Comparison of mathematical models for red marrow and blood absorbed dose estimation in the radioiodine treatment of advanced differentiated thyroid carcinoma.

IF 3.4 3区 医学 Q2 ENGINEERING, BIOMEDICAL Physics in medicine and biology Pub Date : 2015-02-07 Epub Date: 2015-01-14 DOI:10.1088/0031-9155/60/3/1141
A Miranti, A Giostra, E Richetta, E Gino, R E Pellerito, M Stasi
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引用次数: 10

Abstract

Metastatic and recurrent differentiated thyroid carcinoma is preferably treated with (131)I, whose administered activity is limited by red marrow (RM) toxicity, originally correlated by Benua to a blood absorbed dose higher than 2 Gy. Afterward a variety of dosimetric approaches has been proposed. The aim of this work is to compare the results of the Benua formula with the ones of other three blood and RM absorbed dose formulae. Materials and methods have been borrowed by the dosimetric protocol of the Italian Internal Dosimetry group and adapted to the routine of our centre. Wilcoxon t-tests and percentage differences have been applied for comparison purposes. Results are significantly different (p < 0.05) from each other, with an average percentage difference between Benua versus other results of -22%. The dosimetric formula applied to determine blood or RM absorbed dose may contribute significantly to increase heterogeneity in absorbed dose and dose-response results. Standardization should be a major objective.

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放射碘治疗晚期分化甲状腺癌红骨髓与血液吸收剂量估算的数学模型比较。
转移性和复发性分化甲状腺癌最好使用(131)I治疗,其给药活性受到红骨髓(RM)毒性的限制,最初通过Benua与高于2 Gy的血液吸收剂量相关。之后,提出了各种剂量学方法。本工作的目的是比较贝努阿方与其他三种血液和RM吸收剂量方的结果。材料和方法借鉴了意大利内剂量组的剂量测定方案,并适应了本中心的日常工作。为了进行比较,采用了Wilcoxon t检验和百分比差异。结果之间有显著差异(p < 0.05), Benua与其他结果的平均百分比差异为-22%。用于测定血液或RM吸收剂量的剂量学公式可能会显著增加吸收剂量和剂量反应结果的异质性。标准化应该是一个主要目标。
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来源期刊
Physics in medicine and biology
Physics in medicine and biology 医学-工程:生物医学
CiteScore
6.50
自引率
14.30%
发文量
409
审稿时长
2 months
期刊介绍: The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry
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