Evidence map›Paper›PMID 37835523›Full record

ArticleCancers2023

Deep Learning for Fully Automatic Tumor Segmentation on Serially Acquired Dynamic Contrast-Enhanced MRI Images of Triple-Negative Breast Cancer.

Zhan Xu, David E Rauch, Rania M Mohamed, Sanaz Pashapoor, Zijian Zhou, Bikash Panthi, Jong Bum Son, Ken-Pin Hwang, Benjamin C Musall, Beatriz E Adrada and 21 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. The Evolution and Clinical Impact of Deep Learning Technologies in Breast MRI.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2025
    Review
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

31 authors.

Zhan XuDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.ORCID 0000-0002-5892-0227
David E RauchDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Rania M MohamedDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.ORCID 0000-0002-6487-3965
Sanaz PashapoorDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Zijian ZhouDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Bikash PanthiDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Jong Bum SonDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Ken-Pin HwangDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.ORCID 0000-0002-8196-3794
Benjamin C MusallDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Beatriz E AdradaDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Rosalind P CandelariaDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Jessica W T LeungDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Huong T C Le-PetrossDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Deanna L LaneDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Frances PerezDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Jason WhiteDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Alyson ClaybornDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Brandy ReedDepartment of Clinical Research Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Huiqin ChenDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Jia SunDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Peng WeiDepartment of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.ORCID 0000-0001-7758-6116
Alastair ThompsonSection of Breast Surgery, Baylor College of Medicine, Houston, TX 77030, USA.
Anil KorkutDepartment of Bioinformatics & Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Lei HuoDepartment of Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Kelly K HuntDepartment of Breast Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Jennifer K LittonDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Vicente ValeroDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Debu TripathyDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.ORCID 0000-0002-5711-2404
Wei YangDepartment of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Clinton YamDepartment of Breast Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Jingfei MaDepartment of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.

Funding

Tumor Evolution and Metastasis ProgramP30CA016672 · NCI · UNIVERSITY OF TX MD ANDERSON CAN CTR · PI Fabien Delerue · 1985 to 2026
$290.8M
NCI NIH HHS P30 CA016672NCI NIH HHS P30CA016672
6 · The paper itself

Abstract

Accurate tumor segmentation is required for quantitative image analyses, which are increasingly used for evaluation of tumors. We developed a fully automated and high-performance segmentation model of triple-negative breast cancer using a self-configurable deep learning framework and a large set of dynamic contrast-enhanced MRI images acquired serially over the patients' treatment course. Among all models, the top-performing one that was trained with the images across different time points of a treatment course yielded a Dice similarity coefficient of 93% and a sensitivity of 96% on baseline images. The top-performing model also produced accurate tumor size measurements, which is valuable for practical clinical applications.

Indexed as

deep learningtriple-negative breast cancertumor segmentation

Identifiers

PMID37835523
PMCPMC10571741

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.