基于机器学习的放射性肺炎多组学预测模型
Machine Learning-Based Multiomics Prediction Model for Radiation Pneumonitis.
作者信息
Zhou Lu, Wen Yuefeng, Zhang Guoqian, Wang Linjing, Wu Shuyu, Zhang Shuxu
机构信息
Department of Radiation Oncology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, China.
出版信息
J Oncol. 2023 Feb 18;2023:5328927. doi: 10.1155/2023/5328927. eCollection 2023.
OBJECTIVE
The study aims to establish and validate an effective CT-based radiation pneumonitis (RP) prediction model using the multiomics method of radiomics and EQD2-based dosiomics.
MATERIALS AND METHODS
The study performed a retrospective analysis on 91 nonsmall cell lung cancer patients who received radiotherapy from 2019 to 2021 in our hospital. The patients with RP grade ≥1 were labeled as 1, and those with RP grade < 1 were labeled as 0. The whole lung excluding clinical target volume (lung-CTV) was used as the region of interest (ROI). The radiomic and dosiomic features were extracted from the lung-CTV area's image and dose distribution. Besides, the equivalent dose of the 2 Gy fractionated radiation (EQD) model was used to convert the physical dose to the isoeffect dose, and then, the EQD2-based dosiomic (eqd-dosiomic) features were extracted from the isoeffect dose distribution. Four machine learning (ML) models, including DVH, radiomics combined with DVH (radio + DVH), radiomics combined with dosiomics (radio + dose), and radiomics combined with eqd-dosiomics (radio + eqdose), were established to construct the prediction model via eleven different classifiers. The fivefold cross-validation was used to complete the classification experiment. The area under the curve (AUC) of the receiver operating characteristics (ROC), accuracy, precision, recall, and F1-score were calculated to assess the performance level of the prediction models.
RESULTS
Compared with the DVH, radio + DVH, and radio + dose model, the value of the training AUC, accuracy, and F1-score of radio + eqdose was higher, and the difference was statistically significant ( < 0.05). Besides, the average value of the precision and recall of radio + eqdose was higher, but the difference was not statistically significant ( > 0.05).
CONCLUSION
The performance of using the ML-based multiomics method of radiomics and eqd-dosiomics to predict RP is more efficient and effective.
目的
本研究旨在利用基于多组学的放射组学和基于等效剂量2（EQD2）的剂量组学方法，建立并验证一种有效的基于CT的放射性肺炎（RP）预测模型。
材料与方法
本研究对2019年至2021年在我院接受放疗的91例非小细胞肺癌患者进行回顾性分析。RP分级≥1级的患者标记为1，RP分级<1级的患者标记为0。将排除临床靶区体积（肺-CTV）后的全肺作为感兴趣区（ROI）。从肺-CTV区域的图像和剂量分布中提取放射组学和剂量组学特征。此外，使用2 Gy分割照射的等效剂量（EQD）模型将物理剂量转换为等效生物效应剂量，然后从等效生物效应剂量分布中提取基于EQD2的剂量组学（eqd-剂量组学）特征。建立了四个机器学习（ML）模型，包括剂量体积直方图（DVH）、放射组学联合DVH（放射组学+DVH）、放射组学联合剂量组学（放射组学+剂量）和放射组学联合eqd-剂量组学（放射组学+等效剂量），通过11种不同的分类器构建预测模型。采用五折交叉验证完成分类实验。计算受试者工作特征曲线（ROC）下面积（AUC）、准确率、精确率、召回率和F1分数，以评估预测模型的性能水平。
结果
与DVH、放射组学+DVH和放射组学+剂量模型相比，放射组学+等效剂量的训练AUC、准确率和F1分数值更高，差异有统计学意义（<0.05）。此外，放射组学+等效剂量的精确率和召回率平均值更高，但差异无统计学意义（>0.05）。
结论
基于ML的放射组学和eqd-剂量组学多组学方法预测RP的性能更高效。
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