Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy Journal Article


Authors: Surucu, M; Shah, K. K.; Mescioglu, I.; Roeske, J. C.; Small, W., Jr; Choi, M; Emami, B
Article Title: Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy
Abstract: OBJECTIVE: To develop decision trees predicting for tumor volume reduction in patients with head and neck (H) cancer using pretreatment clinical and pathological parameters. METHODS: Forty-eight patients treated with definitive concurrent chemoradiotherapy for squamous cell carcinoma of the nasopharynx, oropharynx, oral cavity, or hypopharynx were retrospectively analyzed. These patients were rescanned at a median dose of 37.8 Gy and replanned to account for anatomical changes. The percentages of gross tumor volume (GTV) change from initial to rescan computed tomography (CT; %GTVDelta) were calculated. Two decision trees were generated to correlate %GTVDelta in primary and nodal volumes with 14 characteristics including age, gender, Karnofsky performance status (KPS), site, human papilloma virus (HPV) status, tumor grade, primary tumor growth pattern (endophytic/exophytic), tumor/nodal/group stages, chemotherapy regimen, and primary, nodal, and total GTV volumes in the initial CT scan. The C4.5 Decision Tree induction algorithm was implemented. RESULTS: The median %GTVDelta for primary, nodal, and total GTVs was 26.8%, 43.0%, and 31.2%, respectively. Type of chemotherapy, age, primary tumor growth pattern, site, KPS, and HPV status were the most predictive parameters for primary %GTVDelta decision tree, whereas for nodal %GTVDelta, KPS, site, age, primary tumor growth pattern, initial primary GTV, and total GTV volumes were predictive. Both decision trees had an accuracy of 88%. CONCLUSIONS: There can be significant changes in primary and nodal tumor volumes during the course of H chemoradiotherapy. Considering the proposed decision trees, radiation oncologists can select patients predicted to have high %GTVDelta, who would theoretically gain the most benefit from adaptive radiotherapy, in order to better use limited clinical resources.
Keywords: Radiation Oncology; Decision Trees; adaptive radiotherapy; head and neck cancer; tumor shrinkage; tumor volume change
Journal Title: Technology in cancer research treatment
ISSN: 1533-0338; 1533-0338
Publisher: Unknown  
Date Published: 2015
Language: ENG
DOI/URL:
Notes: LR: 20150304; CI: (c) The Author(s) 2015; JID: 101140941; OTO: NOTNLM; aheadofprint