Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease
PurposeThis study aims to develop hippocampal texture model for predicting cognitive impairment in middle-aged patients with cerebral small vessel disease (CSVD).MethodsThe dataset included 145 CSVD patients (Age, 52.662 ± 5.151) and 99 control subjects (Age, 52.576±4.885). An Unet-based deep learni...
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1462636/full |
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| author | Wei Zheng Xiaoyan Qin Ronghua Mu Peng Yang Bingqin Huang Bingqin Huang Zhixuan Song Xiqi Zhu Xiqi Zhu |
| author_facet | Wei Zheng Xiaoyan Qin Ronghua Mu Peng Yang Bingqin Huang Bingqin Huang Zhixuan Song Xiqi Zhu Xiqi Zhu |
| author_sort | Wei Zheng |
| collection | DOAJ |
| description | PurposeThis study aims to develop hippocampal texture model for predicting cognitive impairment in middle-aged patients with cerebral small vessel disease (CSVD).MethodsThe dataset included 145 CSVD patients (Age, 52.662 ± 5.151) and 99 control subjects (Age, 52.576±4.885). An Unet-based deep learning neural network model was developed to automate the segmentation of the hippocampus. Features were extracted for each subject, and the least absolute shrinkage and selection operator (LASSO) method was used to select radiomic features. This study also included the extraction of total intracranial volume, gray matter, white matter, cerebrospinal fluid, white matter hypertensit, and hippocampus volume. The performance of the models was assessed using the areas under the receiver operating characteristic curves (AUCs). Additionally, decision curve analysis (DCA) was conducted to justify the clinical relevance of the study, and the DeLong test was utilized to compare the areas under two correlated receiver operating characteristic (ROC) curves.ResultsNine texture features of the hippocampus were selected to construct radiomics model. The AUC values of the brain volume, radiomics, and combined models in the test set were 0.593, 0.843, and 0.817, respectively. The combination model of imaging markers and hippocampal texture did not yield improved a better diagnosis compared to the individual model (p > 0.05).ConclusionThe hippocampal texture model is a surrogate imaging marker for predicting cognitive impairment in middle-aged CSVD patients. |
| format | Article |
| id | doaj-art-9f1ed62f0bee4b77b61bd5c5baecd62d |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-9f1ed62f0bee4b77b61bd5c5baecd62d2025-02-11T05:10:26ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-02-011610.3389/fneur.2025.14626361462636Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel diseaseWei Zheng0Xiaoyan Qin1Ronghua Mu2Peng Yang3Bingqin Huang4Bingqin Huang5Zhixuan Song6Xiqi Zhu7Xiqi Zhu8Department of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, ChinaDepartment of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, ChinaDepartment of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, ChinaDepartment of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, ChinaDepartment of Radiology, Nanxishan Hospital of Guangxi Zhuang Autonomous Region, Guilin, ChinaGraduate School, Guilin Medical University, Guilin, ChinaPhilips (China) Investment Co., Ltd., Guangzhou Branch, Guangzhou, ChinaDepartment of Radiology, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, ChinaLife Science and Clinical Medicine Research Center, Affiliated Hospital of Youjiang Medical University for Nationalities, Baise, ChinaPurposeThis study aims to develop hippocampal texture model for predicting cognitive impairment in middle-aged patients with cerebral small vessel disease (CSVD).MethodsThe dataset included 145 CSVD patients (Age, 52.662 ± 5.151) and 99 control subjects (Age, 52.576±4.885). An Unet-based deep learning neural network model was developed to automate the segmentation of the hippocampus. Features were extracted for each subject, and the least absolute shrinkage and selection operator (LASSO) method was used to select radiomic features. This study also included the extraction of total intracranial volume, gray matter, white matter, cerebrospinal fluid, white matter hypertensit, and hippocampus volume. The performance of the models was assessed using the areas under the receiver operating characteristic curves (AUCs). Additionally, decision curve analysis (DCA) was conducted to justify the clinical relevance of the study, and the DeLong test was utilized to compare the areas under two correlated receiver operating characteristic (ROC) curves.ResultsNine texture features of the hippocampus were selected to construct radiomics model. The AUC values of the brain volume, radiomics, and combined models in the test set were 0.593, 0.843, and 0.817, respectively. The combination model of imaging markers and hippocampal texture did not yield improved a better diagnosis compared to the individual model (p > 0.05).ConclusionThe hippocampal texture model is a surrogate imaging marker for predicting cognitive impairment in middle-aged CSVD patients.https://www.frontiersin.org/articles/10.3389/fneur.2025.1462636/fullcerebral small vessel diseasecognitive impairmentmagnetic resonance imagingprediction modelradiomics |
| spellingShingle | Wei Zheng Xiaoyan Qin Ronghua Mu Peng Yang Bingqin Huang Bingqin Huang Zhixuan Song Xiqi Zhu Xiqi Zhu Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease Frontiers in Neurology cerebral small vessel disease cognitive impairment magnetic resonance imaging prediction model radiomics |
| title | Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease |
| title_full | Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease |
| title_fullStr | Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease |
| title_full_unstemmed | Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease |
| title_short | Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease |
| title_sort | prediction models for cognitive impairment in middle aged patients with cerebral small vessel disease |
| topic | cerebral small vessel disease cognitive impairment magnetic resonance imaging prediction model radiomics |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1462636/full |
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