{"title":"基于18f - fdg - pet的深度学习预测阿尔茨海默病临床谱系中非痴呆老年人的认知能力下降","authors":"Beomseok Sohn, Seok Jong Chung, Jeong Ryong Lee, Dosik Hwang, Wanying Xie, Ling Ling Chan, Yoon Seong Choi","doi":"10.1093/radadv/umae021","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>With disease-modifying treatments for Alzheimer's disease (AD), prognostic tools for the pre-dementia stage are needed. This study aimed to evaluate the prognostic value of an <sup>18</sup>F-fluorodeoxyglucose-positron emission tomography (<sup>18</sup>F-FDG-PET)-based deep-learning (DL) model in the pre-dementia stage of mild cognitive impairment (MCI) and normal cognition (NC).</p><p><strong>Materials and methods: </strong>A <sup>18</sup>F-FDG-PET-based DL model was developed to classify diagnosis of AD-dementia vs NC using AD Neuroimaging Initiative (ADNI) and Japanese-ADNI (J-ADNI) datasets (<i>n</i> = 756), which provided the degree of similarity to AD-dementia. The prognostic value of the DL output for cognitive decline was assessed in the ADNI MCI (<i>n</i> = 663), J-ADNI MCI (<i>n</i> = 129), and Harvard Aging Brain Study (HABS) NC (<i>n</i> = 274) participants using Cox regression and calculating the integrated area under the time-dependent ROC curves (iAUC), along with clinical information and <sup>18</sup>F-FDG-PET standardized uptake value ratio (SUVR). Subgroup analysis in the amyloid-positive ADNI MCI participants was performed using Cox regression and calculating the area under the time-dependent ROC (tdAUC) curves at 4-year follow-up to assess prognostic value of DL output over clinical information, <sup>18</sup>F-FDG-PET SUVR, and amyloid PET Centiloids.</p><p><strong>Results: </strong>DL output remained independently prognostic among other factors in all three datasets (<i>P</i> < .05 for all by Cox regression). By adding DL output to other prognostic factors, prediction significantly improved in ADNI-MCI (iAUC differences 0.020 [0.007-0.034] before and after adding DL output) and improved without statistical significance in J-ADNI (0.020 [-0.005 to 0.044], and HABS-NC sets (0.059 [-0.003 to 0.126]). DL output showed independent (<i>P</i> = .002 by Cox regression) and significant added prognostic value (tdROC difference 0.019 [<0.001-0.036]) over clinical information, <sup>18</sup>F-FDG-PET SUVR, and Centiloids in the amyloid-positive ADNI MCI participants.</p><p><strong>Conclusion: </strong>The <sup>18</sup>F-FDG-PET-based DL model demonstrated the potential to improve cognitive decline prediction beyond clinical information, and conventional measures from <sup>18</sup>F-FDG-PET and amyloid PET and may prove useful for clinical trial recruitment and individualized management.</p>","PeriodicalId":519940,"journal":{"name":"Radiology advances","volume":"1 3","pages":"umae021"},"PeriodicalIF":0.0000,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12429239/pdf/","citationCount":"0","resultStr":"{\"title\":\"<sup>18</sup>F-FDG-PET-based deep learning for predicting cognitive decline in non-demented elderly across the Alzheimer's disease clinical spectrum.\",\"authors\":\"Beomseok Sohn, Seok Jong Chung, Jeong Ryong Lee, Dosik Hwang, Wanying Xie, Ling Ling Chan, Yoon Seong Choi\",\"doi\":\"10.1093/radadv/umae021\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>With disease-modifying treatments for Alzheimer's disease (AD), prognostic tools for the pre-dementia stage are needed. This study aimed to evaluate the prognostic value of an <sup>18</sup>F-fluorodeoxyglucose-positron emission tomography (<sup>18</sup>F-FDG-PET)-based deep-learning (DL) model in the pre-dementia stage of mild cognitive impairment (MCI) and normal cognition (NC).</p><p><strong>Materials and methods: </strong>A <sup>18</sup>F-FDG-PET-based DL model was developed to classify diagnosis of AD-dementia vs NC using AD Neuroimaging Initiative (ADNI) and Japanese-ADNI (J-ADNI) datasets (<i>n</i> = 756), which provided the degree of similarity to AD-dementia. The prognostic value of the DL output for cognitive decline was assessed in the ADNI MCI (<i>n</i> = 663), J-ADNI MCI (<i>n</i> = 129), and Harvard Aging Brain Study (HABS) NC (<i>n</i> = 274) participants using Cox regression and calculating the integrated area under the time-dependent ROC curves (iAUC), along with clinical information and <sup>18</sup>F-FDG-PET standardized uptake value ratio (SUVR). Subgroup analysis in the amyloid-positive ADNI MCI participants was performed using Cox regression and calculating the area under the time-dependent ROC (tdAUC) curves at 4-year follow-up to assess prognostic value of DL output over clinical information, <sup>18</sup>F-FDG-PET SUVR, and amyloid PET Centiloids.</p><p><strong>Results: </strong>DL output remained independently prognostic among other factors in all three datasets (<i>P</i> < .05 for all by Cox regression). By adding DL output to other prognostic factors, prediction significantly improved in ADNI-MCI (iAUC differences 0.020 [0.007-0.034] before and after adding DL output) and improved without statistical significance in J-ADNI (0.020 [-0.005 to 0.044], and HABS-NC sets (0.059 [-0.003 to 0.126]). DL output showed independent (<i>P</i> = .002 by Cox regression) and significant added prognostic value (tdROC difference 0.019 [<0.001-0.036]) over clinical information, <sup>18</sup>F-FDG-PET SUVR, and Centiloids in the amyloid-positive ADNI MCI participants.</p><p><strong>Conclusion: </strong>The <sup>18</sup>F-FDG-PET-based DL model demonstrated the potential to improve cognitive decline prediction beyond clinical information, and conventional measures from <sup>18</sup>F-FDG-PET and amyloid PET and may prove useful for clinical trial recruitment and individualized management.</p>\",\"PeriodicalId\":519940,\"journal\":{\"name\":\"Radiology advances\",\"volume\":\"1 3\",\"pages\":\"umae021\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12429239/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Radiology advances\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/radadv/umae021\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/9/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Radiology advances","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/radadv/umae021","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/9/1 0:00:00","PubModel":"eCollection","JCR":"","JCRName":"","Score":null,"Total":0}
18F-FDG-PET-based deep learning for predicting cognitive decline in non-demented elderly across the Alzheimer's disease clinical spectrum.
Background: With disease-modifying treatments for Alzheimer's disease (AD), prognostic tools for the pre-dementia stage are needed. This study aimed to evaluate the prognostic value of an 18F-fluorodeoxyglucose-positron emission tomography (18F-FDG-PET)-based deep-learning (DL) model in the pre-dementia stage of mild cognitive impairment (MCI) and normal cognition (NC).
Materials and methods: A 18F-FDG-PET-based DL model was developed to classify diagnosis of AD-dementia vs NC using AD Neuroimaging Initiative (ADNI) and Japanese-ADNI (J-ADNI) datasets (n = 756), which provided the degree of similarity to AD-dementia. The prognostic value of the DL output for cognitive decline was assessed in the ADNI MCI (n = 663), J-ADNI MCI (n = 129), and Harvard Aging Brain Study (HABS) NC (n = 274) participants using Cox regression and calculating the integrated area under the time-dependent ROC curves (iAUC), along with clinical information and 18F-FDG-PET standardized uptake value ratio (SUVR). Subgroup analysis in the amyloid-positive ADNI MCI participants was performed using Cox regression and calculating the area under the time-dependent ROC (tdAUC) curves at 4-year follow-up to assess prognostic value of DL output over clinical information, 18F-FDG-PET SUVR, and amyloid PET Centiloids.
Results: DL output remained independently prognostic among other factors in all three datasets (P < .05 for all by Cox regression). By adding DL output to other prognostic factors, prediction significantly improved in ADNI-MCI (iAUC differences 0.020 [0.007-0.034] before and after adding DL output) and improved without statistical significance in J-ADNI (0.020 [-0.005 to 0.044], and HABS-NC sets (0.059 [-0.003 to 0.126]). DL output showed independent (P = .002 by Cox regression) and significant added prognostic value (tdROC difference 0.019 [<0.001-0.036]) over clinical information, 18F-FDG-PET SUVR, and Centiloids in the amyloid-positive ADNI MCI participants.
Conclusion: The 18F-FDG-PET-based DL model demonstrated the potential to improve cognitive decline prediction beyond clinical information, and conventional measures from 18F-FDG-PET and amyloid PET and may prove useful for clinical trial recruitment and individualized management.
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