Evidence map›Paper›PMID 38790322›Full record

ReviewBioengineering (Basel, Switzerland)2024

Where Does Auto-Segmentation for Brain Metastases Radiosurgery Stand Today?

Matthew Kim, Jen-Yeu Wang, Weiguo Lu, Hao Jiang, Strahinja Stojadinovic, Zabi Wardak, Tu Dan, Robert Timmerman, Lei Wang, Cynthia Chuang and 7 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Bioengineering (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

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

17 authors.

Matthew KimDepartment of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
Jen-Yeu WangDepartment of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.ORCID 0000-0002-2278-4470
Weiguo LuDepartment of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA.
Hao JiangNeuralRad LLC, Madison, WI 53717, USA.
Strahinja StojadinovicDepartment of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA.ORCID 0000-0002-6840-5747
Zabi WardakDepartment of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA.
Tu DanDepartment of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA.
Robert TimmermanDepartment of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA.
Lei WangDepartment of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
Cynthia ChuangDepartment of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
Gregory SzalkowskiDepartment of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
Lianli LiuDepartment of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.ORCID 0000-0002-1095-0953
Erqi PollomDepartment of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
Elham RahimyDepartment of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.
Scott SoltysDepartment of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.ORCID 0000-0002-5360-7063
Mingli ChenDepartment of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX 75390, USA.
Xuejun GuDepartment of Radiation Oncology, Stanford University, Stanford, CA 94305, USA.

Funding

An artificial intelligence-driven distributed stereotactic radiosurgery strategy for multiple brain metastases managementR01CA235723 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI GU, XUEJUN, LU, WEIGUO · 2019 to 2024
$2.9M
NIH HHS R01 CA235723NIH HHS SBIR 75N91021C00031
6 · The paper itself

Abstract

Detection and segmentation of brain metastases (BMs) play a pivotal role in diagnosis, treatment planning, and follow-up evaluations for effective BM management. Given the rising prevalence of BM cases and its predominantly multiple onsets, automated segmentation is becoming necessary in stereotactic radiosurgery. It not only alleviates the clinician's manual workload and improves clinical workflow efficiency but also ensures treatment safety, ultimately improving patient care. Recent strides in machine learning, particularly in deep learning (DL), have revolutionized medical image segmentation, achieving state-of-the-art results. This review aims to analyze auto-segmentation strategies, characterize the utilized data, and assess the performance of cutting-edge BM segmentation methodologies. Additionally, we delve into the challenges confronting BM segmentation and share insights gleaned from our algorithmic and clinical implementation experiences.

Indexed as

brain metastases (BMs)deep learningsegmentation

Identifiers

PMID38790322
PMCPMC11117895

What OpenQuestion holds

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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.