Radiomic and clinical nomogram for cognitive impairment prediction in Wilson’s disease
ObjectiveTo investigate potential biomarkers for the early detection of cognitive impairment in patients with Wilson’s disease (WD), we developed a computer-assisted radiomics model to distinguish between WD and WD cognitive impairment.MethodsOverall, 136 T1-weighted MR images were retrieved from th...
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| Format: | Article |
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Frontiers Media S.A.
2023-04-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2023.1131968/full |
| _version_ | 1827957876047478784 |
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| author | Liwei Tian Ting Dong Ting Dong Sheng Hu Chenling Zhao Guofang Yu Huibing Hu Wenming Yang Wenming Yang |
| author_facet | Liwei Tian Ting Dong Ting Dong Sheng Hu Chenling Zhao Guofang Yu Huibing Hu Wenming Yang Wenming Yang |
| author_sort | Liwei Tian |
| collection | DOAJ |
| description | ObjectiveTo investigate potential biomarkers for the early detection of cognitive impairment in patients with Wilson’s disease (WD), we developed a computer-assisted radiomics model to distinguish between WD and WD cognitive impairment.MethodsOverall, 136 T1-weighted MR images were retrieved from the First Affiliated Hospital of Anhui University of Chinese Medicine, including 77 from patients with WD and 59 from patients with WD cognitive impairment. The images were divided into training and test groups at a ratio of 70:30. The radiomic features of each T1-weighted image were extracted using 3D Slicer software. R software was used to establish clinical and radiomic models based on clinical characteristics and radiomic features, respectively. The receiver operating characteristic profiles of the three models were evaluated to assess their diagnostic accuracy and reliability in distinguishing between WD and WD cognitive impairment. We combined relevant neuropsychological test scores of prospective memory to construct an integrated predictive model and visual nomogram to effectively assess the risk of cognitive decline in patients with WD.ResultsThe area under the curve values for distinguishing WD and WD cognitive impairment for the clinical, radiomic, and integrated models were 0.863, 0.922, and 0.935 respectively, indicative of excellent performance. The nomogram based on the integrated model successfully differentiated between WD and WD cognitive impairment.ConclusionThe nomogram developed in the current study may assist clinicians in the early identification of cognitive impairment in patients with WD. Early intervention following such identification may help improve long-term prognosis and quality of life of these patients. |
| first_indexed | 2024-04-09T15:26:55Z |
| format | Article |
| id | doaj.art-668aa1ed4e1f4a409e35b22bd6e792ee |
| institution | Directory Open Access Journal |
| issn | 1664-2295 |
| language | English |
| last_indexed | 2024-04-09T15:26:55Z |
| publishDate | 2023-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj.art-668aa1ed4e1f4a409e35b22bd6e792ee2023-04-28T14:00:54ZengFrontiers Media S.A.Frontiers in Neurology1664-22952023-04-011410.3389/fneur.2023.11319681131968Radiomic and clinical nomogram for cognitive impairment prediction in Wilson’s diseaseLiwei Tian0Ting Dong1Ting Dong2Sheng Hu3Chenling Zhao4Guofang Yu5Huibing Hu6Wenming Yang7Wenming Yang8Graduate School, Anhui University of Traditional Chinese Medicine, Hefei, ChinaDepartment of Neurology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, ChinaKey Laboratory of Xin’An Medicine, Ministry of Education, Hefei, Anhui, ChinaCenters for Biomedical Engineering, University of Science and Technology of China, Hefei, ChinaGraduate School, Anhui University of Traditional Chinese Medicine, Hefei, ChinaGraduate School, Anhui University of Traditional Chinese Medicine, Hefei, ChinaQimen People's Hospital, Huangshan, Anhui, ChinaDepartment of Neurology, The First Affiliated Hospital of Anhui University of Traditional Chinese Medicine, Hefei, ChinaKey Laboratory of Xin’An Medicine, Ministry of Education, Hefei, Anhui, ChinaObjectiveTo investigate potential biomarkers for the early detection of cognitive impairment in patients with Wilson’s disease (WD), we developed a computer-assisted radiomics model to distinguish between WD and WD cognitive impairment.MethodsOverall, 136 T1-weighted MR images were retrieved from the First Affiliated Hospital of Anhui University of Chinese Medicine, including 77 from patients with WD and 59 from patients with WD cognitive impairment. The images were divided into training and test groups at a ratio of 70:30. The radiomic features of each T1-weighted image were extracted using 3D Slicer software. R software was used to establish clinical and radiomic models based on clinical characteristics and radiomic features, respectively. The receiver operating characteristic profiles of the three models were evaluated to assess their diagnostic accuracy and reliability in distinguishing between WD and WD cognitive impairment. We combined relevant neuropsychological test scores of prospective memory to construct an integrated predictive model and visual nomogram to effectively assess the risk of cognitive decline in patients with WD.ResultsThe area under the curve values for distinguishing WD and WD cognitive impairment for the clinical, radiomic, and integrated models were 0.863, 0.922, and 0.935 respectively, indicative of excellent performance. The nomogram based on the integrated model successfully differentiated between WD and WD cognitive impairment.ConclusionThe nomogram developed in the current study may assist clinicians in the early identification of cognitive impairment in patients with WD. Early intervention following such identification may help improve long-term prognosis and quality of life of these patients.https://www.frontiersin.org/articles/10.3389/fneur.2023.1131968/fullWilson’s diseaseradiomicscognitive impairmentmachine learningMRI |
| spellingShingle | Liwei Tian Ting Dong Ting Dong Sheng Hu Chenling Zhao Guofang Yu Huibing Hu Wenming Yang Wenming Yang Radiomic and clinical nomogram for cognitive impairment prediction in Wilson’s disease Frontiers in Neurology Wilson’s disease radiomics cognitive impairment machine learning MRI |
| title | Radiomic and clinical nomogram for cognitive impairment prediction in Wilson’s disease |
| title_full | Radiomic and clinical nomogram for cognitive impairment prediction in Wilson’s disease |
| title_fullStr | Radiomic and clinical nomogram for cognitive impairment prediction in Wilson’s disease |
| title_full_unstemmed | Radiomic and clinical nomogram for cognitive impairment prediction in Wilson’s disease |
| title_short | Radiomic and clinical nomogram for cognitive impairment prediction in Wilson’s disease |
| title_sort | radiomic and clinical nomogram for cognitive impairment prediction in wilson s disease |
| topic | Wilson’s disease radiomics cognitive impairment machine learning MRI |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2023.1131968/full |
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