Evidence map›Paper›PMID 19272976›Full record

ArticleIEEE transactions on bio-medical engineering2009

Fluorescence spectroscopy: an adjunct diagnostic tool to image-guided core needle biopsy of the breast.

Changfang Zhu, Elizabeth S Burnside, Gale A Sisney, Lonie R Salkowski, Josephine M Harter, Bing Yu, Nirmala Ramanujam

Open access · greenAbstract read
In one paragraph

Article in IEEE transactions on bio-medical engineering, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
3.5field-weighted citation impact, top 8% 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

8 citing papers in PubMed, 34 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Diagnosis of breast cancer by tissue analysis.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2013
    Article
  5. Review
  6. Fluorescence lifetime techniques in medical applications.Annals of biomedical engineering · 2012
    Review
  7. Article
  8. 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

7 authors at 2 institutions in 1 country.

Changfang ZhuDepartment of Electrical and Computer Engineering, University of Wisconsin, Madison, WI 53706, USA. czhu@uwalumni.com
Elizabeth S Burnside
Gale A Sisney
Lonie R Salkowski
Josephine M Harter
Bing Yu
Nirmala Ramanujam
University of Wisconsin–Madison · USDuke University · US

Funding

Diagnosis of Breast Cancer with Optical SpectroscopyR01CA100559 · NCI · UNIVERSITY OF WISCONSIN MADISON · PI RAMANUJAM, NIRMALA · 2004 to 2007
$1.0M
NCI NIH HHS R01 CA100559NCI NIH HHS R01CA100559-01
6 · The paper itself

Abstract

We explored the use of a fiber-optic probe for in vivo fluorescence spectroscopy of breast tissues during percutaneous image-guided breast biopsy. A total of 121 biopsy samples with accompanying histological diagnosis were obtained clinically and investigated in this study. The tissue spectra were analyzed using partial least-squares analysis and represented using a set of principal components (PCs) with dramatically reduced data dimension. For nonmalignant tissue samples, a set of PCs that account for the largest amount of variance in the spectra displayed correlation with the percent tissue composition. For all tissue samples, a set of PCs was identified using a Wilcoxon rank-sum test as showing statistically significant differences between: 1) malignant and fibrous/benign; 2) malignant and adipose; and 3) malignant and nonmalignant breast samples. These PCs were used to distinguish malignant from other nonmalignant tissue types using a binary classification scheme based on both linear and nonlinear support vector machine (SVM) and logistic regression (LR). For the sample set investigated in this study, the SVM classifier provided a cross-validated sensitivity and specificity of up to 81% and 87%, respectively, for discrimination between malignant and fibrous/benign samples, and up to 81% and 81%, respectively, for discriminating between malignant and adipose samples. Classification based on LR was used to generate receiver operator curves with an area under the curve (AUC) of 0.87 for discriminating malignant versus fibrous/benign tissues, and an AUC of 0.84 for discriminating malignant from adipose tissue samples. This study demonstrates the feasibility of performing fluorescence spectroscopy during clinical core needle breast biopsy, and the potential of this technique for identifying breast malignancy in vivo.

Indexed as

Biopsy, NeedleSpectrometry, FluorescenceSurgery, Computer-AssistedAlgorithmsArea Under CurveArtificial IntelligenceBreastBreast NeoplasmsEquipment DesignFemaleFiber Optic TechnologyHumansLeast-Squares AnalysisLogistic ModelsPrincipal Component AnalysisReproducibility of Results

Identifiers

PMID19272976
PMCPMC2791790
OpenAlexW2123478653

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.