Visible near infrared reflectance molecular chemical imaging of human ex vivo carcinomas and murine in vivo carcinomas

Significance: A key risk faced by oncological surgeons continues to be complete removal of tumor. Currently, there is no intraoperative imaging device to detect kidney tumors during excision. Aim: We are evaluating molecular chemical imaging (MCI) as a technology for real-time tumor detection and ma...

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Bibliographic Details
Published in:Journal of biomedical optics 2020-02, Vol.25 (2), p.026003-026003
Main Authors: Stewart, Shona, Darr, Marlena, Gomer, Heather, Smith, Aaron, Samiei, Arash, Post, James Christopher, Miller, Ralph J, Lyne, John, Cohen, Jeffrey, Treado, Patrick J
Format: Article
Language:English
Subjects:
Algorithms
Augmented reality
Biomarkers
Breast
Breast cancer
Cancer
Carcinoma
Computer vision
Contrast agents
Diffuse reflectance spectroscopy
Discriminant analysis
Feasibility studies
Hyperspectral imaging
Image processing
Imaging
Infrared reflection
Kidney cancer
Kidneys
Localization
Lungs
Medical imaging
Near infrared radiation
Reflectance
Spectrum analysis
Surgeons
Surgery
Technology
Tissues
Tumors
Ultrasonic imaging
Xenografts
Xenotransplantation
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Summary:Significance: A key risk faced by oncological surgeons continues to be complete removal of tumor. Currently, there is no intraoperative imaging device to detect kidney tumors during excision. Aim: We are evaluating molecular chemical imaging (MCI) as a technology for real-time tumor detection and margin assessment during tumor removal surgeries. Approach: In exploratory studies, we evaluate visible near infrared (Vis-NIR) MCI for differentiating tumor from adjacent tissue in ex vivo human kidney specimens, and in anaesthetized mice with breast or lung tumor xenografts. Differentiation of tumor from nontumor tissues is made possible with diffuse reflectance spectroscopic signatures and hyperspectral imaging technology. Tumor detection is achieved by score image generation to localize the tumor, followed by application of computer vision algorithms to define tumor border. Results: Performance of a partial least squares discriminant analysis (PLS-DA) model for kidney tumor in a 22-patient study is 0.96 for area under the receiver operating characteristic curve. A PLS-DA model for in vivo breast and lung tumor xenografts performs with 100% sensitivity, 83% specificity, and 89% accuracy. Conclusion: Detection of cancer in surgically resected human kidney tissues is demonstrated ex vivo with Vis-NIR MCI, and in vivo on mice with breast or lung xenografts.
ISSN:1083-3668
1560-2281
DOI:10.1117/1.JBO.25.2.026003