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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  
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  ...  specific needs of each patient.  ...  Transfer learning reduces the need for large training time and large datasets by using the lower layers of pre-trained CNNs.  ... 
doi:10.1109/jbhi.2018.2879381 pmid:30596591 fatcat:rqmjhmdmr5h3rdaody264ogs24

Context aware deep learning for brain tumor segmentation, subtype classification, and survival prediction using radiology images

Linmin Pei, Lasitha Vidyaratne, Md Monibor Rahman, Khan M Iftekharuddin
2020 Scientific Reports  
This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI).  ...  Finally, we perform survival prediction using a hybrid method of deep learning and machine learning.  ...  Acknowledgements We would like to acknowledge Megan Anita Witherow for her help in editing this manuscript. This work was partially funded through NIH/NIBIB grant under award number R01EB020683.  ... 
doi:10.1038/s41598-020-74419-9 pmid:33184301 pmcid:PMC7665039 fatcat:c2vbgfsprbasnky3daocwqh3uy

Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures

Dongming Liu, Jiu Chen, Xinhua Hu, Kun Yang, Yong Liu, Guanjie Hu, Honglin Ge, Wenbin Zhang, Hongyi Liu
2021 Frontiers in Oncology  
AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis.  ...  This article reviews the recent progress in the utilization of the imaging-genomics analysis in GB patients, focusing on its implications and prospects in individualized diagnosis and management.  ...  GB, Glioblastoma; OS, Overall Survival; PFS, Progression free survival.  ... 
doi:10.3389/fonc.2021.699265 fatcat:yhxwbmfdyzfrjff2ajuou25exi

Residual Convolutional Neural Network for the Determination ofIDHStatus in Low- and High-Grade Gliomas from MR Imaging

Ken Chang, Harrison X. Bai, Hao Zhou, Chang Su, Wenya Linda Bi, Ena Agbodza, Vasileios K. Kavouridis, Joeky T. Senders, Alessandro Boaro, Andrew Beers, Biqi Zhang, Alexandra Capellini (+18 others)
2017 Clinical Cancer Research  
: We developed a deep learning technique to noninvasively predict IDH genotype in grade II-IV glioma using conventional MR imaging using a multi-institutional data set.  ...  for 201 patients from the Hospital of University of Pennsylvania (HUP), 157 patients from Brigham and Women's Hospital (BWH), and 138 patients from The Cancer Imaging Archive (TCIA) and divided into training  ...  The authors declare no potential conflicts of interest Acknowledgments This project was supported by a training grant from the NIH Blueprint for Neuroscience Research (T90DA022759/R90DA023427) to K.  ... 
doi:10.1158/1078-0432.ccr-17-2236 pmid:29167275 pmcid:PMC6051535 fatcat:jzzhd5itgbaxfe66tjqr5bd63e

Hemodynamic Imaging in Cerebral Diffuse Glioma—Part B: Molecular Correlates, Treatment Effect Monitoring, Prognosis, and Future Directions

Vittorio Stumpo, Lelio Guida, Jacopo Bellomo, Christiaan Hendrik Bas Van Niftrik, Martina Sebök, Moncef Berhouma, Andrea Bink, Michael Weller, Zsolt Kulcsar, Luca Regli, Jorn Fierstra
2022 Cancers  
From a neuroimaging point of view, these specific molecular and histopathological features may be used to yield imaging biomarkers as surrogates for distinct tumor genotypes and phenotypes.  ...  and survival.  ...  Perfusional tumor heterogeneity can also be used to extract the radiomic features needed to train deep learning models for the prediction of glioblastoma recurrence patterns [226] .  ... 
doi:10.3390/cancers14051342 pmid:35267650 pmcid:PMC8909110 fatcat:a6262pobrzgufgahuzdzk7rdgy

Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

E. J. Limkin, R. Sun, L. Dercle, E. I. Zacharaki, C. Robert, S. Reuzé, A. Schernberg, N. Paragios, E. Deutsch, C. Ferté
2017 Annals of Oncology  
Both authors contributed equally as senior authors.  ...  One can observe a rapid increase in the number of computational medical imaging publications-milestones that have highlighted the utility of imaging biomarkers in oncology.  ...  Verjat for preparation of the figures. Funding The authors of this review received no grant from any funding agency; no grant number is applicable.  ... 
doi:10.1093/annonc/mdx034 pmid:28168275 fatcat:son4hx7ixvb6jntxmifdypigym

Radiomics and radiogenomics in gliomas: a contemporary update

Gagandeep Singh, Sunil Manjila, Nicole Sakla, Alan True, Amr H Wardeh, Niha Beig, Anatoliy Vaysberg, John Matthews, Prateek Prasanna, Vadim Spektor
2021 British Journal of Cancer  
We elucidate novel radiomic and radiogenomic workflow concepts and state-of-the-art descriptors in sub-visual MR image processing, with relevant literature on applications of such machine learning techniques  ...  Radiomics and radiogenomics promise to offer precise diagnosis, predict prognosis, and assess tumour response to modern chemotherapy/immunotherapy and radiation therapy.  ...  ACKNOWLEDGEMENTS The authors wish to thank Maria Figueroa, for her assistance with logistics and proofreading. AUTHOR CONTRIBUTIONS  ... 
doi:10.1038/s41416-021-01387-w pmid:33958734 pmcid:PMC8405677 fatcat:x3b3bmjobfaepbkocurc2v3zeq

Imaging in neuro-oncology

Hari Nandu, Patrick Y. Wen, Raymond Y. Huang
2018 Therapeutic Advances in Neurological Disorders  
, and rising challenges of imaging with immunotherapy.  ...  This review aims to provide an overview of several advanced imaging modalities including magnetic resonance imaging and positron emission tomography (PET), including advances in new PET agents, and summarize  ...  overall-survival outcomes for these patients can impact their treatment decision-making.  ... 
doi:10.1177/1756286418759865 pmid:29511385 pmcid:PMC5833173 fatcat:4tvld3yhnvetvfitsrqbk6veom

TCGA-TCIA Impact on Radiogenomics Cancer Research: A Systematic Review

Zanfardino, Pane, Mirabelli, Salvatore, Franzese
2019 International Journal of Molecular Sciences  
Finally, we outlined the potential clinical impact of radiogenomics to improve the accuracy of diagnosis and the prediction of patient outcomes in oncology.  ...  In this review, we systematically collected and analyzed radiogenomic studies based on TCGA-TCIA data.  ...  The results showed that patients with deep white matter tracts and ependymal invasion on imaging (defined Class A) had a significant decrease in overall survival, whereas, in patients with the absence  ... 
doi:10.3390/ijms20236033 pmid:31795520 pmcid:PMC6929079 fatcat:xmcrk3kmujem3bgopeyezozce4

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
However, most deep learning-based objective outcome prediction and grading paradigms are based on histology or genomics alone and do not make use of the complementary information in an intuitive manner  ...  In this work, we propose Pathomic Fusion, an interpretable strategy for end-to-end multimodal fusion of histology image and genomic (mutations, CNV, RNA-Seq) features for survival outcome prediction.  ...  make use of phenotypic and genotypic information in an integrative manner.  ... 
arXiv:1912.08937v3 fatcat:uruvdqhve5fu3e3amoce5pykmy

Deep Learning Can Differentiate IDH-Mutant from IDH-Wild GBM

Luca Pasquini, Antonio Napolitano, Emanuela Tagliente, Francesco Dellepiane, Martina Lucignani, Antonello Vidiri, Giulio Ranazzi, Antonella Stoppacciaro, Giulia Moltoni, Matteo Nicolai, Andrea Romano, Alberto Di Napoli (+1 others)
2021 Journal of Personalized Medicine  
Our aim was to develop a GBM-tailored deep-learning model for IDH prediction by applying convoluted neural networks (CNN) on multiparametric MRI.  ...  Lower performance was achieved on ADC maps. We present a GBM-specific deep-learning model for IDH mutation prediction, with a maximal accuracy of 83% on rCBV maps.  ...  Our study proposes a new deep learning model tailored to GBM, with high prediction performance for IDH status on MRI sequences.  ... 
doi:10.3390/jpm11040290 pmid:33918828 pmcid:PMC8069494 fatcat:n7faqjtucna5tbnj25uj4pia5u

Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential

Xingping Zhang, Yanchun Zhang, Guijuan Zhang, Xingting Qiu, Wenjun Tan, Xiaoxia Yin, Liefa Liao
2022 Frontiers in Oncology  
In addition, deep learning-based techniques for automatic segmentation and radiomic analysis are being analyzed to address limitations such as rigorous workflow, manual/semi-automatic lesion annotation  ...  institutions, and prospective researches and clinical trials), and thoughts on future directions (the capability to achieve clinical applications and open platform for radiomics analysis).  ...  and prediction of overall survival.  ... 
doi:10.3389/fonc.2022.773840 pmid:35251962 pmcid:PMC8891653 fatcat:3h5tnm3aznb33k5ylkcd6tvs4e

Multi-Channel 3D Deep Feature Learning for Survival Time Prediction of Brain Tumor Patients Using Multi-Modal Neuroimages

Dong Nie, Junfeng Lu, Han Zhang, Ehsan Adeli, Jun Wang, Zhengda Yu, LuYan Liu, Qian Wang, Jinsong Wu, Dinggang Shen
2019 Scientific Reports  
Accurate pre-operative prognosis for this cohort can lead to better treatment planning. Conventional survival prediction based on clinical information is subjective and could be inaccurate.  ...  We propose a two-stage learning-based method to predict the overall survival (OS) time of high-grade gliomas patient.  ...  imaging phenotypes and genotypes.  ... 
doi:10.1038/s41598-018-37387-9 pmid:30705340 pmcid:PMC6355868 fatcat:ux6wmn5n7rfmzcx64b54ewb4jy

Reverse Engineering Glioma Radiomics to Conventional Neuroimaging

Manabu KINOSHITA, Yonehiro KANEMURA, Yoshitaka NARITA, Haruhiko KISHIMA
2021 Neurologia medico-chirurgica  
This novel concept for analyzing medical images brought extensive interest to the neuro-oncology and neuroradiology research community to build a diagnostic workflow to detect clinically relevant genetic  ...  At the same time, many significant insights were discovered through this research project, some of which could be "reverse engineered" to improve conventional non-radiomic MR image acquisition.  ...  .: The preliminary radiogenomics association between MR perfusion imaging parameters and genomic biomarkers, and their predictive performance of overall survival in patients with glioblastoma.  ... 
doi:10.2176/nmc.ra.2021-0133 pmid:34373429 pmcid:PMC8443974 fatcat:i7mzjddf6rfbpknnwp4pirce34

Radiomics: from qualitative to quantitative imaging

William Rogers, Sithin Thulasi Seetha, Turkey A. G. Refaee, Relinde I. Y. Lieverse, Renée W. Y. Granzier, Abdalla Ibrahim, Simon A. Keek, Sebastian Sanduleanu, Sergey P. Primakov, Manon P. L. Beuque, Damiënne Marcus, Alexander M. A. van der Wiel (+5 others)
2020 British Journal of Radiology  
to make predictions, such as survival, or for detection and classification used in diagnostics.  ...  Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling  ...  [97] [98] [99] An iterative study by radiomic researchers found strong evidence of radiomic features in predicting survival and treatment response of patients with glioblastoma using pre-treatment imaging  ... 
doi:10.1259/bjr.20190948 pmid:32101448 fatcat:mnhaur7dyrhanio63v6kdbdthm
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