Evidence map›Paper›PMID 27390615›Full record

ArticleProceedings. IEEE International Symposium on Biomedical Imaging2016

PHENOTYPIC CHARACTERIZATION OF BREAST INVASIVE CARCINOMA VIA TRANSFERABLE TISSUE MORPHOMETRIC PATTERNS LEARNED FROM GLIOBLASTOMA MULTIFORME.

Ju Han, Gerald V Fontenay, Yunfu Wang, Jian-Hua Mao, Hang Chang

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

Article in Proceedings. IEEE International Symposium on Biomedical Imaging, 2016. 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
0.4field-weighted citation impact, top 21% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

5 authors at 2 institutions in 2 countries.

Ju HanDepartment of Electrical and Biomedical Engineering, University of Nevada, Reno, Nevada, USA.
Gerald V FontenayBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Yunfu WangBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA; Department of Neurology, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, China.
Jian-Hua MaoBiological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Hang ChangDepartment of Electrical and Biomedical Engineering, University of Nevada, Reno, Nevada, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, Berkeley, California, USA.
Lawrence Berkeley National Laboratory · USUniversity of Nevada, Reno · US

Funding

Molecular and Therapeutic Basis of Morphometric Aberrations in Brain TumorsR01CA184476 · NCI · UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB · PI CHANG, HANG · 2015 to 2019
$2.0M
NCI NIH HHS R01 CA184476
6 · The paper itself

Abstract

Quantitative analysis of whole slide images (WSIs) in a large cohort may provide predictive models of clinical outcome. However, the performance of the existing techniques is hindered as a result of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. Although unsupervised feature learning provides a promising way in learning pertinent features without human intervention, its capability can be greatly limited due to the lack of well-curated examples. In this paper, we explored the transferability of knowledge acquired from a well-curated Glioblastoma Multiforme (GBM) dataset through its application to the representation and characterization of tissue histology from the Cancer Genome Atlas (TCGA) Breast Invasive Carcinoma (BRCA) cohort. Our experimental results reveals two major phenotypic subtypes with statistically significantly different survival curves. Further differential expression analysis of these two subtypes indicates enrichment of genes regulated by NF-kB in response to TNF and genes up-regulated in response to IFNG.

Indexed as

Breast invasive carcinomaconsensus clusteringenrichment analysisknowledge sharingpredictive sparse decompositionsurvival analysisunsupervised feature learning

Identifiers

PMID27390615
PMCPMC4932846
OpenAlexW2420280888

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.