Analyzing oropharyngeal cancer survival outcomes: a decision tree approach

Francesca De Felice, Laia Humbert-Vidan, Mary Lei, Andrew King, Teresa Guerrero Urbano

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Number of pages1
JournalThe British journal of radiology
Volume93
Issue number1111
DOIs
Publication statusPublished - 1 Jul 2020

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