Importance of complementary data to histopathological image analysis of oral leukoplakia and carcinoma using deep neural networks

Background Oral cancer is one of the most common types of cancer in men causing mortality if not diagnosed early. In recent years, computer-aided diagnosis (CAD) using artificial intelligence techniques, in particular, deep neural networks have been investigated and several approaches have been proposed to deal with the automated detection of various pathologies using digital images. Recent studies indicate that the fusion of images with the patient’s clinical information is important for the final clinical diagnosis. As such dataset does not yet exist for oral cancer, as far as the authors are aware, a new dataset was collected consisting of histopathological images, demographic and clinical data. This study evaluated the importance of complementary data to histopathological image analysis of oral leukoplakia and carcinoma for CAD.

Methods A new dataset (NDB-UFES) was collected from 2011 to 2021 consisting of histopathological images and information. The 237 samples were curated and analyzed by oral pathologists generating the gold standard for classification. State-of-the-art image fusion architectures and complementary data (Concatenation, Mutual Attention, MetaBlock and MetaNet) using the latest deep learning backbones were investigated for 4 distinct tasks to identify oral squamous cell carcinoma, leukoplakia with dysplasia and leukoplakia without dysplasia. We evaluate them using balanced accuracy, precision, recall and area under the ROC curve metrics.

Results Experimental results indicate that the best models present balanced accuracy of $83.24\%$ using images, demographic and clinical information with MetaBlock fusion and ResNetV2 backbone. It represents an improvement in performance of $30.68\%$ (19.54 pp) in the task to differentiate samples diagnosed with oral squamous cell carcinoma and leukoplakia with or without dysplasia.

Conclusion This study indicates that cured demographic and clinical data may positively influence the performance of artificial intelligence models in automated classification of oral cancer.