Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy.
OBJECTIVE
To develop decision trees predicting for tumor volume reduction in patients with head and neck (H&N) 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; %GTVΔ) were calculated. Two decision trees were generated to correlate %GTVΔ 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 %GTVΔ 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 %GTVΔ decision tree, whereas for nodal %GTVΔ, 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&N chemoradiotherapy. Considering the proposed decision trees, radiation oncologists can select patients predicted to have high %GTVΔ, who would theoretically gain the most benefit from adaptive radiotherapy, in order to better use limited clinical resources.
Department of Radiation Oncology, Loyola University Chicago, Maywood, IL, USA msurucu@lumc.edu.
Murat
M
Surucu
Department of Radiation Oncology, Loyola University Chicago, Maywood, IL, USA.
Karan K
KK
Shah
Department of Management Information Systems, Lewis University, Romeoville, IL, USA.
Ibrahim
I
Mescioglu
Department of Radiation Oncology, Loyola University Chicago, Maywood, IL, USA.
John C
JC
Roeske
Department of Radiation Oncology, Loyola University Chicago, Maywood, IL, USA.
William
W
Small
Department of Radiation Oncology, Loyola University Chicago, Maywood, IL, USA.
Mehee
M
Choi
Department of Radiation Oncology, Loyola University Chicago, Maywood, IL, USA.
Bahman
B
Emami
D002294:Carcinoma, Squamous Cell / Q000000981:diagnostic imaging* / Q000473:pathology / Q000532:radiotherapy; D059248:Chemoradiotherapy; D003663:Decision Trees; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000532:radiotherapy; D006801:Humans; D011857:Radiographic Image Interpretation, Computer-Assisted; D011880:Radiotherapy Planning, Computer-Assisted; D012189:Retrospective Studies; D047368:Tumor Burden / Q000528:radiation effects
adaptive radiotherapy; decision trees; head and neck cancer; tumor shrinkage; tumor volume change