郑思思,夏彤,蔡尚,等.放疗引起的淋巴细胞减少症的预测与预防进展[J].中华放射医学与防护杂志,2026,46(4):427-431.Zheng Sisi,Xia Tong,Cai Shang,et al.Advances in the prediction and prevention of radiation-induced lymphopenia[J].Chin J Radiol Med Prot,2026,46(4):427-431
放疗引起的淋巴细胞减少症的预测与预防进展
Advances in the prediction and prevention of radiation-induced lymphopenia
投稿时间:2025-07-09  
DOI:10.3760/cma.j.cn112271-20250709-00239
中文关键词:  放射治疗|淋巴细胞减少症|预测模型|预防策略
英文关键词:Radiotherapy|Lymphopenia|Prediction model|Prevention strategy
基金项目:江苏省青年科技人才托举工程(JSTJ-2024-113);江苏省333高层次人才培养工程(2024,3-2481);苏州大学苏州医学院临床高峰项目(ML12300723);放射医学与辐射防护国家重点实验室资助项目(GZK1202405);苏州市姑苏卫生人才计划(GSWS2021025);苏州大学附属第二医院人才托举项目(XKTJ-RC202407)
作者单位E-mail
郑思思 苏州大学附属第二医院布拉格治疗中心, 苏州 215004  
夏彤 苏州大学附属第二医院放疗科, 苏州 215004  
蔡尚 苏州大学附属第二医院布拉格治疗中心, 苏州 215004
放射医学与辐射防护国家重点实验室(苏州大学), 苏州 215123 
cbibbys@163.com 
单秋洁 江苏省淮安市第五人民医院放疗科, 淮安 223300  
田野 苏州大学附属第二医院放疗科, 苏州 215004
放射医学与辐射防护国家重点实验室(苏州大学), 苏州 215123 
 
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中文摘要:
      放疗引起的淋巴细胞减少症是实体肿瘤放射治疗中的常见并发症,严重淋巴细胞减少症常影响抗肿瘤治疗疗效并预示着不良的生存结局。淋巴细胞检测时机直接影响放疗诱发淋巴细胞减少症的风险评估与预后判断的准确性。通过循环血液剂量学模型和人工智能模型有望精准预测放疗引起的淋巴细胞减少症发生风险。限制免疫危及器官的受照射剂量体积,采用质子、碳离子治疗等先进放疗设备,以及应用超高剂量率放疗技术,可有效降低放疗引起的淋巴细胞减少症的风险,为改善恶性肿瘤患者预后提供有效手段。
英文摘要:
      Radiation-induced lymphopenia (RIL) represents a common complication in radiotherapy for solid tumors. Severe RIL adversely affects the treatment efficacy of tumors and portends poor survival outcomes. The timing of lymphocyte tests directly influences the accuracy of risk assessment and prognostic evaluation for RIL. The precise prediction of RIL risk is expected to be achieved using predictive models, including circulating blood-related dosimetric models and artificial intelligence (AI) models. The RIL risks can be effectively reduced by employing strategies such as limiting the radiation dose-volume to lymphocyte-related organs at risk (LOARs), using advanced radiotherapy equipment for proton and carbon ion therapy, and applying ultra-high dose rate radiotherapy (UHDR-RT). These strategies serve as effective means to improve the prognosis of patients with malignant tumors.
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