies. We aimed to identify reproducible PD subtypes robust to variations in the number of patients and features.

Methods

We applied multiple feature-reduction and cluster-analysis methods to cross-sectional and timeless data, extracted from longitudinal datasets (years 0, 1, 2 & 4; Parkinson's Progressive Marker Initiative; 885 PD/163 healthy-control visits; 35 datasets with combinations of non-imaging, conventional-imaging, and radiomics features from DAT-SPECT images). Hybrid machine-learning systems were constructed invoking 16 feature-reduction algorithms, 8 clustering algorithms, and 16 classifiers (C-index clustering evaluation used on each trajectory). We subsequently performed: i) identification of optimal subtypes, ii) multiple independent tests to assess reproducibility, iii) further confirmation by a statistical approach, iv) test of reproducibility to the size of the samples.

Results

When using no radiomics features, the clusters were not robust to variations in features, whereas, utilizing radiomics information enabled consistent generation of clusters through ensemble analysis of trajectories. We arrived at 3 distinct subtypes, confirmed using the training and testing process of k-means, as well as Hotelling's T2 test. The 3 identified PD subtypes were 1) mild; 2) intermediate; and 3) severe, especially in terms of dopaminergic deficit (imaging), with some escalating motor and non-motor manifestations.

Conclusion

Appropriate hybrid systems and independent statistical tests enable robust identification of 3 distinct PD subtypes. This was assisted by utilizing radiomics features from SPECT images (segmented using MRI). The PD subtypes provided were robust to the number of the subjects, and features.

中文翻译：

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使用放射线学和混合机器学习对帕金森氏病亚型进行可靠的鉴定

目标

重要的是将帕金森氏病（PD）细分为亚型，以实现潜在的早期疾病识别和量身定制的治疗策略。我们旨在确定对患者数量和特征变化具有鲁棒性的可再现PD亚型。

方法

我们对从纵向数据集（第0、1、2和4年；帕金森氏渐进标记计划； 885 PD / 163健康对照访视； 885个健康对照访视； 35个数据集）中提取的横截面和永恒数据应用了多种特征缩减和聚类分析方法。 DAT-SPECT图像的非成像，常规成像和放射学特征的组合）。构造了混合机器学习系统，该系统调用了16个特征约简算法，8个聚类算法和16个分类器（在每个轨迹上使用C索引聚类评估）。我们随后进行了：i）识别最佳亚型，ii）多个独立测试以评估可重复性，iii）通过统计方法进一步确认，iv）对样本量的可重复性测试。

结果

当不使用放射线要素时，聚类对特征的变化不稳健，而利用放射线信息则可以通过轨迹的整体分析来一致地生成聚类。我们通过k均值的训练和测试过程以及Hotelling的T2检验确定了3种不同的亚型。确定的3种PD亚型为1）轻度；2）中级；3）严重，尤其是多巴胺能缺乏症（影像），运动和非运动表现逐渐升高。

结论

适当的混合系统和独立的统计测试可以可靠地识别3种不同的PD亚型。这是通过利用SPECT图像的放射线特征（使用MRI分割）来辅助的。提供的PD亚型对受试者和特征的数量具有鲁棒性。