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Derek Yecies, Rashad Jabarkheel, Michelle Han, Yong-Hun Kim, Lisa Bruckert, Katie Shpanskaya, Augustus Perez, Michael S. B. Edwards, Gerald A. Grant, and Kristen W. Yeom

OBJECTIVE

Posterior fossa syndrome (PFS) is a common postoperative complication following resection of posterior fossa tumors in children. It typically presents 1 to 2 days after surgery with mutism, ataxia, emotional lability, and other behavioral symptoms. Recent structural MRI studies have found an association between PFS and hypertrophic olivary degeneration, which is detectable as T2 hyperintensity in the inferior olivary nuclei (IONs) months after surgery. In this study, the authors investigated whether immediate postoperative diffusion tensor imaging (DTI) of the ION can serve as an early imaging marker of PFS.

METHODS

The authors retrospectively reviewed pediatric brain tumor patients treated at their institution, Lucile Packard Children’s Hospital at Stanford, from 2004 to 2016. They compared the immediate postoperative DTI studies obtained in 6 medulloblastoma patients who developed PFS to those of 6 age-matched controls.

RESULTS

Patients with PFS had statistically significant increased mean diffusivity (MD) in the left ION (1085.17 ± 215.51 vs 860.17 ± 102.64, p = 0.044) and variably increased MD in the right ION (923.17 ± 119.2 vs 873.67 ± 60.16, p = 0.385) compared with age-matched controls. Patients with PFS had downward trending fractional anisotropy (FA) in both the left (0.28 ± 0.06 vs 0.23 ± 0.03, p = 0.085) and right (0.29 ± 0.06 vs 0.25 ± 0.02, p = 0.164) IONs compared with age-matched controls, although neither of these values reached statistical significance.

CONCLUSIONS

Increased MD in the ION is associated with development of PFS. ION MD changes may represent an early imaging marker of PFS.

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Jennifer L. Quon, Michelle Han, Lily H. Kim, Mary Ellen Koran, Leo C. Chen, Edward H. Lee, Jason Wright, Vijay Ramaswamy, Robert M. Lober, Michael D. Taylor, Gerald A. Grant, Samuel H. Cheshier, John R. W. Kestle, Michael S. B. Edwards, and Kristen W. Yeom

OBJECTIVE

Imaging evaluation of the cerebral ventricles is important for clinical decision-making in pediatric hydrocephalus. Although quantitative measurements of ventricular size, over time, can facilitate objective comparison, automated tools for calculating ventricular volume are not structured for clinical use. The authors aimed to develop a fully automated deep learning (DL) model for pediatric cerebral ventricle segmentation and volume calculation for widespread clinical implementation across multiple hospitals.

METHODS

The study cohort consisted of 200 children with obstructive hydrocephalus from four pediatric hospitals, along with 199 controls. Manual ventricle segmentation and volume calculation values served as “ground truth” data. An encoder-decoder convolutional neural network architecture, in which T2-weighted MR images were used as input, automatically delineated the ventricles and output volumetric measurements. On a held-out test set, segmentation accuracy was assessed using the Dice similarity coefficient (0 to 1) and volume calculation was assessed using linear regression. Model generalizability was evaluated on an external MRI data set from a fifth hospital. The DL model performance was compared against FreeSurfer research segmentation software.

RESULTS

Model segmentation performed with an overall Dice score of 0.901 (0.946 in hydrocephalus, 0.856 in controls). The model generalized to external MR images from a fifth pediatric hospital with a Dice score of 0.926. The model was more accurate than FreeSurfer, with faster operating times (1.48 seconds per scan).

CONCLUSIONS

The authors present a DL model for automatic ventricle segmentation and volume calculation that is more accurate and rapid than currently available methods. With near-immediate volumetric output and reliable performance across institutional scanner types, this model can be adapted to the real-time clinical evaluation of hydrocephalus and improve clinician workflow.