In one paragraphArticle in ArXiv, 2024. 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
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
24 authors.
Kareem A WahidDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0002-0503-0175 Cem DedeDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0002-0543-9325 Dina M El-HabashyDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0001-5182-9976 Serageldin KamelDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0002-0046-4337 Michael K RooneyDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0002-2860-4653 Yomna KhamisDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0001-8531-0849 Moamen R A AbdelaalDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0003-4476-2122 Sara AhmedDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0002-1791-8366 Kelsey L CorriganDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-1111-2222-3333 Enoch ChangDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0002-2879-5445 Stephanie O DudzinskiDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0001-9222-472X Travis C SalzilloDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0001-6271-9879 Brigid A McDonaldDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0003-4230-1330 Samuel L MulderDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0001-5185-4805 Lucas McCullumDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0001-9788-7987 Qusai AlakaylehDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0009-0005-0050-6567 Carlos SjogreenDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0002-6584-7670 Renjie HeDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0001-9166-6286 Abdallah S R MohamedDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0003-2064-7613 Stephen Y LaiDepartment of Head and Neck Surgery, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0003-2064-7613 Andrew J SchaeferDepartment of Computational Applied Mathematics and Operations Research, Rice University, Houston, TX, USA.ORCID 0000-0002-0379-741X Mohamed A NaserDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0003-1020-4966 Clifton D FullerDepartment of Radiation Oncology, The University of Texas MD Anderson Cancer, Houston, Texas, USA.ORCID 0000-0002-5264-3994 Funding
Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI DIANE BODURKA · 1985 to 2026
$290.8MDevelopment of functional magnetic resonance imaging-guided adaptive radiotherapy for head and neck cancer patients using novel MR-Linac deviceR01DE028290 · NIDCR · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI CHRISTODOULEAS, JOHN PAUL, FULLER, CLIFTON DAVID · 2019 to 2023
$4.3MSCH: Personalized Rescheduling of Adaptive Radiation Therapy for Head & Neck CancerR01CA257814 · NCI · RICE UNIVERSITY · PI FULLER, CLIFTON DAVID, SCHAEFER, ANDREW J · 2021 to 2024
$1.8MImage Guided Cancer Therapy Training ProgramT32CA261856 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Kristy Brock, Clifton David Fuller · 2022 to 2026
$1.2MProbabilistic Deep Learning Cervical Lymph-Node Auto-Segmentation For Imaging-enhanced Evaluation of Extracapsular Extension Risk (PDL-CLASIFIER)R03DE033550 · NIDCR · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI FULLER, CLIFTON DAVID, NASER, MOHAMED · 2024 to 2025
$499kNCI NIH HHS R01 CA257814NCI NIH HHS T32 CA261856NIDCR NIH HHS R01 DE028290NIDCR NIH HHS R03 DE033550
6 · The paper itselfAbstract
Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. This challenge addresses the scarcity of large, publicly available AI-ready adaptive RT datasets in HNC and explores the potential of incorporating multi-timepoint data to enhance RT auto-segmentation performance. Participants tackled two HNC segmentation tasks: automatic delineation of primary gross tumor volume (GTVp) and gross metastatic regional lymph nodes (GTVn) on pre-RT (Task 1) and mid-RT (Task 2) T2-weighted scans. The challenge provided 150 HNC cases for training and 50 for testing, hosted on grand-challenge.org using a Docker submission framework. In total, 19 independent teams from across the world qualified by submitting both their algorithms and corresponding papers, resulting in 18 submissions for Task 1 and 15 submissions for Task 2. Evaluation using the mean aggregated Dice Similarity Coefficient showed top-performing AI methods achieved scores of 0.825 in Task 1 and 0.733 in Task 2. These results surpassed clinician interobserver variability benchmarks, marking significant strides in automated tumor segmentation for MR-guided RT applications in HNC.
Indexed as
artificial intelligencecontouringdata challengedeep learningHead and neck cancerimage-guidedmagnetic resonance imagingradiotherapysegmentation
Identifiers
PMID39650598
PMCPMC11623708
What OpenQuestion holds
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LicenceCC BY
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