Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images.
Predicting survival of glioblastoma from automatic whole-brain and tumor segmentation of MR images.
复制标题
DOI：
10.1038/s41598-022-19223-3
复制
发表时间：
2022-11-17
影响因子：
4.6
通讯作者：
Van Leemput, Koen
中科院分区：
文献类型：
--
作者：
Palsson, Sveinn;Cerri, Stefano;Poulsen, Hans Skovgaard;Urup, Thomas;Law, Ian;Van Leemput, Koen
生存预测模型有可能用于指导胶质母细胞瘤患者的治疗。然而，目前可用的含有预后信息的 MR 成像生物标志物通常难以解释，难以在数据采集中进行概括，或者仅适用于术前 MR 数据。在本文中，我们旨在通过引入新颖的成像特征来解决这些问题，这些特征可以根据 MR 图像自动计算并输入机器学习模型以预测患者的生存率。我们提出的特征具有直接的解剖功能解释：它们测量肿瘤对周围大脑结构造成的变形，将患者大脑中各种结构的形状与健康个体的预期形状进行比较。为了获得所需的分割，我们使用了一种对比度自适应且对缺失模态具有鲁棒性的自动方法，使得这些特征可以跨扫描仪和成像协议推广。由于我们提出的特征不依赖于肿瘤区域本身的特征，因此它们也适用于术后图像，而在生存预测的背景下对术后图像的研究要少得多。使用涉及术前和术后数据的实验，我们表明所提出的特征在总体和无进展生存方面具有预后价值，超过了传统的非影像学特征。
Survival prediction models can potentially be used to guide treatment of glioblastoma patients. However, currently available MR imaging biomarkers holding prognostic information are often challenging to interpret, have difficulties generalizing across data acquisitions, or are only applicable to pre-operative MR data. In this paper we aim to address these issues by introducing novel imaging features that can be automatically computed from MR images and fed into machine learning models to predict patient survival. The features we propose have a direct anatomical–functional interpretation: They measure the deformation caused by the tumor on the surrounding brain structures, comparing the shape of various structures in the patient’s brain to their expected shape in healthy individuals. To obtain the required segmentations, we use an automatic method that is contrast-adaptive and robust to missing modalities, making the features generalizable across scanners and imaging protocols. Since the features we propose do not depend on characteristics of the tumor region itself, they are also applicable to post-operative images, which have been much less studied in the context of survival prediction. Using experiments involving both pre- and post-operative data, we show that the proposed features carry prognostic value in terms of overall- and progression-free survival, over and above that of conventional non-imaging features.
登录
查看更多内容
影响因子：
19.7
作者：
Bae, Sohi;Choi, Yoon Seong;Lee, Seung-Koo
通讯作者：
Lee, Seung-Koo
影响因子：
5.7
作者：
Fischl, Bruce
通讯作者：
Fischl, Bruce
影响因子：
158.5
作者：
Hegi, ME;Diserens, A;Stupp, R
通讯作者：
Stupp, R
影响因子：
19.7
作者：
Gutman, David A.;Cooper, Lee A. D.;Brat, Daniel J.
通讯作者：
Brat, Daniel J.
影响因子：
15.9
作者：
Ellingson, Benjamin M.;Abrey, Lauren E.;Cloughesy, Timothy F.
通讯作者：
Cloughesy, Timothy F.