ABSTRACT
Background and Objectives@#The purpose of this study was to analyze the survival data of salivary gland cancer (SGCs) patients to construct machine learning and deep learning models that can predict survival and use them to stratify SGC patients according to risk estimate.Subjects and Method We retrospectively analyzed the clinicopathologic data from 460 patients with SGCs from 2006 to 2018. @*Results@#In Cox proportional hazard (CPH) model, pM, stage, lymphovascular invasion, lymph node ratio, and age exhibited significant correlation with patient’s survival. In the CPH model, the c-index value for the training set was 0.85, and that for the test set was 0.81. In the Random Survival Forest model, the c-index value for the training set was 0.86, and that for the test set was 0.82. Stage and age exhibited high importance in both the Random Survival Forest and CPH models. In the deep learning-based model, the c-index value was 0.72 for the training set and 0.72 for the test set. Among the three models mentioned above, the Random Survival Forest model exhibited the highest performance in predicting the survival of SGC patients. @*Conclusion@#A survival prediction model using machine learning techniques showed acceptable performance in predicting the survival of SGC patients. Although large-scale clinical and multicenter studies should be conducted to establish more powerful predictive model, we expect that individualized treatment can be realized according to risk stratification made by the machine learning model.
ABSTRACT
Background and Objectives@#Previous studies reported abnormalities in MRI as a poor prognostic indicator of sudden sensorineural hearing loss (SSNHL). Since abnormalities in three-dimensional (3D) fluid-attenuated inversion recovery (FLAIR) are strongly correlated with the initial hearing function, the prognostic value of the 3D FLAIR images should be carefully evaluated to avoid collinearity. We aimed to evaluate abnormalities on the 3D FLAIR images as an independent prognostic factor in the matched SSNHL groups.Subjects and Method We retrospectively reviewed medical records of 179 patients with SSNHL who underwent temporal MRI, including the 3D FLAIR sequence, between January 2015 and December 2019. Patients were divided based on the presence of cochlear abnormalities on the 3D FLAIR images. Hearing prognosis was evaluated with and without matching for initial hearing and treatment interval. @*Results@#The groups were similar in sex (p=0.091), age (p=0.925), treatment interval (p= 0.216), and MRI interval (p=0.828). Notably, patients with cochlear abnormalities on the 3D FLAIR images showed distinctly more severe hearing loss (p<0.001) at the initial pure tone average (PTA) assessment and poorer outcomes (p<0.001) compared to those without abnormality. After matching for initial hearing and treatment interval, the hearing outcome, measured by PTA, was similar between the groups (p=0.681). @*Conclusion@#Cochlear signal abnormality in 3D FLAIR MRI was associated with poor initial hearing. However, it did not affect hearing recovery outcomes when the groups were matched.