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Prediction of Shunt Malfunction Using Automated Ventricular Volume Analysis and Radiomics
Ryan T Kellogg1, Jan Vargas2, Matthew Nguyen3
1Neurosurgery, University of Virginia, Charlottesville , Virginia , USA.
Background And Objectives:
The assessment of ventricle size is crucial in diagnosing hydrocephalus and in detecting shunt malfunctions. Current methods primarily involve 2-dimensional measurements or ratios. We evaluated the accuracy of volumetric analysis and radiomics in diagnosing hydrocephalus and shunt malfunction.
Methods:
We identified patients that underwent shunt surgery between January 2018 and August 2020 and collected head CTs from patients who were imaged before the placement of their shunt, with a functional shunt, or with a shunt malfunction. We performed automated ventricle segmentation on the computed tomography (CT) scans to compute ventricle volumes. For each patient, the ventricular volume was compared against a reference normative data set to determine if the ventricular volume was within a given range of SDs. Radiomics analyses were performed on the pathological and a normal data set, combined with clinical features, and used to train classifiers to identify patients with a malfunctioning shunt.
Results:
A total of 145 head CTs from 66 patients were collected and segmented. Comparing pathological ventricular volumes to a normative data set yielded an accuracy of 70% to 73%, depending on the SD cutoff (area under the curve [AUC] of 0.772). When radiomics analysis was performed on 145 pathological and 73 normal scans, the best performing model was a support vector machine model that predicted malfunctioning shunt with an AUC of 0.92 and F1-score of 0.848. Furthermore, the support vector machine model was tested using a held-out testing data set that achieved an AUC of 0.933.
Conclusion:
Automated ventricle segmentation using convolutional neural networks combined with radiomics analysis can be used with age and sex to assist in the diagnosis of hydrocephalus and shunt malfunctions when combined with a reference normative data set. It offers a time-efficient alternative to manual segmentation, reduces interobserver variability, and holds promise in improving patient outcomes by facilitating early and accurate diagnosis of hydrocephalus/shunt malfunction.
