Evidence map›Paper›PMID 29047975›Full record

ArticleApplied optics2017

Breast density quantification using structured-light-based diffuse optical tomography simulations.

Jessica Ruiz, Farouk Nouizi, Jaedu Cho, Jie Zheng, Yifan Li, Jeon-Hor Chen, Min-Ying Su, Gultekin Gulsen

Abstract read
In one paragraph

Article in Applied optics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
2.4field-weighted citation impact, top 12% 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

9 citing papers in PubMed, 20 citations in OpenAlex.

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  7. Automatic cancer tissue detection using multispectral photoacoustic imaging.International journal of computer assisted radiology and surgery · 2020
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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

8 authors at 1 institution in 1 country.

Jessica Ruiz
Farouk Nouizi
Jaedu Cho
Jie Zheng
Yifan Li
Jeon-Hor Chen
Min-Ying Su
Gultekin Gulsen
University of California, Irvine · US

Funding

Univ.of Calif., Irvine Cancer Center Support GrantP30CA062203 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI Melanie Funes · 1994 to 2026
$57.9M
Prediction of Neoadjuvant Chemo Pathological Response Using MRI MarkersR01CA127927 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI SU, MIN-YING L. · 2008 to 2017
$5.0M
Development of A Multi-Modality System for Onco-ImagingR33CA120175 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI GULSEN, GULTEKIN · 2008 to 2010
$1.5M
Tomographic Molecular Imaging for Breast CancerR01EB008716 · NIBIB · UNIVERSITY OF CALIFORNIA-IRVINE · PI GULSEN, GULTEKIN · 2008 to 2011
$1.3M
Mammographic Density and Metabolic Genotyping for Predicting Cancer PrognosisR21CA208938 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI CHEN, JEON-HOR, SU, MIN-YING L. · 2017 to 2018
$370k
Volume and Morphology of Fibroglandular Tissue for Breast Cancer Risk PredictionR21CA170955 · NCI · UNIVERSITY OF CALIFORNIA-IRVINE · PI CHEN, JEON-HOR, SU, MIN-YING L. · 2013 to 2014
$363k
Fluorescence Tomography in Small Animal Imaging using an Ultra-fast RTE SolverR21EB013387 · NIBIB · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI GAO, HAO · 2011 to 2012
$345k
NCI NIH HHS P30 CA062203NCI NIH HHS R01 CA127927NCI NIH HHS R21 CA170955NCI NIH HHS R21 CA208938NCI NIH HHS R33 CA120175NIBIB NIH HHS R01 EB008716NIBIB NIH HHS R21 EB013387
6 · The paper itself

Abstract

We present the feasibility of structured-light-based diffuse optical tomography (DOT) to quantify the breast density with an extensive simulation study. This study is performed on multiple numerical breast phantoms built from magnetic resonance imaging (MRI) images. These phantoms represent realistic tissue morphologies and are given typical breast optical properties. First, synthetic data are simulated at five wavelengths using our structured-light-based DOT forward problem. Afterwards, the inverse problem is solved to obtain the absorption images and subsequently the chromophore concentration maps. Parameters, such as segmented volumes and mean concentrations, are extracted from these maps and used in a regression model to estimate the percent breast densities. These estimations are correlated with the true values from MRI, r=0.97, showing that our new technique is promising in measuring breast density.

Indexed as

AlgorithmsBreast DensityPhantoms, ImagingBreastFeasibility StudiesFemaleHumansMagnetic Resonance ImagingTomography, Optical

Identifiers

PMID29047975
PMCPMC6691974
OpenAlexW2748976997

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.