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Is Texture Predictive for Age and Sex in Brain MRI? [article]

Nick Pawlowski, Ben Glocker
2019 arXiv   pre-print
We explore whether this translates to certainmedical imaging tasks such as age and sex prediction from a T1-weighted brain MRI scans.  ...  Deep learning builds the foundation for many medical image analysis tasks where neuralnetworks are often designed to have a large receptive field to incorporate long spatialdependencies.  ...  Acknowledgments NP is supported by Microsoft Research PhD Scholarship and the EPSRC Centre for Doctoral Training in High Performance Embedded and Distributed Systems (HiPEDS, Grant Reference EP/L016796  ... 
arXiv:1907.10961v1 fatcat:t6alt3xszrf7xkij5ircy3qzni

MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes

Martin Bretzner, Anna K. Bonkhoff, Markus D. Schirmer, Sungmin Hong, Adrian V. Dalca, Kathleen L. Donahue, Anne-Katrin Giese, Mark R. Etherton, Pamela M. Rist, Marco Nardin, Razvan Marinescu, Clinton Wang (+44 others)
2021 Frontiers in Neuroscience  
Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients' brain health.  ...  The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD  ...  However, little is known about the relationship between the texture of the brain and WMH accumulation.  ... 
doi:10.3389/fnins.2021.691244 pmid:34321995 pmcid:PMC8312571 fatcat:ixzkolwa6jhxbbksyzuc3f34re

Early detection of Alzheimer's disease using MRI hippocampal texture

Lauge Sørensen, Christian Igel, Naja Liv Hansen, Merete Osler, Martin Lauritzen, Egill Rostrup, Mads Nielsen
2015 Human Brain Mapping  
Cognitive impairment in patients with Alzheimer's disease (AD) is associated with reduction in hippocampal volume in magnetic resonance imaging (MRI).  ...  Texture statistics remained significant after adjustment for volume in all cases, and the combination of texture and volume did not improve diagnostic or prognostic AUCs significantly.  ...  Acknowledgments Lauge Sørensen is employee at Biomediq A/S. Mads Nielsen is employee at Biomediq A/S and shareholder in Biomediq A/S. The remaining authors declare no conflicts of interest.  ... 
doi:10.1002/hbm.23091 pmid:26686837 fatcat:occ25f4rwrbcvktoichq4wecyq

MRI Radiomic Signature of White Matter Hyperintensities Is Associated with Clinical Phenotypes [article]

Martin Bretzner, Anna K. Bonkhoff, Markus D. Schirmer, Sungmin Hong, Adrian Dalca, Kathleen L. Donahue, Anne-Katrin Giese, Mark R. Etherton, Pamela M. Rist, Marco Nardin, Razvan Marinescu, Clinton Wang (+41 others)
2021 bioRxiv   pre-print
By means of describing the texture of conventional images beyond what meets the naked eye, radiomic analyses hold potential for evaluating brain health.  ...  The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and DM, CV4 by HTN, CV5 by AF and DM, CV6 by CAD, and CV7 by CAD and DM.  ...  MRE is supported by the American Academy of Neurology and MGH Executive Council on Research. TT was supported by the Helsinki University Central Hospital, Sigrid  ... 
doi:10.1101/2021.01.24.427986 fatcat:oemzvexwuner3p2yam3whqzpdi

Delusional Severity Is Associated with Abnormal Texture in FLAIR MRI

Marc A. Khoury, Mohamad-Ali Bahsoun, Ayad Fadhel, Shukrullah Shunbuli, Saanika Venkatesh, Abdollah Ghazvanchahi, Samir Mitha, Karissa Chan, Luis R. Fornazzari, Nathan W. Churchill, Zahinoor Ismail, David G. Munoz (+4 others)
2022 Brain Sciences  
Sex, age, education, APOE4 and baseline cerebrospinal fluid (CSF) tau were included as co-variates.  ...  The NABM region, which is gray matter (GM) and white matter (WM) combined, was automatically segmented in FLAIR MRI volumes with intensity standardization and thresholding.  ...  Although interest in MRI texture analysis is growing [22] , there are limited studies in subjects with neurodegenerative disorders and delusions.  ... 
doi:10.3390/brainsci12050600 fatcat:zirue3gynbe6vbbmf5tk6nt4fu

Sex differences in predicting fluid intelligence of adolescent brain from T1-weighted MRIs [article]

Sara Ranjbar, Kyle W. Singleton, Lee Curtin, Susan Christine Massey, Andrea Hawkins-Daarud, Pamela R. Jackson, Kristin R. Swanson
2019 arXiv   pre-print
As part of the Adolescent Brain Cognitive Development Neurocognitive Prediction Challenge 2019, we sought to predict Gf in children aged 9-10 from T1-weighted (T1W) MRIs.  ...  The data included atlas-aligned volumetric T1W images, atlas-defined segmented regions, age, and sex for 3739 subjects used for training and internal validation and 415 subjects used for external validation  ...  Acknowledgements The authors would like to thank the Challenge Organizers and ABCD Study Researchers for the opportunity to participate and utilize their data.  ... 
arXiv:1908.02333v1 fatcat:p7sdkjmicbhbpjzcsfpapcl3fu

Deep Ordinal Ranking for Multi-Category Diagnosis of Alzheimer's Disease using Hippocampal MRI data [article]

Hongming Li, Mohamad Habes, Yong Fan
2017 arXiv   pre-print
In this paper, we propose a deep ordinal ranking model for distinguishing NC, stable MCI (sMCI), pMCI, and AD at an individual subject level, taking into account the inherent ordinal severity of brain  ...  However, identifying individuals with AD and MCI, especially MCI individuals who will convert to AD (progressive MCI, pMCI), in a single setting, is needed to achieve the goal of early diagnosis of AD.  ...  Acknowledgments This work was supported in part by National Institutes of Health grants (Nos. EB022573, CA189523, MH107703, DA039215, and DA039002).  ... 
arXiv:1709.01599v2 fatcat:b2td3jcxrnar5i7eou2jh6y4om

Alzheimer's disease: 3-Dimensional MRI texture for prediction of conversion from mild cognitive impairment

Collin C. Luk, Abdullah Ishaque, Muhammad Khan, Daniel Ta, Sneha Chenji, Yee-Hong Yang, Dean Eurich, Sanjay Kalra
2018 Alzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring  
A composite model including texture features, APOE-ε4 genotype, Mini-Mental Status Examination score, sex, and hippocampal occupancy resulted in an area under curve of 0.905.  ...  Our main objective was to determine whether MRI texture could be used to predict conversion of MCI to AD.  ...  It is associated with the accumulation of amyloid and tau proteins in the brain and is the most common cause of dementia, accounting for nearly 70% of dementia cases [1] .  ... 
doi:10.1016/j.dadm.2018.09.002 pmid:30480081 pmcid:PMC6240791 fatcat:hxahficwjfayhgioebyifjtnee

Multimodal MRI features predict isocitrate dehydrogenase genotype in high-grade gliomas

Biqi Zhang, Ken Chang, Shakti Ramkissoon, Shyam Tanguturi, Wenya Linda Bi, David A. Reardon, Keith L. Ligon, Brian M. Alexander, Patrick Y. Wen, Raymond Y. Huang
2016 Neuro-Oncology  
The features that contributed most to IDH genotype prediction in our model included age and MRI parametric intensity, texture, and shape features.  ...  To assess the impact of MRI features alone, we generated a model excluding age and sex; this model achieved prediction accuracies of 81% (AUC = 0.81) in the training cohort and 90% (AUC = 0.90) in the  ... 
doi:10.1093/neuonc/now121 pmid:27353503 pmcid:PMC5193019 fatcat:liqqe5pwlfde5otjd2xelfittu

Brain MRI Radiomics Analysis of School-Aged Children with Tetralogy of Fallot

Yiwei Pu, Songmei Li, Siyu Ma, Yuanli Hu, Qinghui Hu, Yuting Liu, Mengting Wu, Jia An, Ming Yang, Xuming Mo, Jianxin Shi
2021 Computational and Mathematical Methods in Medicine  
School-aged TOF patients and their healthy peers were recruited for MRI and neurodevelopmental assessment. LASSO regression was used for dimension reduction.  ...  The radiomics on the conventional MRI can help predict the neurodevelopment of school-aged children and provide parents with rehabilitation advice as early as possible.  ...  In other following studies, we will try to interrogate the prediction of conventional MRI for late neural development.  ... 
doi:10.1155/2021/2380346 pmid:34745322 pmcid:PMC8570890 fatcat:nsb4zztc2jft7fwbczw3xu6uce

A deep learning model for early prediction of Alzheimer's disease dementia based on hippocampal MRI [article]

Hongming Li, Mohamad Habes, David A. Wolk, Yong Fan
2019 arXiv   pre-print
Methods: A deep learning method is developed and validated based on MRI scans of 2146 subjects (803 for training and 1343 for validation) to predict MCI subjects' progression to AD dementia in a time-to-event  ...  It is challenging at baseline to predict when and which individuals who meet criteria for mild cognitive impairment (MCI) will ultimately progress to Alzheimer's disease (AD) dementia.  ...  ) with age, sex, education and APOE4 status as covariates, as demonstrated by the Kaplan-Meier plots in Fig. 5 .  ... 
arXiv:1904.07282v1 fatcat:j5i6z6flvneafbnfxydo5e7gay

MRI Texture Analysis Reveals Brain Abnormalities in Medically Refractory Trigeminal Neuralgia

Hayden Danyluk, Abdullah Ishaque, Daniel Ta, Yee Hong Yang, B. Matthew Wheatley, Sanjay Kalra, Tejas Sankar
2021 Frontiers in Neurology  
recruited 14 medically refractory classical TN patients and 20 healthy subjects. 3-Tesla T1-weighted brain MRI scans were acquired in all participants.  ...  These findings further implicate structural brain changes in the development and maintenance of TN.  ...  ACKNOWLEDGMENTS The authors would like to acknowledge Anureet Tiwana for assistance in manuscript formatting for submission.  ... 
doi:10.3389/fneur.2021.626504 pmid:33643203 pmcid:PMC7907508 fatcat:wtgqlxxeefdxdaysxqjax6omg4

Machine Learning Approach For Identifying Dementia From Mri Images

S. K. Aruna, S. Chitra
2016 Zenodo  
Modelling, that captures the brain's structural variability and which is valid in disease classification and interpretation is very challenging.  ...  Dementia, an age-related cognitive decline is indicated by degeneration of cortical and sub-cortical structures.  ...  Persons in their 40s and 50s of both sexes can have dementia, but it is more common in men [11] . Dementia is of many types and each has its causes.  ... 
doi:10.5281/zenodo.1124465 fatcat:gylsoiagujdx5kwpdzio4rmgqq

A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study

Jing Zhang, Kuan Yao, Panpan Liu, Zhenyu Liu, Tao Han, Zhiyong Zhao, Yuntai Cao, Guojin Zhang, Junting Zhang, Jie Tian, Junlin Zhou
2020 EBioMedicine  
The clinicoradiomic model derived from the fusing MRI sequences and sex resulted in the best discrimination ability for risk prediction of brain invasion, with areas under the curves (AUCs) of 0•857 (95%  ...  Our clinicoradiomic model showed good performance and high sensitivity for risk prediction of brain invasion in meningioma, and can be applied in patients with meningiomas.  ...  Acknowledgements We would like to thank Xiaobin Hu (from Lanzhou University) for the study design guidance and data interpretation.  ... 
doi:10.1016/j.ebiom.2020.102933 pmid:32739863 pmcid:PMC7393568 fatcat:24tt6xzdabfn5dbih5qz4voxra

A Predictive Clinical-Radiomics Nomogram for Survival Prediction of Glioblastoma Using MRI

Samy Ammari, Raoul Sallé de Chou, Corinne Balleyguier, Emilie Chouzenoux, Mehdi Touat, Arnaud Quillent, Sarah Dumont, Sophie Bockel, Gabriel C. T. E. Garcia, Mickael Elhaik, Bidault Francois, Valentin Borget (+3 others)
2021 Diagnostics  
Glioblastoma (GBM) is the most common and aggressive primary brain tumor in adult patients with a median survival of around one year.  ...  Then, using this signature along with the age of the patients for training classification models, we obtained on test-sets AUCs of 0.85, 0.74 and 0.58 (0.92, 0.88 and 0.75 on the training-sets) for survival  ...  Acknowledgments: The authors would like to thank the patients studied in this paper. Figure 1 has been designed using resources from Flaticon.com (accessed on 25 September 2021).  ... 
doi:10.3390/diagnostics11112043 pmid:34829395 pmcid:PMC8624566 fatcat:5ccqfajj3jfvhoxgkykrldzgcy
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