Evidence map›Paper›PMID 42098640›Full record

ArticleBMC medical imaging2026

Integration of glymphatic system function and hippocampal radiomics for diagnosis and conversion prediction of Alzheimer's disease.

Xiaohan Mao, Di Zhang, Danqing Ying, Juncheng Yu, Yongqian Ge, Zhongzheng Jia

Abstract read
In one paragraph

Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Hippocampal texture asymmetry features onFrontiers in immunology · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Xiaohan Mao *Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, NO. 20 Xisi Road, Nantong, 226001, People's Republic of China.
Di Zhang *Department of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, NO. 20 Xisi Road, Nantong, 226001, People's Republic of China.
Danqing YingDepartment of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, NO. 20 Xisi Road, Nantong, 226001, People's Republic of China.
Juncheng YuDepartment of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, NO. 20 Xisi Road, Nantong, 226001, People's Republic of China.
Yongqian GeDepartment of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, NO. 20 Xisi Road, Nantong, 226001, People's Republic of China.
Zhongzheng JiaDepartment of Medical Imaging, Affiliated Hospital of Nantong University, Medical School of Nantong University, NO. 20 Xisi Road, Nantong, 226001, People's Republic of China. jzz2397@163.com.

Funding

Jiangsu Provincial Health Commission BJ23030Jiangsu Provincial Research Hospital YJXYY202204-YSB73Postgraduate Research & Practice Innovation Program of Jiangsu Province SJCX24_2048
6 · The paper itself

Abstract

backgroundGlymphatic system (GS) function and hippocampal microstructural changes are promising imaging markers of Alzheimer's disease (AD). This study aims to investigate the effectiveness of combining diffusion tensor image analysis along the perivascular space (DTI-ALPS) with hippocampal radiomics for diagnosing AD, and to develop an innovative multivariable model integrating hippocampal radiomics and clinical biomarkers for predicting mild cognitive impairment (MCI) progression.

methodsWe included three cohorts from two databases retrospectively, using an internal (n = 210) and an external dataset (n = 430) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The ALPS index was employed to measure GS function, and 3D-T1WI hippocampal radiomics features were extracted to construct machine learning models for classifying and diagnosing AD. Conversion of MCI to AD was assessed through integrating the hippocampal radiomics features, ALPS index, and AD-related clinical biomarkers.

resultsThe ALPS index was lower in patients with AD than in healthy controls (HCs) in both the internal and external cohorts (p < 0.001). The combined hippocampal radiomics features and ALPS index model demonstrated good performance in AD classification. The multivariable prediction model of MCI progression to AD achieved an area under the curve of 0.97 and 0.92 for the training and testing cohorts, respectively.

conclusionsIntegrated ALPS index and hippocampal-based radiomics features can improve diagnostic performance in patients with AD, showing predictive capability for identifying the MCI conversion.

Indexed as

Alzheimer DiseaseCognitive DysfunctionDiffusion Tensor ImagingGlymphatic SystemHippocampusAgedAged, 80 and overDisease ProgressionFemaleHumansMachine LearningMaleRadiomicsRetrospective StudiesAlzheimer’s diseaseCognitive declineGlymphatic systemMagnetic resonance imagingRadiomics

Identifiers

PMID42098640
PMCPMC13321504

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.