Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model?
Simple Summary: Mathematical tumor growth models have been proposed for decades to capture the growth of gliomas, an aggressive form of brain tumor. However, the estimation of the tumor cell-density distribution at diagnosis and model parameters from partial observations provided by magnetic resonan...
| Publicado en: | Cancers Vol. 14; no. 10; pp. 2530 - 2559 |
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| Autores principales: | , , , , , , , |
| Formato: | computer program diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
MDPI
May2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157147435&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157147435 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20726694 B74B jtl: Cancers issn: 20726694 maglogo: N pubinfo: dt: May2022 vid: 14 iid: 10 pid: 97109 pub: MDPI artinfo: ui: 157147435 157147435 157147435 10.3390/cancers14102530 157147435 ppf: 2530 ppct: 29 formats: tig: atl: Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model? aug: au: Martens, Corentin Rovai, Antonin Bonatto, Daniele Metens, Thierry Debeir, Olivier Decaestecker, Christine Goldman, Serge Van Simaeys, Gaetan affil: Department of Nuclear Medicine, Hôpital Erasme, Université libre de Bruxelles, Route de Lennik 808, 1070 Brussels, Belgium sug: subj: Deep Learning Glioma Diagnosis Glioma Pathology Magnetic Resonance Imaging Models, Statistical Neural Networks (Computer) Image Processing, Computer Assisted Individualized Medicine Human Brain Pathology Cancer Patients Image Enhancement Imaging, Three-Dimensional Cell Proliferation Glioma Prognosis Glioma Therapy ab: Simple Summary: Mathematical tumor growth models have been proposed for decades to capture the growth of gliomas, an aggressive form of brain tumor. However, the estimation of the tumor cell-density distribution at diagnosis and model parameters from partial observations provided by magnetic resonance imaging are ill-posed problems. In this work, we propose a deep learning-based approach to address these problems. 1200 synthetic tumors are first generated using the mathematical model over brain geometries of 6 volunteers. Two deep convolutional neural networks are then trained to (i) reconstruct a whole tumor cell-density distribution and (ii) evaluate the model parameters from partial observations provided in the form of threshold-like imaging contours, with state-of-the-art results. From the estimated cell-density distribution and parameter values, the spatio-temporal evolution of the tumor can ultimately be accurately captured by the mathematical model. Such an approach could be of great interest for glioma characterization and therapy planning. Reaction-diffusion models have been proposed for decades to capture the growth of gliomas, the most common primary brain tumors. However, ill-posedness of the initialization at diagnosis time and parameter estimation of such models have restrained their clinical use as a personalized predictive tool. In this work, we investigate the ability of deep convolutional neural networks (DCNNs) to address commonly encountered pitfalls in the field. Based on 1200 synthetic tumors grown over real brain geometries derived from magnetic resonance (MR) data of six healthy subjects, we demonstrate the ability of DCNNs to reconstruct a whole tumor cell-density distribution from only two imaging contours at a single time point. With an additional imaging contour extracted at a prior time point, we also demonstrate the ability of DCNNs to accurately estimate the individual diffusivity and proliferation parameters of the model. From this knowledge, the spatio-temporal evolution of the tumor cell-density distribution at later time points can ultimately be precisely captured using the model. We finally show the applicability of our approach to MR data of a real glioblastoma patient. This approach may open the perspective of a clinical application of reaction-diffusion growth models for tumor prognosis and treatment planning. pubtype: Academic Journal doctype: computer program diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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