论文标题
自动MRI驱动模型校准用于晚期脑肿瘤进展分析
Automatic MRI-Driven Model Calibration for Advanced Brain Tumor Progression Analysis
论文作者
论文摘要
我们的目标是对单个多参数扫描的数学肿瘤生长模型的校准。目标问题是术前胶质母细胞瘤（GBM）扫描的分析。 To this end, we present a fully automatic tumor-growth calibration methodology that integrates a single-species reaction-diffusion partial differential equation (PDE) model for tumor progression with multiparametric Magnetic Resonance Imaging (mpMRI) scans to robustly extract patient specific biomarkers i.e., estimates for (i) the tumor cell proliferation rate, (ii) the tumor cell migration rate, and (iii) the肿瘤起始的原始位置。我们的方法基于肿瘤初始位置（TIL）的稀疏重建算法。由于非线性，不适和调节性不良，这个问题尤其具有挑战性。我们提出了一个粗到三的多分辨率延续方案，具有参数分解以稳定反转。我们通过将提出的方法应用于206名GBM患者的临床数据，证明了我们方法的鲁棒性和实用性。我们分析了提取的生物标志物，并将肿瘤起源与患者的整体生存相关联，将前者映射到一个共同的地图集空间中。我们提出了初步结果，当通过估计的生物物理参数增强一组成像特征时，提高了预测患者总生存的准确性。所有提取的特征，肿瘤初始位置和生物物理生长参数均可公开用于进一步分析。据我们所知，这是第一个可以处理多灶性肿瘤并可以将其定位到几毫米的全自动方案。
Our objective is the calibration of mathematical tumor growth models from a single multiparametric scan. The target problem is the analysis of preoperative Glioblastoma (GBM) scans. To this end, we present a fully automatic tumor-growth calibration methodology that integrates a single-species reaction-diffusion partial differential equation (PDE) model for tumor progression with multiparametric Magnetic Resonance Imaging (mpMRI) scans to robustly extract patient specific biomarkers i.e., estimates for (i) the tumor cell proliferation rate, (ii) the tumor cell migration rate, and (iii) the original, localized site(s) of tumor initiation. Our method is based on a sparse reconstruction algorithm for the tumor initial location (TIL). This problem is particularly challenging due to nonlinearity, ill-posedeness, and ill conditioning. We propose a coarse-to-fine multi-resolution continuation scheme with parameter decomposition to stabilize the inversion. We demonstrate robustness and practicality of our method by applying the proposed method to clinical data of 206 GBM patients. We analyze the extracted biomarkers and relate tumor origin with patient overall survival by mapping the former into a common atlas space. We present preliminary results that suggest improved accuracy for prediction of patient overall survival when a set of imaging features is augmented with estimated biophysical parameters. All extracted features, tumor initial positions, and biophysical growth parameters are made publicly available for further analysis. To our knowledge, this is the first fully automatic scheme that can handle multifocal tumors and can localize the TIL to a few millimeters.
