Evidence map›Paper›PMID 42325604›Full record

ArticleNeuro-oncology advances

A WEb-Accessible comprehensiVE platform for automatic vestibular schwannoma segmentation and longitudinal volumetric tracking.

Riya Prashad, Gregory Szalkowski, Jen-Yeu Wang, Fred C Lam, Ahed H Kattaa, Vivek Sanker, Cynthia Chuang, Lei Wang, Lianli Liu, Qingying Wang and 12 more

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

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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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.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Riya PrashadDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.ORCID https://orcid.org/0009-0004-6053-5303
Gregory SzalkowskiDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.
Jen-Yeu WangDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.
Fred C LamDepartment of Neurosurgery, Stanford University, Stanford, California, USA.
Ahed H KattaaDepartment of Neurosurgery, Stanford University, Stanford, California, USA.
Vivek SankerDepartment of Neurosurgery, Stanford University, Stanford, California, USA.
Cynthia ChuangDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.
Lei WangDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.
Lianli LiuDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.
Qingying WangDepartment of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Yinheng ZhuDepartment of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Mingli ChenDepartment of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Iris GibbsDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.
Scott G SoltysDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.
Erqi L PollomDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.ORCID https://orcid.org/0000-0003-2908-6293
Elham RahimyDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.
David J ParkDepartment of Neurosurgery, Stanford University, Stanford, California, USA.
Yusuke S HoriDepartment of Neurosurgery, Stanford University, Stanford, California, USA.
Steven D ChangDepartment of Neurosurgery, Stanford University, Stanford, California, USA.
Hao JiangDepartment of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Weiguo LuDepartment of Radiation Oncology, The University of Texas Southwestern Medical Center, Dallas, Texas, USA.
Xuejun GuDepartment of Radiation Oncology, Stanford University, Stanford, California, USA.ORCID https://orcid.org/0000-0001-6725-6906

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Vestibular schwannomas (VS) require long-term tracking for treatment decisions and outcome assessment. This study aims to develop a WEb-Accessible comprehensiVE (WEAVE) platform that combines AI-driven segmentation with a user-friendly interface to enable longitudinal volume tracking for disease assessment, planning, and monitoring. Methods: WEAVE was built using nnU-Net as the backbone for auto-segmentation, with 3 models trained and validated on combinations of various image modalities. Auto-segmentation performance was evaluated with multiple metrics including absolute and relative volume differences (AVD/RVD), Dice score, mean surface-to-surface distance, and 95th percentile Hausdorff distance (HD95). The platform features a central database with DICOM-RT import/export capabilities, and its interface is built using Rust and WebAssembly. Results: Three models demonstrated comparable performance without significant differences with mean Dice scores, AVD, and RVD ranged from (0.89 to 0.90), (0.11 to 0.13 cc), and (10.70% to 13.44%), respectively. Mean surface-to-surface distance and HD95 values were consistently low (0.14-0.19 and 0.74-0.88 mm, respectively). Average inference time was ∼60 s per case. The platform successfully enabled longitudinal tumor volume tracking and provided flexible visualization options, including single and multiple image views and a graphical representation of volume changes over time. Conclusions: WEAVE is a comprehensive platform that combines automated segmentation with longitudinal tracking to support VS management. The AI models achieved Dice scores comparable to interobserver variability in manual contouring, indicating clinical adequacy. The tracking capability provides consistency in treatment planning and monitoring and opens the possibility to advance AI-driven segmentation and streamline workflows for other intracranial pathologies.

Indexed as

auto-segmentationlongitudinal volume trackingstereotactic radiosurgerytreatment follow-upvestibular schwannoma

Identifiers

PMID42325604
PMCPMC13278798

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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.