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基于图谱的前列腺癌放疗自动分割准确性比较

Comparison of atlas-based auto-segmentation accuracy for radiotherapy in prostate cancer.

作者信息

Aoyama Takahiro, Shimizu Hidetoshi, Kitagawa Tomoki, Yokoi Kazushi, Koide Yutaro, Tachibana Hiroyuki, Suzuki Kojiro, Kodaira Takeshi

机构信息

Department of Radiation Oncology, Aichi Cancer Centre, 1-1 Kanokoden, Chikusa-Ku, Nagoya, Aichi 464-8681, Japan.

Graduate School of Medicine, Aichi Medical University, 1-1 Yazako-karimata, Nagakute, Aichi 480-1195, Japan.

出版信息

Phys Imaging Radiat Oncol. 2021 Aug 25;19:126-130. doi: 10.1016/j.phro.2021.08.002. eCollection 2021 Jul.

DOI:10.1016/j.phro.2021.08.002
PMID:34485717
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8397888/
Abstract

Atlas-based auto-segmentation (ABS) procedure used in radiotherapy can be classified into two groups, one using one atlas per patient (sSM) and the other using multiple atlases (sMM). This study evaluated auto-contouring accuracy and contouring time in patients with prostate cancer using the two procedures. The Dice similarity coefficient of sMM was significantly better than that of sSM (prostate [median, 0.81 (range, 0.66-0.91) vs. 0.64 (0.27-0.71),  < 0.01], seminal vesicles [0.49 (0.31-0.80) vs. 0.18 (0.01-0.60),  < 0.05], and rectum [0.81 (0.37-0.91) vs. 0.57 (0.31-0.77),  < 0.01]). The median contouring times were 2.6 (sMM) and 1.3 min (sSM).

摘要

放射治疗中使用的基于图谱的自动分割(ABS)程序可分为两类,一类是为每位患者使用一个图谱(单图谱方法),另一类是使用多个图谱(多图谱方法)。本研究使用这两种程序评估了前列腺癌患者的自动轮廓勾画准确性和轮廓勾画时间。多图谱方法的骰子相似系数显著优于单图谱方法(前列腺[中位数,0.81(范围,0.66 - 0.91)对0.64(0.27 - 0.71),<0.01],精囊[0.49(0.31 - 0.80)对0.18(0.01 - 0.60),<0.05],以及直肠[0.81(0.37 - 0.91)对0.57(0.31 - 0.77),<0.01])。轮廓勾画的中位时间分别为2.6分钟(多图谱方法)和1.3分钟(单图谱方法)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2a4/8397888/4edcfed4bc5c/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2a4/8397888/c84be135f251/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2a4/8397888/4edcfed4bc5c/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2a4/8397888/c84be135f251/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b2a4/8397888/4edcfed4bc5c/gr2.jpg

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本文引用的文献

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Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.深度学习在前列腺癌放射治疗计划中的应用:系统评价。
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ESTRO ACROP consensus guideline on CT- and MRI-based target volume delineation for primary radiation therapy of localized prostate cancer.ESTRO ACROP 共识指南:基于 CT 和 MRI 的局限性前列腺癌放射治疗靶区勾画。
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Evaluation of rectum and bladder dose accumulation from external beam radiotherapy and brachytherapy for cervical cancer using two different deformable image registration techniques.
基于人工智能的自动分割:在轮廓描绘、吸收剂量分布及后勤保障方面的优势
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Real-world validation of Artificial Intelligence-based Computed Tomography auto-contouring for prostate cancer radiotherapy planning.基于人工智能的计算机断层扫描自动轮廓勾画在前列腺癌放射治疗计划中的真实世界验证
Phys Imaging Radiat Oncol. 2023 Oct 13;28:100501. doi: 10.1016/j.phro.2023.100501. eCollection 2023 Oct.
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Radiat Oncol. 2022 Sep 5;17(1):152. doi: 10.1186/s13014-022-02121-3.
使用两种不同的可变形图像配准技术评估宫颈癌外照射放疗和近距离放疗时直肠和膀胱的剂量累积情况。
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A review of interventions to reduce inter-observer variability in volume delineation in radiation oncology.减少放射肿瘤学中靶区勾画观察者间差异的干预措施综述。
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