507 Rational Radiomic Design for Stepwise Diagnosis of Posterior Fossa Pediatric Tumors. (1st April 2022)

• Record Type: Journal Article
• Authors: Zhang, Michael; Wong, Samuel; Wright, Jason; Toescu, Sebastian; Mohammadzadeh, Maryam; Han, Michelle; Lummus, Seth; Wagner, Matt; Yecies, Derek W.; Lai, Hollie; Eghbal, Azam; Radmanesh, Alireza; Nemelka, Jordan; Harward, Stephen C.; Malinzak, Michael; Laughlin, Suzanne; Perreault, Sebastien; Braun, Kristina; Vosough, Arastoo; Poussaint, Tina Y.; Goetti, Robert; Ertl-Wagner, Birgit; Ho, Chang; Oztekin, Ozgur; Ramaswamy, Vijay; Mankad, Kshitij; Vitanza, Nick; Cheshier, Samuel H.; Said, Mourad; Aquilina, Kristian; Thompson, Eric M.; Jaju, Alok; Grant, Gerald A.; Lober, Robert; Yeom, Kristen;
• Is Part Of: Neurosurgery. Volume 68(2022)Supplement 1
• Page Start: 127
• Page End: 128
• Publication Date: 2022-04-01
• Subjects: Nervous system -- Surgery -- Periodicals; 617.48005
• DOI: 10.1227/NEU.0000000000001880_507
• Languages: English
• ISSNs: 0148-396X

Abstract

Abstract : INTRODUCTION: Medulloblastoma (MB), pilocytic astrocytoma (PA), and ependymoma (EP) compose the majority of posterior fossa (PF) pediatric tumors and share similar features on MRI. Pre-operative anticipation of pathology can inform the surgical approach, extent of resection, and potential complications. METHODS: We extracted 1800 Image features from T2- and gadolinium-enhanced T1-weighted images in a multinational cohort of 274 MB, 156 PA, and 97 EP. We designed a two-step classifier—first ruling out PA with a three-way classifier, and next distinguishing MB from EP with a binary classifier. For each step, we selected the best performing classifier model from six candidates following LASSO-feature reduction. Final multi-class classifier performance was measured on a holdout test set with the micro-averaged F1-score. RESULTS: Optimal diagnostic performance was achieved using two decision steps, each with their respective feature set and classifier method. An initial three-way (MB, PA, and EP) logistic regression classifier exhibited a micro-averaged F1-score of 0.739. From a reduced feature set of 61 features, T2-Uniformity and T1-Contrast were the most relevant for distinguishing PA. A subsequent two-way neural net classifier distinguished MB from EP with F1-score 0.9189. In the second reduced feature set of 39 features, T2-Sphericity and T1-Flatness were most relevant. Performing the two classifiers sequentially for MB, PA, and EP classification produced a micro-averaged F1-score of 0.9179. CONCLUSION: A sequential radiomic improved upon a single-step radiographic classifier for pediatric PF tumors. Strong overlap between MB and EP prevented a single-step classifier from distinguishing all three pathologies at once. PA-relevant features aligned with avid gadolinium-enhancement and non-enhancing cystic components. MB and EP-related features correlated to how each conform within and extrude from the fourth ventricle, respectively.