Machine Learning Algorithms for Predicting Treatment Outcomes of Oropharyngeal Cancer After Surgery
نویسندگان
چکیده
Background and Objectives This study analyzed data from patients who were diagnosed with human papilloma virus (HPV)-associated oropharyngeal (OPC) treated surgically to construct a machine learning survival prediction model.Subjects Method We retrospectively the clinico-pathological of 203 HPV-associated squamous cell carcinoma (OPSCC) 2007 2015.Results In Cox proportional hazard (CPH) model, c-index values for training set test 0.81 0.59, respectively. The univariate analysis showed that contralateral lymph nodes (LNs) metastasis, lymphovascular invasion, pN, stage, surgical margin status, histologic grade, pT, number metastatic LNs had significant correlations survival. Contrastively, multivariate pT grade have correlation random forest 0.83 0.87, DeepSurv cindex 0.75 0.83. Among three models mentioned above, Random Survival Forest best performance predicting OPSCC patients.Conclusion confirmed model using deep algorithms reasonable estimates patients.
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ژورنال
عنوان ژورنال: Korean journal of otorhinolaryngology-head and neck surgery
سال: 2023
ISSN: ['2092-6529', '2092-5859']
DOI: https://doi.org/10.3342/kjorl-hns.2022.00794