Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression.
Estimating Glioblastoma Biophysical Growth Parameters Using Deep Learning Regression.
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DOI：
10.1007/978-3-030-72084-1_15
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Bakas S
中科院分区：
文献类型：
--
作者：
Pati S;Sharma V;Aslam H;Thakur SP;Akbari H;Mang A;Subramanian S;Biros G;Davatzikos C;Bakas S
胶质母细胞瘤（GBM）可以说是最具侵袭性、浸润性和异质性的成人脑肿瘤类型。GBM生长的生物物理建模有助于更明智的临床决策。然而，将生物物理模型部署到临床环境是具有挑战性的，因为底层计算非常昂贵，并且使用现有技术可能需要几个小时。在这里，我们提出了一个计划，以加快计算。特别是，我们提出了一个基于深度学习（DL）的逻辑回归模型来估计GBM的生物物理增长。这种生长由三个肿瘤特异性参数定义：1）白色物质中的扩散系数（Dw），其规定了肿瘤细胞在白色物质中的浸润速率，2）质量效应参数（Mp），其定义了平均肿瘤扩张，和3）肿瘤已经生长的以天数计的估计时间（T）。术前结构多参数MRI（mpMRI）扫描n = 135例受试者的TCGA-GBM成像收集用于定量评价我们的方法。我们考虑由异常FLAIR信号包络定义的区域内的mpMRI强度，用于为每个肿瘤特异性生长参数训练一个DL模型。我们训练和验证基于DL的预测对来自生物物理反演模型的参数。我们基于DL的估计和生物物理参数之间的平均Pearson相关系数分别为Dw为0.85，Mp为0.90，T为0.94。这项研究从生物物理肿瘤生长估计中揭示了肿瘤特异性参数的能力。它为临床翻译铺平了道路，并为在未来的研究中利用先进的放射组学描述符打开了大门，与生物物理生长建模方法相比，参数重建速度要快得多。
Glioblastoma (GBM) is arguably the most aggressive, infiltrative, and heterogeneous type of adult brain tumor. Biophysical modeling of GBM growth has contributed to more informed clinical decision-making. However, deploying a biophysical model to a clinical environment is challenging since underlying computations are quite expensive and can take several hours using existing technologies. Here we present a scheme to accelerate the computation. In particular, we present a deep learning (DL)-based logistic regression model to estimate the GBM’s biophysical growth in seconds. This growth is defined by three tumor-specific parameters: 1) a diffusion coefficient in white matter (Dw), which prescribes the rate of infiltration of tumor cells in white matter, 2) a mass-effect parameter (Mp), which defines the average tumor expansion, and 3) the estimated time (T) in number of days that the tumor has been growing. Preoperative structural multi-parametric MRI (mpMRI) scans from n = 135 subjects of the TCGA-GBM imaging collection are used to quantitatively evaluate our approach. We consider the mpMRI intensities within the region defined by the abnormal FLAIR signal envelope for training one DL model for each of the tumor-specific growth parameters. We train and validate the DL-based predictions against parameters derived from biophysical inversion models. The average Pearson correlation coefficients between our DL-based estimations and the biophysical parameters are 0.85 for Dw, 0.90 for Mp, and 0.94 for T, respectively. This study unlocks the power of tumor-specific parameters from biophysical tumor growth estimation. It paves the way towards their clinical translation and opens the door for leveraging advanced radiomic descriptors in future studies by means of a significantly faster parameter reconstruction compared to biophysical growth modeling approaches.
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影响因子：
10.6
作者：
Gooya A;Biros G;Davatzikos C
通讯作者：
Davatzikos C
DOI：
10.1007/978-3-319-30858-6_13
发表时间：
2016-01-01
期刊：
Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)
影响因子：
--
作者：
Bakas, Spyridon;Zeng, Ke;Davatzikos, Christos
通讯作者：
Davatzikos, Christos
影响因子：
4.4
作者：
Clark, Kenneth;Vendt, Bruce;Prior, Fred
通讯作者：
Prior, Fred
影响因子：
19.7
作者：
Jain, Rajan;Poisson, Laila M.;Flanders, Adam
通讯作者：
Flanders, Adam
影响因子：
2.4
作者：
Davatzikos, Christos;Rathore, Saima;Kontos, Despina
通讯作者：
Kontos, Despina