Differentiating intradiploic orbital dermoid and epidermoid cysts utilizing clinical features and machine learning.

Purpose: The purpose of this study was to characterize intradiploic dermoid and epidermoid orbital cysts to determine any differences in clinical, radiographic, or surgical features. Methods: A retrospective review was performed of patients presenting with intradiplopic dermoid or epidermoid cysts. Additionally, a complete review of the literature was performed to identify cases of intradiplopic orbital dermoid and epidermoid cysts. Data collected included age, sex, presenting symptoms, location of intradiplopic cyst, ophthalmic findings, treatment, and follow-up. Clinical features of dermoid versus epidermoid cyst were compared. Additionally, machine-learning algorithms were developed to predict histopathology based on clinical features. Results: There were 55 cases of orbital intradiploic cysts, 49 from literature review and six from our cohort. Approximately 31% had dermoid and 69% had epidermoid histopathology. Average age of patients with dermoid cysts was significantly lesser than that of patients with epidermoid cysts (23 vs. 35 years, respectively; P = 0.048). There was no difference between sex predilection, presenting symptoms, radiographic findings, or surgical treatment of dermoids and epidermoids. The majority of patients (64%) underwent craniotomy for surgical removal. Machine-learning algorithms KStar and Neural Network were able to distinguish dermoid from epidermoid with accuracies of 76.3% and 69%, respectively. Conclusion: Orbital intradiploic cysts are more commonly epidermoid in origin. Dermoid cysts presented in younger patients; however, there were no other significant differences in features including ophthalmic or radiographic findings. Despite similar features, machine learning was able to identify dermoid versus epidermoid with good accuracy. Future studies may examine the role of machine learning for clinical guidance as well as new surgical options for intervention.