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Feng J, Hui D, Zheng Q, Guo Y, Xia Y, Shi F, Zhou Q, Yu F, He X, Wang S, Li C. Automatic detection of cognitive impairment in patients with white matter hyperintensity and causal analysis of related factors using artificial intelligence of MRI. Comput Biol Med 2024; 178:108684. [PMID: 38852399 DOI: 10.1016/j.compbiomed.2024.108684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2023] [Revised: 05/28/2024] [Accepted: 05/31/2024] [Indexed: 06/11/2024]
Abstract
PURPOSE White matter hyperintensity (WMH) is a common feature of brain aging, often linked with cognitive decline and dementia. This study aimed to employ deep learning and radiomics to develop models for detecting cognitive impairment in WMH patients and to analyze the causal relationships among cognitive impairment and related factors. MATERIALS AND METHODS A total of 79 WMH patients from hospital 1 were randomly divided into a training set (62 patients) and a testing set (17 patients). Additionally, 29 patients from hospital 2 were included as an independent testing set. All participants underwent formal neuropsychological assessments to determine cognitive status. Automated identification and segmentation of WMH were conducted using VB-net, with extraction of radiomics features from cortex, white matter, and nuclei. Four machine learning classifiers were trained on the training set and validated on the testing set to detect cognitive impairment. Model performances were evaluated and compared. Causal analyses were conducted among cortex, white matter, nuclei alterations, and cognitive impairment. RESULTS Among the models, the logistic regression (LR) model based on white matter features demonstrated the highest performance, achieving an AUC of 0.819 in the external test dataset. Causal analyses indicated that age, education level, alterations in cortex, white matter, and nuclei were causal factors of cognitive impairment. CONCLUSION The LR model based on white matter features exhibited high accuracy in detecting cognitive impairment in WMH patients. Furthermore, the possible causal relationships among alterations in cortex, white matter, nuclei, and cognitive impairment were elucidated.
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Affiliation(s)
- Junbang Feng
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China; Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Dongming Hui
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Qingqing Zheng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College, Chongqing, China
| | - Yi Guo
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Yuwei Xia
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Qing Zhou
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd., Shanghai, China
| | - Fei Yu
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China
| | - Xiaojing He
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Shike Wang
- Department of Radiology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chuanming Li
- Medical Imaging Department, Chongqing University Central Hospital, Chongqing Emergency Medical Center, Chongqing, China.
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Yan Y, He X, Xu Y, Zhao F, Peng J, Shao Y. Pattern of heterogeneity in normal brain ageing: screening for mild cognitive impairment and its risk of progression with a radiomics model. Age Ageing 2024; 53:afae140. [PMID: 38984695 DOI: 10.1093/ageing/afae140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 05/01/2024] [Indexed: 07/11/2024] Open
Abstract
PURPOSE This study aimed to develop a normal brain ageing model based on magnetic resonance imaging and radiomics, therefore identifying radscore, an imaging indicator representing white matter heterogeneity and exploring the significance of radscore in detecting people's cognitive changes. METHODS Three hundred sixty cognitively normal (CN) subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database and 105 CN subjects from the Parkinson's Progression Markers Initiative database were used to develop the model. In ADNI, 230 mild cognitive impairment (MCI) subjects were matched with 230 CN old-aged subjects to evaluate their heterogeneity difference. One hundred four MCI subjects with 48 months of follow-up were divided into low and high heterogeneity groups. Kaplan-Meier survival curve analysis was used to observe the importance of heterogeneity results for predicting MCI progression. RESULTS The area under the receiver operating characteristic curve of the model in the training, internal test and external test sets was 0.7503, 0.7512 and 0.7514, respectively. There was a significantly positive correlation between age and radscore of CN subjects (r = 0.501; P < .001). The radscore of MCI subjects was significantly higher than that of matched CN subjects (P < .001). The median radscore ratios of MCI to CN from four age groups (66-70y, 71-75y, 76-80y and 81-85y) were 1.611, 1.760, 1.340 and 1.266, respectively. The probability to progression of low and high heterogeneity groups had a significant difference (P = .002). CONCLUSION When radscore is significantly higher than that of normal ageing, it is necessary to alert the possibility of cognitive impairment and deterioration.
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Affiliation(s)
- Yuting Yan
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou 310014, Zhejiang, China
| | - Xiaodong He
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou 310014, Zhejiang, China
| | - Yuyun Xu
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou 310014, Zhejiang, China
| | - Fanfan Zhao
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou 310014, Zhejiang, China
| | - Jiaxuan Peng
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou 310014, Zhejiang, China
| | - Yuan Shao
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical College, Hangzhou 310014, Zhejiang, China
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Das SR, Ilesanmi A, Wolk DA, Gee JC. Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ? Magn Reson Med Sci 2024; 23:367-376. [PMID: 38880615 PMCID: PMC11234947 DOI: 10.2463/mrms.rev.2024-0053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 05/20/2024] [Indexed: 06/18/2024] Open
Abstract
The most commonly used neuroimaging biomarkers of brain structure, particularly in neurodegenerative diseases, have traditionally been summary measurements from ROIs derived from structural MRI, such as volume and thickness. Advances in MR acquisition techniques, including high-field imaging, and emergence of learning-based methods have opened up opportunities to interrogate brain structure in finer detail, allowing investigators to move beyond macrostructural measurements. On the one hand, superior signal contrast has the potential to make appearance-based metrics that directly analyze intensity patterns, such as texture analysis and radiomics features, more reliable. Quantitative MRI, particularly at high-field, can also provide a richer set of measures with greater interpretability. On the other hand, use of neural networks-based techniques has the potential to exploit subtle patterns in images that can now be mined with advanced imaging. Finally, there are opportunities for integration of multimodal data at different spatial scales that is enabled by developments in many of the above techniques-for example, by combining digital histopathology with high-resolution ex-vivo and in-vivo MRI. Some of these approaches are at early stages of development and present their own set of challenges. Nonetheless, they hold promise to drive the next generation of validation and biomarker studies. This article will survey recent developments in this area, with a particular focus on Alzheimer's disease and related disorders. However, most of the discussion is equally relevant to imaging of other neurological disorders, and even to other organ systems of interest. It is not meant to be an exhaustive review of the available literature, but rather presented as a summary of recent trends through the discussion of a collection of representative studies with an eye towards what the future may hold.
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Affiliation(s)
- Sandhitsu R. Das
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Ademola Ilesanmi
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - David A. Wolk
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
- Penn Memory Center, University of Pennsylvania, Philadelphia, PA, USA
| | - James C. Gee
- Penn Image Computing and Science Laboratory (PICSL), Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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Yin TT, Cao MH, Yu JC, Shi TY, Mao XH, Wei XY, Jia ZZ. T1-Weighted Imaging-Based Hippocampal Radiomics in the Diagnosis of Alzheimer's Disease. Acad Radiol 2024:S1076-6332(24)00370-2. [PMID: 38902110 DOI: 10.1016/j.acra.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2024] [Revised: 06/01/2024] [Accepted: 06/05/2024] [Indexed: 06/22/2024]
Abstract
RATIONALE AND OBJECTIVES To investigate the potential of T1-weighted imaging (T1WI)-based hippocampal radiomics as imaging markers for the diagnosis of Alzheimer's disease (AD) and their efficacy in discriminating between mild cognitive impairment (MCI) and dementia in AD. METHODS A total of 126 AD patients underwent T1WI-based magnetic resonance imaging (MRI) examinations, along with 108 age-sex-matched healthy controls (HC). This was a retrospective, single-center study conducted from November 2021 to February 2023. AD patients were categorized into two groups based on disease progression and cognitive function: AD-MCI and dementia (AD-D). T1WI-based radiomics features of the bilateral hippocampi were extracted. To diagnose AD and differentiate between AD-MCI and AD-D, predictive models were developed using random forest (RF), logistic regression (LR), and support vector machine (SVM). We compared radiomics features between the AD and HC groups, as well as within the subgroups of AD-MCI and AD-D. Area under the curve (AUC), accuracy, sensitivity, and specificity were all used to assess model performance. Furthermore, correlations between radiomics features and Mini-Mental State Examination (MMSE) scores, tau protein phosphorylated at threonine 181 (P-tau-181), and amyloid β peptide1-42 (Aβ1-42) were analyzed. RESULTS The RF model demonstrated superior performance in distinguishing AD from HC (AUC=0.961, accuracy=90.8%, sensitivity=90.7%, specificity=90.9%) and in identifying AD-MCI and AD-D (AUC=0.875, accuracy=80.7%, sensitivity=87.2%, specificity=73.2%) compared to the other models. Additionally, radiomics features were correlated with MMSE scores, P-tau-181, and Aβ1-42 levels in AD. CONCLUSION T1WI-based hippocampal radiomics features are valuable for diagnosing AD and identifying AD-MCI and AD-D.
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Affiliation(s)
- Ting Ting Yin
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (T.T.Y., J.C.Y., T.Y.S., X.H.M., X.Y.W., Z.Z.J.)
| | - Mao Hong Cao
- Department of Neurology, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (M.H.C.)
| | - Jun Cheng Yu
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (T.T.Y., J.C.Y., T.Y.S., X.H.M., X.Y.W., Z.Z.J.)
| | - Ting Yan Shi
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (T.T.Y., J.C.Y., T.Y.S., X.H.M., X.Y.W., Z.Z.J.)
| | - Xiao Han Mao
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (T.T.Y., J.C.Y., T.Y.S., X.H.M., X.Y.W., Z.Z.J.)
| | - Xin Yue Wei
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (T.T.Y., J.C.Y., T.Y.S., X.H.M., X.Y.W., Z.Z.J.)
| | - Zhong Zheng Jia
- Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, Nantong, China (T.T.Y., J.C.Y., T.Y.S., X.H.M., X.Y.W., Z.Z.J.).
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Li J, Li J, Zhu H, Liu M, Li T, He Y, Xu Y, Huang F, Qin Q. Prediction of Cognitive Impairment Risk among Older Adults: A Machine Learning-Based Comparative Study and Model Development. Dement Geriatr Cogn Disord 2024; 53:169-179. [PMID: 38776891 DOI: 10.1159/000539334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Accepted: 05/09/2024] [Indexed: 05/25/2024] Open
Abstract
INTRODUCTION The prevalence of cognitive impairment and dementia in the older population is increasing, and thereby, early detection of cognitive decline is essential for effective intervention. METHODS This study included 2,288 participants with normal cognitive function from the Ma'anshan Healthy Aging Cohort Study. Forty-two potential predictors, including demographic characteristics, chronic diseases, lifestyle factors, anthropometric indices, physical function, and baseline cognitive function, were selected based on clinical importance and previous research. The dataset was partitioned into training, validation, and test sets in a proportion of 60% for training, 20% for validation, and 20% for testing, respectively. Recursive feature elimination was used for feature selection, followed by six machine learning algorithms that were employed for model development. The performance of the models was evaluated using area under the curve (AUC), specificity, sensitivity, and accuracy. Moreover, SHapley Additive exPlanations (SHAP) was conducted to access the interpretability of the final selected model and to gain insights into the impact of features on the prediction outcomes. SHAP force plots were established to vividly show the application of the prediction model at the individual level. RESULTS The final predictive model based on the Naive Bayes algorithm achieved an AUC of 0.820 (95% CI, 0.773-0.887) on the test set, outperforming other algorithms. The top ten influential features in the model included baseline Mini-Mental State Examination (MMSE), education, self-reported economic status, collective or social activities, Pittsburgh sleep quality index (PSQI), body mass index, systolic blood pressure, diastolic blood pressure, instrumental activities of daily living, and age. The model demonstrated the potential to identify individuals at a higher risk of cognitive impairment within 3 years from older adults. CONCLUSION The predictive model developed in this study contributes to the early detection of cognitive impairment in older adults by primary healthcare staff in community settings.
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Affiliation(s)
- Jianwei Li
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Jie Li
- The Department of Health Promotion and Behavioral Sciences, School of Public Health, Anhui Medical University, Hefei, China
| | - Huafang Zhu
- Ma'anshan Center for Disease Control and Prevention, Ma'anshan, China
| | - Mengyu Liu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Tengfei Li
- The Department of Health Promotion and Behavioral Sciences, School of Public Health, Anhui Medical University, Hefei, China
| | - Yeke He
- The Department of Health Promotion and Behavioral Sciences, School of Public Health, Anhui Medical University, Hefei, China
| | - Yuan Xu
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Fen Huang
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, China
| | - Qirong Qin
- Department of Epidemiology and Health Statistics, School of Public Health, Anhui Medical University, Hefei, China
- Ma'anshan Center for Disease Control and Prevention, Ma'anshan, China
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Jang H, Na DL, Kwon JC, Jung NY, Moon Y, Lee JS, Park KW, Lee AY, Cho H, Lee JH, Kim BC, Park KH, Lee BC, Choi H, Kim J, Park MY. A Two-Year Observational Study to Evaluate Conversion Rates from High- and Low-Risk Patients with Amnestic Mild Cognitive Impairment to Probable Alzheimer's Disease in a Real-World Setting. J Alzheimers Dis Rep 2024; 8:851-862. [PMID: 38910942 PMCID: PMC11191635 DOI: 10.3233/adr-230189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2023] [Accepted: 03/20/2024] [Indexed: 06/25/2024] Open
Abstract
Background Predicting conversion to probable Alzheimer&s disease (AD) from amnestic mild cognitive impairment (aMCI) is difficult but important. A nomogram was developed previously for determining the risk of 3-year probable AD conversion in aMCI. Objective To compare the probable AD conversion rates with cognitive and neurodegenerative changes for 2 years from high- and low risk aMCI groups classified using the nomogram. Methods This prospective, multicenter, observational study was conducted in Korea. A total of patients were classified as high- or low-risk aMCI according to the nomogram and followed-up for 2 years to compare the annual conversion rate to probable AD and brain structure changes between the two groups. Results In total, 176 (high-risk, 85; low-risk, 91) and 160 (high-risk, 77; low-risk, 83) patients completed the 1-year and 2-year follow-up, respectively. The probable AD conversion rate was significantly higher in the high-risk (Year 1, 28.9%; Year 2, 46.1%) versus low-risk group (Year 1, 0.0%; Year 2, 4.9%, both p < 0.0001). Mean changes from baseline in Seoul Neuropsychological Screening Battery-Dementia Version, Clinical Dementia Rating-Sum of Box, and Korean version of the Instrumental Activities of Daily Living scores and cortical atrophy index at Years 1 and 2 were significantly greater in the high-risk group (p < 0.0001). Conclusions The high-risk aMCI group, as determined by the nomogram, had a higher conversion rate to probable AD and faster cognitive decline and neurodegeneration change than the low-risk group. These real-world results have clinical implications that help clinicians in accurately predicting patient outcomes and facilitating early decision-making.Trial Registration: ClinicalTrials.gov (NCT03448445).
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Affiliation(s)
- Hyemin Jang
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Duk L. Na
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Jay Cheol Kwon
- Department of Neurology, Changwon Fatima Hospital, Changwon, Republic of Korea
| | - Na-Yeon Jung
- Department of Neurology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Republic of Korea
| | - Yeonsil Moon
- Department of Neurology, Konkuk University Medical Center, Konkuk University School of Medicine, Seoul, Republic of Korea
| | - Jung Seok Lee
- Department of Neurology, Jeju National University College of Medicine, Jeju, Republic of Korea
| | - Kyung-Won Park
- Department of Neurology, Cognitive Disorders and Dementia Center, Dong-A University College of Medicine and Institute of Convergence Bio-Health, Busan, Republic of Korea
| | - Ae Young Lee
- Department of Neurology, Chungnam National University School of Medicine, Daejeon, Republic of Korea
| | - Hanna Cho
- Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Jae-Hong Lee
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Byeong C. Kim
- Department of Neurology, Chonnam National University Medical School & Hospital, Gwangju, Republic of Korea
| | - Kee Hyung Park
- Department of Neurology, College of Medicine, Gachon University Gil Hospital, Incheon, Republic of Korea
| | - Byung-Chul Lee
- Department of Neurology, College of Medicine, Hallym University, Seoul, Republic of Korea
| | - Hojin Choi
- Department of Neurology, Hanyang University Guri Hospital, Guri, Republic of Korea
| | - Jieun Kim
- Department of Medical, Eisai Korea Inc., Seoul, Republic of Korea
| | - Mee Young Park
- Department of Neurology, Yeungnam University College of Medicine, Daegu, Republic of Korea
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Lu L, Qin L, Zhao X, Liu Z, Qiu X, Yang S, Song H, Yang J. Metabolites of intestinal fora can be used as diagnostic and progressive markers for mild cognitive impairment. Front Cell Infect Microbiol 2024; 14:1351523. [PMID: 38404286 PMCID: PMC10885801 DOI: 10.3389/fcimb.2024.1351523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 01/10/2024] [Indexed: 02/27/2024] Open
Abstract
Purpose The aim of the work was to analyze the metabolites of the intestinal microbiota from the patients with mild cognitive impairment (MCI) and progressive MCI due to Alzheimer's disease (AD). Method Two cohorts were established. The first one included 87 subjects with 30 healthy controls (NC), 22 patients with MCI due to AD, and 35 patients with AD. The second cohort included 87 patients with MCI due to AD, who were followed up for 2 years and finally were divided into progressive MCI due to AD group (P-G) and unprogressive MCI due to AD group (U-G) according their cognitive levels. Fecal samples were collected to all patients at the baseline time point. Differential metabolites were subjected to pathway analysis by MetaboAnalyst. Results In the first cohort, we found 21 different metabolites among the three groups (AD, MCI, and NC). In the second cohort, we identified 19 differential metabolites between the P-G and U-G groups. By machine learning analysis, we found that seven characteristic metabolites [Erythrodiol, alpha-Curcumene, Synephrine, o-Hydroxylaminobenzoate, 3-Amino-4-hydroxybenzoic acid, 2-Deoxystreptamine, and 9(S] were of characteristic significance for the diagnosis of MCI due to AD, and six metabolites (Indolelactate, Indole-3-acetaldehyde, L-Proline, Perillyl, Mesaconate, and Sphingosine) were the characteristic metabolites of early warning for the progression of MCI due to AD. D-Glucuronic acid was negatively correlated with Apolipoprotein E4 (APOE4). Perillyl alcohol was negatively correlated with all of the five biomarkers [P-tau181, Neurofilament light chain (NF-light), Aβ1-42, Aβ1-40, and glial fibrillary acidic protein (GFAP)], but Indoleacetaldehyde was positively correlated with three biomarkers (P-tau181, Aβ1-42, and GFAP). Three characteristic metabolites (3-Amino-4-hydroxybenzoate, 2-Deoxystreptamine, and p-Synephrine) were positively correlated with Aβ1-42. 2-Deoxystreptamine, 9(S)-HPOT, and Indoleacetaldehyde were positively correlated with GFAP. L-Proline and Indoleacetaldehyde were positively correlated with NF-light. Conclusion Specific metabolites of intestinal fora can be used as diagnostic and progressive markers for MCI.
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Affiliation(s)
- Liquan Lu
- Department of Laboratory Medicine, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Lei Qin
- Department of Neurology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Xiaohui Zhao
- Department of Neurology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Zanhua Liu
- Department of Neurology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Xiaoting Qiu
- Department of Social Work, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Shuo Yang
- Department of Neurology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
| | - Haihan Song
- Central Lab, Shanghai Key Laboratory of Pathogenic Fungi Medical Testing, Shanghai Pudong New Area People’s Hospital, Shanghai, China
- Department of Immunology, DICAT Biomedical Computation Centre, Vancouver, BC, Canada
| | - Juan Yang
- Department of Neurology, Shanghai Pudong New Area People’s Hospital, Shanghai, China
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Chen J, Yang J, Shen D, Wang X, Lin Z, Chen H, Cui G, Zhang Z. A Predictive Model of the Progression to Alzheimer's Disease in Patients with Mild Cognitive Impairment Based on the MRI Enlarged Perivascular Spaces. J Alzheimers Dis 2024; 101:159-173. [PMID: 39177602 DOI: 10.3233/jad-240523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/24/2024]
Abstract
Background Mild cognitive impairment (MCI) is a heterogeneous condition that can precede various forms of dementia, including Alzheimer's disease (AD). Identifying MCI subjects who are at high risk of progressing to AD is of major clinical relevance. Enlarged perivascular spaces (EPVS) on MRI are linked to cognitive decline, but their predictive value for MCI to AD progression is unclear. Objective This study aims to assess the predictive value of EPVS for MCI to AD progression and develop a predictive model combining EPVS grading with clinical and laboratory data to estimate conversion risk. Methods We analyzed 358 patients with MCI from the ADNI database, consisting of 177 MCI-AD converters and 181 non-converters. The data collected included demographic information, imaging data (including perivascular spaces grade), clinical assessments, and laboratory test results. Variable selection was conducted using the Least Absolute Shrinkage and Selection Operator (LASSO) method, followed by logistic regression to develop predictive model. Results In the univariate logistic regression analysis, both moderate (OR = 5.54, 95% CI [3.04-10.18]) and severe (OR = 25.04, 95% CI [10.07-62.23]) enlargements of the centrum semiovale perivascular space (CSO-PVS) were found to be strong predictors of disease progression. LASSO analyses yielded 12 variables, refined to six in the final model: APOE4 genotype, ADAS11 score, CSO-PVS grade, and volumes of entorhinal, fusiform, and midtemporal regions, with an AUC of 0.956 in the training and 0.912 in the validation cohort. Conclusions Our predictive model, emphasizing EPVS assessment, provides clinicians with a practical tool for early detection and management of AD risk in MCI patients.
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Affiliation(s)
- Jun Chen
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Jingwen Yang
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Dayong Shen
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Xi Wang
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Zihao Lin
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Hao Chen
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Guiyun Cui
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
| | - Zuohui Zhang
- Department of Neurology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China
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Mousa D, Zayed N, Yassine IA. Correlation transfer function analysis as a biomarker for Alzheimer brain plasticity using longitudinal resting-state fMRI data. Sci Rep 2023; 13:21559. [PMID: 38057476 PMCID: PMC10700324 DOI: 10.1038/s41598-023-48693-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 11/29/2023] [Indexed: 12/08/2023] Open
Abstract
Neural plasticity is the ability of the brain to alter itself functionally and structurally as a result of its experience. However, longitudinal changes in functional connectivity of the brain are still unrevealed in Alzheimer's disease (AD). This study aims to discover the significant connections (SCs) between brain regions for AD stages longitudinally using correlation transfer function (CorrTF) as a new biomarker for the disease progression. The dataset consists of: 29 normal controls (NC), and 23, 24, and 23 for early, late mild cognitive impairments (EMCI, LMCI), and ADs, respectively, along three distant visits. The brain was divided into 116 regions using the automated anatomical labeling atlas, where the intensity time series is calculated, and the CorrTF connections are extracted for each region. Finally, the standard t-test and ANOVA test were employed to investigate the SCs for each subject's visit. No SCs, along three visits, were found For NC subjects. The most SCs were mainly directed from cerebellum in case of EMCI and LMCI. Furthermore, the hippocampus connectivity increased in LMCI compared to EMCI whereas missed in AD. Additionally, the patterns of longitudinal changes among the different AD stages compared to Pearson Correlation were similar, for SMC, VC, DMN, and Cereb networks, while differed for EAN and SN networks. Our findings define how brain changes over time, which could help detect functional changes linked to each AD stage and better understand the disease behavior.
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Affiliation(s)
- Doaa Mousa
- Computers and Systems Department, Electronics Research Institute, Cairo, Egypt.
| | - Nourhan Zayed
- Computers and Systems Department, Electronics Research Institute, Cairo, Egypt
- Mechanical Engineering Department, The British University in Egypt, Cairo, Egypt
| | - Inas A Yassine
- Systems and Biomedical Engineering Department, Cairo University, Giza, Egypt
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Saravi B, Zink A, Ülkümen S, Couillard-Despres S, Wollborn J, Lang G, Hassel F. Clinical and radiomics feature-based outcome analysis in lumbar disc herniation surgery. BMC Musculoskelet Disord 2023; 24:791. [PMID: 37803313 PMCID: PMC10557221 DOI: 10.1186/s12891-023-06911-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 09/24/2023] [Indexed: 10/08/2023] Open
Abstract
BACKGROUND Low back pain is a widely prevalent symptom and the foremost cause of disability on a global scale. Although various degenerative imaging findings observed on magnetic resonance imaging (MRI) have been linked to low back pain and disc herniation, none of them can be considered pathognomonic for this condition, given the high prevalence of abnormal findings in asymptomatic individuals. Nevertheless, there is a lack of knowledge regarding whether radiomics features in MRI images combined with clinical features can be useful for prediction modeling of treatment success. The objective of this study was to explore the potential of radiomics feature analysis combined with clinical features and artificial intelligence-based techniques (machine learning/deep learning) in identifying MRI predictors for the prediction of outcomes after lumbar disc herniation surgery. METHODS We included n = 172 patients who underwent discectomy due to disc herniation with preoperative T2-weighted MRI examinations. Extracted clinical features included sex, age, alcohol and nicotine consumption, insurance type, hospital length of stay (LOS), complications, operation time, ASA score, preoperative CRP, surgical technique (microsurgical versus full-endoscopic), and information regarding the experience of the performing surgeon (years of experience with the surgical technique and the number of surgeries performed at the time of surgery). The present study employed a semiautomatic region-growing volumetric segmentation algorithm to segment herniated discs. In addition, 3D-radiomics features, which characterize phenotypic differences based on intensity, shape, and texture, were extracted from the computed magnetic resonance imaging (MRI) images. Selected features identified by feature importance analyses were utilized for both machine learning and deep learning models (n = 17 models). RESULTS The mean accuracy over all models for training and testing in the combined feature set was 93.31 ± 4.96 and 88.17 ± 2.58. The mean accuracy for training and testing in the clinical feature set was 91.28 ± 4.56 and 87.69 ± 3.62. CONCLUSIONS Our results suggest a minimal but detectable improvement in predictive tasks when radiomics features are included. However, the extent of this advantage should be considered with caution, emphasizing the potential of exploring multimodal data inputs in future predictive modeling.
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Affiliation(s)
- Babak Saravi
- Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany.
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany.
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, Salzburg, 5020, Austria.
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Alisia Zink
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany
| | - Sara Ülkümen
- Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Sebastien Couillard-Despres
- Institute of Experimental Neuroregeneration, Spinal Cord Injury and Tissue Regeneration Center Salzburg (SCI-TReCS), Paracelsus Medical University, Salzburg, 5020, Austria
- Austrian Cluster for Tissue Regeneration, Vienna, Austria
| | - Jakob Wollborn
- Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Gernot Lang
- Department of Orthopedics and Trauma Surgery, Faculty of Medicine, Medical Center - University of Freiburg, University of Freiburg, Freiburg, Germany
| | - Frank Hassel
- Department of Spine Surgery, Loretto Hospital, Freiburg, Germany
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11
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Ge X, Cui K, Qin Y, Chen D, Han H, Yu H. Screening strategies and dynamic risk prediction models for Alzheimer's disease. J Psychiatr Res 2023; 166:92-99. [PMID: 37757706 DOI: 10.1016/j.jpsychires.2023.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/16/2023] [Accepted: 09/15/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND Characterizing the progression from Mild cognitive impairment (MCI) to Alzheimer's disease (AD) is essential for early AD prevention and targeted intervention. Our goal was to construct precise screening schemes for individuals with different risk of AD and to establish prognosis models for them. METHODS We constructed a retrospective cohort by reviewing individuals with baseline diagnosis of MCI and at least one follow-up visits between November 2005 and May 2021. They were stratified into high-risk and low-risk groups with longitudinal cognitive trajectory. Then, we established a screening framework and obtained optimal screening strategies for two risk groups. Cox and random survival forest (RSF) models were developed for dynamic prognosis prediction. RESULTS In terms of screening strategies, the combination of Clinical Dementia Rating Sum of Boxes (CDRSB) and hippocampus volume was recommended for the high-risk MCI group, while the combination of Alzheimer's Disease Assessment Scale Cognitive 13 items (ADAS13) and FAQ was recommended for low-risk MCI group. The concordance index (C-index) of the Cox model for the high-risk group was 0.844 (95% CI: 0.815-0.873) and adjustments for demographic information and APOE ε4. The RSF model incorporating longitudinal ADAS13, FAQ, and demographic information and APOE ε4 performed for the low-risk group. CONCLUSION This precise screening scheme will optimize allocation of medical resources and reduce the economic burden on individuals with low risk of MCI. Moreover, dynamic prognosis models may be helpful for early identification of individuals at risk and clinical decisions, which will promote the secondary prevention of AD.
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Affiliation(s)
- Xiaoyan Ge
- Department of Health Statistics, School of Public Health, Jinzhou Medical University, 40 SongPo Road, Jinzhou, China.
| | - Kai Cui
- Department of Health Statistics, School of Public Health, Jinzhou Medical University, 40 SongPo Road, Jinzhou, China.
| | - Yao Qin
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China.
| | - Durong Chen
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China.
| | - Hongjuan Han
- Department of Mathematics, School of Basic Medical Sciences, Shanxi Medical University, Taiyuan, China.
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 XinJian South Road, Taiyuan, China; Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, 56 XinJian South Road, Taiyuan, China.
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12
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Wu Y, Wang X, Gu C, Zhu J, Fang Y. Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals. Age Ageing 2023; 52:afad182. [PMID: 37740920 PMCID: PMC10518045 DOI: 10.1093/ageing/afad182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Indexed: 09/25/2023] Open
Abstract
BACKGROUND Mild cognitive impairment (MCI) is the early stage of AD, and about 10-12% of MCI patients will progress to AD every year. At present, there are no effective markers for the early diagnosis of whether MCI patients will progress to AD. This study aimed to develop machine learning-based models for predicting the progression from MCI to AD within 3 years, to assist in screening and prevention of high-risk populations. METHODS Data were collected from the Alzheimer's Disease Neuroimaging Initiative, a representative sample of cognitive impairment population. Machine learning models were applied to predict the progression from MCI to AD, using demographic, neuropsychological test and MRI-related biomarkers. Data were divided into training (56%), validation (14%) and test sets (30%). AUC (area under ROC curve) was used as the main evaluation metric. Key predictors were ranked utilising their importance. RESULTS The AdaBoost model based on logistic regression achieved the best performance (AUC: 0.98) in 0-6 month prediction. Scores from the Functional Activities Questionnaire, Modified Preclinical Alzheimer Cognitive Composite with Trails test and ADAS11 (Unweighted sum of 11 items from The Alzheimer's Disease Assessment Scale-Cognitive Subscale) were key predictors. CONCLUSION Through machine learning, neuropsychological tests and MRI-related markers could accurately predict the progression from MCI to AD, especially in a short period time. This is of great significance for clinical staff to screen and diagnose AD, and to intervene and treat high-risk MCI patients early.
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Affiliation(s)
- Yafei Wu
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
| | - Xing Wang
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
| | - Chenming Gu
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
| | - Junmin Zhu
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
| | - Ya Fang
- School of Public Health, Xiamen University, Xiamen, Fujian, China
- Key Laboratory of Health Technology Assessment of Fujian Province, Xiamen University, Xiamen, Fujian, China
- National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, China
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Wang W, Peng J, Hou J, Yuan Z, Xie W, Mao G, Pan Y, Shao Y, Shu Z. Predicting mild cognitive impairment progression to Alzheimer's disease based on machine learning analysis of cortical morphological features. Aging Clin Exp Res 2023:10.1007/s40520-023-02456-1. [PMID: 37405620 DOI: 10.1007/s40520-023-02456-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 05/25/2023] [Indexed: 07/06/2023]
Abstract
PURPOSE To establish a model for predicting mild cognitive impairment (MCI) progression to Alzheimer's disease (AD) using morphological features extracted from a joint analysis of voxel-based morphometry (VBM) and surface-based morphometry (SBM). METHODS We analyzed data from 121 MCI patients from the Alzheimer's Disease Neuroimaging Initiative, 32 of whom progressed to AD during a 4-year follow-up period and were classified as the progression group, while the remaining 89 were classified as the non-progression group. Patients were divided into a training set (n = 84) and a testing set (n = 37). Morphological features measured by VBM and SBM were extracted from the cortex of the training set and dimensionally reduced to construct morphological biomarkers using machine learning methods, which were combined with clinical data to build a multimodal combinatorial model. The model's performance was evaluated using receiver operating characteristic curves on the testing set. RESULTS The Alzheimer's Disease Assessment Scale (ADAS) score, apolipoprotein E (APOE4), and morphological biomarkers were independent predictors of MCI progression to AD. The combinatorial model based on the independent predictors had an area under the curve (AUC) of 0.866 in the training set and 0.828 in the testing set, with sensitivities of 0.773 and 0.900 and specificities of 0.903 and 0.747, respectively. The number of MCI patients classified as high-risk for progression to AD was significantly different from those classified as low-risk in the training set, testing set, and entire dataset, according to the combinatorial model (P < 0.05). CONCLUSION The combinatorial model based on cortical morphological features can identify high-risk MCI patients likely to progress to AD, potentially providing an effective tool for clinical screening.
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Affiliation(s)
- Wei Wang
- Jinzhou Medical University Postgraduate Education Base (Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College), Hangzhou, Zhejiang, China
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College, Chongqing, China
| | - Jiaxuan Peng
- Jinzhou Medical University Postgraduate Education Base (Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College), Hangzhou, Zhejiang, China
| | - Jie Hou
- Jinzhou Medical University Postgraduate Education Base (Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College), Hangzhou, Zhejiang, China
| | - Zhongyu Yuan
- Jinzhou Medical University Postgraduate Education Base (Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College), Hangzhou, Zhejiang, China
| | - Wutao Xie
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical and Pharmaceutical College, Chongqing, China
| | - Guohe Mao
- Banan Hospital of Chongqing Medical University, Chongqing, China
| | - Yaling Pan
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China
| | - Yuan Shao
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China
| | - Zhenyu Shu
- Center for Rehabilitation Medicine, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, No. 158 Shangtang Road, Hangzhou City, Zhejiang Province, China.
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Liu B, Meng S, Cheng J, Zeng Y, Zhou D, Deng X, Kuang L, Wu X, Tang L, Wang H, Liu H, Liu C, Li C. Diagnosis of Subcortical Ischemic Vascular Cognitive Impairment With No Dementia Using Radiomics of Cerebral Cortex and Subcortical Nuclei in High-Resolution T1-Weighted MR Imaging. Front Oncol 2022; 12:852726. [PMID: 35463351 PMCID: PMC9027106 DOI: 10.3389/fonc.2022.852726] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To investigate whether the combination of radiomics derived from brain high-resolution T1-weighted imaging and automatic machine learning could diagnose subcortical ischemic vascular cognitive impairment with no dementia (SIVCIND) accurately. Methods A total of 116 right-handed participants involving 40 SIVCIND patients and 76 gender-, age-, and educational experience-matched normal controls (NM) were recruited. A total of 7,106 quantitative features from the bilateral thalamus, hippocampus, globus pallidus, amygdala, nucleus accumbens, putamen, caudate nucleus, and 148 areas of the cerebral cortex were automatically calculated from each subject. Six methods including least absolute shrinkage and selection operator (LASSO) were utilized to lessen the redundancy of features. Three supervised machine learning approaches of logistic regression (LR), random forest (RF), and support vector machine (SVM) employing 5-fold cross-validation were used to train and establish diagnosis models, and 10 times 10-fold cross-validation was used to evaluate the generalization performance of each model. Correlation analysis was performed between the optimal features and the neuropsychological scores of the SIVCIND patients. Results Thirteen features from the right amygdala, right hippocampus, left caudate nucleus, left putamen, left thalamus, and bilateral nucleus accumbens were included in the optimal subset. Among all the three models, the RF produced the highest diagnostic performance with an area under the receiver operator characteristic curve (AUC) of 0.990 and an accuracy of 0.948. According to the correlation analysis, the radiomics features of the right amygdala, left caudate nucleus, left putamen, and left thalamus were found to be significantly correlated with the neuropsychological scores of the SIVCIND patients. Conclusions The combination of radiomics derived from brain high-resolution T1-weighted imaging and machine learning could diagnose SIVCIND accurately and automatically. The optimal radiomics features are mostly located in the right amygdala, left caudate nucleus, left putamen, and left thalamus, which might be new biomarkers of SIVCIND.
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Affiliation(s)
- Bo Liu
- Department of Radiology, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- Department of Radiology, Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Shan Meng
- Department of Radiology, The Second People’s Hospital of Jiulongpo District, Chongqing, China
| | - Jie Cheng
- Department of Ultrasound, Chongqing Maternal and Child Health Hospital, Chongqing, China
| | - Yan Zeng
- Department of Radiology, Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Daiquan Zhou
- Department of Radiology, Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaojuan Deng
- Department of Radiology, Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lianqin Kuang
- Department of Radiology, Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Xiaojia Wu
- Department of Radiology, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Lin Tang
- Department of Radiology, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Haolin Wang
- Medical Data Science Academy, Chongqing Medical University, Chongqing, China
| | - Huan Liu
- Department of Data Analysis, GE Healthcare, Shanghai, China
| | - Chen Liu
- Department of Radiology, The First Affiliated Hospital of Army Medical University, Chongqing, China
- *Correspondence: Chen Liu, ; Chuanming Li,
| | - Chuanming Li
- Department of Radiology, Second Affiliated Hospital of Chongqing Medical University, Chongqing, China
- *Correspondence: Chen Liu, ; Chuanming Li,
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Yu G, Yang W, Zhang J, Zhang Q, Zhou J, Hong Y, Luo J, Shi Q, Yang Z, Zhang K, Tu H. Application of a nomogram to radiomics labels in the treatment prediction scheme for lumbar disc herniation. BMC Med Imaging 2022; 22:51. [PMID: 35305577 PMCID: PMC8934490 DOI: 10.1186/s12880-022-00778-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 03/09/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Objective
To investigate and verify the efficiency and effectiveness of a nomogram based on radiomics labels in predicting the treatment of lumbar disc herniation (LDH).
Methods
By reviewing medical records that were analysed over the past three years, clinical and imaging data of 200 lumbar disc patients at the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine were obtained. The collected cases were randomly divided into a training group (n = 140) and a testing group (n = 60) at a ratio of 7:3. Two radiologists with experience in reading orthopaedics images independently segmented the ROIs. The whole intervertebral disc with the most obvious protrusion in the sagittal plane T2WI lumbar MRI as a mask (ROI) is sketched. The LASSO (Least Absolute Shrinkage And Selection Operator) algorithm was used to filter the features after extracting the radiomics features. The multivariate logistic regression model was used to construct a quantitative imaging Rad‑Score for the selected features with nonzero coefficients. The radiomics labels and nomogram were evaluated using the receiver operating characteristic curve (ROC) and the area under the curve (AUC). The calibration curve was used to evaluate the consistency between the nomogram prediction and the actual treatment plan. The DCA decision curve was used to evaluate the clinical applicability of the nomogram.
Result
Following feature extraction, 11 radiomics features were used to construct the radiomics label for predicting the treatment plan of LDH. A nomogram was then constructed. The AUC was 0.93 (95% CI: 0.90–0.97), with a sensitivity of 89%, a specificity of 91%, a positive predictive value of 92.7%, a negative predictive value of 89.4%, and an accuracy of 91%. The calibration curve showed that there was good consistency between the prediction and the actual observation. The DCA decision curve analysis showed that the nomogram of the imaging group has great potential for clinical application when the risk threshold is between 5 and 72%.
Conclusion
A nomogram based on radiomics labels has good predictive value for the treatment of LDH and can be used as a reference for clinical decision-making.
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Chen Y, Sun Y, Luo Z, Lin J, Qi B, Kang X, Ying C, Guo C, Yao M, Chen X, Wang Y, Wang Q, Chen J, Chen S. Potential Mechanism Underlying Exercise Upregulated Circulating Blood Exosome miR-215-5p to Prevent Necroptosis of Neuronal Cells and a Model for Early Diagnosis of Alzheimer's Disease. Front Aging Neurosci 2022; 14:860364. [PMID: 35615585 PMCID: PMC9126031 DOI: 10.3389/fnagi.2022.860364] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 03/21/2022] [Indexed: 02/05/2023] Open
Abstract
Exercise is crucial for preventing Alzheimer's disease (AD), although the exact underlying mechanism remains unclear. The construction of an accurate AD risk prediction model is beneficial as it can provide a theoretical basis for preventive exercise prescription. In recent years, necroptosis has been confirmed as an important manifestation of AD, and exercise is known to inhibit necroptosis of neuronal cells. In this study, we extracted 67 necroptosis-related genes and 32 necroptosis-related lncRNAs and screened for key predictive AD risk genes through a random forest analysis. Based on the neural network Prediction model, we constructed a new logistic regression-based AD risk prediction model in order to provide a visual basis for the formulation of exercise prescription. The prediction model had an area under the curve (AUC) value of 0.979, indicative of strong predictive power and a robust clinical application prospect. In the exercise group, the expression of exosomal miR-215-5p was found to be upregulated; miR-215-5p could potentially inhibit the expressions of IDH1, BCL2L11, and SIRT1. The single-cell SCENIC assay was used to identify key transcriptional regulators in skeletal muscle. Among them, CEBPB and GATA6 were identified as putative transcriptional regulators of miR-215. After "skeletal muscle removal of load," the expressions of CEBPB and GATA6 increased substantially, which in turn led to the elevation of miR-215 expression, thereby suggesting a putative mechanism for negative feedback regulation of exosomal homeostasis.
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Affiliation(s)
- Yisheng Chen
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yaying Sun
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhiwen Luo
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jinrong Lin
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Beijie Qi
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xueran Kang
- Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Chenting Ying
- Department of Orthopaedics, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Chenyang Guo
- Department of Orthopedics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Mengxuan Yao
- Department of Orthopaedic Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China
- Key Laboratory of Biomechanics of Hebei Province, Orthopaedic Research Institution of Hebei Province, Shijiazhuang, China
| | | | - Yi Wang
- Huashan Hospital, Fudan University, Shanghai, China
| | - Qian Wang
- Department of Central Laboratory, The Affiliated Taian City Central Hospital of Qingdao University, Tai’an, China
- *Correspondence: Qian Wang,
| | - Jiwu Chen
- Department of Orthopedics, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai, China
- Jiwu Chen,
| | - Shiyi Chen
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
- Shiyi Chen,
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Kudo K. Editorial for "Individualized Prediction of Early Alzheimer's Disease Based on MRI Radiomics, Clinical and Laboratory Examinations: A 60-Month Follow-up Study". J Magn Reson Imaging 2021; 54:1658-1659. [PMID: 34085337 DOI: 10.1002/jmri.27760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 05/20/2021] [Indexed: 11/09/2022] Open
Affiliation(s)
- Kohsuke Kudo
- Department of Diagnostic Imaging, Hokkaido University Faculty of Medicine, Sapporo, Japan
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