Evidence map›Paper›PMID 37396608›Full record

ArticleArXiv2023

The Brain Tumor Segmentation (BraTS) Challenge 2023:

Maruf Adewole, Jeffrey D Rudie, Anu Gbdamosi, Oluyemisi Toyobo, Confidence Raymond, Dong Zhang, Olubukola Omidiji, Rachel Akinola, Mohammad Abba Suwaid, Adaobi Emegoakor and 41 more

Open access · greenAbstract readPreprint
In one paragraph

Article in ArXiv, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

51 authors at 20 institutions in 7 countries.

Maruf AdewoleMedical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria.
Jeffrey D RudieDepartment of Radiology, University of California, San Diego.
Anu GbdamosiMedical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria.
Oluyemisi ToyoboMedical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria.
Confidence RaymondMedical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria.
Dong ZhangMedical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria.
Olubukola OmidijiMedical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria.
Rachel AkinolaLagos State University Teaching Hospital, Ikeja, Lagos, Nigeria.
Mohammad Abba SuwaidNSIA-Kano Diagnostic Center, Kano Nigeria.
Adaobi EmegoakorNnamdi Azikiwe University Teaching Hospital, Nnewi, Anambra State, Nigeria.
Nancy OjoFederal Medical Centre, Abeokuta, Ogun State, Nigeria.
Kenneth AguhFederal Medical Centre, Umahia, Abia State, Nigeria.
Chinasa KalaiwoNational Hospital Abuja, FCT, Nigeria.
Gabriel BabatundeLagos University Teaching Hospital, Lagos, Nigeria.
Afolabi OgunleyeLagos State University Teaching Hospital, Ikeja, Lagos, Nigeria.
Yewande GbadamosiLagos State University Teaching Hospital, Ikeja, Lagos, Nigeria.
Kator IorpagherBenue State University Teaching Hospital, Markurdi, Benue State, Nigeria.
Evan CalabreseDuke University Medical Center, Department of Radiology, USA.
Mariam AboianYale University, New Haven, CT, USA.
Marius LinguraruChildren's National Hospital, Washington DC, USA.
Jake AlbrechtSage Bionetworks, USA.
Benedikt WiestlerDepartment of Neuroradiology, Technical University of Munich, Munich, Germany.
Florian KoflerDepartment of Neuroradiology, Technical University of Munich, Munich, Germany.
Anastasia JanasYale University, New Haven, CT, USA.
Dominic LaBellaDuke University Medical Center, Department of Radiation Oncology, USA.
Anahita Fathi KzerooniChildren's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA.
Hongwei Bran LiDepartment of Neuroradiology, Technical University of Munich, Munich, Germany.
Juan Eugenio IglesiasAthinoula A Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA.
Keyvan FarahaniCancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD 20814, USA.
James EddySage Bionetworks, USA.
Timothy BergquistSage Bionetworks, USA.
Verena ChungSage Bionetworks, USA.
Russell Takeshi ShinoharaCenter for AI and Data Science for Integrated Diagnostics (AI2D) & Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
Walter WigginsDuke University Medical Center, Department of Radiology, USA.
Zachary ReitmanDuke University Medical Center, Department of Radiation Oncology, USA.
Chunhao WangDuke University Medical Center, Department of Radiation Oncology, USA.
Xinyang LiuChildren's National Hospital, Washington DC, USA.
Zhifan JiangChildren's National Hospital, Washington DC, USA.
Ariana FamiliarChildren's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, USA.
Koen Van LeemputDepartment of Applied Mathematics and Computer Science, Technical University of Denmark, Denmark.
Christina BukasHelmholtz Research Center, Munich, Germany.
Maire PiraudHelmholtz Research Center, Munich, Germany.
Gian-Marco ConteMayo Clinic, MN, USA.
Elaine JohanssonPrecision FDA, U.S. Food and Drug Administration, Silver Spring, MD, USA.
Zeke MeierBooz Allen Hamilton, McLean, VA, USA.
Bjoern H MenzeDepartment of Neuroradiology, Technical University of Munich, Munich, Germany.
Ujjwal BaidCenter for AI and Data Science for Integrated Diagnostics (AI2D) & Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
Spyridon BakasCenter for AI and Data Science for Integrated Diagnostics (AI2D) & Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA.
Farouk DakoCenter for Global Health, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Abiodun FatadeMedical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria.
Udunna C AnazodoMedical Artificial Intelligence Laboratory (MAI Lab), Lagos, Nigeria.
Artificial Intelligence in Medicine (Canada) · CASage Bionetworks · USLagos State University Teaching Hospital · NGTechnical University of Munich · DEUniversity of Pennsylvania · USGeorge Washington University · USHelmholtz Zentrum München · DEYale University · USBooz Allen Hamilton (United States) · USCenter for Global Health · USChildren's Hospital of Philadelphia · USChildren's National · USFederal Medical Centre · NGLagos University Teaching Hospital · NGMassachusetts General Hospital · USMayo Clinic in Arizona · USNnamdi Azikiwe University Teaching Hospital · NGPhiladelphia University · USTechnical University of Denmark · DKUniversity of California, San Diego · US

Funding

Advancing method benchmarking and data sharing through crowd-sourced competitions in cancer researchU24CA248265 · NCI · SAGE BIONETWORKS · PI BOUTROS, PAUL CHRISTOPHER, VARMA, SUSHEEL · 2020 to 2024
$4.1M
The Federated Tumor Segmentation (FeTS) platform: An intuitive tool facilitating secure multi-institutional collaborationU01CA242871 · NCI · UNIVERSITY OF PENNSYLVANIA · PI BAKAS, SPYRIDON · 2019 to 2021
$1.1M
NCI NIH HHS U01 CA242871NCI NIH HHS U24 CA248265
6 · The paper itself

Abstract

Gliomas are the most common type of primary brain tumors. Although gliomas are relatively rare, they are among the deadliest types of cancer, with a survival rate of less than 2 years after diagnosis. Gliomas are challenging to diagnose, hard to treat and inherently resistant to conventional therapy. Years of extensive research to improve diagnosis and treatment of gliomas have decreased mortality rates across the Global North, while chances of survival among individuals in low- and middle-income countries (LMICs) remain unchanged and are significantly worse in Sub-Saharan Africa (SSA) populations. Long-term survival with glioma is associated with the identification of appropriate pathological features on brain MRI and confirmation by histopathology. Since 2012, the Brain Tumor Segmentation (BraTS) Challenge have evaluated state-of-the-art machine learning methods to detect, characterize, and classify gliomas. However, it is unclear if the state-of-the-art methods can be widely implemented in SSA given the extensive use of lower-quality MRI technology, which produces poor image contrast and resolution and more importantly, the propensity for late presentation of disease at advanced stages as well as the unique characteristics of gliomas in SSA (i.e., suspected higher rates of gliomatosis cerebri). Thus, the BraTS-Africa Challenge provides a unique opportunity to include brain MRI glioma cases from SSA in global efforts through the BraTS Challenge to develop and evaluate computer-aided-diagnostic (CAD) methods for the detection and characterization of glioma in resource-limited settings, where the potential for CAD tools to transform healthcare are more likely.

Indexed as

AIartificial intelligencebrainBraTSchallengemachine learningsegmentationSub-Saharan Africatumor

Identifiers

PMID37396608
PMCPMC10312814
OpenAlexW4382918219

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.