Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles

Michael Zhang, Edward Wang, Derek Yecies, Lydia T. Tam, Michelle Han, Sebastian Toescu, Jason N. Wright, Emre Altinmakas, Eric Chen, Alireza Radmanesh, Jordan Nemelka, Ozgur Oztekin, Matthias W. Wagner, Robert M. Lober, Birgit Ertl-Wagner, Chang Y. Ho, Kshitij Mankad, Nicholas A. Vitanza, Samuel H. Cheshier, Tom S. JacquesPaul G. Fisher, Kristian Aquilina, Mourad Said, Alok Jaju, Stefan Pfister, Michael D. Taylor, Gerald A. Grant, Sarah Mattonen, Vijay Ramaswamy, Kristen W. Yeom

Research output: Contribution to journalArticlepeer-review

9 Scopus citations


Background: The risk profile for posterior fossa ependymoma (EP) depends on surgical and molecular status [Group A (PFA) versus Group B (PFB)]. While subtotal tumor resection is known to confer worse prognosis, MRI-based EP risk-profiling is unexplored. We aimed to apply machine learning strategies to link MRI-based biomarkers of high-risk EP and also to distinguish PFA from PFB. Methods: We extracted 1800 quantitative features from presurgical T2-weighted (T2-MRI) and gadolinium-enhanced T1-weighted (T1-MRI) imaging of 157 EP patients. We implemented nested cross-validation to identify features for risk score calculations and apply a Cox model for survival analysis. We conducted additional feature selection for PFA versus PFB and examined performance across three candidate classifiers. Results: For all EP patients with GTR, we identified four T2-MRI-based features and stratified patients into high- and low-risk groups, with 5-year overall survival rates of 62% and 100%, respectively (P <. 0001). Among presumed PFA patients with GTR, four T1-MRI and five T2-MRI features predicted divergence of high- and low-risk groups, with 5-year overall survival rates of 62.7% and 96.7%, respectively (P =. 002). T1-MRI-based features showed the best performance distinguishing PFA from PFB with an AUC of 0.86. Conclusions: We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy.

Original languageEnglish (US)
Pages (from-to)986-994
Number of pages9
Issue number6
StatePublished - Jun 1 2022


  • ependymoma
  • machine learning
  • molecular subgroup
  • posterior fossa tumor
  • radiomics

ASJC Scopus subject areas

  • Clinical Neurology
  • Oncology
  • Cancer Research


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