Shi Zhenwei, Foley Kieran G, Pablo de Mey Juan, Spezi Emiliano, Whybra Philip, Crosby Tom, van Soest Johan, Dekker Andre, Wee Leonard
Department of Radiation Oncology (MAASTRO), GROW School for Oncology and Developmental Biology, Maastricht University Medical Centre+, Maastricht, Netherlands.
Velindre Cancer Centre, Cardiff, United Kingdom.
Front Oncol. 2019 Dec 16;9:1411. doi: 10.3389/fonc.2019.01411. eCollection 2019.
Radiation-induced lung disease (RILD), defined as dyspnea in this study, is a risk for patients receiving high-dose thoracic irradiation. This study is a TRIPOD (Transparent Reporting of A Multivariable Prediction Model for Individual Prognosis or Diagnosis) Type 4 validation of previously-published dyspnea models via secondary analysis of esophageal cancer SCOPE1 trial data. We quantify the predictive performance of these two models for predicting the maximal dyspnea grade ≥ 2 within 6 months after the end of high-dose chemo-radiotherapy for primary esophageal cancer. We tested the performance of two previously published dyspnea risk models using baseline, treatment and follow-up data on 258 esophageal cancer patients in the UK enrolled into the SCOPE1 multi-center trial. The tested models were developed from lung cancer patients treated at MAASTRO Clinic (The Netherlands) from the period 2002 to 2011. The adverse event of interest was dyspnea ≥ Grade 2 (CTCAE v3) within 6 months after the end of radiotherapy. As some variables were missing randomly and cannot be imputed, 212 patients in the SCOPE1 were used for validation of model 1 and 255 patients were used for validation of model 2. The model parameter Forced Expiratory Volume in 1 s (FEV), as a predictor to both validated models, was imputed using the WHO performance status. External validation was performed using an automated, decentralized approach, without exchange of individual patient data. Out of 258 patients with esophageal cancer in SCOPE1 trial data, 38 patients (14.7%) developed radiation-induced dyspnea (≥ Grade 2) within 6 months after chemo-radiotherapy. The discrimination performance of the models in esophageal cancer patients treated with high-dose external beam radiotherapy was moderate, area under curve (AUC) of 0.68 (95% CI 0.55-0.76) and 0.70 (95% CI 0.58-0.77), respectively. The curves and AUCs derived by distributed learning were identical to the results from validation on a local host. We have externally validated previously published dyspnea models using an esophageal cancer dataset. FEV that is not routinely measured for esophageal cancer was imputed using WHO performance status. Prediction performance was not statistically different from previous training and validation sets. Risk estimates were dominated by WHO score in Model 1 and baseline dyspnea in Model 2. The distributed learning approach gave the same answer as local processing, and could be performed without accessing a validation site's individual patients-level data.
放射性肺病（RILD）在本研究中定义为呼吸困难，是接受高剂量胸部放疗患者面临的一种风险。本研究是一项通过对食管癌SCOPE1试验数据进行二次分析，对先前发表的呼吸困难模型进行的TRIPOD（个体预后或诊断多变量预测模型的透明报告）4型验证。我们量化了这两个模型在预测原发性食管癌高剂量放化疗结束后6个月内最大呼吸困难分级≥2级方面的预测性能。我们使用了英国SCOPE1多中心试验中258例食管癌患者的基线、治疗和随访数据，测试了两个先前发表的呼吸困难风险模型的性能。所测试的模型是基于2002年至2011年期间在荷兰马斯特里赫特诊所接受治疗的肺癌患者开发的。感兴趣的不良事件是放疗结束后6个月内呼吸困难≥2级（CTCAE v3）。由于一些变量随机缺失且无法插补，SCOPE1中的212例患者用于模型1的验证，255例患者用于模型2的验证。作为两个验证模型的预测指标，1秒用力呼气量（FEV）使用世界卫生组织的体能状态进行插补。外部验证采用自动化、分散式方法进行，无需交换个体患者数据。在SCOPE1试验数据中的258例食管癌患者中，38例（14.7%）在放化疗后6个月内出现放射性呼吸困难（≥2级）。在接受高剂量外照射放疗的食管癌患者中，模型的辨别性能中等，曲线下面积（AUC）分别为0.68（95%CI 0.55 - 0.76）和0.70（95%CI 0.58 - 0.77）。分布式学习得出的曲线和AUC与在本地主机上验证的结果相同。我们使用食管癌数据集对先前发表的呼吸困难模型进行了外部验证。使用世界卫生组织的体能状态对食管癌患者未常规测量的FEV进行了插补。预测性能与先前的训练和验证集相比无统计学差异。风险估计在模型1中以世界卫生组织评分为主，在模型2中以基线呼吸困难为主。分布式学习方法得出的结果与本地处理相同，并且可以在不访问验证站点个体患者水平数据的情况下进行。
