Abstract：
Lymphopenia is known for its significance on poor survivals in breast cancer patients. Considering full dosimetric data, this study aimed to develop and validate predictive models for lymphopenia after radiotherapy (RT) in breast cancer.
Patients with breast cancer treated with adjuvant RT were eligible in this multicenter study. The study endpoint was lympopenia, defined as the reduction in absolute lymphocytes and graded lymphopenia after RT. The dose-volume histogram (DVH) data of related critical structures and clinical factors were taken into account for the development of dense neural network (DNN) predictive models. The developed DNN models were validated using external patient cohorts.
A total of 918 consecutive patients with invasive breast cancer enrolled. The training, testing, and external validating datasets consisted of 589, 203, and 126 patients, respectively. Treatment volumes at nearly all dose levels of the DVH were significant predictors for lymphopenia following RT, including volumes at very low-dose 1 Gy (V1) of organs at risk (OARs) including lung, heart and body, especially ipsilateral-lung V1. A final DNN model, combining full DVH dosimetric parameters of OARs and three key clinical factors, achieved a predictive accuracy of 75 % or higher.
This study demonstrated and externally validated the significance of full dosimetric data, particularly the volume of low dose at as low as 1 Gy of critical structures on lymphopenia after radiation in patients with breast cancer. The significance of V1 deserves special attention, as modern VMAT RT technology often has a relatively high value of this parameter. Further study is warranted for RT plan optimization.
Patients with breast cancer treated with adjuvant RT were eligible in this multicenter study. The study endpoint was lympopenia, defined as the reduction in absolute lymphocytes and graded lymphopenia after RT. The dose-volume histogram (DVH) data of related critical structures and clinical factors were taken into account for the development of dense neural network (DNN) predictive models. The developed DNN models were validated using external patient cohorts.
A total of 918 consecutive patients with invasive breast cancer enrolled. The training, testing, and external validating datasets consisted of 589, 203, and 126 patients, respectively. Treatment volumes at nearly all dose levels of the DVH were significant predictors for lymphopenia following RT, including volumes at very low-dose 1 Gy (V1) of organs at risk (OARs) including lung, heart and body, especially ipsilateral-lung V1. A final DNN model, combining full DVH dosimetric parameters of OARs and three key clinical factors, achieved a predictive accuracy of 75 % or higher.
This study demonstrated and externally validated the significance of full dosimetric data, particularly the volume of low dose at as low as 1 Gy of critical structures on lymphopenia after radiation in patients with breast cancer. The significance of V1 deserves special attention, as modern VMAT RT technology often has a relatively high value of this parameter. Further study is warranted for RT plan optimization.
摘要：
背景：已知淋巴细胞减少对乳腺癌（BC）患者的不良生存率具有重要意义。考虑到完整的剂量测定数据，本研究旨在建立和验证BC放疗后淋巴细胞减少症的预测模型.
方法：接受辅助RT治疗的BC患者符合这项多中心研究的条件。研究终点是淋巴减少症，定义为RT后绝对淋巴细胞减少和分级淋巴细胞减少。考虑了相关关键结构和临床因素的剂量-体积直方图（DVH）数据，以开发密集神经网络（DNN）预测模型。使用外部患者队列验证开发的DNN模型。
结果：共纳入918例侵袭性BC患者。训练,测试,外部验证数据集包括589、203和126名患者，分别。几乎所有剂量水平的DVH治疗量都是RT后淋巴细胞减少的重要预测因子。包括极低剂量1Gy（V1）的危险器官（OAR）包括肺的体积，心脏和身体，尤其是同侧肺V1。最终的DNN模型，结合OARs的完整DVH剂量学参数和三个关键临床因素，达到75%或更高的预测精度。
结论：这项研究证明并外部验证了完整剂量学数据的重要性，特别是低剂量的体积，在低至1Gy的关键结构的BC患者放疗后淋巴细胞减少。V1的意义值得特别关注,因为现代VMATRT技术通常具有相对较高的此参数值。需要进一步的研究以优化RT计划。
方法：接受辅助RT治疗的BC患者符合这项多中心研究的条件。研究终点是淋巴减少症，定义为RT后绝对淋巴细胞减少和分级淋巴细胞减少。考虑了相关关键结构和临床因素的剂量-体积直方图（DVH）数据，以开发密集神经网络（DNN）预测模型。使用外部患者队列验证开发的DNN模型。
结果：共纳入918例侵袭性BC患者。训练,测试,外部验证数据集包括589、203和126名患者，分别。几乎所有剂量水平的DVH治疗量都是RT后淋巴细胞减少的重要预测因子。包括极低剂量1Gy（V1）的危险器官（OAR）包括肺的体积，心脏和身体，尤其是同侧肺V1。最终的DNN模型，结合OARs的完整DVH剂量学参数和三个关键临床因素，达到75%或更高的预测精度。
结论：这项研究证明并外部验证了完整剂量学数据的重要性，特别是低剂量的体积，在低至1Gy的关键结构的BC患者放疗后淋巴细胞减少。V1的意义值得特别关注,因为现代VMATRT技术通常具有相对较高的此参数值。需要进一步的研究以优化RT计划。
