Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States.
Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, United States.
Oral Oncol. 2021 May;116:105241. doi: 10.1016/j.oraloncology.2021.105241. Epub 2021 Feb 25.
To develop nomograms predicting overall survival (OS), freedom from locoregional recurrence (FFLR), and freedom from distant metastasis (FFDM) for patients receiving chemoradiation for laryngeal squamous cell carcinoma (LSCC).
Clinical and treatment data for patients with LSCC enrolled on NRG Oncology/RTOG 0129 and 0522 were extracted from the RTOG database. The dataset was partitioned into 70% training and 30% independent validation datasets. Significant predictors of OS, FFLR, and FFDM were obtained using univariate analysis on the training dataset. Nomograms were built using multivariate analysis with four a priori variables (age, gender, T-stage, and N-stage) and significant predictors from the univariate analyses. These nomograms were internally and externally validated using c-statistics (c) on the training and validation datasets, respectively.
The OS nomogram included age, gender, T stage, N stage, and number of cisplatin cycles. The FFLR nomogram included age, gender, T-stage, N-stage, and time-equivalent biologically effective dose. The FFDM nomogram included age, gender, N-stage, and number of cisplatin cycles. Internal validation of the OS nomogram, FFLR nomogram, and FFDM nomogram yielded c = 0.66, c = 0.66 and c = 0.73, respectively. External validation of these nomograms yielded c = 0.59, c = 0.70, and c = 0.73, respectively. Using nomogram score cutoffs, three risk groups were separated for each outcome.
We have developed and validated easy-to-use nomograms for LSCC outcomes using prospective cooperative group trial data.
为接受喉鳞状细胞癌(LSCC)放化疗的患者开发预测总生存期(OS)、无局部区域复发(FFLR)和无远处转移(FFDM)的列线图。
从 RTOG 数据库中提取 NRG 肿瘤学/RTOG 0129 和 0522 入组的 LSCC 患者的临床和治疗数据。数据集分为 70%的训练集和 30%的独立验证数据集。在训练数据集上进行单因素分析,获得 OS、FFLR 和 FFDM 的显著预测因素。使用多因素分析,结合四个先验变量(年龄、性别、T 期和 N 期)和单因素分析中的显著预测因素,构建列线图。使用训练和验证数据集的 C 统计量(c)分别对这些列线图进行内部和外部验证。
OS 列线图包括年龄、性别、T 期、N 期和顺铂周期数。FFLR 列线图包括年龄、性别、T 期、N 期和时间等效生物有效剂量。FFDM 列线图包括年龄、性别、N 期和顺铂周期数。OS 列线图、FFLR 列线图和 FFDM 列线图的内部验证分别产生 c=0.66、c=0.66 和 c=0.73。这些列线图的外部验证分别产生 c=0.59、c=0.70 和 c=0.73。使用列线图评分截断值,为每个结果分离了三个风险组。
我们使用前瞻性合作组试验数据为 LSCC 结果开发并验证了易于使用的列线图。