Browsing by Author "Mirsky, David M"
Now showing 1 - 2 of 2
Results Per Page
Sort Options
Item Open Access Improved prediction of postoperative pediatric cerebellar mutism syndrome using an artificial neural network.(Neuro-oncology advances, 2022-01) Sidpra, Jai; Marcus, Adam P; Löbel, Ulrike; Toescu, Sebastian M; Yecies, Derek; Grant, Gerald; Yeom, Kristen; Mirsky, David M; Marcus, Hani J; Aquilina, Kristian; Mankad, KshitijBackground
Postoperative pediatric cerebellar mutism syndrome (pCMS) is a common but severe complication that may arise following the resection of posterior fossa tumors in children. Two previous studies have aimed to preoperatively predict pCMS, with varying results. In this work, we examine the generalization of these models and determine if pCMS can be predicted more accurately using an artificial neural network (ANN).Methods
An overview of reviews was performed to identify risk factors for pCMS, and a retrospective dataset was collected as per these defined risk factors from children undergoing resection of primary posterior fossa tumors. The ANN was trained on this dataset and its performance was evaluated in comparison to logistic regression and other predictive indices via analysis of receiver operator characteristic curves. The area under the curve (AUC) and accuracy were calculated and compared using a Wilcoxon signed-rank test, with P < .05 considered statistically significant.Results
Two hundred and four children were included, of whom 80 developed pCMS. The performance of the ANN (AUC 0.949; accuracy 90.9%) exceeded that of logistic regression (P < .05) and both external models (P < .001).Conclusion
Using an ANN, we show improved prediction of pCMS in comparison to previous models and conventional methods.Item Open Access Robust deep learning classification of adamantinomatous craniopharyngioma from limited preoperative radiographic images.(Scientific reports, 2020-10) Prince, Eric W; Whelan, Ros; Mirsky, David M; Stence, Nicholas; Staulcup, Susan; Klimo, Paul; Anderson, Richard CE; Niazi, Toba N; Grant, Gerald; Souweidane, Mark; Johnston, James M; Jackson, Eric M; Limbrick, David D; Smith, Amy; Drapeau, Annie; Chern, Joshua J; Kilburn, Lindsay; Ginn, Kevin; Naftel, Robert; Dudley, Roy; Tyler-Kabara, Elizabeth; Jallo, George; Handler, Michael H; Jones, Kenneth; Donson, Andrew M; Foreman, Nicholas K; Hankinson, Todd CDeep learning (DL) is a widely applied mathematical modeling technique. Classically, DL models utilize large volumes of training data, which are not available in many healthcare contexts. For patients with brain tumors, non-invasive diagnosis would represent a substantial clinical advance, potentially sparing patients from the risks associated with surgical intervention on the brain. Such an approach will depend upon highly accurate models built using the limited datasets that are available. Herein, we present a novel genetic algorithm (GA) that identifies optimal architecture parameters using feature embeddings from state-of-the-art image classification networks to identify the pediatric brain tumor, adamantinomatous craniopharyngioma (ACP). We optimized classification models for preoperative Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and combined CT and MRI datasets with demonstrated test accuracies of 85.3%, 83.3%, and 87.8%, respectively. Notably, our GA improved baseline model performance by up to 38%. This work advances DL and its applications within healthcare by identifying optimized networks in small-scale data contexts. The proposed system is easily implementable and scalable for non-invasive computer-aided diagnosis, even for uncommon diseases.