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

Overview of attention for article published in Breast Cancer Research, October 2014
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Title
Pathologic response prediction to neoadjuvant chemotherapy utilizing pretreatment near-infrared imaging parameters and tumor pathologic criteria
Published in
Breast Cancer Research, October 2014
DOI 10.1186/s13058-014-0456-0
Pubmed ID
Authors

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

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 and deoxygenated hemoglobin concentrations (oxyHb and deoxyHb) 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, an average sensitivity of 56.8%, specificity of 88.9%, PPV and NPV of 84.8% and 70.9%, and AUC of 84.0% were obtained from the testing data. Among the hemoglobin predictors with and without tumor pathological variables, the best predictor is tHb combined with tumor pathological variables followed by oxyHb with pathological variables. When tHb was included as an additional predictor to tumor pathological variables, the corresponding measures improved to 79%, 94%, 90%, 86%, and AUC 92.4%, respectively. When oxyHb was included as an additional predictor to tumor variables, these measures improved to 77%, 85%, 83%, 83%, and AUC 90.6%, respectively. The addition of tHb or oxyHb significantly improves the prediction sensitivity, NPV and AUC as 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 initiation of the treatment.Trial registrationClincalTrials.gov, NCT00908609, registered 22 May 2009.

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The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 1 2%
Brazil 1 2%
Unknown 52 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 14 26%
Researcher 8 15%
Student > Master 4 7%
Lecturer 3 6%
Student > Doctoral Student 3 6%
Other 12 22%
Unknown 10 19%
Readers by discipline Count As %
Medicine and Dentistry 14 26%
Engineering 12 22%
Biochemistry, Genetics and Molecular Biology 5 9%
Physics and Astronomy 5 9%
Agricultural and Biological Sciences 4 7%
Other 3 6%
Unknown 11 20%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 July 2015.
All research outputs
#18,381,794
of 22,768,097 outputs
Outputs from Breast Cancer Research
#1,592
of 1,894 outputs
Outputs of similar age
#186,280
of 260,390 outputs
Outputs of similar age from Breast Cancer Research
#40
of 53 outputs
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