Histopathology-Based Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning

Abstract

Oral squamous cell carcinoma (OSCC) is prevalent around the world and is associated with poor prognosis. OSCC is typically diagnosed from tissue biopsy sections by pathologists who rely on their empirical experience. Deep learning models may improve the accuracy and speed of image classification, thus reducing human error and workload. Here we developed a custom-made deep learning model to assist pathologists in detecting OSCC from histopathology images. We collected and analyzed a total of 2,025 images, among which 1,925 images were included in the training set and 100 images were included in the testing set. Our model was able to automatically evaluate these images and arrive at a diagnosis with a sensitivity of 0.98, specificity of 0.92, positive predictive value of 0.924, negative predictive value of 0.978, and F1 score of 0.951. Using a subset of 100 images, we examined whether our model could improve the diagnostic performance of junior and senior pathologists. We found that junior pathologists were able to delineate OSCC in these images 6.26 min faster when assisted by the model than when working alone. When the clinicians were assisted by the model, their average F1 score improved from 0.9221 to 0.9566 in the case of junior pathologists and from 0.9361 to 0.9463 in the case of senior pathologists. Our findings indicate that deep learning can improve the accuracy and speed of OSCC diagnosis from histopathology images.

Artificial Intelligence-Based Diagnosis of Oral Lichen Planus Using Deep Convolutional Neural Networks

“…24 AI techniques using CNN for disease diagnosis (classification) have been studied in many fields using radiography, clinical examination, or histopathology. [7][8][9][10][11][12][13][14][15][16][17][18] The application of CNN in diagnosing skin lesions based on the clinical appearance and color has been studied previously. 25,26 Skin and oral mucosal lesions share similar diagnostic principles.…”

“…12 Furthermore, histopathological diagnosis of oral squamous cell carcinoma using CNN has a sensitivity of 98% and specificity of 92%. 13 The application of CNN using panoramic radiographs has also shown significant results, including diagnosis of radiolucent lesions, 14 mesiodens, 15 taurodontism, 16 cystic lesions, 17 or even fractures. 18…”

F. nucleatum facilitates oral squamous cell carcinoma progression via GLUT1-driven lactate production

“…Oral squamous cell carcinoma (OSCC) is a prevalent subtype of head and neck squamous cell carcinoma (HNSCC). 1 , 2 , 3 Despite progress in the diagnosis and treatment of OSCC, the 5-year survival rate remains low due to metastasis or recurrence. 4 , 5 Cohort studies have suggested that, in addition to the genetic, epigenetic, and stromal microenvironment elements, the features of the microbiota within tissues are associated with the hallmarks of cancer, including risks, pathological types, and prognosis.…”

Deep learning and clustering approaches for dental implant size classification based of periapical radiographs

“…The application of artificial intelligence (AI) in medical imaging analysis has expanded rapidly [9][10][11][12][13][14] . Also, in dentistry, AI models were used for the diagnosis of dental diseases such as dental caries or periodontal disease [15][16][17] .…”