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

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

doi:10.1117/1.jbo.22.8.086009

Amy Y. Chen, Baowei Fei + Show 7 more

Open Access

https://doi.org/10.1117/1.jbo.22.8.086009

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Abstract

.A label-free, hyperspectral imaging (HSI) approach has been proposed for tumor margin assessment. HSI data, i.e., hypercube , 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 , sensitivity of , and specificity of . For tissue specimens from the thyroid, the method achieved an average accuracy of , sensitivity of , and specificity of . 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.

Highlights

There are ∼15.2 million new cases of cancer each year, and more than 80% of these patients will require surgery, some several times.[1]
We evaluated the performance of the hyperspectral image classification using the areas under the curve (AUC), accuracy, sensitivity, and specificity, as defined in the following equations [true negative (TN), true positive (TP), false positive (FP), and false negative (FN)]: EQ-TARGET;temp:intralink-;sec2.9;326;675
Fresh surgical tissue specimens were collected from 16 head and neck (H&N) cancer patients

Summary

Introduction

There are ∼15.2 million new cases of cancer each year, and more than 80% of these patients will require surgery, some several times.[1]. Head and neck (H&N), lung, colon, and pancreatic cancers, complete resection is associated with a threefold to fivefold improvement in the patient survival compared to a partial or incomplete resection. 7–10

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