Label-free reflectance hyperspectral imaging for tumor margin assessment: a pilot study on surgical specimens of cancer patients

Baowei Fei, Guolan Lu, Xu Wang, Hongzheng Zhang, James V Little, Mihir R Patel, Christopher C Griffith, Mark W El-Diery, Amy Y Chen, Baowei Fei, Guolan Lu, Xu Wang, Hongzheng Zhang, James V Little, Mihir R Patel, Christopher C Griffith, Mark W El-Diery, Amy Y Chen

Abstract

A label-free, hyperspectral imaging (HSI) approach has been proposed for tumor margin assessment. HSI data, i.e., hypercube (x,y,λ), consist of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on an HSI image has an optical spectrum. In this pilot clinical study, a pipeline of a machine-learning-based quantification method for HSI data was implemented and evaluated in patient specimens. Spectral features from HSI data were used for the classification of cancer and normal tissue. Surgical tissue specimens were collected from 16 human patients who underwent head and neck (H&N) cancer surgery. HSI, autofluorescence images, and fluorescence images with 2-deoxy-2-[(7-nitro-2,1,3-benzoxadiazol-4-yl)amino]-D-glucose (2-NBDG) and proflavine were acquired from each specimen. Digitized histologic slides were examined by an H&N pathologist. The HSI and classification method were able to distinguish between cancer and normal tissue from the oral cavity with an average accuracy of 90%±8%, sensitivity of 89%±9%, and specificity of 91%±6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94%±6%, sensitivity of 94%±6%, and specificity of 95%±6%. HSI outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study demonstrated the feasibility of label-free, HSI for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the HSI technology is warranted for its application in image-guided surgery.

Keywords: cancer detection; head and neck cancer; hyperspectral imaging; image classification; image quantification; image-guided surgery; label-free; tumor margin assessment.

(2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Figures

Fig. 1
Fig. 1
Study design for the HSI of surgical specimens of H&N cancer patients.
Fig. 2
Fig. 2
Flowchart of the machine-learning-based quantification pipeline for hyperspectral images.
Fig. 3
Fig. 3
Surgical specimens of tumor, normal tissue, and tumor with adjacent normal tissue from a tongue cancer patient. Left: tissue and corresponding histological slides. Right: 2-NBDG and proflavine fluorescence images for each tissue.
Fig. 4
Fig. 4
Tumor margin detection of surgical specimens from an H&N cancer patient. After hyperspectral image acquisitions, the tissue was processed histologically, and tumor margins were outlined on the pathology image (bottom right) by a pathologist (J.V.L.), which was used to validate the results of the classification (top-right). The average spectral curves are shown at the bottom left for each type of tissue, i.e., tumor, normal, and tumor with adjacent normal tissue.

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