Literature DB >> 31428998

Radiomics approach in the neurodegenerative brain.

Christian Salvatore1, Isabella Castiglioni2, Antonio Cerasa3,4.   

Abstract

As claimed by Robert Gilles et al., "Images are more than pictures, they are data". This statement refers to the power of imaging to provide large amounts of quantitative features for improving diagnosis, prognosis and therapy response. The conversion of digital medical images into high-dimensional mineable data is called radiomics. Radiomics analysis is based on data-characterisation algorithms which have the potential to uncover disease heterogeneity characteristics that might escape from the expert evaluation. This method has been widely applied in oncology and genetic fields, while the literature on neurodegenerative disorders is in its relative infancy. Here, we provide a preliminary evaluation of the main results reached applying radiomics analyses on well-established MRI features of patients with Alzheimer's Disease and Parkinson's disease.

Entities:  

Keywords:  Alzheimer’s disease; Neuroimaging; Parkinson’s disease; Radiomics

Mesh:

Year:  2019        PMID: 31428998     DOI: 10.1007/s40520-019-01299-z

Source DB:  PubMed          Journal:  Aging Clin Exp Res        ISSN: 1594-0667            Impact factor:   3.636


  7 in total

1.  Phantom validation of quantitative susceptibility and dynamic contrast-enhanced permeability MR sequences across instruments and sites.

Authors:  Nicholas Hobson; Sean P Polster; Ying Cao; Kelly Flemming; Yunhong Shu; John Huston; Chandra Y Gerrard; Reed Selwyn; Marc Mabray; Atif Zafar; Romuald Girard; Julián Carrión-Penagos; Yu Fen Chen; Todd Parrish; Xiaohong Joe Zhou; James I Koenig; Robert Shenkar; Agnieszka Stadnik; Janne Koskimäki; Alexey Dimov; Dallas Turley; Timothy Carroll; Issam A Awad
Journal:  J Magn Reson Imaging       Date:  2019-09-12       Impact factor: 4.813

2.  Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type.

Authors:  Helge C Kniep; Frederic Madesta; Tanja Schneider; Uta Hanning; Michael H Schönfeld; Gerhard Schön; Jens Fiehler; Tobias Gauer; René Werner; Susanne Gellissen
Journal:  Radiology       Date:  2018-12-11       Impact factor: 11.105

3.  Brain MR Radiomics to Differentiate Cognitive Disorders.

Authors:  Sara Ranjbar; Stefanie N Velgos; Amylou C Dueck; Yonas E Geda; J Ross Mitchell
Journal:  J Neuropsychiatry Clin Neurosci       Date:  2019-01-14       Impact factor: 2.198

4.  MRI Characterizes the Progressive Course of AD and Predicts Conversion to Alzheimer's Dementia 24 Months Before Probable Diagnosis.

Authors:  Christian Salvatore; Antonio Cerasa; Isabella Castiglioni
Journal:  Front Aging Neurosci       Date:  2018-05-24       Impact factor: 5.750

5.  Corpus Callosum Radiomics-Based Classification Model in Alzheimer's Disease: A Case-Control Study.

Authors:  Qi Feng; Yuanjun Chen; Zhengluan Liao; Hongyang Jiang; Dewang Mao; Mei Wang; Enyan Yu; Zhongxiang Ding
Journal:  Front Neurol       Date:  2018-07-26       Impact factor: 4.003

6.  Predicting the Development of Normal-Appearing White Matter With Radiomics in the Aging Brain: A Longitudinal Clinical Study.

Authors:  Yuan Shao; Zhonghua Chen; Shuai Ming; Qin Ye; Zhenyu Shu; Cheng Gong; Peipei Pang; Xiangyang Gong
Journal:  Front Aging Neurosci       Date:  2018-11-28       Impact factor: 5.750

7.  Radiomics: a novel feature extraction method for brain neuron degeneration disease using 18F-FDG PET imaging and its implementation for Alzheimer's disease and mild cognitive impairment.

Authors:  Yupeng Li; Jiehui Jiang; Jiaying Lu; Juanjuan Jiang; Huiwei Zhang; Chuantao Zuo
Journal:  Ther Adv Neurol Disord       Date:  2019-03-29       Impact factor: 6.570

  7 in total
  8 in total

1.  Radiomics Model for Frontotemporal Dementia Diagnosis Using T1-Weighted MRI.

Authors:  Benedetta Tafuri; Marco Filardi; Daniele Urso; Roberto De Blasi; Giovanni Rizzo; Salvatore Nigro; Giancarlo Logroscino
Journal:  Front Neurosci       Date:  2022-06-20       Impact factor: 5.152

Review 2.  Application of radiomics in precision prediction of diagnosis and treatment of gastric cancer.

Authors:  Getao Du; Yun Zeng; Dan Chen; Wenhua Zhan; Yonghua Zhan
Journal:  Jpn J Radiol       Date:  2022-10-19       Impact factor: 2.701

3.  Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging.

Authors:  Panshi Liu; Han Wang; Shilei Zheng; Fan Zhang; Xianglin Zhang
Journal:  Front Neurol       Date:  2020-04-08       Impact factor: 4.003

Review 4.  Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential.

Authors:  Xingping Zhang; Yanchun Zhang; Guijuan Zhang; Xingting Qiu; Wenjun Tan; Xiaoxia Yin; Liefa Liao
Journal:  Front Oncol       Date:  2022-02-17       Impact factor: 6.244

5.  Radiomic Features of the Hippocampus for Diagnosing Early-Onset and Late-Onset Alzheimer's Disease.

Authors:  Yang Du; Shaowei Zhang; Yuan Fang; Qi Qiu; Lu Zhao; Wenjing Wei; Yingying Tang; Xia Li
Journal:  Front Aging Neurosci       Date:  2022-01-26       Impact factor: 5.750

6.  Detection of Microstructural Medial Prefrontal Cortex Changes Using Magnetic Resonance Imaging Texture Analysis in a Post-Traumatic Stress Disorder Rat Model.

Authors:  Shilei Zheng; Han Wang; Fang Han; Jianyi Chu; Fan Zhang; Xianglin Zhang; Yuxiu Shi; Lili Zhang
Journal:  Front Psychiatry       Date:  2022-04-21       Impact factor: 4.157

7.  CT-based radiomic nomogram for preoperative prediction of DNA mismatch repair deficiency in gastric cancer.

Authors:  Qingwen Zeng; Yanyan Zhu; Leyan Li; Zongfeng Feng; Xufeng Shu; Ahao Wu; Lianghua Luo; Yi Cao; Yi Tu; Jianbo Xiong; Fuqing Zhou; Zhengrong Li
Journal:  Front Oncol       Date:  2022-09-16       Impact factor: 5.738

8.  Aberrant functional connectivity and activity in Parkinson's disease and comorbidity with depression based on radiomic analysis.

Authors:  Xulian Zhang; Xuan Cao; Chen Xue; Jingyi Zheng; Shaojun Zhang; Qingling Huang; Weiguo Liu
Journal:  Brain Behav       Date:  2021-03-10       Impact factor: 2.708

  8 in total

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