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

Abstract

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.

Artificial intelligence in head and neck cancer diagnosis

“…The algorithm used was Inception V3 and AUC was 0.994 for the validation dataset and 0.981 for the testing dataset. 23 …”

Detection of laryngeal carcinoma during endoscopy using artificial intelligence

“…By extracting several images per patient, we increased our dataset to a total of 1448 images. These numbers are in vast contrast with some exceptional databases from recent studies 25,26,28–30 . We, therefore, used the dataset collected by Yin et al to increase the number of data 29 .…”

“…Only in recent years has there been a constant rise in publications of articles investigating the application of AI in laryngoscopic images. Although we did not performed a systematic review of literature, a thorough literature search revealed over 15 publications in the last 6 years 16–32 . A rough summary of these articles showed that all but one research group investigated still images (i.e., photos) of laryngeal lesions in a postprocessing method.…”

Artificial intelligence in clinical endoscopy: Insights in the field of videomics

“…In this field, He et al ( 9 ) applied CNN to interpret images of laryngeal squamous cell carcinoma using static NBI frames to determine whether a lesion was benign or malignant. The model reached an accuracy of 90.6%, a sensitivity of 88.8%, and a specificity of 92.2%.…”