Yan Yujie, Zhu Yaoyao, Yang Shuangyan, Qian Cheng, Zhang Ying, Yuan Xiaoshuai, Hu Min, Kang Jingjing, Jiang Chenxue, Hu Minren, Zhao Ruifeng, Zhao Lan, Xu Yaping
Department of Radiation Oncology, Shanghai Pulmonary Hospital, Tongji University Medical School Cancer Institute, Tongji University School of Medicine, Shanghai, China.
Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, China.
Transl Lung Cancer Res. 2024 May 31;13(5):1069-1083. doi: 10.21037/tlcr-24-328. Epub 2024 May 29.
Severe radiation pneumonitis (RP), one of adverse events in patients with lung cancer receiving thoracic radiotherapy, is more likely to lead to more mortality and poor quality of life, which could be predicted by clinical information and treatment scheme. In this study, we aimed to explore the clinical predict model for severe RP.
We collected information on lung cancer patients who received radiotherapy from August 2020 to August 2022. Clinical features were obtained from 690 patients, including baseline and treatment data as well as radiation dose measurement parameters, including lung volume exceeding 5 Gy (V5), lung volume exceeding 20 Gy (V20), lung volume exceeding 30 Gy (V30), mean lung dose (MLD), etc. Among them, 621 patients were in the training cohort, and 69 patients were in the test cohort. Three models were built using different screening methods, including multivariate logistics regression (MLR), backward stepwise regression (BSR), and random forest regression (RFR), to evaluate their predictive power. Overoptimism in the training cohorts was evaluated by four validation methods, including hold-out, 10-fold, leave-one-out, and bootstrap methods, and test cohort was used to evaluate the predictive performance of the model. Model calibration, decision curve analysis (DCA), and evaluation of the nomograms for the three models were completed.
Severe RP was up to 9.4%. The results of multivariate analysis of logistics regression in all patients showed that patients with subclinical (untreated and asymptomatic) interstitial lung disease (ILD) could increase the risk of severe RP, and patients with a better lung diffusion function and received standardized steroids treatment could decrease the risk of severe RP. The three models built by MLR, BSR, and RFR all had good accuracy (>0.850) and moderate κ value (>0.4), and the model 2 built by BSR had the highest area under the receiver operating characteristic (ROC) curve (AUC) in three models, which was 0.958 [95% confidence interval (CI): 0.932-0.985]. The calibration curve showed good agreement between the predicted and actual values, and the DCA showed a positive net benefit for the model 2 which drew the nomogram. The model 2 included subclinical ILD, diffusing capacity of the lung for carbon monoxide (DLCO), ipsilateral lung V20, and standardized steroid treatment, which could affect the incidence of severe RP.
Subclinical ILD, DLCO, ipsilateral lung V20, and with or not standardized steroid treatment could affect the incidence of severe RP. Strict lung dose limitation and standardized steroid treatment could contribute to a decrease in severe RP.
严重放射性肺炎（RP）是肺癌患者接受胸部放疗后的不良事件之一，更易导致更高的死亡率和生活质量下降，可通过临床信息和治疗方案进行预测。在本研究中，我们旨在探索严重RP的临床预测模型。
我们收集了2020年8月至2022年8月接受放疗的肺癌患者的信息。从690例患者中获取临床特征，包括基线和治疗数据以及辐射剂量测量参数，如肺体积超过5 Gy（V5）、肺体积超过20 Gy（V20）、肺体积超过30 Gy（V30）、平均肺剂量（MLD）等。其中，621例患者在训练队列中，69例患者在测试队列中。使用不同的筛选方法构建了三个模型，包括多因素逻辑回归（MLR）、向后逐步回归（BSR）和随机森林回归（RFR），以评估它们的预测能力。通过四种验证方法评估训练队列中的过度乐观情况，包括留出法、10折交叉验证法、留一法和自助法，并使用测试队列评估模型的预测性能。完成了三个模型的模型校准、决策曲线分析（DCA）和列线图评估。
严重RP发生率高达9.4%。所有患者的逻辑回归多因素分析结果显示，亚临床（未治疗且无症状）间质性肺疾病（ILD）患者会增加严重RP的风险，而肺弥散功能较好且接受标准化类固醇治疗的患者可降低严重RP的风险。由MLR、BSR和RFR构建的三个模型均具有良好的准确性（>0.850）和中等的κ值（>0.4），并且由BSR构建的模型2在三个模型中具有最高的受试者操作特征（ROC）曲线下面积（AUC），为0.958 [95%置信区间（CI）：0.932 - 0.985]。校准曲线显示预测值与实际值之间具有良好的一致性，DCA显示绘制列线图的模型2具有正的净效益。模型2包括亚临床ILD、一氧化碳弥散量（DLCO）、患侧肺V20和标准化类固醇治疗，这些因素可影响严重RP的发生率。
亚临床ILD、DLCO、患侧肺V20以及是否接受标准化类固醇治疗可影响严重RP的发生率。严格的肺剂量限制和标准化类固醇治疗有助于降低严重RP的发生率。
