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Breast cancer differential diagnosis using diffuse optical spectroscopic imaging and regression with z-score normalized data
Significance: Current imaging paradigms for differential diagnosis of suspicious breast lesions suffer from high false positive rates that force patients to undergo unnecessary biopsies. Diffuse optical spectroscopic imaging (DOSI) noninvasively probes functional hemodynamic and compositional parame...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Society of Photo-Optical Instrumentation Engineers
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901858/ https://www.ncbi.nlm.nih.gov/pubmed/33624457 http://dx.doi.org/10.1117/1.JBO.26.2.026004 |
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author | Cochran, Jeffrey M. Leproux, Anais Busch, David R. O’Sullivan, Thomas D. Yang, Wei Mehta, Rita S. Police, Alice M. Tromberg, Bruce J. Yodh, Arjun G. |
author_facet | Cochran, Jeffrey M. Leproux, Anais Busch, David R. O’Sullivan, Thomas D. Yang, Wei Mehta, Rita S. Police, Alice M. Tromberg, Bruce J. Yodh, Arjun G. |
author_sort | Cochran, Jeffrey M. |
collection | PubMed |
description | Significance: Current imaging paradigms for differential diagnosis of suspicious breast lesions suffer from high false positive rates that force patients to undergo unnecessary biopsies. Diffuse optical spectroscopic imaging (DOSI) noninvasively probes functional hemodynamic and compositional parameters in deep tissue and has been shown to be sensitive to contrast between normal and malignant tissues. Aim: DOSI methods are under investigation as an adjunct to mammography and ultrasound that could reduce false positive rates and unnecessary biopsies, particularly in radiographically dense breasts. Methods: We performed a retrospective analysis of 212 subjects with suspicious breast lesions who underwent DOSI imaging. Physiological tissue parameters were [Formula: see text]-score normalized to the patient’s contralateral breast tissue and input to univariate logistic regression models to discriminate between malignant tumors and the surrounding normal tissue. The models were then used to differentiate malignant lesions from benign lesions. Results: Models incorporating several individual hemodynamic parameters were able to accurately distinguish malignant tumors from both the surrounding background tissue and benign lesions with area under the curve (AUC) [Formula: see text]. [Formula: see text]-score normalization improved the discriminatory ability and calibration of these predictive models relative to unnormalized or ratio-normalized data. Conclusions: Findings from a large subject population study show how DOSI data normalization that accounts for normal tissue heterogeneity and quantitative statistical regression approaches can be combined to improve the ability of DOSI to diagnose malignant lesions. This improved diagnostic accuracy, combined with the modality’s inherent logistical advantages of portability, low cost, and nonionizing radiation, could position DOSI as an effective adjunct modality that could be used to reduce the number of unnecessary invasive biopsies. |
format | Online Article Text |
id | pubmed-7901858 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Society of Photo-Optical Instrumentation Engineers |
record_format | MEDLINE/PubMed |
spelling | pubmed-79018582021-02-24 Breast cancer differential diagnosis using diffuse optical spectroscopic imaging and regression with z-score normalized data Cochran, Jeffrey M. Leproux, Anais Busch, David R. O’Sullivan, Thomas D. Yang, Wei Mehta, Rita S. Police, Alice M. Tromberg, Bruce J. Yodh, Arjun G. J Biomed Opt Imaging Significance: Current imaging paradigms for differential diagnosis of suspicious breast lesions suffer from high false positive rates that force patients to undergo unnecessary biopsies. Diffuse optical spectroscopic imaging (DOSI) noninvasively probes functional hemodynamic and compositional parameters in deep tissue and has been shown to be sensitive to contrast between normal and malignant tissues. Aim: DOSI methods are under investigation as an adjunct to mammography and ultrasound that could reduce false positive rates and unnecessary biopsies, particularly in radiographically dense breasts. Methods: We performed a retrospective analysis of 212 subjects with suspicious breast lesions who underwent DOSI imaging. Physiological tissue parameters were [Formula: see text]-score normalized to the patient’s contralateral breast tissue and input to univariate logistic regression models to discriminate between malignant tumors and the surrounding normal tissue. The models were then used to differentiate malignant lesions from benign lesions. Results: Models incorporating several individual hemodynamic parameters were able to accurately distinguish malignant tumors from both the surrounding background tissue and benign lesions with area under the curve (AUC) [Formula: see text]. [Formula: see text]-score normalization improved the discriminatory ability and calibration of these predictive models relative to unnormalized or ratio-normalized data. Conclusions: Findings from a large subject population study show how DOSI data normalization that accounts for normal tissue heterogeneity and quantitative statistical regression approaches can be combined to improve the ability of DOSI to diagnose malignant lesions. This improved diagnostic accuracy, combined with the modality’s inherent logistical advantages of portability, low cost, and nonionizing radiation, could position DOSI as an effective adjunct modality that could be used to reduce the number of unnecessary invasive biopsies. Society of Photo-Optical Instrumentation Engineers 2021-02-23 2021-02 /pmc/articles/PMC7901858/ /pubmed/33624457 http://dx.doi.org/10.1117/1.JBO.26.2.026004 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/ Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. |
spellingShingle | Imaging Cochran, Jeffrey M. Leproux, Anais Busch, David R. O’Sullivan, Thomas D. Yang, Wei Mehta, Rita S. Police, Alice M. Tromberg, Bruce J. Yodh, Arjun G. Breast cancer differential diagnosis using diffuse optical spectroscopic imaging and regression with z-score normalized data |
title | Breast cancer differential diagnosis using diffuse optical spectroscopic imaging and regression with z-score normalized data |
title_full | Breast cancer differential diagnosis using diffuse optical spectroscopic imaging and regression with z-score normalized data |
title_fullStr | Breast cancer differential diagnosis using diffuse optical spectroscopic imaging and regression with z-score normalized data |
title_full_unstemmed | Breast cancer differential diagnosis using diffuse optical spectroscopic imaging and regression with z-score normalized data |
title_short | Breast cancer differential diagnosis using diffuse optical spectroscopic imaging and regression with z-score normalized data |
title_sort | breast cancer differential diagnosis using diffuse optical spectroscopic imaging and regression with z-score normalized data |
topic | Imaging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7901858/ https://www.ncbi.nlm.nih.gov/pubmed/33624457 http://dx.doi.org/10.1117/1.JBO.26.2.026004 |
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