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

• Published in: Journal of biomedical optics Vol. 25; no. 2; p. 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: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.02.2020; S P I E - International Society for
• 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; United States > US; kidney cancer; lung cancer; intraoperative imaging; molecular chemical imaging; breast cancer; renal cancer; hyperspectral imaging; surgery
• ISSN: 1083-3668; 1560-2281
• DOI: 10.1117/1.JBO.25.2.026003

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.