深度学习重建对CT检查中辐射剂量降低和癌症风险的影响:一个真实世界的临床分析。

IF 4.7 2区 医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
European Radiology Pub Date : 2025-06-01 Epub Date: 2024-11-29 DOI:10.1007/s00330-024-11212-6
Naoki Kobayashi, Takeshi Nakaura, Naofumi Yoshida, Yasunori Nagayama, Masafumi Kidoh, Hiroyuki Uetani, Daisuke Sakabe, Yuki Kawamata, Yoshinori Funama, Takashi Tsutsumi, Toshinori Hirai
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引用次数: 0

摘要

目的:本研究的目的是利用真实世界的临床数据,估计深度学习重建(DLR)的实施可以在多大程度上降低CT检查中辐射诱发癌症的风险。方法:我们回顾性分析了与DLR实施相关的两个阶段的成年患者的扫描数据:使用混合迭代重建的12个月DLR前阶段(n = 5553)和使用常规CT重建过渡到DLR的12个月DLR后阶段(n = 5494)。为了确保两组之间的可比性,我们采用基于年龄、性别和体重指数的1:1匹配倾向评分。收集剂量数据以估计器官特异性等效剂量和总有效剂量。我们评估了dlr实施后的平均剂量减少量,并估计了dlr实施前后每次CT检查的癌症终身归因风险(LAR)。还估计了实施DLR前后辐射诱发癌症的数量。结果:经倾向评分匹配后,两组共5247例纳入最终分析。DLR后,全身有效CT总剂量由DLR前的28.1±14.0 mSv显著降低至15.5±10.3 mSv (p)。结论:与迭代重建相比,DLR的实施有可能使辐射剂量降低45%,辐射致癌风险从0.247降至0.130%。与混合迭代重建相比,在常规CT扫描中实施深度学习重建(DLR)能否显著降低辐射剂量和辐射致癌风险?结果DLR降低了45%的有效体CT总剂量(从28.1±14.0 mSv降至15.5±10.3 mSv),并将估计癌症发病率从0.247降至0.130%。临床实践中采用DLR大大降低了CT检查的辐射暴露和癌症风险,提高了患者(特别是年轻女性)的安全性,并强调了先进成像技术的重要性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Impact of deep learning reconstruction on radiation dose reduction and cancer risk in CT examinations: a real-world clinical analysis.

Purpose: The purpose of this study is to estimate the extent to which the implementation of deep learning reconstruction (DLR) may reduce the risk of radiation-induced cancer from CT examinations, utilizing real-world clinical data.

Methods: We retrospectively analyzed scan data of adult patients who underwent body CT during two periods relative to DLR implementation at our facility: a 12-month pre-DLR phase (n = 5553) using hybrid iterative reconstruction and a 12-month post-DLR phase (n = 5494) with routine CT reconstruction transitioning to DLR. To ensure comparability between two groups, we employed propensity score matching 1:1 based on age, sex, and body mass index. Dose data were collected to estimate organ-specific equivalent doses and total effective doses. We assessed the average dose reduction post-DLR implementation and estimated the Lifetime Attributable Risk (LAR) for cancer per CT exam pre- and post-DLR implementation. The number of radiation-induced cancers before and after the implementation of DLR was also estimated.

Results: After propensity score matching, 5247 cases from each group were included in the final analysis. Post-DLR, the total effective body CT dose significantly decreased to 15.5 ± 10.3 mSv from 28.1 ± 14.0 mSv pre-DLR (p < 0.001), a 45% reduction. This dose reduction significantly lowered the radiation-induced cancer risk, especially among younger women, with the estimated annual cancer incidence from 0.247% pre-DLR to 0.130% post-DLR.

Conclusion: The implementation of DLR has the possibility to reduce radiation dose by 45% and the risk of radiation-induced cancer from 0.247 to 0.130% as compared with the iterative reconstruction.

Key points: Question Can implementing deep learning reconstruction (DLR) in routine CT scans significantly reduce radiation dose and the risk of radiation-induced cancer compared to hybrid iterative reconstruction? Findings DLR reduced the total effective body CT dose by 45% (from 28.1 ± 14.0 mSv to 15.5 ± 10.3 mSv) and decreased estimated cancer incidence from 0.247 to 0.130%. Clinical relevance Adopting DLR in clinical practice substantially lowers radiation exposure and cancer risk from CT exams, enhancing patient safety, especially for younger women, and underscores the importance of advanced imaging techniques.

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来源期刊
European Radiology
European Radiology 医学-核医学
CiteScore
11.60
自引率
8.50%
发文量
874
审稿时长
2-4 weeks
期刊介绍: European Radiology (ER) continuously updates scientific knowledge in radiology by publication of strong original articles and state-of-the-art reviews written by leading radiologists. A well balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes ER an indispensable source for current information in this field. This is the Journal of the European Society of Radiology, and the official journal of a number of societies. From 2004-2008 supplements to European Radiology were published under its companion, European Radiology Supplements, ISSN 1613-3749.
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