A deep convolutional neural network-based method for laryngeal squamous cell carcinoma diagnosis

Background: Laryngeal squamous cell carcinoma (LSCC) is one of the most common tumors of the respiratory tract. Currently, the diagnosis of LSCC is mainly based on a laryngoscopy analysis and pathological findings. Deep-learning algorithms have been shown to provide accurate clinical diagnoses. Methods: We developed a deep convolutional neural network (CNN) model, and evaluated its application to narrow-band imaging (NBI) endoscopy and pathological diagnoses of LSCC at several hospitals. A total of 4,591 patients' laryngeal NBI scans (1,927 benign and 2,664 LSCC) were used to test and validate the model. Additionally, 3,458 pathological images (752 benign and 2,706 LSCC) of 1,228 patients' hematoxylin and eosin staining slides (318 benign and 910 LSCC) were used for the pathological diagnosis training and validation. The images were randomly divided into training, validation and testing images at the ratio of 70:15:15. An independent test cohort of LSCC NBI scans and pathological images from other institutions were also used. Results: In the NBI group, the areas under the curve of the validation, test, and independent test data sets were 0.966, 0.964, and 0.873, respectively, and those of the pathology group were 0.994, 0.981, and 0.982, respectively. Our method was highly accurate at diagnosing LSCC. Conclusions: In this study, the CNN model performed well in the NBI and pathological diagnosis of LSCC. More accurate and faster diagnoses could be achieved with the assistance of this algorithm.