Share：
Share this content in WeChat
X
[Chinese][PDF]102928
• Review •
Research progress of MRI radiomics in mild cognitive impairment
CAI Lina  LI Xiaoling  PAN Yang  WANG Peng  PENG Cailiang  HAN Shengwang  HOU Yu  WANG Yang  GAO Ruixue 

Cite this article as: Cai LN, Li XL, Pan Y, et al. Research progress of MRI radiomics in mild cognitive impairment[J]. Chin J Magn Reson Imaging, 2022, 13(6): 131-134. DOI:10.12015/issn.1674-8034.2022.06.027.


[Abstract] Mild cognitive impairment (MCI) is the early manifestation of Alzheimer's disease (AD). At present, there is no effective radical cure for AD. Therefore, early diagnosis and intervention of MCI are of great significance to prevent or delay the development of AD. MRI radiomics can extract and analyze the features of MCI patients' images in a non-invasive way, which can provide more potential imaging biomarker information, and then guide clinical accurate diagnosis and treatment, which has a broad development prospect. This paper reviews the concept of radiomics and the research progress of MRI radiomics in the diagnosis, classification and conversion prediction of MCI.
[Keywords] magnetic resonance imaging；radiomics；mild cognitive impairment；Alzheimer's disease

CAI Lina1   LI Xiaoling2#   PAN Yang3*   WANG Peng2   PENG Cailiang2   HAN Shengwang4   HOU Yu5   WANG Yang2   GAO Ruixue1  

1 Graduate School of Heilongjiang University of Chinese Medicine, Harbin 150040, China

2 Department of CT & MR, the First Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, Harbin 150040, China

3 Heilongjiang College of Traditional Chinese Medicine, Harbin 150036, China

4 Department of Rehabilitation, the Second Affiliated Hospital of Heilongjiang University of Traditional Chinese Medicine, Harbin 150001, China

5 Department of Gynecology, Harbin Hospital of Traditional Chinese Medicine, Harbin 150010, China

Pan Y, E-mail: pannyangyang1323@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS National Natural Science Foundation of China (No. 82074537); Natural Science Foundation of Heilongjiang Province (No. LH2020H103); Harbin Science and Technology Innovation Outstanding Academic Leaders Fund (No. 2016RAXYJ096).
Received  2022-02-28
Accepted  2022-05-27
DOI: 10.12015/issn.1674-8034.2022.06.027
Cite this article as: Cai LN, Li XL, Pan Y, et al. Research progress of MRI radiomics in mild cognitive impairment[J]. Chin J Magn Reson Imaging, 2022, 13(6): 131-134. DOI:10.12015/issn.1674-8034.2022.06.027.

[1]
Davis M, Connell T O, Johnson S, et al. Estimating Alzheimer's Disease Progression Rates from Normal Cognition Through Mild Cognitive Impairment and Stages of Dementia[J]. Curr Alzheimer Res, 2018, 15(8): 777-788. DOI: 10.2174/1567205015666180119092427.
[2]
Langa KM, Levine DA. The diagnosis and management of mild cognitive impairment: a clinical review[J]. JAMA, 2014, 312(23): 2551-2561. DOI: 10.1001/jama.2014.13806.
[3]
Tang L, Wu X, Liu H, et al. Individualized Prediction of Early Alzheimer's Disease Based on Magnetic Resonance Imaging Radiomics, Clinical, and Laboratory Examinations: A 60-Month Follow-Up Study[J]. J Magn Reson Imaging, 2021, 54(5): 1647-1657. DOI: 10.1002/jmri.27689.
[4]
Wang G, He L, Yuan C, et al. Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma[J]. Eur J Radiol, 2018, 98: 100-106. DOI: 10.1016/j.ejrad.2017.11.007.
[5]
Salvatore C, Castiglioni I, Cerasa A. Radiomics approach in the neurodegenerative brain[J]. Aging Clin Exp Res, 2021, 3(6): 1709-1711. DOI: 10.1007/s40520-019-01299-z.
[6]
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036.
[7]
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169.
[8]
Chaddad A, Desrosiers C, Toews M. Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age[J]. Sci Rep, 2017, 31, 7: 45639. DOI: 10.1038/srep45639.
[9]
Yang SX, Zhang L. Research progress on the reproducibility of radiomic features in image acquisition and reconstruction[J]. International Journal of Medical Radiology, 2020, 43(3): 326-330. DOI: 10.19300/j2020.Z17615.
[10]
Carré A, Klausner G, Edjlali M, et al. Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics[J]. Sci Rep, 2020, 10(1): 12340. DOI: 10.1038/s41598-020-69298-z.
[11]
Scalco E, Belfatto A, Mastropietro A, et al. T2w-MRI signal normalization affects radiomics features reproducibility[J]. Med Phys, 2020, 47: 1680-1691. DOI: 10.1002/mp.14038.
[12]
Won SY, Park YW, Park M, et al. Quality Reporting of Radiomics Analysis in Mild Cognitive Impairment and Alzheimer's Disease: A Roadmap for Moving Forward[J]. Korean J Radiol, 2020, 21(12): 1345-1354. DOI: 10.3348/kjr.2020.0715.
[13]
Song R, Wu XJ, Li CM, et al. Nomogram based on MRI imaging omics and ATN classification system to predict the progress of mild cognitive impairment[J]. Radiol Pract, 2021, 36(12): 1481-1487. DOI: 10.13609/j.cnki.1000-0313.2021.12.005.
[14]
Xie K, Sun HF, Lin T, et al. Research progresses in feature extraction of radiomics[J]. Chin J Med Imaging Technol, 2017, 33(12): 1792-1796. DOI: 10.13929/j.1003-3289.201707022.
[15]
Yang JZ, Wang YF, Li W, et al. A semi-automatic algorithm for callosum segmentation in head MRI[J]. Chin J Med Imaging Technol, 2010, 26(9): 1632-1635. DOI: 10.13929/j.1003-3289.2010.09.075.
[16]
Larue RT, Defraene G, De Ruysscher D, et al. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures[J]. Br J Radiol, 2017, 90(1070): 20160665. DOI: 10.1259/bjr.20160665.
[17]
Hu YM, Zhao HP, Li W, et al. Semantic image segmentation of brain MRI with deep learning[J]. Journal of Central South University (Medical Science), 2021, 28, 46(8): 858-864. DOI: 10.11817/j.issn.1672-7347.2021.200744.
[18]
Shen H, Huang J, Zheng Q, et al. A Deep-Learning-Based, Fully Automated Program to Segment and Quantify Major Spinal Components on Axial Lumbar Spine Magnetic Resonance Images[J]. Phys Ther, 2021, 101(6): pzab041. DOI: 10.1093/ptj/pzab041.
[19]
Gerb J, Ahmadi SA, Kierig E, et al. VOLT: a novel open-source pipeline for automatic segmentation of endolymphatic space in inner ear MRI[J]. J Neurol, 2020, 267(1): 185-196. DOI: 10.1007/s00415-020-10062-8.
[20]
Kong Z, Li T, Luo J, et al. Automatic Tissue Image Segmentation Based on Image Processing and Deep Learning[J]. J Healthc Eng, 2019, 2019: 2912458. DOI: 10.1155/2019/2912458.
[21]
Madusanka N, Choi HK, So JH, et al. One-year Follow-up Study of Hippocampal Subfield Atrophy in Alzheimer's Disease and Normal Aging[J]. Curr Med Imaging Rev, 2019, 15(7): 699-709. DOI: 10.2174/1573405615666190327102052.
[22]
Das S, Panigrahi P, Chakrabarti S. Corpus Callosum Atrophy in Detection of Mild and Moderate Alzheimer's Disease Using Brain Magnetic Resonance Image Processing and Machine Learning Techniques[J]. J Alzheimers Dis Rep, 2021, 5(1): 771-788. DOI: 10.3233/ADR-210314.
[23]
Platero C, Lin L, Tobar MC. Longitudinal Neuroimaging Hippocampal Markers for Diagnosing Alzheimer's Disease[J]. Neuroinformatics, 2019, 17(1): 43-61. DOI: 10.1007/s12021-018-9380-2.
[24]
Caminiti SP, Ballarini T, Sala A, et al. FDG-PET and CSF biomarker accuracy in prediction of conversion to different dementias in a large multicentre MCI cohort[J]. Neuroimage Clin, 2018, 28(18): 167-177. DOI: 10.1016/j.nicl.2018.01.019.
[25]
Huang K, Lin Y, Yang L, et al. Alzheimer's Disease Neuroimaging Initiative. A multipredictor model to predict the conversion of mild cognitive impairment to Alzheimer's disease by using a predictive nomogram[J]. Neuropsychopharmacology, 2020, 45(2): 358-366. DOI: 10.1038/s41386-019-0551-0.
[26]
Cheung EYW, Chau ACM, Tang FH. On Behalf Of The Alzheimer's Disease Neuroimaging Initiative. Radiomics-Based Artificial Intelligence Differentiation of Neurodegenerative Diseases with Reference to the Volumetry[J]. Life (Basel), 2022, 12(4): 514. DOI: 10.3390/life12040514.
[27]
Zhang X, Zhang Y, Zhang G, et al. Deep Learning With Radiomics for Disease Diagnosis and Treatment: Challenges and Potential[J]. Front Oncol, 2022, 17(12): 773840. DOI: 10.3389/fonc.2022.773840.
[28]
Zhou J, Lu J, Gao C, et al. Predicting the response to neoadjuvant chemotherapy for breast cancer: wavelet transforming radiomics in MRI[J]. BMC Cancer, 2020, 20(1): 100. DOI: 10.1186/s12885-020-6523-2.
[29]
Cummings JL, Tong G, Ballard C. Treatment Combinations for Alzheimer's Disease: Current and Future Pharmacotherapy Options[J]. J Alzheimers Dis, 2019, 67(3): 779-794. DOI: 10.3233/JAD-180766.
[30]
Lombardi G, Crescioli G, Cavedo E, et al. Structural magnetic resonance imaging for the early diagnosis of dementia due to Alzheimer's disease in people with mild cognitive impairment[J]. Cochrane Database Syst Rev, 2020, 3(3): CD009628. DOI: 10.1002/14651858.
[31]
Jha D, Kim JI, Kwon GR. Diagnosis of Alzheimer's Disease Using Dual-Tree Complex Wavelet Transform, PCA, and Feed-Forward Neural Network[J]. J Healthc Eng, 2017, 2017: 9060124. DOI: 10.1155/2017/9060124.
[32]
Yao LL, Fan Z. A machine learning model for early diagnosis of Alzheimer's disease[J]. Chin J Magn Reson Imaging, 2021, 12(6): 78-82. DOI: 10.12015/issn.1674-8034.2021.06.015.
[33]
Carpenter KA, Huang X. Machine Learning-based Virtual Screening and Its Applications to Alzheimer's Drug Discovery: A Review[J]. Curr Pharm Des, 2018, 24(28): 3347-3358. DOI: 10.2174/1381612824666180607124038.
[34]
Rathore S, Habes M, Iftikhar MA, et al. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer's disease and its prodromal stages[J]. Neuroimage, 2017, 155: 530-548. DOI: 10.1016/j.neuroimage.2017.03.057.
[35]
Guo S, Xiao B, Wu C. Alzheimer's Disease Neuroimaging Initiative. Identifying subtypes of mild cognitive impairment from healthy aging based on multiple cortical features combined with volumetric measurements of the hippocampal subfields[J]. Quant Imaging Med Surg, 2020, 10(7): 1477-1489. DOI: 10.21037/qims-19-872.
[36]
Li A, Li F, Elahifasaee F, et al. Alzheimer's Disease Neuroimaging Initiative. Hippocampal shape and asymmetry analysis by cascaded convolutional neural networks for Alzheimer's disease diagnosis[J]. Brain Imaging Behav, 2021, 15(5): 2330-2339. DOI: 10.1007/s11682-020-00427-y.
[37]
Leandrou S, Lamnisos D, Mamais I, et al. Alzheimer's Disease and Neuroimaging Initiative. Assessment of Alzheimer's Disease Based on Texture Analysis of the Entorhinal Cortex[J]. Front Aging Neurosci, 2020, 2(12): 176. DOI: 10.3389/fnagi.2020.00176.
[38]
So JH, Madusanka N, Choi HK, et al. Deep Learning for Alzheimer's Disease Classification using Texture Features[J]. Curr Med Imaging Rev, 2019, 15(7): 689-698. DOI: 10.2174/1573405615666190404163233.
[39]
Feng Q, Niu J, Wang L, et al. Comprehensive classification models based on amygdala radiomic features for Alzheimer's disease and mild cognitive impairment[J]. Brain Imaging Behav, 2021, 15(5): 2377-2386. DOI: 10.1007/s11682-020-00434-z.
[40]
Feng W, Halm-Lutterodt NV, Tang H, et al. Automated MRI-Based Deep Learning Model for Detection of Alzheimer's Disease Process[J]. Int J Neural Syst, 2020, 30(6): 2050032. DOI: 10.1142/S012906572050032X.
[41]
Liu M, Li F, Yan H, et al. Alzheimer's Disease Neuroimaging Initiative. A multi-model deep convolutional neural network for automatic hippocampus segmentation and classification in Alzheimer's disease[J]. Neuroimage, 2020, 208: 116459. DOI: 10.1016/j.neuroimage.2019.116459.
[42]
Spasov S, Passamonti L, Duggento A, et al. Alzheimer's Disease Neuroimaging Initiative. A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease[J]. Neuroimage, 2019, 189: 276-287. DOI: 10.1016/j.neuroimage.2019.01.031.
[43]
Luk CC, Ishaque A, Khan M, et al. Alzheimer's Disease Neuroimaging Initiative. Alzheimer's disease: 3-Dimensional MRI texture for prediction of conversion from mild cognitive impairment[J]. Alzheimers Dement (Amst), 2018, 2(10): 755-763. DOI: 10.1016/j.dadm.2018.09.002.
[44]
Kim JP, Kim J, Jang H, et al. Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach[J]. Sci Rep, 2021, 11(1): 6954. DOI: 10.1038/s41598-021-86114-4.
[45]
Park YW, Choi D, Park M, et al. Alzheimer's Disease Neuroimaging Initiative. Predicting Amyloid Pathology in Mild Cognitive Impairment Using Radiomics Analysis of Magnetic Resonance Imaging[J]. J Alzheimers Dis, 2021, 79(2): 483-491. DOI: 10.3233/JAD-200734.
[46]
Ranjbar S, Velgos SN, Dueck AC, et al. Alzheimer's Disease Neuroimaging Initiative. Brain MR Radiomics to Differentiate Cognitive Disorders[J]. J Neuropsychiatry Clin Neurosci, 2019, 31(3): 210-219. DOI: 10.1176/appi.neuropsych.17120366.
[47]
Shu ZY, Mao DW, Xu YY, et al. Prediction of the progression from mild cognitive impairment to Alzheimer's disease using a radiomics-integrated model[J]. Ther Adv Neurol Disord, 2021, 15(14): 17562864211029551. DOI: 10.1177/17562864211029551.
[48]
Shen T, Jiang J, Li Y, et al. Decision Supporting Model for One-year Conversion Probability from MCI to AD using CNN and SVM[J]. Annu Int Conf IEEE Eng Med Biol Soc, 2018, 2018: 738-741. DOI: 10.1109/EMBC.2018.8512398.
[49]
Lin W, Tong T, Gao Q, et al. Alzheimer's Disease Neuroimaging Initiative. Convolutional Neural Networks-Based MRI Image Analysis for the Alzheimer's Disease Prediction From Mild Cognitive Impairment[J]. Front Neurosci, 2018, 12: 777. DOI: 10.3389/fnins.2018.00777.
[50]
Kang L, Jiang J, Huang J, et al. Identifying Early Mild Cognitive Impairment by Multi-Modality MRI-Based Deep Learning[J]. Front Aging Neurosci, 2020, 12: 206. DOI: 10.3389/fnagi.2020.00206.

PREV Brucellosis with sternoclavicular arthritis and spondylitis: One case report
NEXT Research progress of multimodal MRI and radiomics in evaluation of Parkinson,s disease with depression
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn