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. 2017 Sep 15;23(18):5426-5436.
doi: 10.1158/1078-0432.CCR-17-0906. Epub 2017 Jun 13.

Detection of Head and Neck Cancer in Surgical Specimens Using Quantitative Hyperspectral Imaging

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Free PMC article

Detection of Head and Neck Cancer in Surgical Specimens Using Quantitative Hyperspectral Imaging

Guolan Lu et al. Clin Cancer Res. .
Free PMC article

Abstract

Purpose: This study intends to investigate the feasibility of using hyperspectral imaging (HSI) to detect and delineate cancers in fresh, surgical specimens of patients with head and neck cancers.Experimental Design: A clinical study was conducted in order to collect and image fresh, surgical specimens from patients (N = 36) with head and neck cancers undergoing surgical resection. A set of machine-learning tools were developed to quantify hyperspectral images of the resected tissue in order to detect and delineate cancerous regions which were validated by histopathologic diagnosis. More than two million reflectance spectral signatures were obtained by HSI and analyzed using machine-learning methods. The detection results of HSI were compared with autofluorescence imaging and fluorescence imaging of two vital-dyes of the same specimens.Results: Quantitative HSI differentiated cancerous tissue from normal tissue in ex vivo surgical specimens with a sensitivity and specificity of 91% and 91%, respectively, and which was more accurate than autofluorescence imaging (P < 0.05) or fluorescence imaging of 2-NBDG (P < 0.05) and proflavine (P < 0.05). The proposed quantification tools also generated cancer probability maps with the tumor border demarcated and which could provide real-time guidance for surgeons regarding optimal tumor resection.Conclusions: This study highlights the feasibility of using quantitative HSI as a diagnostic tool to delineate the cancer boundaries in surgical specimens, and which could be translated into the clinic application with the hope of improving clinical outcomes in the future. Clin Cancer Res; 23(18); 5426-36. ©2017 AACR.

Conflict of interest statement

Disclosure of Potential Conflicts of Interest

M.R. Patel is a consultant/advisory board member for AstraZeneca and Intuitive Surgical. No potential conflicts of interest were disclosed by the other authors.

Figures

Figure 1
Figure 1
Overview of the clinical study design for fresh surgical specimen imaging.
Figure 2
Figure 2
Average spectral curve of tumor and normal tissue samples from various head and neck cancer sites, including the oral cavity, thyroid, larynx, pharynx, parotid, paranasal sinus, and nasal cavity of human patients. The solid line and dash line represent the mean spectra of cancer and normal tissue, and the shaded area centered on the two lines represents the standard deviation.
Figure 3
Figure 3
Diagnostic performance of HSI with the intrapatient classification method. A, Different block sizes and classifiers for the distinction of tumor from normal tissue in multiple, anatomic sites in head and neck cancer patients. C–F, Shows an example of a thyroid cancer detection result. C, Training hypercube with a tumor specimen and a normal specimen. D, Testing a hypercube with tumor and normal interface tissue. E, Cancer probability map generated by the ensemble LDA classifier. The green line is the tumor border generated by thresholding on the probability map. The color bar shows the likelihood of it being cancerous tissue. F, Pathology gold standard with the tumor region outlined within the green region by a clinically experienced pathologist.
Figure 4
Figure 4
Tongue cancer detection using the intrapatient classification method. A, D, G, and J are the RGB composite image from hypercube, autofluorecence imaging, 2-NBDG, and proflavine fluorescence imaging, with the green and yellow solid lines outlining the cancer and normal tissue regions for training predictive models. B, E, H, and K are the corresponding RGB composite image of cancer–normal interface tissue for testing model performance, with the green and yellow dashed line outlining the region that we are certain to be tumor and normal for quantitative evaluation. C, F, I, and L are the predicted cancer map for HSI, autofluorescence, 2-NBDG, and proflavine imaging, with magenta and blue color denoting predicted malignant and normal tissue. Only regions within the green and yellow curve are used for quantitative evaluation. Glare pixels identified from hyperspectral images were excluded from classification, and, therefore, not labeled in the prediction map. M is the registered histology gold standard image, with the cancerous region outlined inside the green line by an clinically experienced pathologist. N and O are the enlarged cancer and normal histology image from the selected region of the image in M.
Figure 5
Figure 5
ROC curves of intrapatient classification (A) and interpatient classification (B) with HSI for individual patients.

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