Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles. Issue 6 (25th November 2021)

• Record Type: Journal Article
• Authors: Zhang, Michael; Wang, Edward; Yecies, Derek; Tam, Lydia T; Han, Michelle; Toescu, Sebastian; Wright, Jason N; Altinmakas, Emre; Chen, Eric; Radmanesh, Alireza; Nemelka, Jordan; Oztekin, Ozgur; Wagner, Matthias W; Lober, Robert M; Ertl-Wagner, Birgit; Ho, Chang Y; Mankad, Kshitij; Vitanza, Nicholas A; Cheshier, Samuel H; Jacques, Tom S; Fisher, Paul G; Aquilina, Kristian; Said, Mourad; Jaju, Alok; Pfister, Stefan; Taylor, Michael D; Grant, Gerald A; Mattonen, Sarah; Ramaswamy, Vijay; Yeom, Kristen W
• Is Part Of: Neuro-oncology. Volume 24:Issue 6(2022)
• Page Start: 986
• Page End: 994
• Publication Date: 2021-11-25
• Subjects: ependymoma -- machine learning -- molecular subgroup -- posterior fossa tumor -- radiomics; Brain Neoplasms -- Periodicals; Brain -- Tumors -- Periodicals; Brain -- Cancer -- Periodicals; Nervous system -- Cancer -- Periodicals; 616.99481
• DOI: 10.1093/neuonc/noab272
• Languages: English
• ISSNs: 1522-8517

Abstract

Abstract: 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.