Article(id=1320381134756757547, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320381108315870143, articleNumber=null, orderNo=null, doi=10.11876/mimt202603001, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1774800000000, receivedDateStr=2026-03-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792732994020, onlineDateStr=2026-10-23, pubDate=1781020800000, pubDateStr=2026-06-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792732994020, onlineIssueDateStr=2026-10-23, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792732994020, creator=13701087609, updateTime=1792732994020, updator=13701087609, issue=Issue{id=1320381108315870143, tenantId=1146029695717560320, journalId=1319373289681944729, year='2026', volume='32', issue='3', pageStart='1', pageEnd='132', issueExtLink='null', onlineDate='null', pubDate='1781020800000', pubDateStr='2026-06-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792732987716, creator='13701087609', updateTime=1792746098645, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1320436099600728986, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320381108315870143, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1320436099600728987, tenantId=1146029695717560320, journalId=1319373289681944729, issueId=1320381108315870143, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=11, ext={EN=ArticleExt(id=1320381134911946796, articleId=1320381134756757547, tenantId=1146029695717560320, journalId=1319373289681944729, language=EN, title=A risk prediction model for the conversion from mild cognitive impairment to Alzheimer's disease based on TabPFN with interpretability, columnId=null, journalTitle=Modern Instruments & Medical Treatment, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective To establish a prediction model for the conversion risk from mild cognitive impairment (MCI) to Alzheimer's disease (AD) based on structural MRI features, and employ interpretable algorithms to quantify the contribution of critical brain region features to model discrimination.
Methods A total of 356 sets of T1-weighted structural MRI data were enrolled from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. All samples were stratified and randomly divided into a training set containing 237 subjects [119 mild cognitive impairment converters (MCIc) and 118 mild cognitive impairment non-converters (MCInc)] and an independent test set consisting of 119 subjects (60 MCIc and 59 MCInc). Five-fold cross-validation was conducted on the training set to evaluate model stability. FreeSurfer v7.4.1 software was used to extract 1,481 brain structural features for constructing the feature matrix. Boruta nonlinear wrapper feature selection was performed, and 17 core features were retained ultimately. Four models, including support vector machine (SVM), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM) and tabular prior-data fitted network (TabPFN), were constructed using the original and filtered feature sets separately. Logistic regression combined with LASSO regression (LR+LASSO) was introduced as a classic linear baseline for comparative analysis. SHapley Additive exPlanations (SHAP) was further applied to interpret brain region features associated with high conversion risk.
Results After Boruta feature screening, the separability of samples in the principal component space was improved substantially. The 17 retained features mainly involve brain regions such as the hippocampus, amygdala, temporal lobe and parietal lobe. On the independent test set, the Boruta+TabPFN pipeline achieved an accuracy of 0.765, an area under curve (AUC) of 0.823 and a false negative rate (FNR) of 0.200. Compared with LR+LASSO, Boruta+TabPFN achieved better performance in accuracy, precision, specificity and AUC. However, LR+LASSO yielded slightly higher recall at the target recall threshold, indicating that the linear baseline model prioritizes high sensitivity at the expense of specificity.
Conclusion Effective nonlinear feature screening is required to build robust discriminative models with high-dimensional structural MRI features. Although Boruta+TabPFN did not obtain the optimal value for every single evaluation metric, it delivered steady performance in AUC, recall and false negative control, which better satisfies the clinical requirement of identifying high-risk individuals in the early screening of MCI-to-AD conversion. SHAP interpretability analysis demonstrated that the risk of MCI progression to AD is driven by coordinated morphological changes across multiple brain regions including the hippocampus, amygdala, temporal lobe, parietal lobe and limbic system, providing interpretable imaging evidence for early disease risk evaluation.
, authors=Jiaao Deng, Chenkai Xia, Liping Sun, Qingyun Meng, Huijiang Du
*, authorsList=Jiaao Deng, Chenkai Xia, Liping Sun, Qingyun Meng, Huijiang Du, authorCompany=null, correspAuthors=Huijiang Du, authorNote=null, correspAuthorsNote=null, copyrightStatement=
All copyrights in 2026 belong to the sponsor of the journal.Not content published by the journal may be reproduced or abridged without authorization., copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1320381135167799341, articleId=1320381134756757547, tenantId=1146029695717560320, journalId=1319373289681944729, language=CN, title=基于TabPFN的可解释轻度认知障碍向阿尔茨海默病转化的风险预测模型研究, columnId=1320381126389117454, journalTitle=现代仪器与医疗, columnName=智能医疗器械专栏, runingTitle=null, highlight=null, articleAbstract=
目的 基于结构MRI特征构建轻度认知障碍（mild cognitive impairment，MCI）向阿尔茨海默病（Alzheimer's disease，AD）转化的风险预测模型，并引入可解释方法揭示关键脑区特征对模型判别的贡献。
方法 选取阿尔茨海默病神经影像计划（Alzheimer's Disease Neuroimaging Initiative，ADNI）数据库中356例T1结构MRI数据，采用分层随机划分为训练集237例[转化型MCI（mild cognitive impairment converters，MCIc）119例，非转化型MCI（mild cognitive impairment non-converters，MCInc）118例]和独立测试集119例（MCIc 60例，MCInc 59例），并在训练集中采用5折交叉验证评估模型稳定性。采用FreeSurfer v7.4.1软件提取1 481项脑结构特征并构建特征矩阵。对特征进行Boruta非线性包裹式筛选，最终保留17项关键特征。基于筛选前后特征分别构建支持向量机（support vector machine，SVM）、极端梯度提升（extreme gradient boosting，XGBoost）、轻量级梯度提升（light gradient boosting machine，LightGBM）及表格先验数据拟合网络（tabular prior-data fitted network，TabPFN）模型，并引入Logistic回归结合LASSO回归（LR+LASSO）作为经典线性基线方法进行补充对比。进一步采用SHAP方法对高风险相关脑区特征进行解释。
结果 经Boruta筛选后，样本在主成分空间中的分离趋势增强，17项特征主要涉及海马、杏仁核、颞叶及顶叶等脑区。在独立测试集中，Boruta+TabPFN的准确率、曲线下面积（area under curve，AUC）和假阴性率（false negative rate，FNR）分别为0.765、0.823和0.200。与LR+LASSO相比，Boruta+TabPFN在准确率、精确率、特异度及AUC方面更优，但LR+LASSO在目标召回阈值下召回率略高，提示线性基线更偏向高敏感度而牺牲特异度。
结论 高维结构MRI特征需要通过有效、非线性的特征筛选以构建稳定判别模型。Boruta+TabPFN并非在所有单项指标上均最高，但其在AUC、召回率及漏判控制方面表现较稳定，更符合MCI转化风险早期筛查中对高风险个体识别的需求。SHAP代理解释结果提示，MCI向AD转化风险涉及海马、杏仁核、颞叶、顶叶及边缘系统等多脑区协同变化，为疾病早期风险评估提供了可解释的影像学依据。
, authors=邓嘉奥, 夏宸楷, 孙丽萍, 孟青云, 杜慧江
*, authorsList=邓嘉奥, 夏宸楷, 孙丽萍, 孟青云, 杜慧江, authorCompany=null, correspAuthors=杜慧江, authorNote=
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杜慧江，男，硕士研究生，工程师，研究方向：计算机应用，医学影像智能算法
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2026年版权归本刊主办单位所有未经本刊书面许可, 不得转载、摘编本刊所有文章内容。, copyrightOwner=中国科学器材公司, extLink=null, articleAbsUrl=null, sourceXml=slOMNBfLi992+dB/C1XC4A==, magXml=LaadoXcZ5OotI5YZCGmrXw==, pdfUrl=null, pdf=Wsgnvm34TEIddtJByRNhbg==, pdfFileSize=3744534, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=uoiXqx0oheIsUebvitVbIA==, mapNumber=null, fund=null)}, authors=[Author(id=1320381135440429106, tenantId=1146029695717560320, journalId=1319373289681944729, articleId=1320381134756757547, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=null, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1320381135520120884, tenantId=1146029695717560320, journalId=1319373289681944729, articleId=1320381134756757547, authorId=1320381135440429106, language=EN, stringName=Jiaao Deng, firstName=Jiaao, middleName=null, lastName=Deng, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=School of Medical Devices, Shanghai University of Medicine & Health Sciences, Shanghai 201318, China, bio=null, bioImg=null, bioContent=null, aboutCorrespAuthor=null), CN=AuthorExt(id=1320381135583035445, tenantId=1146029695717560320, journalId=1319373289681944729, articleId=1320381134756757547, authorId=1320381135440429106, language=CN, stringName=邓嘉奥, firstName=null, middleName=null, lastName=null, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=null, address=上海健康医学院医疗器械学院，上海 201318, bio={"content":"
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