Deep Learning for Clinical Image Analyses in Oral Squamous Cell Carcinoma A Review

IMPORTANCE Oral squamous cell carcinoma (SCC) is a lethal malignant neoplasm with a high rate of tumor metastasis and recurrence. Accurate diagnosis, prognosis prediction, and metastasis detection can improve patient outcomes. Deep learning for clinical image analysis can be used for diagnosis and prognosis in cancers, including oral SCC; its use in these areas can improve patient care and outcome. OBSERVATIONS This review is a summary of the use of deep learning models for diagnosis, prognosis, and metastasis detection for oral SCC by analyzing information from pathological and radiographic images. Specifically, deep learning has been used to classify different cell types, to differentiate cancer cells from nonmalignant cells, and to identify oral SCC from other cancer types. It can also be used to predict survival, to differentiate between tumor grades, and to detect lymph node metastasis. In general, the performance of these deep learning models has an accuracy ranging from 77.89% to 97.51% and 76% to 94.2% with the use of pathological and radiographic images, respectively. The review also discusses the importance of using good-quality clinical images in sufficient quantity on model performance. CONCLUSIONS AND RELEVANCE Applying pathological and radiographic images in deep learning models for diagnosis and prognosis of oral SCC has been explored, and most studies report results showing good classification accuracy. The successful use of deep learning in these areas has a high clinical translatability in the improvement of patient care.