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Spatial Dose-Response Pattern Associated With Severe Radiation-Induced Lymphopenia in the Liver: A Voxel-Based Analysis

Authors
 Kim, Seohan  ;  Byun, Hwa Kyung  ;  Sung, Wonmo 
Citation
 INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, Vol.123(2) : 563-571, 2025-10 
Journal Title
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
ISSN
 0360-3016 
Issue Date
2025-10
Abstract
Purpose: This study aimed to investigate the dose-response pattern of severe radiation-induced lymphopenia (SRIL) in patients with hepatocellular carcinoma (HCC) undergoing radiation therapy (RT). We focused on identifying specific liver regions associated with SRIL development. Methods and Materials: We analyzed data from 75 patients with HCC treated with RT. Segment-wise and voxel-based analyses (VBAs) were conducted to investigate the spatial relationship between delivered dose and SRIL occurrence (absolute lymphocyte count [ALC] < 500/mL). Logistic regression was performed for segment-wise analysis, whereas generalized linear models and Mann-Whitney U tests were employed for VBAs. The liver was divided into Couinaud segments, and dose distributions were analyzed at both the segment and voxel levels. Results: Segment-wise logistic regression revealed that pre-RT ALC (odds ratio [OR], 0.006; P = .002), liver segments 1 (OR, 1.228; P = .048), and 7 (OR, 1.314; P = .016) were statistically associated with SRIL occurrence. VBAs demonstrated heterogeneous dose-response patterns across the liver, with segment 1 consistently showing the strongest association with SRIL across different statistical methods. Segment 1 contained the highest proportion of statistically significant voxels (94%) in relation to SRIL occurrence among all liver segments. Conclusions: This study revealed an inhomogeneous dose-response pattern regarding SRIL manifestation in the liver. Our results suggest that certain regions within a single organ may require rigorous dosimetric constraints to mitigate SRIL. (c) 2025 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
Full Text
https://www.sciencedirect.com/science/article/pii/S0360301625003578
DOI
10.1016/j.ijrobp.2025.03.082
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Radiation Oncology (방사선종양학교실) > 1. Journal Papers
Yonsei Authors
Byun, Hwa Kyung(변화경) ORCID logo https://orcid.org/0000-0002-8964-6275
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/208379
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