Zhang Zhen, Wang Zhixiang, Yan Meng, Yu Jiaqi, Dekker Andre, Zhao Lujun, Wee Leonard
Department of Radiation Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands.
Department of Radiation Oncology, MAASTRO, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center+, Maastricht, The Netherlands.
Int J Radiat Oncol Biol Phys. 2023 Mar 1;115(3):746-758. doi: 10.1016/j.ijrobp.2022.08.047. Epub 2022 Aug 27.
Radiation pneumonitis (RP) is one of the common side effects of radiation therapy in the thoracic region. Radiomics and dosiomics quantify information implicit within medical images and radiation therapy dose distributions. In this study we demonstrate the prognostic potential of radiomics, dosiomics, and clinical features for RP prediction.
Radiomics, dosiomics, dose-volume histogram (DVH) metrics, and clinical parameters were obtained on 314 retrospectively collected and 35 prospectively enrolled patients diagnosed with lung cancer between 2013 to 2019. A radiomics risk score (R score) and dosiomics risk score (D score), as well as a DVH-score, were calculated based on logistic regression after feature selection. Six models were built using different combinations of R score, D score, DVH score, and clinical parameters to evaluate their added prognostic power. Overoptimism was evaluated by bootstrap resampling from the training set, and the prospectively collected cohort was used as the external test set. Model calibration and decision-curve characteristics of the best-performing models were evaluated. For ease of further evaluation, nomograms were constructed for selected models.
A model built by integrating all of the R score, D score, and clinical parameters had the best discriminative ability with areas under the curve of 0.793 (95% confidence interval [CI], 0.735-0.851), 0.774 (95% CI, 0.762-0.786), and 0.855 (95% CI, 0.719-0.990) in the training, bootstrapping, and external test sets, respectively. The calibration curve image showed good agreement between the predicted and actual values, with a slope of 1.21 and intercept of -0.04. The decision curve image showed a positive net benefit for the final model based on the nomogram.
Radiomic and dosiomic features have the potential to assist with the prediction of RP, and the combination of radiomics, dosiomics, and clinical parameters led to the best prognostic model in the present study.
放射性肺炎（RP）是胸部放疗常见的副作用之一。放射组学和剂量组学可量化医学图像和放射治疗剂量分布中隐含的信息。在本研究中，我们展示了放射组学、剂量组学和临床特征在预测RP方面的预后潜力。
对2013年至2019年间确诊为肺癌的314例回顾性收集患者和35例前瞻性纳入患者获取了放射组学、剂量组学、剂量体积直方图（DVH）指标和临床参数。在特征选择后，基于逻辑回归计算放射组学风险评分（R评分）、剂量组学风险评分（D评分）以及DVH评分。使用R评分、D评分、DVH评分和临床参数的不同组合构建了六个模型，以评估它们额外的预后能力。通过从训练集中进行自助重采样评估过度乐观情况，并将前瞻性收集的队列用作外部测试集。评估了表现最佳模型的模型校准和决策曲线特征。为便于进一步评估，为选定模型构建了列线图。
整合所有R评分、D评分和临床参数构建的模型具有最佳的判别能力，在训练集、自助重采样集和外部测试集中曲线下面积分别为0.793（95%置信区间[CI]，0.735 - 0.851）、0.774（95% CI，0.762 - 0.786）和0.855（95% CI，0.719 - 0.990）。校准曲线图像显示预测值与实际值之间具有良好的一致性，斜率为1.21，截距为 - 0.04。决策曲线图像显示基于列线图的最终模型具有正的净效益。
放射组学和剂量组学特征有潜力辅助预测RP，并且在本研究中，放射组学、剂量组学和临床参数的组合产生了最佳的预后模型。
