2021
DOI: 10.1093/neuonc/noab272
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Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles

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… Show more

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Cited by 13 publications
(1 citation statement)
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“…A previous study extracted 1800 quantitative features from preoperative T2WI and T1WI+C images from 157 patients with posterior fossa EP and submitted these features to a Cox proportional hazards regression model for survival analysis. The results showed that radiomic features based on T1WI+C could effectively distinguish between PFA and PFB (AUC = 0.86), and that the risk score based on T2WI radiomic features could significantly distinguish between the high‐risk group and the low‐risk group [ 25 ].…”
Section: Application Status Of Radiomics In Pediatric Diseasesmentioning
confidence: 99%
“…A previous study extracted 1800 quantitative features from preoperative T2WI and T1WI+C images from 157 patients with posterior fossa EP and submitted these features to a Cox proportional hazards regression model for survival analysis. The results showed that radiomic features based on T1WI+C could effectively distinguish between PFA and PFB (AUC = 0.86), and that the risk score based on T2WI radiomic features could significantly distinguish between the high‐risk group and the low‐risk group [ 25 ].…”
Section: Application Status Of Radiomics In Pediatric Diseasesmentioning
confidence: 99%