Potential Determinants for Radiation-Induced Lymphopenia in Patients With Breast Cancer Using Interpretable Machine Learning Approach.

Autor:	Yu H; Institute of Biomedical and Health Engineering, Chinese Academy of Sciences Shenzhen Institutes of Advanced Technology, Shenzhen, China., Chen F; Department of Clinical Oncology, University of Hong Kong-Shenzhen Hospital, Shenzhen, China.; Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China., Lam KO; Department of Clinical Oncology, University of Hong Kong-Shenzhen Hospital, Shenzhen, China.; Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China., Yang L; Department of Clinical Oncology, University of Hong Kong-Shenzhen Hospital, Shenzhen, China., Wang Y; Biomedical Engineering, Shenzhen Polytechnic, Shenzhen, China., Jin JY; University Hospitals/Cleverland Medical Center, Seidman Cancer Center and Case Western Reserve University, Cleveland, OH, United States., Ei Helali A; Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China., Kong FS; Department of Clinical Oncology, University of Hong Kong-Shenzhen Hospital, Shenzhen, China.; Department of Clinical Oncology, The University of Hong Kong, Hong Kong, Hong Kong SAR, China.
Jazyk:	angličtina
Zdroj:	Frontiers in immunology [Front Immunol] 2022 Jun 21; Vol. 13, pp. 768811. Date of Electronic Publication: 2022 Jun 21 (Print Publication: 2022).
DOI:	10.3389/fimmu.2022.768811
Abstrakt:	Radiation-induced lymphopenia is known for its survival significance in patients with breast cancer treated with radiation therapy. This study aimed to evaluate the impact of radiotherapy on lymphocytes by applying machine learning strategies. We used Extreme Gradient Boosting (XGboost) to predict the event of lymphopenia (grade≥1) and conduced an independent validation. Then, we induced feature attribution analysis (Shapley additive explanation, SHAP) in explaining the XGboost models to explore the directional contribution of each feature to lymphopenia. Finally, we implemented the proof-of-concept clinical validation. The results showed that the XGboost models had rigorous generalization performances (accuracies 0.764 and ROC-AUC 0.841, respectively) in the independent cohort. The baseline lymphocyte counts are the most protective feature (SHAP = 5.226, direction of SHAP = -0.964). Baseline platelets and monocytes also played important protective roles. The usage of taxane only chemotherapy was less risk on lymphopenia than the combination of anthracycline and taxane. By the contribution analysis of dose, we identified that firstly lymphocytes were sensitive to a radiation dose less than 4Gy; secondly the irradiation volume was more important in promoting lymphopenia than the irradiation dose; thirdly the irradiation dose promoted the event of lymphopenia when the irradiation volume was fixed. Overall, our findings paved the way to clarifying the radiation dose volume effect. To avoid radiation-induced lymphopenia, irradiation volume should be kept to a minimum during the planning process, as long as the target coverage is not compromised. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Yu, Chen, Lam, Yang, Wang, Jin, EI Helali and Kong.)
Databáze:	MEDLINE
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