Optical biopsy of head and neck cancer using hyperspectral imaging and convolutional neural networks

Abstract

For patients undergoing surgical cancer resection of squamous cell carcinoma (SCCa), cancer-free surgical margins are essential for good prognosis. We developed a method to use hyperspectral imaging (HSI), a noncontact optical imaging modality, and convolutional neural networks (CNNs) to perform an optical biopsy of ex-vivo, surgical gross-tissue specimens, collected from 21 patients undergoing surgical cancer resection. Using a cross-validation paradigm with data from different patients, the CNN can distinguish SCCa from normal aerodigestive tract tissues with an area under the receiver operator curve (AUC) of 0.82. Additionally, normal tissue from the upper aerodigestive tract can be subclassified into squamous epithelium, muscle, and gland with an average AUC of 0.94. After separately training on thyroid tissue, the CNN can differentiate between thyroid carcinoma and normal thyroid with an AUC of 0.95, 92% accuracy, 92% sensitivity, and 92% specificity. Moreover, the CNN can discriminate medullary thyroid carcinoma from benign multinodular goiter (MNG) with an AUC of 0.93. Classical-type papillary thyroid carcinoma is differentiated from MNG with an AUC of 0.91. Our preliminary results demonstrate that an HSI-based optical biopsy method using CNNs can provide multicategory diagnostic information for normal and cancerous head-and-neck tissue, and more patient data are needed to fully investigate the potential and reliability of the proposed technique.

Most Relevant Spectral Bands Identification for Brain Cancer Detection Using Hyperspectral Imaging

“…In particular, HSI has started to achieve promising results in the recent years with respect to cancer detection through the utilization of cutting-edge machine-learning algorithms [4,[19][20][21]. Several types of cancer have been investigated using HSI including both in vivo and ex vivo tissue samples, such as gastric and colon cancer [22][23][24][25], breast cancer [26,27], head and neck cancer [28][29][30][31][32][33], and brain cancer [34][35][36], among others.…”

Adaptive deep learning for head and neck cancer detection using hyperspectral imaging

“…7, 8, 9 and Table 1. Under the same conditions, our proposed method performed better than the traditional classification methods [10,31], and the CNN based deep learning method [45,[50][51][52]. However, we obtain unsatisfied performance on some mice.…”

“…4. The optimal dimensions of compressed feature are 55,50,40,45,60,45,55,30,60,55,60, and 60 corresponding to the highest accuracy for the 12 mice, respectively.…”

“…It also can improve the detection and classification performance on HSI [44]. An HSI-based optical biopsy method was proposed using CNN, which could provide multicategory diagnostic information for normal head-andneck tissue, squamous-cell carcinoma, and thyroid carcinomas [45][46][47]. A CNN-based modeling framework was introduced for the analysis of hyperspectral images for the detection of head and neck cancer in an animal model [48].…”

In-Vivo and Ex-Vivo Tissue Analysis through Hyperspectral Imaging Techniques: Revealing the Invisible Features of Cancer

“…Also in this work, normal multi-class sub-classification was performed with an AUC of 0.94 for detection of normal squamous epithelium, mucosal gland, and skeletal muscle. For thyroid cancers, it was determined that benign thyroid hyperplasia can be detected from medullary or papillary thyroid carcinomas separately with above 0.91 AUC [141]. These preliminary results suggest that HSI has potential to be used in optical biopsies for H&N tissues to provide information beyond just binary cancer classification, but the study employed only 21 patient-excised surgical specimens.…”