Yu Hao, Lam Ka-On, Wu Huanmei, Green Michael, Wang Weili, Jin Jian-Yue, Hu Chen, Jolly Shruti, Wang Yang, Kong Feng-Ming Spring
Biomedical Engineering, Shenzhen Polytechnic, Shenzhen, China.
BioHealth Informatics, School of Informatics and Computing, Indiana University - Purdue University Indianapolis (IUPUI), Indianapolis, IN, United States.
Front Oncol. 2021 Feb 1;10:601979. doi: 10.3389/fonc.2020.601979. eCollection 2020.
Radiation-induced lung fibrosis (RILF) is an important late toxicity in patients with non-small-cell lung cancer (NSCLC) after radiotherapy (RT). Clinically significant RILF can impact quality of life and/or cause non-cancer related death. This study aimed to determine whether pre-treatment plasma cytokine levels have a significant effect on the risk of RILF and investigate the abilities of machine learning algorithms for risk prediction.
This is a secondary analysis of prospective studies from two academic cancer centers. The primary endpoint was grade≥2 (RILF2), classified according to a system consistent with the consensus recommendation of an expert panel of the AAPM task for normal tissue toxicity. Eligible patients must have at least 6 months' follow-up after radiotherapy commencement. Baseline levels of 30 cytokines, dosimetric, and clinical characteristics were analyzed. Support vector machine (SVM) algorithm was applied for model development. Data from one center was used for model training and development; and data of another center was applied as an independent external validation.
There were 57 and 37 eligible patients in training and validation datasets, with 14 and 16.2% RILF2, respectively. Of the 30 plasma cytokines evaluated, SVM identified baseline circulating CCL4 as the most significant cytokine associated with RILF2 risk in both datasets ( = 0.003 and 0.07, for training and test sets, respectively). An SVM classifier predictive of RILF2 was generated in Cohort 1 with CCL4, mean lung dose (MLD) and chemotherapy as key model features. This classifier was validated in Cohort 2 with accuracy of 0.757 and area under the curve (AUC) of 0.855.
Using machine learning, this study constructed and validated a weighted-SVM classifier incorporating circulating CCL4 levels with significant dosimetric and clinical parameters which predicts RILF2 risk with a reasonable accuracy. Further study with larger sample size is needed to validate the role of CCL4, and this SVM classifier in RILF2.
放射性肺纤维化（RILF）是接受放疗（RT）的非小细胞肺癌（NSCLC）患者重要的晚期毒性反应。具有临床意义的RILF会影响生活质量和/或导致非癌症相关死亡。本研究旨在确定治疗前血浆细胞因子水平是否对RILF风险有显著影响，并研究机器学习算法的风险预测能力。
这是对来自两个学术癌症中心的前瞻性研究的二次分析。主要终点为≥2级（RILF2），根据与美国医学物理学家协会（AAPM）正常组织毒性任务专家小组共识建议一致的系统进行分类。符合条件的患者放疗开始后必须至少随访6个月。分析了30种细胞因子的基线水平、剂量学和临床特征。应用支持向量机（SVM）算法进行模型开发。来自一个中心的数据用于模型训练和开发；另一个中心的数据用作独立的外部验证。
训练集和验证集中分别有57例和37例符合条件的患者，RILF2发生率分别为14%和16.2%。在评估的30种血浆细胞因子中，SVM确定基线循环CCL4是两个数据集中与RILF2风险最相关的细胞因子（训练集和测试集的P值分别为0.003和0.07）。在队列1中生成了一个以CCL4、平均肺剂量（MLD）和化疗为关键模型特征的预测RILF2的SVM分类器。该分类器在队列2中得到验证，准确率为0.757，曲线下面积（AUC）为0.855。
本研究利用机器学习构建并验证了一个加权SVM分类器，该分类器将循环CCL4水平与重要的剂量学和临床参数相结合，能够以合理的准确率预测RILF2风险。需要更大样本量的进一步研究来验证CCL4以及该SVM分类器在RILF2中的作用。
