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.

中文翻译：

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使用单次 mpMRI 扫描全自动校准肿瘤生长模型。

我们的目标是从单个多参数扫描校准数学肿瘤生长模型。目标问题是术前胶质母细胞瘤 (GBM) 扫描的分析。为此，我们提出了一种全自动肿瘤生长校准方法，该方法将用于肿瘤进展的单物种反应扩散偏微分方程 (PDE) 模型与多参数磁共振成像 (mpMRI) 扫描相结合，以稳健地提取患者特异性生物标志物，即，估计（ 一世 ) 肿瘤 细胞增殖率 , ( ii ) 肿瘤 细胞迁移率 ， 和 （ 三 ） 原本的， 肿瘤起始的局部部位 . 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、不适定性和病态条件，这个问题特别具有挑战性。我们提出了一种从粗到细的多分辨率延续方案，通过参数分解来稳定反演。我们通过将所提出的方法应用于 206 名 GBM 患者的临床数据，证明了我们方法的稳健性和实用性。我们分析提取的生物标志物，并通过将前者映射到公共图谱空间来将肿瘤起源与患者总生存率联系起来。我们提出的初步结果表明，当一组成像特征通过估计的生物物理参数增强时，可以提高预测患者总体生存率的准确性。所有提取的特征，肿瘤初始位置，和生物物理生长参数被公开以供进一步分析。据我们所知，这是第一个可以处理多灶性肿瘤并将 TIL 定位到几毫米的全自动方案。