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椭球计算与手动肿瘤勾画在胶质母细胞瘤肿瘤体积评估中的比较。

Ellipsoid calculations versus manual tumor delineations for glioblastoma tumor volume evaluation.

机构信息

Department of Radiotherapy, ICANS, Institut Cancérologie Strasbourg Europe, 17 Rue Albert Calmette, 67200, Strasbourg Cedex, France.

Department of Radiotherapy, Institut Gustave Roussy, Paris-Saclay University, Villejuif, France.

出版信息

Sci Rep. 2022 Jun 22;12(1):10502. doi: 10.1038/s41598-022-13739-4.

DOI:10.1038/s41598-022-13739-4
PMID:35732848
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9217851/
Abstract

In glioblastoma, the response to treatment assessment is essentially based on the 2D tumor size evolution but remains disputable. Volumetric approaches were evaluated for a more accurate estimation of tumor size. This study included 57 patients and compared two volume measurement methods to determine the size of different glioblastoma regions of interest: the contrast-enhancing area, the necrotic area, the gross target volume and the volume of the edema area. The two methods, the ellipsoid formula (the calculated method) and the manual delineation (the measured method) showed a high correlation to determine glioblastoma volume and a high agreement to classify patients assessment response to treatment according to RANO criteria. This study revealed that calculated and measured methods could be used in clinical practice to estimate glioblastoma volume size and to evaluate tumor size evolution.

摘要

在胶质母细胞瘤中,治疗反应评估主要基于 2D 肿瘤大小的演变,但仍然存在争议。已经评估了各种体积评估方法,以更准确地估计肿瘤大小。本研究纳入了 57 名患者,比较了两种体积测量方法,以确定不同胶质母细胞瘤感兴趣区域的大小:增强区域、坏死区域、大体肿瘤靶区和水肿区域体积。这两种方法,即椭球公式(计算法)和手动勾画(测量法),显示出高度相关性,可用于确定胶质母细胞瘤的体积,并且高度一致,可以根据 RANO 标准对患者的治疗反应进行分类。本研究表明,计算法和测量法可在临床实践中用于估计胶质母细胞瘤的体积大小,并评估肿瘤大小的演变。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54b0/9217851/7c8800acf37f/41598_2022_13739_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54b0/9217851/80f608bc3caf/41598_2022_13739_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54b0/9217851/f07aec50240b/41598_2022_13739_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54b0/9217851/7c8800acf37f/41598_2022_13739_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54b0/9217851/80f608bc3caf/41598_2022_13739_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54b0/9217851/f07aec50240b/41598_2022_13739_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/54b0/9217851/7c8800acf37f/41598_2022_13739_Fig3_HTML.jpg

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本文引用的文献

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Noninvasive diffusion magnetic resonance imaging of brain tumour cell size for the early detection of therapeutic response.无创性扩散磁共振成像检测脑肿瘤细胞大小以早期发现治疗反应。
Sci Rep. 2020 Jun 8;10(1):9223. doi: 10.1038/s41598-020-65956-4.
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Automated Brain Tumor Segmentation Using Multimodal Brain Scans: A Survey Based on Models Submitted to the BraTS 2012-2018 Challenges.基于 BraTS 2012-2018 挑战赛提交模型的多模态脑扫描的自动脑肿瘤分割:调查
IEEE Rev Biomed Eng. 2020;13:156-168. doi: 10.1109/RBME.2019.2946868. Epub 2019 Oct 11.
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Radiomics in Glioblastoma: Current Status and Challenges Facing Clinical Implementation.
胶质母细胞瘤治疗后早期的MRI表型提示患者的总体生存情况。
Neurooncol Adv. 2023 Oct 12;5(1):vdad133. doi: 10.1093/noajnl/vdad133. eCollection 2023 Jan-Dec.
胶质母细胞瘤中的放射组学:临床应用现状与面临的挑战
Front Oncol. 2019 May 21;9:374. doi: 10.3389/fonc.2019.00374. eCollection 2019.
4
Apparent diffusion coefficient and tumor volume measurements help stratify progression-free survival of bevacizumab-treated patients with recurrent glioblastoma multiforme.表观扩散系数和肿瘤体积测量有助于对接受贝伐单抗治疗的复发性多形性胶质母细胞瘤患者的无进展生存期进行分层。
Neuroradiol J. 2019 Aug;32(4):241-249. doi: 10.1177/1971400919847184. Epub 2019 May 8.
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Automated quantitative tumour response assessment of MRI in neuro-oncology with artificial neural networks: a multicentre, retrospective study.基于人工神经网络的 MRI 神经肿瘤学中肿瘤自动定量反应评估:多中心回顾性研究。
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