Evidence map›Paper›PMID 34850171›Full record

ArticleNeuro-oncology2022

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 and 20 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Neuro-oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

30 authors.

Michael ZhangDepartment of Neurosurgery, Stanford Hospital and Clinics, Stanford, California, USA.ORCID 0000-0002-7067-0689
Edward WangDepartment of Medical Biophysics, Western University, London, Ontario, Canada.
Derek YeciesDepartment of Neurosurgery, Stanford Hospital and Clinics, Stanford, California, USA.ORCID 0000-0001-8534-4651
Lydia T TamStanford School of Medicine, Stanford University, Stanford, California, USA.
Michelle HanDepartment of Pediatrics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.
Sebastian ToescuDepartment of Neurosurgery, Great Ormond Street Institute of Child Health, London, UK.ORCID 0000-0001-8768-9231
Jason N WrightDepartment of Radiology, Seattle Children's Hospital, and Harborview Medical Center, Seattle, Washington, USA.
Emre AltinmakasDepartment of Radiology, Koç University School of Medicine, Istanbul, Turkey.
Eric ChenDepartment of Clinical Radiology & Imaging Sciences, Riley Children's Hospital, Indianapolis, Indiana, USA.
Alireza RadmaneshDepartment of Radiology, New York University Grossman School of Medicine, New York, New York, USA.
Jordan NemelkaDivision of Pediatric Neurosurgery, Department of Neurosurgery, Huntsman Cancer Institute, University of Utah School of Medicine, Intermountain Healthcare Primary Children's Hospital, Salt Lake City, Utah, USA.
Ozgur OztekinDepartment of Neuroradiology, Cigli Education and Research Hospital, and Tepecik Education and Research Hospital, Izmir, Turkey.
Matthias W WagnerDepartment of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada.
Robert M LoberDivision of Neurosurgery, Dayton Children's Hospital, Dayton, Ohio, USA.
Birgit Ertl-WagnerDepartment of Diagnostic Imaging, The Hospital for Sick Children, Toronto, Ontario, Canada.ORCID 0000-0002-7896-7049
Chang Y HoDepartment of Clinical Radiology & Imaging Sciences, Riley Children's Hospital, Indianapolis, Indiana, USA.
Kshitij MankadDepartment of Radiology, Great Ormond Street Institute of Child Health, London, UK.
Nicholas A VitanzaDivision of Pediatric Hematology/Oncology, Department of Pediatrics, Seattle Children's Hospital, Seattle, Washington, USA.
Samuel H CheshierDivision of Pediatric Neurosurgery, Department of Neurosurgery, Huntsman Cancer Institute, University of Utah School of Medicine, Intermountain Healthcare Primary Children's Hospital, Salt Lake City, Utah, USA.
Tom S JacquesDepartment of Developmental Biology & Cancer, University College London Great Ormond Street Institute of Child Health, and Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
Paul G FisherDepartment of Neurology, Lucile Packard Children's Hospital, Stanford University, Palo Alto, California, USA.
Kristian AquilinaDepartment of Neurosurgery, Great Ormond Street Institute of Child Health, London, UK.
Mourad SaidRadiology Department Centre International Carthage Médicale, Monastir, Tunisia.
Alok JajuDepartment of Medical Imaging, Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA.
Stefan PfisterDepartment of Pediatrics, Hopp Children' Cancer Center, Heidelberg, Germany.
Michael D TaylorDivision of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario, Canada.
Gerald A GrantDepartment of Neurosurgery, Lucile Packard Children's Hospital, Stanford, California, USA.
Sarah MattonenDepartment of Medical Biophysics, Western University, London, Ontario, Canada.
Vijay RamaswamyDivision of Haematology/Oncology, Programme in Developmental and Stem Cell Biology, The Hospital for Sick Children, Toronto, Ontario, Canada.ORCID 0000-0002-6557-895X
Kristen W YeomDepartment of Radiology, Lucile Packard Children's Hospital, Stanford, California, USA.

Funding

STANFORD CANCER IMAGING TRAINING (SCIT) PROGRAMT32CA009695 · NCI · STANFORD UNIVERSITY · PI BRUCE L DANIEL, Jeremy Dahl · 1993 to 2026
$7.7M
Cancer Research UKCIHRDepartment of HealthNCI NIH HHS T32 CA009695
6 · The paper itself

Abstract

backgroundThe 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.

methodsWe 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.

resultsFor 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.

conclusionsWe 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.

Indexed as

EpendymomaHumansMachine LearningMagnetic Resonance ImagingPrognosisRetrospective Studiesependymomamachine learningmolecular subgroupposterior fossa tumorradiomics

Identifiers

PMID34850171
PMCPMC9159456

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.