부산대학교 도서관

Our objective was to develop a predictive model using a machine learning signature to identify patients at high risk of relapse or death after treatment for HPV-positive oropharyngeal carcinoma. Pre-treatment variables of 450 patients with HPV-positive oropharyngeal carcinoma treated with a curative intent comprised clinical items, imaging parameters and histological findings. The events considered were progression or residual disease after treatment, the recurrent disease after a disease-free interval and death. The endpoints were the prediction of events and progression-free survival. After feature Z-score normalisation and selection, random forest classifier models were trained. The best models were evaluated on recall, the F-score, and the ROC AUC metric. The clinical relevance of the best prediction model was evaluated using Kaplan–Meier analysis with a log-rank test. The best random forest model predicted the 5-year risk of relapse-free survival with a recall of 79.1%, an F1-score of 81.08%, and an AUC of the ROC curve of 0.89. The models performed poorly for the prediction of specific events of progression only, recurrence only or death only. The clinical relevance of the model was validated with a 5-year relapse-free survival of high-risk patients versus low-risk patients of 23.5% and 80%, respectively (p < 0.0001). Patients with HPV-driven oropharyngeal carcinoma at high risk of relapse-free survival could be identified with a predictive machine learning model using patient data before treatment. • Machine learning predicted well the 5-year risk of relapse-free survival. • Short-term prediction of respective risks of death or relapse scored badly. • Main ranked variables were radiological tumour sizes and SUVmax. • Our model will help stratify low-risk and high-risk patients. [ABSTRACT FROM AUTHOR]