Department of Clinical Oncology, Shenzhen Key Laboratory for Cancer Metastasis and Personalized Therapy, The University of Hong Kong-Shenzhen Hospital, Shenzhen, China; Department of Clinical Oncology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China.
Department of Radiotherapy, The First Affiliated Hospital of Hainan Medical University, China.
Radiother Oncol. 2024 Aug;197:110333. doi: 10.1016/j.radonc.2024.110333. Epub 2024 May 19.
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.
淋巴细胞减少症因其对乳腺癌患者生存预后的重要意义而受到关注。本研究旨在考虑全剂量学数据，建立并验证预测乳腺癌患者放疗后淋巴细胞减少症的模型。
本多中心研究纳入接受辅助放疗的乳腺癌患者。研究终点为淋巴细胞减少症，定义为放疗后绝对淋巴细胞数和分级淋巴细胞减少症。考虑相关危及器官（OAR）的剂量-体积直方图（DVH）数据和临床因素，建立密集神经网络（DNN）预测模型。使用外部患者队列验证所建立的 DNN 模型。
共纳入 918 例浸润性乳腺癌患者。训练、测试和外部验证数据集分别包含 589、203 和 126 例患者。DVH 中接近所有剂量水平的治疗体积均是放疗后淋巴细胞减少症的显著预测因子，包括 OAR 中的极低剂量 1 戈瑞（V1）体积，如肺、心脏和身体，特别是同侧肺 V1。最终的 DNN 模型结合 OAR 的全剂量学参数和三个关键临床因素，预测准确率达到 75%或更高。
本研究表明并在外部验证了全剂量学数据的重要性，特别是在接受乳腺癌放疗的患者中，关键结构低至 1 戈瑞的剂量体积与放疗后淋巴细胞减少症相关。V1 具有重要意义，因为现代 VMAT RT 技术通常具有相对较高的 V1 值。需要进一步研究以优化放疗计划。
