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Clinical decision support systems for brain tumor characterization using advanced magnetic resonance imaging techniques

Evangelia Tsolaki
2014 World Journal of Radiology  
The first is to review and evaluate the progress that has been made towards the utilization of CDSS based on data from advanced MRI techniques.  ...  The second is to analyze and propose the future work that has to be done, based on the existing problems and challenges, especially taking into account the new imaging techniques and parameters that can  ...  The authors proposed a method to create a "nosologic image" in order to extract information about the brain tumor type and the grade based on long TE 1 H-MRSI data, since biopsy does not always reveal  ... 
doi:10.4329/wjr.v6.i4.72 pmid:24778769 pmcid:PMC4000611 fatcat:zbpqhixrhfaqlezqfgsngqd4tq

Combined fluorescence and reflectance spectroscopy for in vivo quantification of cancer biomarkers in low- and high-grade glioma surgery

Pablo A. Valdés, Anthony Kim, Frederic Leblond, Olga M. Conde, Brent T. Harris, Keith D. Paulsen, Brian C. Wilson, David W. Roberts
2011 Journal of Biomedical Optics  
Gliomas represent a heterogeneous group of brain tumors with marked intra-and inter-tumor variability.  ...  fluorescence image guidance.  ...  Kim and Wilson have a provisional patent (61, 297,969) for the intraoperative probe used in this study.  ... 
doi:10.1117/1.3646916 pmid:22112112 pmcid:PMC3221714 fatcat:jgbtjczkcbhfne3zm2uei2pjgm

Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis [article]

Richard J. Chen, Ming Y. Lu, Jingwen Wang, Drew F. K. Williamson, Scott J. Rodig, Neal I. Lindeman, Faisal Mahmood
2020 arXiv   pre-print
The proposed method establishes insight and theory on how to train deep networks on multimodal biomedical data in an intuitive manner, which will be useful for other problems in medicine that seek to combine  ...  deep networks trained on histology and genomic data alone.  ...  As described in our previous work [51] , the conditional GAN framework consists of two networks (a generator G and a discriminator D) that compete against each other in a min-max game to respectively  ... 
arXiv:1912.08937v3 fatcat:uruvdqhve5fu3e3amoce5pykmy

Artificial Intelligence in Tumor Subregion Analysis Based on Medical Imaging: A Review [article]

Mingquan Lin, Jacob Wynne, Yang Lei, Tonghe Wang, Walter J. Curran, Tian Liu, Xiaofeng Yang
2021 arXiv   pre-print
This paper reviews AI-based tumor subregion analysis in medical imaging. We summarize the latest AI-based methods for tumor subregion analysis and their applications.  ...  Medical imaging is widely used in cancer diagnosis and treatment, and artificial intelligence (AI) has achieved tremendous success in various tasks of medical image analysis.  ...  ACKNOWLEDGEMENTS This research was supported in part by the National Cancer Institute of the National Institutes of Health under Award Number R01CA215718 and Emory Winship Cancer Institute pilot grant.  ... 
arXiv:2103.13588v1 fatcat:3fxgny7u3bcxzcmlkhzz5fdvv4

MORONET: Multi-omics Integration via Graph Convolutional Networks for Biomedical Data Classification [article]

Tongxin Wang, Wei Shao, Zhi Huang, Haixu Tang, Zhengming Ding, Kun Huang
2020 bioRxiv   pre-print
We present a novel multi-omics integrative method named Multi-Omics gRaph cOnvolutional NETworks (MORONET) for biomedical classification.  ...  , DNA methylation data, and miRNA expression data.  ...  Acknowledgements This work was supported by Indiana University Precision Health Initiative and National Institute of Biomedical Imaging and Bioengineering (R01EB025018).  ... 
doi:10.1101/2020.07.02.184705 fatcat:lemnficphncsxdkayxyruligbm

Application of Deep Learning in Automated Analysis of Molecular Images in Cancer: A Survey

Yong Xue, Shihui Chen, Jing Qin, Yong Liu, Bingsheng Huang, Hanwei Chen
2017 Contrast Media & Molecular Imaging  
Research on cancer molecular images using deep learning techniques is also increasing dynamically.  ...  Molecular imaging enables the visualization and quantitative analysis of the alterations of biological procedures at molecular and/or cellular level, which is of great significance for early detection  ...  To achieve fully automated quantitative analysis of the brain tumor metabolism based on 11 C-methionine (MET) PET, Hirata et al.  ... 
doi:10.1155/2017/9512370 pmid:29114182 pmcid:PMC5661078 fatcat:ev3zrlx67vfo5mt23e5u3y2t64

Analysis of Cellular Feature Differences of Astrocytomas with Distinct Mutational Profiles Using Digitized Histopathology Images [article]

Mousumi Roy, Fusheng Wang, George Teodoro, Jose Velazqeuz Vega, Daniel Brat, Jun Kong
2018 arXiv   pre-print
Our analysis is generic and can be applied to a wide set of cell-based biomedical research.  ...  In this study, we propose a self-reliant and efficient analysis framework that supports quantitative analysis of cellular phenotypic difference across distinct molecular groups.  ...  Our analysis is generic to a wide set of cell-based biomedical research. Fig. 1 : 1 Overall schema of cell analysis is presented.  ... 
arXiv:1806.09093v1 fatcat:u7xysjeb4vhhpg776sh3aucdau

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, Levente Lanczi, Elizabeth Gerstner (+56 others)
2015 IEEE Transactions on Medical Imaging  
The method is based on our work focusing on high-grade glioma [39] , with further technical details available in [51] . The context sensitivity arises from two components in the framework.  ...  Synthetic image data: The synthetic data of the BRATS 2012 challenge consisted of simulated images for 35 high-grade and 30 low-grade gliomas that exhibit comparable tissue contrast properties and segmentation  ... 
doi:10.1109/tmi.2014.2377694 pmid:25494501 pmcid:PMC4833122 fatcat:csrnfqc4i5eilh7wk5howvpr4u

Radiogenomics for Precision Medicine With A Big Data Analytics Perspective

Andreas S. Panayides, Marios Pattichis, Stephanos Leandrou, Costas Pitris, Anastasia Constantinidou, Constantinos S. Pattichis
2019 IEEE journal of biomedical and health informatics  
challenges from a big data analytics perspective, and discuss standardization and open data initiatives that will facilitate the adoption of precision medicine methods and practices.  ...  Using evidence-based substratification of patients, the objective is to achieve better prognosis, diagnosis, and treatment that will transform existing clinical pathways toward optimizing care for the  ...  More precisely, three studies investigated the prediction of IDH1 mutation status for Lower Grade Glioma (LGG) [75] , [77] and low and high grade glioma [80] , respectively, while another [78] .  ... 
doi:10.1109/jbhi.2018.2879381 pmid:30596591 fatcat:rqmjhmdmr5h3rdaody264ogs24

Pattern recognition of abscesses and brain tumors through MR spectroscopy: Comparison of experimental conditions and radiological findings

Bruno Hebling Vieira, Antonio Carlos dos Santos, Carlos Ernesto Garrido Salmon
2017 Research on Biomedical Engineering  
against metastases, grade I-II gliomas and glioblastomas (0.980), meningiomas and glioblastomas (0.956), grade I-II gliomas and metastases (0.989), meningiomas and metastases (0.990), and between healthy  ...  Methods: Pairwise classification between abscesses, brain tumor classes, and healthy subjects tissue spectra was performed, also the multiclass classification between meningiomas, grade I-II-III gliomas  ...  A lower result was found in the discrimination between healthy subjects tissue and grade III gliomas in short TE data, achieving an AUC of 0.801 in short TE 1.5 T spectra.  ... 
doi:10.1590/2446-4740.00617 fatcat:kow5r237ebaalooo5aymnbjnby

Whole slide images reflect DNA methylation patterns of human tumors

Hong Zheng, Alexandre Momeni, Pierre-Louis Cedoz, Hannes Vogel, Olivier Gevaert
2020 npj Genomic Medicine  
In this work, we investigate the interaction between cancer histopathology images and DNA methylation profiles to provide a better understanding of tumor pathobiology at the epigenetic level.  ...  Our results provide new insights into the link between histopathological and molecular data.  ...  ACKNOWLEDGEMENTS This work was supported by National Institute of Dental & Craniofacial Research (NIDCR) (U01 DE025188), the National Institute of Biomedical Imaging and Bioengineering (R01 EB020527),  ... 
doi:10.1038/s41525-020-0120-9 pmid:32194984 pmcid:PMC7064513 fatcat:eepy6wv3dnbsjbce6svuoxdxsq

PRIMAGE project: predictive in silico multiscale analytics to support childhood cancer personalised evaluation empowered by imaging biomarkers

Luis Martí-Bonmatí, Ángel Alberich-Bayarri, Ruth Ladenstein, Ignacio Blanquer, J. Damian Segrelles, Leonor Cerdá-Alberich, Polyxeni Gkontra, Barbara Hero, J. M. García-Aznar, Daniel Keim, Wolfgang Jentner, Karine Seymour (+22 others)
2020 European Radiology Experimental  
The open cloud-based platform will offer precise clinical assistance for phenotyping (diagnosis), treatment allocation (prediction), and patient endpoints (prognosis), based on the use of imaging biomarkers  ...  The decision support prototype will be constructed and validated on two paediatric cancers: neuroblastoma and diffuse intrinsic pontine glioma.  ...  Acknowledgements PRIMAGE (PRedictive In-silico Multiscale Analytics to support cancer personalised diaGnosis and prognosis, empowered by imaging biomarkers) Business Place is a Horizon 2020 | RIA (Topic  ... 
doi:10.1186/s41747-020-00150-9 pmid:32246291 fatcat:qujua7cvcnbvvelwuolpaygduu

Modality specific U-Net variants for biomedical image segmentation: A survey [article]

Narinder Singh Punn, Sonali Agarwal
2022 arXiv   pre-print
image segmentation to address the automation in identification and detection of the target regions or sub-regions.  ...  Finally, the strengths and similarities of these U-Net variants are analysed along with the challenges involved in biomedical image segmentation to uncover promising future research directions in this  ...  ACKNOWLEDGMENT We thank our institute, Indian Institute of Information Technology Allahabad (IIITA), India and Big Data Analytics (BDA) lab for allocating the necessary resources to perform this research  ... 
arXiv:2107.04537v3 fatcat:r4gmhvaxsnauvckcwyp7sdhzzi

A Multiparametric MRI-Based Radiomics Analysis to Efficiently Classify Tumor Subregions of Glioblastoma: A Pilot Study in Machine Learning

Fang-Ying Chiu, Nguyen Quoc Khanh Le, Cheng-Yu Chen
2021 Journal of Clinical Medicine  
A total of 1316 features on the raw MR images were selected for each annotated area.  ...  Accurate demarcation on magnetic resonance imaging (MRI) between the active tumor region and perifocal edematous extension is essential for planning stereotactic biopsy, GBM resection, and radiotherapy  ...  The ADC threshold value of 1185 × 10 −6 mm 2 /s has a sensitivity of 97.6% and specificity of 53.1% in the discrimination of high-grade (grades III and IV) and low-grade (grade II) gliomas.  ... 
doi:10.3390/jcm10092030 pmid:34068528 fatcat:aholl3idunhxpdayvugta7jdcm

Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Messaoud Hameurlaine, Abdelouahab Moussaoui
2019 Nano Biomedicine and Engineering  
Brain tumor detection and segmentation is one of the most challenging and time consuming task in medical image processing.  ...  The image segmentation is a very difficult job in the image processing and challenging task for clinical diagnostic tools.  ...  For this purpose, biomedical image processing techniques are applied to MRI scans.  ... 
doi:10.5101/nbe.v11i2.p178-191 fatcat:gh5jemeth5hapa62bomn7ypwgm
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