Lee Tsair-Fwu, Chao Pei-Ju, Ting Hui-Min, Chang Liyun, Huang Yu-Jie, Wu Jia-Ming, Wang Hung-Yu, Horng Mong-Fong, Chang Chun-Ming, Lan Jen-Hong, Huang Ya-Yu, Fang Fu-Min, Leung Stephen Wan
Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung, Taiwan, ROC.
Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan, ROC.
PLoS One. 2014 Feb 28;9(2):e89700. doi: 10.1371/journal.pone.0089700. eCollection 2014.
The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT.
Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3(+) xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R(2), chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC.
Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R(2) was satisfactory and corresponded well with the expected values.
Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT.
本研究旨在开发一种带有最小绝对收缩选择算子（LASSO）的多变量逻辑回归模型，以对接受调强放疗（IMRT）的头颈癌（HNC）患者中、重度患者自评口干症的发生率做出有效预测。
分析了206例HNC患者的生活质量问卷数据集。采用欧洲癌症研究与治疗组织QLQ-H&N35和QLQ-C30问卷作为终点评估。主要终点（3级（+）口干症）定义为IMRT完成后3个月（XER3m）和12个月（XER12m）时的中、重度口干症。建立了正常组织并发症概率（NTCP）模型。使用带有自抽样技术的LASSO确定多变量逻辑回归模型的最佳和次优预后因素数量。使用标准化Brier评分、Nagelkerke R²、卡方检验、综合检验、Hosmer-Lemeshow检验和AUC进行统计分析。
LASSO在3个月时间点选择了8个预后因素：平均剂量-c、平均剂量-i、年龄、经济状况、T分期、美国癌症联合委员会（AJCC）分期、吸烟和教育程度。在12个月时间点选择了9个预后因素：平均剂量-i、教育程度、平均剂量-c、吸烟、T分期、基线口干症情况、酗酒、家族史和淋巴结分类。在通过LASSO选择次优预后因素数量时，通过Hosmer-Lemeshow检验和AUC对3个次优预后因素进行了微调，即3个月时间点的平均剂量-c、平均剂量-i和年龄。在12个月时间点也选择了5个次优预后因素，即平均剂量-i、教育程度、平均剂量-c、吸烟和T分期。NTCP模型在两个时间点的总体表现，就标准化Brier评分、综合检验和Nagelkerke R²而言令人满意，且与预期值吻合良好。
带有LASSO的多变量NTCP模型可用于预测IMRT后患者自评的口干症。
