Detecting Oral Cancer Using Tabular Deep Learning

Oral cancer has one of the lowest five-year survival rates among major cancer types. Therefore, early detection is crucial for histopathological confirmation. State-of-the-art methods reported in the literature largely analyze images only for oral cancer prediction. The use of deep learning networks for related tabular medical data remains unexplored for oral cancer and understudied in general. As part of our multimodal AI/ML approach toward reliable prediction of candidate lesions to biopsy, we describe our work in deep learning approaches on a fielded clinical structured text data in spreadsheet format (tabular data) on a subset comprising 1791 patients drawn from a large ongoing oral cancer study to classify patients with a cancerous lesion from those with a precancerous lesion (i.e., direct precursor to cancer). We compare two tabular deep learning methods and one conventional algorithm for the predictive data analysis. The experimental results on a hold-out test set demonstrate a promising performance for all models (Youden index > 0.6 and AUC > 0.9). In addition, we examine and analyze the interpretability of models. All models indicate that lesion characteristics are crucial predictive features. The insights and results obtained from this work would be valuable to the research community in application of AI/ML to biomedicine.