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• W4387206972 abstract "Lymphopenia is common after radiotherapy (RT) and is known for its significance on poor survival outcomes in patients with breast cancer. Previous work has demonstrated the significance of point dosimetric factors like the volume receiving 5 Gy. Considering the full dosimetric data together, this study aimed to develop and validate predictive models for lymphopenia after RT in breast cancer.Patients with breast cancer treated with radiation therapy in adjuvant setting and with complete dosimetric data were eligible. Combining dose-volume histogram (DVH) dosimetric and clinical factors, dense neural network (DNN) models were developed to predict both the reduction in lymphocyte counts and the graded lymphopenia in breast cancer patients after adjuvant RT. A Shapley additive explanation was applied to explain each feature's directional contributions. The generalization of DNN models was validated in both internal and independent external validation cohorts. P<0.05 was considered to be significant.A total of 928 consecutive patients with invasive breast cancer were eligible for this study. Treatment volumes of nearly all irradiation dose levels of DVH were significant predictors for lymphopenia after RT, including volumes at very low-dose 1 Gy (V1) of all structures considered including the lung, heart and body. DNN models using full DVH dosimetric and clinical factors were built and a simplified model was further established and validated in both internal and external validation cohorts. This simplified DNN AI model, combining full DVH dosimetric parameters of all OARs and five key clinical factors including baseline lymphocyte counts, tumor stage, RT technique, RT fields and RT fractionation, showed a predictive accuracy of 77% and above.This study demonstrated and externally validated the significance of an AI model of combining clinical and full dosimetric data, especially the volume of low dose at as low as 1 Gy of all critical structures on lymphopenia after RT in patients with breast cancer. The significance of V1 deserves special attention, as modern arc RT technology often has relatively high value of this parameter. Further study warranted for breast cancer RT plan optimization." @default.
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• W4387206972 date "2023-10-01" @default.
• W4387206972 modified "2023-10-16" @default.
• W4387206972 title "Interpretable Deep Learning Identified the Significance of 1 Gy Volume on Lymphopenia after Radiotherapy in Breast Cancer" @default.
• W4387206972 doi "https://doi.org/10.1016/j.ijrobp.2023.06.1006" @default.
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