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Bayesian Personalization of Brain Tumor Growth Model
[chapter]
2015
Lecture Notes in Computer Science
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Therefore, we propose a method for conducting the Bayesian personalization of the tumor growth model parameters. ...
Recent work on brain tumor growth modeling for glioblastoma using reaction-diffusion equations suggests that the diffusion coefficient and the proliferation rate can be related to clinically relevant information ...
ConclusionWe presented a Bayesian personalization of the parameters of a tumor growth model on 4 patients. ...
doi:10.1007/978-3-319-24571-3_51
fatcat:zsoydlegpjdqrmpkfrhia5tjru
Geometry-aware neural solver for fast Bayesian calibration of brain tumor models
[article]
2021
arXiv
pre-print
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We test the neural solver on Bayesian tumor model personalization for a cohort of glioma patients. ...
Modeling of brain tumor dynamics has the potential to advance therapeutic planning. ...
, we performed the Bayesian brain tumor model calibration. ...
arXiv:2009.04240v4
fatcat:4oanyxg4b5am7de367qzblwzaa
Personalized Radiotherapy Design for Glioblastoma: Integrating Mathematical Tumor Models, Multimodal Scans and Bayesian Inference
2019
IEEE Transactions on Medical Imaging
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2018 pre-print arXiv:1807.00499v2 fatcat:s37cckt23zf4bjmu2ffgi7ktyiOther Versions
Here, we provide a Bayesian machine learning framework for the rational design of improved, personalized radiotherapy plans using mathematical modeling and patient multimodal medical scans. ...
The proposed integration of multimodal scans and mathematical modeling provides a robust, non-invasive tool to assist personalized radiotherapy design. ...
In the remainder of the paper, Section II introduces the Bayesian framework for model calibration, including the tumor growth and imaging models. ...
doi:10.1109/tmi.2019.2902044
pmid:30835219
pmcid:PMC7170051
fatcat:dbhcbj3hhfevfblzs6h34ogcyu
MRI Based Bayesian Personalization of a Tumor Growth Model
2016
IEEE Transactions on Medical Imaging
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The mathematical modeling of brain tumor growth has been the topic of numerous research studies. ...
Our approach aims at analyzing the uncertainty in the patient specific parameters of a tumor growth model, by sampling from the posterior probability of the parameters knowing the magnetic resonance images ...
The method is based on the Bayesian personalization of a tumor growth model. We specifically apply it to the personalization of glioblastoma growth using a reaction-diffusion model. ...
doi:10.1109/tmi.2016.2561098
pmid:27164582
fatcat:6jmn5hghfzaudcvhysk7isjpbm
Learn-Morph-Infer: a new way of solving the inverse problem for brain tumor modeling
[article]
2021
arXiv
pre-print
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Over recent years a corpus of literature on medical image-based tumor modeling was published. It includes different mathematical formalisms describing the forward tumor growth model. ...
Numerical simulations of tumor growth could complement imaging information by providing estimates of full spatial distributions of tumor cells. ...
Conclusion We present a learnable brain tumor model personalization methodology. ...
arXiv:2111.04090v1
fatcat:lydzeude6zgbngok3lpy2m3bom
Neural parameters estimation for brain tumor growth modeling
[article]
2019
arXiv
pre-print
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2020 accepted arXiv:1907.00973v2 fatcat:6cyjxki6bbfe3l42t2ylagke3u
We test the method on synthetic and real scans of rats injected with brain tumors to calibrate the model and to predict tumor progression. ...
In this work, we propose a learning-based technique for the estimation of tumor growth model parameters from medical scans. ...
Introduction Modeling brain tumor progression holds a promise of optimizing clinical treatment planning. ...
arXiv:1907.00973v1
fatcat:mniy7vgv4vfflpc6c4g4do36ce
Personalized Radiotherapy Planning Based on a Computational Tumor Growth Model
2017
IEEE Transactions on Medical Imaging
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In this article, we propose a proof of concept for the automatic planning of personalized radiotherapy for brain tumors. ...
A computational model of glioblastoma growth is combined with an exponential cell survival model to describe the effect of radiotherapy. ...
To our knowledge, this is the first work that uses a personalized model of brain tumor growth taking into account the uncertainty in tumor growth parameters and the clinician's segmentations in order to ...
doi:10.1109/tmi.2016.2626443
pmid:28113925
fatcat:zqqgtibxnrcx7iydriuvnxtc44
Computational systems biology in cancer brain metastasis
2016
Frontiers in bioscience (Scholar edition)
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INTRODUCTION Systems biology is computational and mathematical modeling of a complex biological Computational systems biology in cancer brain metastasis ...
Bioinformatics has been used for identifying the molecular mechanisms driving brain metastasis and mathematical modeling methods for analyzing dynamics of a system and predicting optimal therapeutic strategies ...
regression model, Bayesian model, Markov model and so on. ...
doi:10.2741/s456
pmid:26709906
fatcat:y2i4eiaslbeqthdnxxuy4dafgq
Current Challenges in Glioblastoma: Intratumour Heterogeneity, Residual Disease, and Models to Predict Disease Recurrence
2015
Frontiers in Oncology
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Examples of mathematical models including neural networks and Bayesian models will then be outlined, and their potential for application to the ield of brain tumor genetics and disease prediction will ...
biomarkers in brain tumors has been of great beneit both diagnostically and for stratifying therapies (3) . ...
doi:10.3389/fonc.2015.00251
pmid:26636033
pmcid:PMC4644939
fatcat:hvqs3zyhwbhqpjjxl4uapk5u4i
Reverse Engineering of Modified Genes by Bayesian Network Analysis Defines Molecular Determinants Critical to the Development of Glioblastoma
2013
PLoS ONE
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In this study we have identified key genes that are critical in development of astrocytic tumors. ...
Reverse engineering of these 646 genes using Bayesian network analysis produced a gene network for each grade of astrocytoma (Grade I-IV), and 'key genes' within each grade were identified. ...
type of malignant brain tumor. ...
doi:10.1371/journal.pone.0064140
pmid:23737970
pmcid:PMC3667850
fatcat:bbhqny5fnrgg7gdqhylxr4qgk4
Investigating Brain Tumor Segmentation and Detection Techniques
2020
Procedia Computer Science
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Segmentation of brain tumor involves separation of abnormal brain tissues from normal tissues of brain. ...
Segmentation of brain tumor involves separation of abnormal brain tissues from normal tissues of brain. ...
Every year, nearabout 11,000 persons are being diagnosed of the brain tumor [2] . Brain tumor is an anomalous lump of flesh comprising of uncontrolled growth and multiplication of cells [3] . ...
doi:10.1016/j.procs.2020.03.189
fatcat:ssn4wkgp7nbihnbtq2ceuhqrau
Development of clinically based prediction models using machine learning and Bayesian statistics
2019
Nucleus
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The models are based on clinical records of patients who underwent radiotherapy treatment due to glioblastoma which is an aggressive brain cancer. ...
In this work, the framework for developing generic clinically based models is emphasized and illustrated with Bayesian statistics neurologic grade prediction models in order to exemplify the type of models ...
The rapid growth of clinical data is dramatically increasing due to the availability of electronic data. Hence, modelling for prognostics and therapeutic purposes is moving forward [1] . ...
doaj:229a3be0315a4f9f8bd2cecdc2b8d922
fatcat:r7usfo6yynesfjttmyndp7py24
Review on Heart Diseases Prediction on Neural Network
2016
Journal of advance research in computer science & enigneering
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A multilayer perception is a feed forward ANN model that is used extensively for the solution of heart diseases problems. An ANN is the same as human brain. ...
Neural network (ANN) tool has been used for solving many decision modeling problems. ...
These are called secondary or metastatic brain tumors. Symptoms of brain tumors depend on the size and location of the tumor. ...
doi:10.53555/nncse.v3i11.405
fatcat:n6lizava5nf27aa5rijwqhgzka
Review on Heart Diseases Prediction on ANN
2016
Journal of advance research in computer science & enigneering
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The file type is application/pdf.
A multilayer perception is a feed forward ANN model that is used extensively for the solution of heart diseases problems. An ANN is the same as human brain. ...
Neural network (ANN) tool has been used for solving many decision modeling problems. ...
These are called secondary or metastatic brain tumors. Symptoms of brain tumors depend on the size and location of the tumor. ...
doi:10.53555/nncse.v3i11.403
fatcat:upjxc7czqvcozkz6hf5gyc45um
Modeling and predicting tumor response in radioligand therapy
2018
Journal of Nuclear Medicine
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Improvement of the method and external validation of the model is ongoing. by on March 22, 2020. For personal use only. jnm.snmjournals.org Downloaded from 3 ...
Methods: A recently developed PBPK model for 177 Lu PSMA I&T RLT was extended to account for tumor (exponential) growth and reduction due to irradiation (linear quadratic model). ...
Tumor Effect Model Tumor growth and reduction was modeled using exponential growth and the linear quadratic model for the surviving fraction. ...
doi:10.2967/jnumed.118.210377
pmid:29748236
fatcat:qmhvuw4phnczxcrcsy7evrgxze
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