Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy
Decision Trees Predicting Tumor Shrinkage for Head and Neck Cancer: Implications for Adaptive Radiotherapy
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DOI：
10.1177/1533034615572638
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发表时间：
2016-02-01
影响因子：
2.8
通讯作者：
Emami, Bahman
中科院分区：
文献类型：
--
作者：
Surucu, Murat;Shah, Karan K.;Emami, Bahman
目的：利用治疗前临床和病理参数，建立预测头颈部肿瘤体积缩小的决策树。方法：回顾分析48例鼻咽、口咽、口腔或下咽鳞状细胞癌同期放化疗患者的临床资料。这些患者被重新扫描，中位剂量为37.8GY，并根据解剖变化进行了重新计划。计算大体肿瘤体积(GTV)从初次扫描到再扫描的百分比(CT；%GTV Delta)。在初始CT扫描中，生成了两个决策树来关联原发和结节体积中的%GTV Delta，14个特征包括年龄、性别、卡诺夫斯基表现状态(KPS)、部位、人乳头瘤病毒(HPV)状态、肿瘤分级、原发肿瘤生长模式(内/外生长)、肿瘤/结节/组分期、化疗方案以及原发、结节和总GTV体积。实现了C4.5决策树归纳算法。结果：原发、结节和总GTV的GTV Delta中位数分别为26.8%、43.0%和31.2%。化疗类型、年龄、原发肿瘤生长方式、部位、KPS和HPV状态是预测原发%GTV Delta决策树的最具预测性的参数，而对于结节%GTV Delta、KPS、部位、年龄、原发肿瘤生长方式、初始原发GTV和总GTV体积是最具预测性的参数。两种决策树的准确率都达到了88%。结论：在H&N放化疗过程中，原发灶和结节瘤体积会发生显著变化。考虑到所提出的决策树，放射肿瘤学家可以选择预计具有高GTV Delta的患者，这些患者理论上将从适应性放射治疗中获得最大好处，以便更好地利用有限的临床资源。
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; %GTV Delta) were calculated. Two decision trees were generated to correlate %GTV Delta 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 %GTV Delta 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 %GTV Delta decision tree, whereas for nodal %GTV Delta, 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 %GTV Delta, who would theoretically gain the most benefit from adaptive radiotherapy, in order to better use limited clinical resources.