Evidence map›Paper›PMID 35537336›Full record

ArticleBiomaterials2022

Deep learning identification of stiffness markers in breast cancer.

Alexandra Sneider, Ashley Kiemen, Joo Ho Kim, Pei-Hsun Wu, Mehran Habibi, Marissa White, Jude M Phillip, Luo Gu, Denis Wirtz

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomaterials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
2.8field-weighted citation impact, top 9% of its field
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

12 citing papers in PubMed, 26 citations in OpenAlex.

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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

9 authors at 1 institution in 1 country.

Alexandra SneiderDepartment of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
Ashley KiemenDepartment of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
Joo Ho KimDepartment of Materials Science and Engineering, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
Pei-Hsun WuDepartment of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
Mehran HabibiJohns Hopkins Breast Center, Johns Hopkins Bayview Medical Center, 4940 Eastern Ave, Baltimore, MD, 21224, USA.
Marissa WhiteDepartment of Pathology, Johns Hopkins School of Medicine, 401 N Broadway, Baltimore, MD, 21231, USA.
Jude M PhillipDepartment of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA; Department of Biomedical Engineering, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
Luo GuDepartment of Materials Science and Engineering, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA.
Denis WirtzDepartment of Chemical and Biomolecular Engineering, Johns Hopkins Physical Sciences-Oncology Center, Institute for NanoBioTechnology, Johns Hopkins University, 3400 N Charles St, Baltimore, MD, 21218, USA; Department of Pathology, Johns Hopkins School of Medicine, 401 N Broadway, Baltimore, MD, 21231, USA; Department of Oncology, Johns Hopkins School of Medicine, 1800 Orleans St, Baltimore, MD, 21205, USA. Electronic address: wirtz@jhu.edu.
Johns Hopkins University · US

Funding

Tech Core 2U54CA268083 · NCI · JOHNS HOPKINS UNIVERSITY · PI Denis Wirtz, Laura DeLong Wood · 2022 to 2026
$10.2M
The Role of Physical Cues in Collective Cell InvasionU54CA210173 · NCI · JOHNS HOPKINS UNIVERSITY · PI KONSTANTOPOULOS, KONSTANTINOS · 2016 to 2020
$9.9M
Single-cell phenotyping for therapeutic stratification in pancreatic cancerR01CA174388 · NCI · JOHNS HOPKINS UNIVERSITY · PI WIRTZ, DENIS · 2012 to 2016
$3.8M
Validation of Nuclear Morphology as a Biomarker of Aging and Aging-Related PhenotypesU01AG060903 · NIA · JOHNS HOPKINS UNIVERSITY · PI WIRTZ, DENIS · 2018 to 2022
$3.1M
Organ Specific ProjectU54AR081774 · NIAMS · JOHNS HOPKINS UNIVERSITY · PI WIRTZ, DENIS · 2022 to 2023
$1.1M
NCI NIH HHS R01 CA174388NCI NIH HHS U54 CA210173NCI NIH HHS U54 CA268083NIAMS NIH HHS U54 AR081774NIA NIH HHS U01 AG060903
6 · The paper itself

Abstract

While essential to our understanding of solid tumor progression, the study of cell and tissue mechanics has yet to find traction in the clinic. Determining tissue stiffness, a mechanical property known to promote a malignant phenotype in vitro and in vivo, is not part of the standard algorithm for the diagnosis and treatment of breast cancer. Instead, clinicians routinely use mammograms to identify malignant lesions and radiographically dense breast tissue is associated with an increased risk of developing cancer. Whether breast density is related to tumor tissue stiffness, and what cellular and non-cellular components of the tumor contribute the most to its stiffness are not well understood. Through training of a deep learning network and mechanical measurements of fresh patient tissue, we create a bridge in understanding between clinical and mechanical markers. The automatic identification of cellular and extracellular features from hematoxylin and eosin (H&E)-stained slides reveals that global and local breast tissue stiffness best correlate with the percentage of straight collagen. Importantly, the percentage of dense breast tissue does not directly correlate with tissue stiffness or straight collagen content.

Indexed as

Breast NeoplasmsDeep LearningBreast DensityCollagenFemaleHumansMammographyCollagenBreast cancerBreast densityDeep learningMechanobiologyStiffness

Identifiers

PMID35537336
PMCPMC9873266
OpenAlexW4225701842

What OpenQuestion holds

Textmetadata
LicenceTDM
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.