Predicting Progression of Oral Lesions to Malignancy Using Machine Learning.

OBJECTIVE: To use large-scale electronic health record (EHR) data to develop machine learning models predicting malignant transformation of oral lesions. METHODS: A multi-institutional health system database was used to identify a retrospective cohort of patients with biopsied oral lesions. The primary outcome was malignant transformation. Chart review and automated system database queries were used to identify a range of demographic, clinical, and pathologic variables. Machine learning was used to develop predictive models for progression to malignancy. RESULTS: There were 2192 patients with a biopsied oral lesion, of whom 1232 had biopsy proven oral dysplasia. There was malignant transformation in 34% of patients in the oral lesions dataset, and in 54% of patients in the dysplasia subset. Multiple machine learning-based models were trained on the data in two experiments, (a) including all patients with biopsied oral lesions and (b) including only patients with biopsy-proven dysplasia. In the first experiment, the best machine learning models predicted malignant transformation among the biopsied oral lesions with an area under the curve (AUC) of 86%. In the second experiment, the random forest model predicted malignant transformation among lesions with dysplasia with an AUC of 0.75. The most influential features were dysplasia grade and the presence of multiple lesions, with smaller influences from other features including anemia, histopathologic description of atypia, and other prior cancer history. CONCLUSION: With diverse features from EHR data, machine learning approaches are feasible and allow for generation of models that predict which oral lesions are likely to progress to malignancy. LEVEL OF EVIDENCE: 3 Laryngoscope, 133:1156-1162, 2023.