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. |
Journal Title: | Technology in cancer research treatment |
ISSN: | 1533-0338; 1533-0338 |
Publisher: | Unknown |
Date Published: | 2015 |
Language: | ENG |
DOI/URL: |
1533034615572638 |
Notes: | LR: 20150304; CI: (c) The Author(s) 2015; JID: 101140941; OTO: NOTNLM; aheadofprint |