Evidence map›Paper›PMID 25349073›Full record

ArticleBreast cancer research : BCR2014

Pathologic response prediction to neoadjuvant chemotherapy utilizing pretreatment near-infrared imaging parameters and tumor pathologic criteria.

Quing Zhu, Liqun Wang, Susan Tannenbaum, Andrew Ricci, Patricia DeFusco, Poornima Hegde

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in Breast cancer research : BCR, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00908609 (3-D Simultaneous Ultrasound and NIR Imaging for Breast Cancer Detection), which is not on this map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
5.2field-weighted citation impact, top 5% 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.

NCT00908609 completednot on this map

3-D Simultaneous Ultrasound and NIR Imaging for Breast Cancer Detection

TypeobservationalSponsorUConn HealthRan2007 to 2012Enrolled340ConditionsBreast Cancer
3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it, 45 citations in OpenAlex.

  1. Pooled it
  2. Role of vascular density and normalization in response to neoadjuvant bevacizumab and chemotherapy in breast cancer patients.Proceedings of the National Academy of Sciences of the United States of America · 2015
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  11. Enhanced diffuse optical tomographic reconstruction using concurrent ultrasound information.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021
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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

6 authors at 5 institutions in 2 countries.

Quing Zhu
Liqun Wang
Susan Tannenbaum
Andrew Ricci
Patricia DeFusco
Poornima Hegde
UConn Health · USHartford Financial Services (United States)Hartford Hospital · USUniversity of Connecticut · USUniversity of Manitoba · CA

Funding

Near Infrared Diffused Light Imaging with Ultrasound Guidance: Predicting Neoadjuvant Chemotherapy ResponseR01EB002136 · NIBIB · WASHINGTON UNIVERSITY · PI ZHU, QUING · 2003 to 2016
$3.4M
NIBIB NIH HHS R01 EB002136
6 · The paper itself

Abstract

introductionThe purpose of this study is to develop a prediction model utilizing tumor hemoglobin parameters measured by ultrasound-guided near-infrared optical tomography (US-NIR) in conjunction with standard pathologic tumor characteristics to predict pathologic response before neoadjuvant chemotherapy (NAC) is given.

methodsThirty-four patients' data were retrospectively analyzed using a multiple logistic regression model to predict response. These patients were split into 30 groups of training (24 tumors) and testing (12 tumors) for cross validation. Tumor vascularity was assessed using US-NIR measurements of total hemoglobin (tHb), oxygenated (oxyHb) and deoxygenated hemoglobin (deoxyHb) concentrations acquired before treatment. Tumor pathologic variables of tumor type, Nottingham score, mitotic index, the estrogen and progesterone receptors and human epidermal growth factor receptor 2 acquired before NAC in biopsy specimens were also used in the prediction model. The patients' pathologic response was graded based on the Miller-Payne system. The overall performance of the prediction models was evaluated using receiver operating characteristic (ROC) curves. The quantitative measures were sensitivity, specificity, positive and negative predictive values (PPV and NPV) and the area under the ROC curve (AUC).

resultsUtilizing tumor pathologic variables alone, average sensitivity of 56.8%, average specificity of 88.9%, average PPV of 84.8%, average NPV of 70.9% and average AUC of 84.0% were obtained from the testing data. Among the hemoglobin predictors with and without tumor pathological variables, the best predictor was tHb combined with tumor pathological variables, followed by oxyHb with pathological variables. When tHb was included with tumor pathological variables as an additional predictor, the corresponding measures improved to 79%, 94%, 90%, 86% and 92.4%, respectively. When oxyHb was included with tumor variables as an additional predictor, these measures improved to 77%, 85%, 83%, 83% and 90.6%, respectively. The addition of tHb or oxyHb significantly improved the prediction sensitivity, NPV and AUC compared with using tumor pathological variables alone.

conclusionsThese initial findings indicate that combining widely used tumor pathologic variables with hemoglobin parameters determined by US-NIR may provide a powerful tool for predicting patient pathologic response to NAC before the start of treatment.

trial registrationClincalTrials.gov ID: NCT00908609 (registered 22 May 2009).

Indexed as

AdultAgedAged, 80 and overBreast NeoplasmsCarcinoma, Ductal, BreastChemotherapy, AdjuvantDiagnostic ImagingFemaleHemoglobinsHumansMiddle AgedNeoadjuvant TherapyRetrospective StudiesROC CurveSpectroscopy, Near-InfraredTreatment OutcomedeoxyhemoglobinHemoglobins

Identifiers

PMID25349073
PMCPMC4303135
OpenAlexW2154528682

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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.