基于多参数MRI数据的贝叶斯空间模型在前列腺癌分类中的应用
Bayesian Spatial Models for Voxel-wise Prostate Cancer Classification Using Multi-parametric MRI Data
摘要：
多参数磁共振成像（mpMRI）在前列腺癌的诊断中发挥着越来越重要的作用。已经提出了各种计算机辅助检测算法，用于通过组合来自各种mpMRI数据分量的信息来进行自动前列腺癌检测。然而，存在mpMRI的其他特征，包括体素之间的空间相关性和mpMRI参数中的患者间异质性，这些特征在文献中尚未充分探索，但如果适当利用，可能会改善癌症检测。本文提出了新的体素贝叶斯分类器的前列腺癌，占mpMRI的空间相关性和患者之间的异质性。空间相关性建模是具有挑战性的，由于数据的极高维度，我们认为三个计算效率的方法，分别使用最近邻高斯过程（NNGP），基于结的降秩近似，和条件自回归（CAR）模型。通过在mpMRI参数模型上添加受试者特异性随机截距来解释患者间异质性。仿真结果表明，适当建模的空间相关性和患者之间的异质性提高分类精度。体内数据的应用表明，分类是通过使用NNGP和降秩近似的空间建模而不是CAR模型来改进的，而对患者间异质性进行建模并不能进一步改进我们的分类器。在我们提出的模型中，基于NNGP的模型被推荐考虑其鲁棒的分类精度和高计算效率。
英文摘要：
Multi-parametric magnetic resonance imaging (mpMRI) plays an increasingly important role in the diagnosis of prostate cancer. Various computer-aided detection algorithms have been proposed for automated prostate cancer detection by combining information from various mpMRI data components. However, there exist other features of mpMRI, including the spatial correlation between voxels and between-patient heterogeneity in the mpMRI parameters, that have not been fully explored in the literature but could potentially improve cancer detection if leveraged appropriately. This paper proposes novel voxel-wise Bayesian classifiers for prostate cancer that account for the spatial correlation and between-patient heterogeneity in mpMRI. Modeling the spatial correlation is challenging due to the extreme high dimensionality of the data, and we consider three computationally efficient approaches using Nearest Neighbor Gaussian Process (NNGP), knot-based reduced-rank approximation, and a conditional autoregressive (CAR) model, respectively. The between-patient heterogeneity is accounted for by adding a subject-specific random intercept on the mpMRI parameter model. Simulation results show that properly modeling the spatial correlation and between-patient heterogeneity improves classification accuracy. Application to in vivo data illustrates that classification is improved by spatial modeling using NNGP and reduced-rank approximation but not the CAR model, while modeling the between-patient heterogeneity does not further improve our classifier. Among our proposed models, the NNGP-based model is recommended considering its robust classification accuracy and high computational efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
