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Bayesian Personalization of Brain Tumor Growth Model [chapter]

Matthieu Lê, Hervé Delingette, Jayashree Kalpathy-Cramer, Elizabeth R. Gerstner, Tracy Batchelor, Jan Unkelbach, Nicholas Ayache
2015 Lecture Notes in Computer Science  
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]

Ivan Ezhov, Tudor Mot, Suprosanna Shit, Jana Lipkova, Johannes C. Paetzold, Florian Kofler, Fernando Navarro, Chantal Pellegrini, Marcel Kollovieh, Marie Metz, Benedikt Wiestler, Bjoern Menze
2021 arXiv   pre-print
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

Jana Lipkova, Panagiotis Angelikopoulos, Stephen Wu, Esther Alberts, Benedikt Wiestler, Christian Diehl, Christine Preibisch, Thomas Pyka, Stephanie Combs, Panagiotis Hadjidoukas, Koen Van Leemput, Petros Koumoutsakos (+2 others)
2019 IEEE Transactions on Medical Imaging  
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

Matthieu Le, Herve Delingette, Jayashree Kalpathy-Cramer, Elizabeth R. Gerstner, Tracy Batchelor, Jan Unkelbach, Nicholas Ayache
2016 IEEE Transactions on Medical Imaging  
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]

Ivan Ezhov, Kevin Scibilia, Katharina Franitza, Felix Steinbauer, Suprosanna Shit, Lucas Zimmer, Jana Lipkova, Florian Kofler, Johannes Paetzold, Luca Canalini, Diana Waldmannstetter, Martin Menten (+3 others)
2021 arXiv   pre-print
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]

Ivan Ezhov, Jana Lipkova, Suprosanna Shit, Florian Kofler, Nore Collomb, Benjamin Lemasson, Emmanuel Barbier, Bjoern Menze
2019 arXiv   pre-print
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

Matthieu Le, Herve Delingette, Jayashree Kalpathy-Cramer, Elizabeth R. Gerstner, Tracy Batchelor, Jan Unkelbach, Nicholas Ayache
2017 IEEE Transactions on Medical Imaging  
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

Xiaobo Zhou
2016 Frontiers in bioscience (Scholar edition)  
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

Hayley P. Ellis, Mark Greenslade, Ben Powell, Inmaculada Spiteri, Andrea Sottoriva, Kathreena M. Kurian
2015 Frontiers in Oncology  
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

Brian W. Kunkle, Changwon Yoo, Deodutta Roy, Kamaleshwar Singh
2013 PLoS ONE  
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

Mansi Lather, Parvinder Singh
2020 Procedia Computer Science  
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

Oscar Daniel Zambrano Ramírez, Jean-Marc FONTBONNE
2019 Nucleus  
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

Vijay Yadav, Ashish Tiwari
2016 Journal of advance research in computer science & enigneering  
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

Vijay Yadav, Ashish Tiwari
2016 Journal of advance research in computer science & enigneering  
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

Peter Kletting, Anne Thieme, Nina Eberhardt, Andreas Rinscheid, Calogero D'Alessandria, Jakob Allmann, Hans-Jürgen Wester, Robert Tauber, Ambros J Beer, Gerhard Glatting, Matthias Eiber
2018 Journal of Nuclear Medicine  
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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