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. 2021 Dec;47(7):931-941.
doi: 10.1111/nan.12710. Epub 2021 Apr 7.

Machine learning-based decision tree classifier for the diagnosis of progressive supranuclear palsy and corticobasal degeneration

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Machine learning-based decision tree classifier for the diagnosis of progressive supranuclear palsy and corticobasal degeneration

Shunsuke Koga et al. Neuropathol Appl Neurobiol. 2021 Dec.

Abstract

Aims: This study aimed to clarify the different topographical distribution of tau pathology between progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD) and establish a machine learning-based decision tree classifier.

Methods: Paraffin-embedded sections of the temporal cortex, motor cortex, caudate nucleus, globus pallidus, subthalamic nucleus, substantia nigra, red nucleus, and midbrain tectum from 1020 PSP and 199 CBD cases were assessed by phospho-tau immunohistochemistry. The severity of tau lesions (i.e., neurofibrillary tangle, coiled body, tufted astrocyte or astrocytic plaque, and tau threads) was semi-quantitatively scored in each region. Hierarchical cluster analysis was performed using tau pathology scores. A decision tree classifier was made with tau pathology scores using 914 cases. Cross-validation was done using 305 cases. An additional ten cases were used for a validation study.

Results: Cluster analysis displayed two distinct clusters; the first cluster included only CBD, and the other cluster included all PSP and six CBD cases. We built a decision tree, which used only seven decision nodes. The scores of tau threads in the caudate nucleus were the most decisive factor for predicting CBD. In a cross-validation, 302 out of 305 cases were correctly diagnosed. In the pilot validation study, three investigators made a correct diagnosis in all cases using the decision tree.

Conclusion: Regardless of the morphology of astrocytic tau lesions, semi-quantitative tau pathology scores in select brain regions are sufficient to distinguish PSP and CBD. The decision tree simplifies neuropathologic differential diagnosis of PSP and CBD.

Keywords: Machine learning; corticobasal degeneration; corticobasal syndrome; decision tree classifier; hierarchical cluster analysis; progressive supranuclear palsy.

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Figures

FIGURE 1
FIGURE 1
Representative images of tau pathology scores in CP13‐immunostained slides. Images of NFT, tau threads and astrocytic plaques are from the motor cortex in CBD cases. Images of tufted astrocytes are from the caudate nucleus, and images of coiled bodies are from the motor cortex in PSP cases. All images are the same magnification. Scale bar =100 μm
FIGURE 2
FIGURE 2
Heatmap and hierarchical clustering based on tau pathology in 1020 PSP and 199 CBD cases. The heatmap reflects the severity of tau pathology, and a colour scale is given at the top right. Each tau lesion type in a brain region is represented with rows, and each case is represented with columns. Two distinct clusters are identified by hierarchical clustering. The left cluster (Cluster 1) mainly contains CBD cases as shown in orange in the pathology label, while the right cluster (Cluster 2) mainly contains PSP cases as shown in blue in the pathology label. Cluster 2 can be divided into four clusters (Cluster 2–1, 2–2, 2–3 and 2–4). Pathologically confirmed CBD cases in Cluster 2 are noted as #1‐6. Note that Case 3 is located at the left end in Cluster 2–2. Abbreviations: AD, Alzheimer's disease; Astro, astrocytic tau lesions; Caudate, caudate nucleus; CB, coiled bodies; CBD, corticobasal degeneration; CBS, corticobasal syndrome; GP, globus pallidus; MBT, midbrain tectum; NFT, neurofibrillary tangle; Red, red nucleus; MBT, midbrain tectum; Motor, motor cortex; PSP, progressive supranuclear palsy; RS, Richardson syndrome; SN, substantia nigra; STN, subthalamic nucleus; Temp, temporal cortex; Threads, tau positive threads
FIGURE 3
FIGURE 3
Decision tree for the diagnosis of PSP and CBD. The colour of nodes indicates the diagnosis: blue =CBD, orange =PSP. The darker colour indicates higher proportion of each disease. (A) A decision tree with the maximum depth of 1. Of 914 cases (762 PSP and 152 CBD) in a training set, 881 cases are correctly classified (accuracy =96.3%). (B) A decision tree with the maximum depth of 3. Of 914 cases, 903 cases are correctly classified (accuracy =98.8%). Abbreviations: Caudate, caudate nucleus; GP, globus pallidus; MBT, midbrain tectum; NFT, neurofibrillary tangle; RN, red nucleus; STN, subthalamic nucleus
FIGURE 4
FIGURE 4
Diagnostic flowchart for PSP and CBD based on the decision tree classifier. Values indicate semi‐quantitative tau pathology scores (0 = none; 1 = mild; 2= moderate; 3 = severe). *This class indicates PSP at 80% accuracy, so also consider CBD. **This class indicates CBD at 67% accuracy, so also consider PSP. Abbreviations: Caudate, caudate nucleus; GP, globus pallidus; MBT, midbrain tectum; NFT, neurofibrillary tangle; RN, red nucleus; STN, subthalamic nucleus

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