Literature DB >> 31287388

Optoacoustic Imaging and Gray-Scale US Features of Breast Cancers: Correlation with Molecular Subtypes.

Basak E Dogan1, Gisela L G Menezes1, Reni S Butler1, Erin I Neuschler1, Roger Aitchison1, Philip T Lavin1, F Lee Tucker1, Stephen R Grobmyer1, Pamela M Otto1, A Thomas Stavros1.   

Abstract

Background Optoacoustic imaging can assess tumor hypoxia coregistered with US gray-scale images. The combination of optoacoustic imaging and US may have a role in distinguishing breast cancer molecular subtypes. Purpose To investigate whether optoacoustic US feature scores correlate with breast cancer molecular subtypes. Materials and Methods A total of 1972 women (with a total of 2055 breast masses) underwent prebiopsy optoacoustic US in a prospective multi-institutional study between December 2012 and September 2015. Seven readers blinded to pathologic diagnosis scored gray-scale US and optoacoustic US features of the known cancers. Optoacoustic US features within (internal) and outside of the tumor boundary (external) were scored. Immunohistochemistry findings were obtained from pathology reports. Multinomial logistic regression analysis was used to fit the US scores, adding optoacoustic US features to the model to investigate the incremental benefit of each feature. Kruskal-Wallis tests were used to analyze the relationship between molecular subtypes and feature scores. Results Among 653 invasive cancers identified in 629 women, a total of 532 cancers in 519 women, all of which had molecular markers available, were included in the analysis. Mean age ± standard deviation was 57.9 years ± 12.6. Mean total external optoacoustic US feature scores of luminal (A and B) breast cancers were higher (9.9 vs 8.8; P < .05) and total internal scores were lower (6.8 vs 7.7; P < .001) than those of triple-negative and human epidermal growth factor receptor 2-positive (HER2+) cancers. A multinomial logistic regression model showed that optoacoustic internal vessel (odds ratio [OR], 0.6; 95% confidence interval [CI]: 0.5, 0.8; P = .002), optoacoustic internal blush (OR, 0.7; 95% CI: 0.5, 0.9; P = .02), and optoacoustic internal hemoglobin (OR, 0.6; 95% CI: 0.5, 0.8; P = .001) were associated with classification of luminal versus triple-negative and HER2+ cancer subtypes. Conclusion Combined optoacoustic US imaging and gray-scale US features may help distinguish luminal breast cancers from triple-negative and human epidermal growth factor receptor 2-positive cancers. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Mann in this issue.

Entities:  

Year:  2019        PMID: 31287388     DOI: 10.1148/radiol.2019182071

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  11 in total

Review 1.  Photoacoustic imaging as a highly efficient and precise imaging strategy for the evaluation of brain diseases.

Authors:  Ting Qiu; Yintao Lan; Weijian Gao; Mengyu Zhou; Shiqi Liu; Wenyan Huang; Sujuan Zeng; Janak L Pathak; Bin Yang; Jian Zhang
Journal:  Quant Imaging Med Surg       Date:  2021-05

Review 2.  Development of Multispectral Optoacoustic Tomography as a Clinically Translatable Modality for Cancer Imaging.

Authors:  William M MacCuaig; Meredith A Jones; Oshaani Abeyakoon; Lacey R McNally
Journal:  Radiol Imaging Cancer       Date:  2020-11-20

3.  Impact of skin tone on photoacoustic oximetry and tools to minimize bias.

Authors:  Yash Mantri; Jesse V Jokerst
Journal:  Biomed Opt Express       Date:  2022-01-20       Impact factor: 3.732

4.  Integration of Multitargeted Polymer-Based Contrast Agents with Photoacoustic Computed Tomography: An Imaging Technique to Visualize Breast Cancer Intratumor Heterogeneity.

Authors:  Lei Li; Deepanjali Patil; Greg Petruncio; Kathleen K Harnden; Jisha V Somasekharan; Mikell Paige; Lihong V Wang; Carolina Salvador-Morales
Journal:  ACS Nano       Date:  2021-01-19       Impact factor: 15.881

Review 5.  The emerging role of photoacoustic imaging in clinical oncology.

Authors:  Li Lin; Lihong V Wang
Journal:  Nat Rev Clin Oncol       Date:  2022-03-23       Impact factor: 66.675

6.  A scalable open-source MATLAB toolbox for reconstruction and analysis of multispectral optoacoustic tomography data.

Authors:  Devin O'Kelly; James Campbell; Jeni L Gerberich; Paniz Karbasi; Venkat Malladi; Andrew Jamieson; Liqiang Wang; Ralph P Mason
Journal:  Sci Rep       Date:  2021-10-06       Impact factor: 4.379

7.  Image processing improvements afford second-generation handheld optoacoustic imaging of breast cancer patients.

Authors:  Jan Kukačka; Stephan Metz; Christoph Dehner; Alexander Muckenhuber; Korbinian Paul-Yuan; Angelos Karlas; Eva Maria Fallenberg; Ernst Rummeny; Dominik Jüstel; Vasilis Ntziachristos
Journal:  Photoacoustics       Date:  2022-03-02

8.  The Potential of Photoacoustic Imaging in Radiation Oncology.

Authors:  Thierry L Lefebvre; Emma Brown; Lina Hacker; Thomas Else; Mariam-Eleni Oraiopoulou; Michal R Tomaszewski; Rajesh Jena; Sarah E Bohndiek
Journal:  Front Oncol       Date:  2022-03-03       Impact factor: 5.738

9.  Photoacoustic imaging phantoms for assessment of object detectability and boundary buildup artifacts.

Authors:  Jorge Palma-Chavez; Keith A Wear; Yash Mantri; Jesse V Jokerst; William C Vogt
Journal:  Photoacoustics       Date:  2022-03-21

Review 10.  Photoacoustic imaging of breast cancer: a mini review of system design and image features.

Authors:  Nikhila Nyayapathi; Jun Xia
Journal:  J Biomed Opt       Date:  2019-11       Impact factor: 3.170

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