Abstract：Objective To discuss the radiomic features of hippocampus related to Alzheimer disease (AD) diagnosis, and to build and compare the right and left hippocampal classification models for AD diagnosis.Methods A prospective study was conducted on AD patients who visited the memory clinic of Zhejiang Provincial People's Hospital from September 2016 to June 2018. Fifty-nine AD subjects and forty-four normal controls (NC) were scanned with three-dimensional T1-weighted magnetization-prepared rapid gradient echo (MPRAGE) sequence by GE 3. 0 T MR. The right and left hippocampus were segmented for each subject using an efficient learning-based deformable model. The two groups of data passed through feature calculation and feature selection respectively. Then two classification models of the right and left hippocampus were built respectively based on the selected features using the Logistic regression model.Results There were 385 features obtained after calculation from each group of hippocampus. And there were five features selected of the left and right hippocampus respectively. The accuracy, area under the receiver operating characteristic curve values, specificity, sensitivity, positive predictive value and negative predictive value of the right hippocampus model evaluation indexes were 0.69, 0.74, 50%, 83%, 68% and 70%, respectively. And those of the left hippocampus model were 0.75, 0.83, 79%, 72%, 81% and 69%,respectively.Conclusionse The Results demonstrated the potential of hippocampal radiomic features as a biomarker for the diagnosis of AD. The radiomic models of right and left hippocampus are helpful for classifying AD from NC subjects. And the model of left hippocampus has a higher diagnostic value than the right.
冯琪, 陈愿君, 吴正旺, 毛德旺, 廖峥娈, 王玫, 蒋弘阳, 于恩彦, 丁忠祥. 阿尔茨海默病海马结构异常的影像组学研究[J]. 中华解剖与临床杂志, 2018, 23(5): 371-376.
Feng Qi, Chen Yuanjun, Wu Zhengwang, Mao Dewang, Liao Zhengluan, Wang Mei, Jiang Hongyang, Yu Enyan, Ding Zhongxiang. Radiomic study for the diagnosis of Alzheimer's disease based on the structure anomaly of hippocampus. Chinese Journal of Anatomy and Clinics, 2018, 23(5): 371-376.
Lepage M, Habib R, Tulving E. Hippocampal PET activations of memory encoding and retrieval: the HIPER model[J]. Hippocampus, 1998, 8(4): 313-322. DOI:10.1002/(SICI)1098-1063(1998)8:4<313::AID-HIPO1>3.0.CO;2-I.
[2]
Braak H, Braak E. Frequency of stages of alzheimer-related lesions in different age categories[J]. Neurobiol Aging, 1997, 18(4): 351-357. DOI:10.1016/S0197-4580(97)00056-0.
[3]
de Flores R, La Joie R, Chételat G. Structural imaging of hippocampal subfields in healthy aging and Alzheimer's disease[J]. Neuroscience, 2015, 309: 29-50. DOI:10.1016/j.neuroscience.2015.08.033.
[4]
de Flores R, Mutlu J, Bejanin A, et al. Intrinsic connectivity of hippocampal subfields in normal elderly and mild cognitive impairment patients[J]. Hum Brain Mapp, 2017, 38(10): 4922-4932. DOI:10.1002/hbm.23704.
[5]
Mak E, Gabel S, Su L, et al. Multi-modal MRI investigation of volumetric and microstructural changes in the hippocampus and its subfields in mild cognitive impairment, Alzheimer's disease, and dementia with Lewy bodies[J]. Int Psychogeriatr, 2017, 29(4): 545-555. DOI:10.1017/S1041610216002143.
[6]
Li Y, Liu X, Qian Z, et al. Genotype prediction of ATRX mutation in lower-grade gliomas using an MRI radiomics signature[J]. Eur Radiol, 2018, 28(7): 2960-2968. DOI:10.1007/s00330-017-5267-0.
[7]
Tang C, Hobbs B, Amer A, et al. Development of an immune-pathology informed radiomics model for non-small cell lung cancer[J]. Sci Rep, 2018, 8(1): 1922. DOI:10.1038/s41598-018-20471-5.
[8]
Meng Y, Zhang Y, Dong D, et al. Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer[J]. J Magn Reson Imaging, 2018,48(3):605-614. DOI:10.1002/jmri.25968.
[9]
Chaddad A, Desrosiers C, Hassan L, et al. Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder[J]. BMC Neurosci, 2017, 18(1): 52. DOI:10.1186/s12868-017-0373-0.
[10]
Sun H, Chen Y, Huang Q, et al. Psychoradiologic utility of MR imaging for diagnosis of attention deficit hyperactivity disorder: a radiomics analysis[J]. Radiology, 2018, 287(2): 620-630. DOI:10.1148/radiol.2017170226.
[11]
de Oliveira MS, Balthazar ML, D'Abreu A, et al. MR imaging texture analysis of the corpus callosum and thalamus in amnestic mild cognitive impairment and mild Alzheimer's disease[J]. AJNR Am J Neuroradiol, 2011, 32(1): 60-66. DOI:10.3174/ajnr.A2232.
[12]
Park S, Kim B, Lee J, et al. GGO nodule volume-preserving nonrigid lung registration using GLCM texture analysis[J]. IEEE Trans Biomed Eng, 2011, 58(10): 2885-2894. DOI:10.1109/TBME.2011.2162330.
[13]
McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer's disease: recommendations from the National Institute on Aging-Alzheimer's Association workgroups on diagnostic guidelines for Alzheimer's disease[J]. Alzheimers Dement, 2011, 7(3): 263-269. DOI:10.1016/j.jalz.2011.03.005.
[14]
Wu Z, Gao Y, Shi F, et al. Segmenting hippocampal subfields from 3T MRI with multi-modality images[J]. Med Image Anal, 2018, 43: 10-22. DOI:10.1016/j.media.2017.09.006.
[15]
Park S, Kim B, Lee J, et al. GGO nodule volume-preserving nonrigid lung registration using GLCM texture analysis[J]. IEEE Trans Biomed Eng, 2011, 58(10): 2885-2894. DOI:10.1109/TBME.2011.2162330.
[16]
Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1973, SMC-3(6): 610-621. DOI:10.1109/TSMC.1973.4309314.
[17]
Bocchino C, Carabellese A, Caruso T,et al. Use of gray value distribution of run lengths for texture analysis[J]. Pattern Recognition Letters, 1990, 11(6): 415-419. DOI:10.1016/0167-8655(90)90112-F.
[18]
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, 7: 45639. DOI:10.1038/srep45639.
[19]
Cui LB, Liu L, Wang HN, et al. Disease definition for schizophrenia by functional connectivity using radiomics strategy[J]. Schizophr Bull, 2018, 44(5): 1053-1059. DOI:10.1093/schbul/sby007.
Hondius DC, van Nierop P, Li KW, et al. Profiling the human hippocampal proteome at all pathologic stages of Alzheimer's disease[J]. Alzheimers Dement, 2016, 12(6): 654-668. DOI:10.1016/j.jalz.2015.11.002.
[22]
Sørensen L, Igel C, Liv Hansen N, et al. Early detection of Alzheimer's disease using MRI hippocampal texture[J]. Hum Brain Mapp, 2016, 37(3): 1148-1161. DOI:10.1002/hbm.23091.
[23]
Sheng C, Xia M, Yu H, et al. Abnormal global functional network connectivity and its relationship to medial temporal atrophy in patients with amnestic mild cognitive impairment[J]. PLoS One, 2017, 12(6): e0179823. DOI:10.1371/journal.pone.0179823.
[24]
Peter J, Sandkamp R, Minkova L, et al. Real-world navigation in amnestic mild cognitive impairment: the relation to visuospatial memory and volume of hippocampal subregions[J]. Neuropsychologia, 2018, 109: 86-94. DOI:10.1016/j.neuropsychologia.2017.12.014.
[25]
Li B, Shi J, Gutman BA, et al. Influence of APOE genotype on hippocampal atrophy over time—an N=1925 surface-based ADNI study[J]. PLoS One, 2016, 11(4): e0152901. DOI:10.1371/journal.pone.0152901.
[26]
Minkova L, Habich A, Peter J, et al. Gray matter asymmetries in aging and neurodegeneration: a review and meta-analysis[J]. Hum Brain Mapp, 2017, 38(12): 5890-5904. DOI:10.1002/hbm.23772.
2018, Vol. 23 
