A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Filters
Radiogenomics for Precision Medicine With A Big Data Analytics Perspective
2019
IEEE journal of biomedical and health informatics
Preserved Fulltext
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
2020
Scientific Reports
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2021
Frontiers in Oncology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2017
Clinical Cancer Research
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
: 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
2022
Cancers
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2017
Annals of Oncology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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
2021
British Journal of Cancer
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2018
Therapeutic Advances in Neurological Disorders
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
, 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
2019
International Journal of Molecular Sciences
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is application/pdf.
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]
2020
arXiv
pre-print
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
Other Versions
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
2021
Journal of Personalized Medicine
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2022
Frontiers in Oncology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2022; you can also visit the original URL.
The file type is application/pdf.
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
2019
Scientific Reports
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf.
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
2021
Neurologia medico-chirurgica
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
2020
British Journal of Radiology
Preserved Fulltext
A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf.
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
« Previous
Showing results 1 — 15 out of 515 results