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

Abstract

Successful outcomes of surgical cancer resection necessitate negative, cancer-free surgical margins. Currently, tissue samples are sent to pathology for diagnostic confirmation. Hyperspectral imaging (HSI) is an emerging, non-contact optical imaging technique. A reliable optical method could serve to diagnose and biopsy specimens in real-time. Using convolutional neural networks (CNNs) as a tissue classifier, we developed a method to use HSI to perform an optical biopsy of ex-vivo surgical specimens, collected from 21 patients undergoing surgical cancer resection. Training and testing on samples from different patients, the CNN can distinguish squamous cell carcinoma (SCCa) from normal aerodigestive tract tissues with an area under the curve (AUC) of 0.82, 81% accuracy, 81% sensitivity, and 80% specificity. Additionally, normal oral tissues can be sub-classified into epithelium, muscle, and glandular mucosa using a decision tree method, with an average AUC of 0.94, 90% accuracy, 93% sensitivity, and 89% specificity. After separately training on thyroid tissue, the CNN differentiates 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 multi-nodular goiter (MNG) with an AUC of 0.93, 87% accuracy, 88% sensitivity, and 85% specificity. Classical-type papillary thyroid carcinoma is differentiated from benign MNG with an AUC of 0.91, 86% accuracy, 86% sensitivity, and 86% specificity. Our preliminary results demonstrate that an HSI-based optical biopsy method using CNNs can provide multi-category diagnostic information for normal head-and-neck tissue, SCCa, and thyroid carcinomas. More patient data are needed in order to fully investigate the proposed technique to establish reliability and generalizability of the work.

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

“…This work was initially presented as a conference proceedings and oral presentation. 25 First, a simple binary classification is performed, i.e., cancer versus normal, and second, multiclass subclassification of normal upper aerodigestive tract samples is investigated. If proven to be reliable and generalizable, this method could help provide intraoperative diagnostic information beyond palpation and visual inspection to the surgeon's resources, perhaps enabling surgeons to achieve more accurate cuts and biopsies, or as a computer-aided diagnostic tool for physicians diagnosing and treating these types of cancer.…”

Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery

“…However, AI applications have expanded greatly in recent years. In terms of image-based analysis, images yielded by rigid endoscopes, laryngoscopes, stroboscopes, computed tomography, magnetic resonance imaging, and multispectral narrow-band imaging [38], as well as hyperspectral imaging [45][46][47][48][49][50][51][52]54], are now interpreted by AI. In voice-based analysis, AI is used to evaluate pathological voice conditions associated with vocal fold disorders, to analyze and decode phonation itself [67], to improve speech perception in noisy conditions, and to improve the hearing of pa-tients with CIs.…”

Information Extraction Techniques in Hyperspectral Imaging Biomedical Applications

“…Also, Wilson et al demonstrated the ability of HSI for melanin detection in histological unstained specimens of melanocytic lesions in the skin and the eye using PCA and false-color representations of data [53]. PCA has been used for extracting the most important features of HS data prior to classification in different applications, such as the detection of in-vivo oral cancer [54], prostate cancer in histological slides [55], the identification of white blood cells in blood smear slides [56] or the intraoperative delineation of brain tumors [57]. Another example of the utility of feature extraction methods was demonstrated by Hadoux et al, where relevant differences between the retinal spectral data from patients with Alzheimer and healthy patients were found after applying an orthogonal projection of data [58].…”

“…Pixel-wise classification SVM Intestinal ischemia identification [36] Gastric cancer detection [37] Prostate cancer [38] Tongue cancer [39] Skin cancer [40] RF In-vivo oral cancer [41] MLR Ulcerative colitis in histological slides [42] SVM, RF Brain cancer in histological slides [43] SVM, RF, LDA Head and neck tumor [44] Feature extraction and feature selection PCA Biliary trees visualization enhancement [52] PCA and false color Melanocytic lesions visualization [53] PCA and supervised classification Detection of in-vivo oral cancer [54] Prostate cancer in histological slides [55] The identification of white blood cells in blood smear slides [56] Intraoperative brain tumor delineation [57] Orthogonal projections Retina analysis for Alzheimer's detection [58] t-SNE and supervised classification…”