Article Figures & Data
Figures
- FIG 1.
Density plots of the top 3 features, including age at diagnosis (A), T2-Cluster Shade (B), and T1-Mean Intensity (C). D, Bar plot measuring the relative influence as calculated by LR of the top 10 reduced features for the binary classifier trained to distinguish embryonal tumors and high-grade gliomas.
- FIG 2.
Density plots of the top 3 features, including T2-kurtosis (A), T1-skewness (B), and T1-information measure of correlation (C). D, Bar plot measuring the relative influence as calculated by XGB of the 4 reduced features for the binary classifier trained to distinguish embryonal tumor and ependymoma.
- FIG 3.
Density plots of the top 3 features, including T1-mean (A), T1-cluster shade (B), and T2-maximal correlation coefficient (C). D, Bar plot measuring the relative influence as calculated by LR of the top 10 reduced features for the binary classifier trained to distinguish ependymomas and high-grade gliomas.
- FIG 4.
Examples of model-derived probability output are shown on test cohorts of supratentorial embryonal tumors (EMB), EP, and HGG that did not participate in training. Due to overlap in macroscopic features of these malignant supratentorial tumors (eg, a wide range in size, morphology, and enhancement/intensity features), independent binary classifiers that specifically targeted feature separation for EMB versus EP (A, XGB), EMB versus HGG (B, LR), and HGG versus EP (C, Neural network [NN]) were found predictive over a single multiclass classifier. Examples of the same EMB tumors that were separately submitted into XGB and LR models are shown (asterisk) and show strong EMB discrimination against EP and HGG, respectively. In 1 example, the same EP tumor could be distinguished from EMB (yellow arrow) but was not predictive against HGG (gray arrow). ATRT indicates atypical teratoid/rhabdoid tumors; ETMR, embryonal tumor with multi-layered rosettes; NB, CNS neuroblastoma; NOS, not otherwise specified.
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