Fig 1.
A) Potential impact of radiographic features on meningioma patient management. Pre-operative radiographic assessment of grade may improve the ability to tailor precision medicine decision trees to individual patients. B) A combined model of semantic and radiomic radiographic features was used to predict meningioma grade and validated on an independent cohort of meningiomas.
Table 1.
Description of radiographic features and filters.
Individual descriptions are given for each group and parameter or feature.
Fig 2.
Schematic of the radiomic feature selection process from the extraction to the final feature set.
Table 2.
Demographic information across the full, training, and validation datasets.
Fig 3.
A) Heatmap of the predictive power of (1) semantic and (2) radiomic features for meningioma grade (n = 175) or presence of histopathologic atypia in low grade meningiomas (n = 103). B) The association between semantic and radiomic features was investigated. Every semantic feature was predicted with each of the radiomic feature in a univariate manner that indicates their relationship. * indicates significance from random after multiple correction.
Table 3.
Univariate results for the semantic features.
Odds ratio, lower and higher 95% confidence interval and p-value (with multiple testing correction) are reported for each features.
Table 4.
Univariate results for the radiomic features.
AUC, lower and higher 95% confidence interval and p-value (with multiple testing correction) are reported for each features.
Fig 4.
Area under the curve (AUC) from random forest models on the independent validation set (n = 44) for meningioma grade classification.
“*” indicates p-value <0.05, “***” indicates p-value <0.0001 from random prediction (Noether test).
Table 5.
Meningioma classification validation (n = 44) for each model is reported.
AUC, lower and higher 95% confidence interval and p-value (from random) are reported for each features.