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Abstract：
BACKGROUND: To integrate radiomics and dosiomics features from multiple regions in the radiation pneumonia (RP grade ≥ 2) prediction for esophageal cancer (EC) patients underwent radiotherapy (RT).
METHODS: Total of 143 EC patients in the authors\' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated.
RESULTS: Models with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively.
CONCLUSIONS: CT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT.
METHODS: Total of 143 EC patients in the authors\' hospital (training and internal validation: 70%:30%) and 32 EC patients from another hospital (external validation) underwent RT from 2015 to 2022 were retrospectively reviewed and analyzed. Patients were dichotomized as positive (RP+) or negative (RP-) according to CTCAE V5.0. Models with radiomics and dosiomics features extracted from single region of interest (ROI), multiple ROIs and combined models were constructed and evaluated. A nomogram integrating radiomics score (Rad_score), dosiomics score (Dos_score), clinical factors, dose-volume histogram (DVH) factors, and mean lung dose (MLD) was also constructed and validated.
RESULTS: Models with Rad_score_Lung&Overlap and Dos_score_Lung&Overlap achieved a better area under curve (AUC) of 0.818 and 0.844 in the external validation in comparison with radiomics and dosiomics models with features extracted from single ROI. Combining four radiomics and dosiomics models using support vector machine (SVM) improved the AUC to 0.854 in the external validation. Nomogram integrating Rad_score, and Dos_score with clinical factors, DVH factors, and MLD further improved the RP prediction AUC to 0.937 and 0.912 in the internal and external validation, respectively.
CONCLUSIONS: CT-based RP prediction model integrating radiomics and dosiomics features from multiple ROIs outperformed those with features from a single ROI with increased reliability for EC patients who underwent RT.
摘要：
背景：将来自多个区域的影像组学和剂量组学特征整合在食管癌（EC）患者接受放疗（RT）的放射性肺炎（RP等级≥2）预测中。
方法：对作者医院的143例EC患者（培训和内部验证：70％：30％）和另一家医院的32例EC患者（外部验证）从2015年至2022年接受RT进行了回顾性回顾和分析。根据CTCAEV5.0将患者分为阳性（RP）或阴性（RP-）。具有从单个感兴趣区域（ROI）提取的影像组学和剂量组学特征的模型，构建并评估了多个ROI和组合模型。整合影像组学评分的列线图(Rad_score)，dosiomics评分(Dos_score)，临床因素，剂量-体积直方图(DVH)因子，和平均肺剂量（MLD）也被构建和验证。
结果：具有Rad_score_Lung&Overlap和Dos_score_Lung&Overlap的模型在外部验证中获得了较好的曲线下面积（AUC），分别为0.818和0.844。使用支持向量机（SVM）结合四种影像组学和剂量组学模型，在外部验证中将AUC提高到0.854。列线图积分Rad_score,和Dos_评分与临床因素，DVH因素,和MLD在内部和外部验证中进一步将RP预测AUC提高到0.937和0.912，分别。
结论：基于CT的RP预测模型综合了来自多个ROI的影像组学和剂量组学特征，优于那些具有来自单个ROI的特征的预测模型，对于接受RT的EC患者具有更高的可靠性。
方法：对作者医院的143例EC患者（培训和内部验证：70％：30％）和另一家医院的32例EC患者（外部验证）从2015年至2022年接受RT进行了回顾性回顾和分析。根据CTCAEV5.0将患者分为阳性（RP）或阴性（RP-）。具有从单个感兴趣区域（ROI）提取的影像组学和剂量组学特征的模型，构建并评估了多个ROI和组合模型。整合影像组学评分的列线图(Rad_score)，dosiomics评分(Dos_score)，临床因素，剂量-体积直方图(DVH)因子，和平均肺剂量（MLD）也被构建和验证。
结果：具有Rad_score_Lung&Overlap和Dos_score_Lung&Overlap的模型在外部验证中获得了较好的曲线下面积（AUC），分别为0.818和0.844。使用支持向量机（SVM）结合四种影像组学和剂量组学模型，在外部验证中将AUC提高到0.854。列线图积分Rad_score,和Dos_评分与临床因素，DVH因素,和MLD在内部和外部验证中进一步将RP预测AUC提高到0.937和0.912，分别。
结论：基于CT的RP预测模型综合了来自多个ROI的影像组学和剂量组学特征，优于那些具有来自单个ROI的特征的预测模型，对于接受RT的EC患者具有更高的可靠性。
