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

• Published in: Clinical cancer research Vol. 23; no. 18; pp. 5426 - 5436
• Main Authors: Lu, Guolan, Little, James V., Wang, Xu, Zhang, Hongzheng, Patel, Mihir R., Griffith, Christopher C., El-Deiry, Mark W., Chen, Amy Y., Fei, Baowei
• Format: Journal Article
• Language: English
• Published: United States American Association for Cancer Research Inc 15.09.2017
• Subjects: Adult; Aged; Aged, 80 and over; Algorithms; Cancer; Diagnostic Imaging - methods; Diagnostic Imaging - standards; Diagnostic software; Diagnostic systems; Experimental design; Feasibility studies; Female; Fluorescence; Head & neck cancer; Head and Neck Neoplasms - diagnosis; Head and Neck Neoplasms - surgery; Humans; Hyperspectral imaging; Image detection; Image Processing, Computer-Assisted; Learning algorithms; Male; Medical personnel; Middle Aged; Models, Theoretical; Optical Imaging - methods; Optical Imaging - standards; Patients; Prognosis; Reflectance; Reproducibility of Results; Research Design; ROC Curve; Sensitivity and Specificity; Spectral signatures; Surgery; Surgical instruments; Workflow
• ISSN: 1078-0432; 1557-3265; 1557-3265
• DOI: 10.1158/1078-0432.CCR-17-0906

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.