Detecting oropharyngeal carcinoma using multispectral, narrow‐band imaging and machine learning

Objective To determine if multispectral narrow‐band imaging (mNBI) can be used for automated, quantitative detection of oropharyngeal carcinoma (OPC). Study Design Prospective cohort study. Methods Multispectral narrow‐band imaging and white light endoscopy (WLE) were used to examine the lymphoepithelial tissues of the oropharynx in a preliminary cohort of 30 patients (20 with biopsy‐proven OPC, 10 healthy). Low‐level image features from five patients were then extracted to train naïve Bayesian classifiers for healthy and malignant tissue. Results Tumors were classified by color features with 65.9% accuracy, 66.8% sensitivity, and 64.9% specificity under mNBI. In contrast, tumors were classified with 52.3% accuracy ( P  = 0.0108), 44.8% sensitivity ( P  = 0.0793), and 59.9% specificity ( P  = 0.312) under WLE. Receiver operating characteristic analysis yielded areas under the curve (AUC) of 72.3% and 54.6% for classification under mNBI and WLE, respectively ( P  = 0.00168). For classification by both color and texture features, AUC under mNBI increased (80.1%, P  = 0.00230) but did not improve under WLE (below 55% for both models, P  = 0.180). Cross‐validation with five folds yielded an AUC above 80% for both mNBI models and below 55% for both WLE models ( P  = 0.0000410 and 0.000116). Conclusion Compared to WLE, mNBI significantly enhanced the performance of a naïve Bayesian classifier trained on low‐level image features of oropharyngeal mucosa. These findings suggest that automated clinical detection of OPC might be used to enhance surgical vision, improve early diagnosis, and allow for high‐throughput screening. Level of Evidence NA. Laryngoscope , 2514–2520, 2018