Francesca De Felice1,2, Laia Humbert-Vidan3,4, Mary Lei1, Andrew King4, Teresa Guerrero Urbano1. 1. Department of Clinical Oncology, Guy's and St Thomas' NHS Foundation Trust, London, UK. 2. Department of Radiotherapy, Policlinico Umberto I, "Sapienza" University of Rome, Rome, Italy. 3. Department of Medical Physics, Guy's and St Thomas' NHS Foundation Trust, London, UK. 4. Department of Biomedical Engineering, King's College London, London, UK.
Abstract
OBJECTIVES: To analyze survival outcomes in patients with oropharygeal cancer treated with primary intensity modulated radiotherapy (IMRT) using decision tree algorithms. METHODS: A total of 273 patients with newly diagnosed oropharyngeal cancer were identified between March 2010 and December 2016. The data set contained nine predictor variables and a dependent variable (overall survival (OS) status). The open-source R software was used. Survival outcomes were estimated by Kaplan-Meier method. Important explanatory variables were selected using the random forest approach. A classification tree that optimally partitioned patients with different OS rates was then built. RESULTS: The 5 year OS for the entire population was 78.1%. The top three important variables identified were HPV status, N stage and early complete response to treatment. Patients were partitioned in five groups on the basis of these explanatory variables. CONCLUSION: The proposed classification tree could help to guide future research in oropharyngeal cancer field. ADVANCES IN KNOWLEDGE: Decision tree method seems to be an appropriate tool to partition oropharyngeal cancer patients.
OBJECTIVES: To analyze survival outcomes in patients with oropharygeal cancer treated with primary intensity modulated radiotherapy (IMRT) using decision tree algorithms. METHODS: A total of 273 patients with newly diagnosed oropharyngeal cancer were identified between March 2010 and December 2016. The data set contained nine predictor variables and a dependent variable (overall survival (OS) status). The open-source R software was used. Survival outcomes were estimated by Kaplan-Meier method. Important explanatory variables were selected using the random forest approach. A classification tree that optimally partitioned patients with different OS rates was then built. RESULTS: The 5 year OS for the entire population was 78.1%. The top three important variables identified were HPV status, N stage and early complete response to treatment. Patients were partitioned in five groups on the basis of these explanatory variables. CONCLUSION: The proposed classification tree could help to guide future research in oropharyngeal cancer field. ADVANCES IN KNOWLEDGE: Decision tree method seems to be an appropriate tool to partition oropharyngeal cancer patients.
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