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Integrated Biophysical Modeling and Image Analysis: Application to Neuro-Oncology.
Annual Review of Biomedical Engineering ( IF 9.7 ) Pub Date : 2020-06-05 , DOI: 10.1146/annurev-bioeng-062117-121105
Andreas Mang 1 , Spyridon Bakas 1 , Shashank Subramanian 2 , Christos Davatzikos 3 , George Biros 2
Affiliation  

Central nervous system (CNS) tumors come with vastly heterogeneous histologic, molecular, and radiographic landscapes, rendering their precise characterization challenging. The rapidly growing fields of biophysical modeling and radiomics have shown promise in better characterizing the molecular, spatial, and temporal heterogeneity of tumors. Integrative analysis of CNS tumors, including clinically acquired multi-parametric magnetic resonance imaging (mpMRI) and the inverse problem of calibrating biophysical models to mpMRI data, assists in identifying macroscopic quantifiable tumor patterns of invasion and proliferation, potentially leading to improved (a) detection/segmentation of tumor subregions and (b) computer-aided diagnostic/prognostic/predictive modeling. This article presents a summary of (a) biophysical growth modeling and simulation,(b) inverse problems for model calibration, (c) these models' integration with imaging workflows, and (d) their application to clinically relevant studies. We anticipate that such quantitative integrative analysis may even be beneficial in a future revision of the World Health Organization (WHO) classification for CNS tumors, ultimately improving patient survival prospects.

中文翻译:


综合生物物理建模和图像分析:在神经肿瘤学中的应用。

中枢神经系统(CNS)肿瘤具有极其异质的组织学、分子和放射学景观,这使得对其精确表征具有挑战性。快速发展的生物物理模型和放射组学领域在更好地表征肿瘤的分子、空间和时间异质性方面显示出了希望。CNS 肿瘤的综合分析,包括临床采集的多参数磁共振成像 (mpMRI) 和根据 mpMRI 数据校准生物物理模型的逆问题,有助于识别宏观可量化的肿瘤侵袭和增殖模式,可能导致改进 (a )检测/肿瘤亚区域的分割和( b )计算机辅助诊断/预后/预测建模。本文总结了 ( a ) 生物物理生长建模和模拟,( b ) 模型校准的反问题,( c ) 这些模型与成像工作流程的集成,以及 ( d ) 它们在临床相关研究中的应用。我们预计,这种定量综合分析甚至可能有利于世界卫生组织(WHO)中枢神经系统肿瘤分类的未来修订,最终改善患者的生存前景。

更新日期:2020-06-05
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