Article(id=1322608133260820597, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322608013010120712, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202504174, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744473600000, receivedDateStr=2025-04-13, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793263951840, onlineDateStr=2026-10-29, pubDate=1762704000000, pubDateStr=2025-11-10, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793263951840, onlineIssueDateStr=2026-10-29, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793263951840, creator=13701087609, updateTime=1793263951840, updator=13701087609, issue=Issue{id=1322608013010120712, tenantId=1146029695717560320, journalId=1227665162245664772, year='2025', volume='52', issue='21', pageStart='3841', pageEnd='4032', issueExtLink='null', onlineDate='null', pubDate='1762704000000', pubDateStr='2025-11-10', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793263923169, creator='13701087609', updateTime=1793264266929, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322609454932149086, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322608013010120712, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322609454932149087, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1322608013010120712, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3900, endPage=3908, ext={EN=ArticleExt(id=1322608133646696566, articleId=1322608133260820597, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Development and multicenter validation of a predictive model for gallstone disease and gallbladder polyps based on multidimensional clinical data, columnId=1240413921954295836, journalTitle=Modern Preventive Medicine, columnName=Epidemiology and Statistical Methods, runingTitle=null, highlight=null, articleAbstract=
Objective

The clinical symptoms and ultrasound images of gallstone disease (GSD) and gallbladder polyps (GP) overlap, yet they have distinct prognoses and management strategies. Misdiagnosis can lead to excessive or delayed intervention. This study aims to develop and validate a differential diagnostic model based on multidimensional clinical indicators to enhance the differentiation between GSD and GP.

Methods

We included 371 518 individuals from health check-ups at a hospital in Kaizhou District, Chongqing, from 2015 to 2020, selecting 24 307 GSD or GP patients. These were split into a training set (19 445 cases) and an internal validation set (4 862 cases) at an 8:2 ratio. Variables were selected using Lasso regression and XGBoost algorithm, and a diagnostic model was built with Logistic regression. The model’s calibration, discrimination, and clinical applicability were evaluated using the calibration curve, the area under the ROC curve (AUC), and decision curve analysis, respectively.For external validation, 14 341 GSD and GP patients undergoing health checkups during the same period at a hospital in the Liangjiang New Area, Chongqing, were included to assess the model’s discriminative performance.

Results

Women, older individuals, those with high BMI, central obesity, elevated GGT and ALT levels, high blood glucose levels, and fatty liver were more likely to be diagnosed with GSD. Individuals with high HDL-C and LDL-C levels were more likely to have GP. The model demonstrated good calibration and discrimination, with an ROC AUC of 69.4% (95% CI: 67.8%-70.9%). Decision curve analysis further indicated that the model had good clinical applicability, and external validation further confirmed its discriminatory performance. An online tool (https://yitingxu129.shinyapps.io/logisticmodel/) was created to aid in differentiating GSD and GP.

Conclusion

This study shows that gender, age, BMI, and blood biochemistry indicators are valuable in differentiating GSD and GP.The developed model has good predictive performance and clinical applicability and is expected to assist radiologists in making accurate differential diagnoses of GSD and GP, reduce misdiagnoses, and lower the disease burden.

, authors=Yi-ting XU1, Meng-lin HE1, Xin WANG1, Hong-yu LI1, Guo-heng JIANG1, Lin-jun XIE1, Yi JIANG1, Jing LUO1, Xuan BAI1, Zi-jie CHEN1, Cui-hua ZHANG1, Hong YANG1, Wen-qian YU1, Min GAO2, Jiong LU2, Xin WANG1, authorsList=Yi-ting XU, Meng-lin HE, Xin WANG, Hong-yu LI, Guo-heng JIANG, Lin-jun XIE, Yi JIANG, Jing LUO, Xuan BAI, Zi-jie CHEN, Cui-hua ZHANG, Hong YANG, Wen-qian YU, Min GAO, Jiong LU, Xin WANG, authorCompany=null, correspAuthors=Xin WANG, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, 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=1322608137136357507, articleId=1322608133260820597, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于多维度临床数据的胆结石与胆囊息肉鉴别预测模型构建与多中心验证研究, columnId=1228016567632462653, journalTitle=现代预防医学, columnName=流行病与统计方法, runingTitle=null, highlight=null, articleAbstract=
目的

胆结石(gallstone disease)与胆囊息肉(gallbladder polyps)的临床表现及超声影像存在重叠,但转归和管理策略差异显著,误诊易导致过度或延误干预。本研究旨在构建并验证一个基于多维度临床指标的鉴别诊断模型,以提升对胆结石与胆囊息肉的鉴别诊断能力。

方法

本研究纳入2015—2020年重庆市开州区某医院体检人群共371 518例,从中选取被诊断为胆结石或胆囊息肉的患者24 307例,并按8:2的比例划分为训练集(19 445例)与内部验证集(4 862例)。通过LASSO回归、XGBoost算法对变量进行筛选,基于logistic回归构建鉴别诊断模型。以校准曲线、受试者工作曲线(receiver operating characteristic curve,ROC)下面积、决策曲线等指标分别评估模型的标定能力、区分能力及临床适用性。同时,选取同期在重庆市两江新区某医院参加健康体检的胆结石和胆囊息肉患者共14 341例,对模型的鉴别诊断能力进行外部验证。

结果

研究发现,女性、高龄、高BMI水平、中心肥胖、高γ-谷氨酰转肽酶、谷丙转氧酶水平、高血糖水平以及脂肪肝患者更易被诊断为胆结石;而高水平高密度脂蛋白胆固醇、高水平低密度脂蛋白胆固醇人群更倾向于被诊断为胆囊息肉。基于上述因素构建的诊断模型,其准确性评价结果显示:预测标定能力良好,预测区分ROC曲线下面积达69.4%(95% CI:67.8% ~ 70.9%),决策曲线也表明该模型具有较好的临床适用性。此外,外部验证结果进一步证实了该模型的鉴别诊断能力。本项目还建立了在线网站(https://yitingxu129.shinyapps.io/logisticmodel/),旨在有效辅助鉴别胆结石与胆囊息肉。

结论

本研究表明,性别、年龄、BMI以及血生化指标等因素在胆结石和胆囊息肉的鉴别诊断中具有潜在价值。基于这些因素构建的鉴别诊断模型,具备良好的预测效果和临床适用性。未来,该模型有望辅助临床影像科医生对胆结石与胆囊息肉进行鉴别诊断,提高诊断准确性,降低疾病负担。

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徐逸婷(2002-),女,本科在读,研究方向:胆结石和胆管癌病因流行病学研究

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Gut, 2024, 73 (3): 521-532., articleTitle=Novel insights into causal effects of serum lipids and lipid-modifying targets on cholelithiasis, refAbstract=null), Reference(id=1322608154219757844, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, doi=null, pmid=null, pmcid=null, year=2024, volume=2024, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=[17], rfOrder=20, authorNames=Li SY, Zhu P, Chen FY, journalName=Gastroenterology Research and Practice, refType=null, unstructuredReference=Li SY, Zhu P, Chen FY, et al. Liver Function-Related indicators and risk of gallstone Diseases-A multicenter study and a systematic review and Meta-Analysis [J]. 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Danish Medical Journal, 2018, 65 (2): B5438., articleTitle=New determinants for gallstone disease?, refAbstract=null), Reference(id=1322608154358169878, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, doi=null, pmid=null, pmcid=null, year=2002, volume=12, issue=2, pageStart=131, pageEnd=140, url=null, language=null, rfNumber=[19], rfOrder=22, authorNames=Boland LL, Folsom AR, Rosamond WD, journalName=Annals of Epidemiology, refType=null, unstructuredReference=Boland LL, Folsom AR, Rosamond WD. Hyperinsulinemia, dyslipidemia, and obesity as risk factors for hospitalized gallbladder disease: a prospective study [J]. Annals of Epidemiology, 2002, 12 (2): 131-140., articleTitle=Hyperinsulinemia, dyslipidemia, and obesity as risk factors for hospitalized gallbladder disease: a prospective study, refAbstract=null), Reference(id=1322608154551107863, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, doi=null, pmid=null, pmcid=null, year=2025, volume=26, issue=8, pageStart=3665, pageEnd=null, url=null, language=null, rfNumber=[20], rfOrder=23, authorNames=Bojan A, Pricop C, Vladeanu MC, journalName=International Journal of Molecular Sciences, refType=null, unstructuredReference=Bojan A, Pricop C, Vladeanu MC, et al. The predictive roles of tumour markers, hemostasis assessment, and inflammation in the early detection and prognosis of gallbladder adenocarcinoma and metaplasia: a clinical study [J]. International Journal of Molecular Sciences, 2025, 26 (8): 3665., articleTitle=The predictive roles of tumour markers, hemostasis assessment, and inflammation in the early detection and prognosis of gallbladder adenocarcinoma and metaplasia: a clinical study, refAbstract=null), Reference(id=1322608154609828120, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, doi=null, pmid=null, pmcid=null, year=2021, volume=31, issue=12, pageStart=1422, pageEnd=1427, url=null, language=null, rfNumber=[21], rfOrder=24, authorNames=Ozer N, Sozutek A, Sahin A, journalName=Journal of the College of Physicians and Surgeons--Pakistan: JCPSP, refType=null, unstructuredReference=Ozer N, Sozutek A, Sahin A, et al. The role of Ca 19-9, Ca 72-4, Cea and cholesterol levels in predicting malignancy in gallbladder polyps [J]. Journal of the College of Physicians and Surgeons--Pakistan: JCPSP, 2021, 31 (12): 1422-1427., articleTitle=The role of Ca 19-9, Ca 72-4, Cea and cholesterol levels in predicting malignancy in gallbladder polyps, refAbstract=null), Reference(id=1322608154672742681, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, doi=null, pmid=null, pmcid=null, year=2016, volume=150, issue=1, pageStart=156, pageEnd=167.e1, url=null, language=null, rfNumber=[22], rfOrder=25, authorNames=Shabanzadeh DM, Sørensen LT, Jørgensen T, journalName=Gastroenterology, refType=null, unstructuredReference=Shabanzadeh DM, Sørensen LT, Jørgensen T. A prediction rule for risk stratification of incidentally discovered gallstones: results from a large cohort study [J]. Gastroenterology, 2016, 150 (1): 156-167.e1., articleTitle=A prediction rule for risk stratification of incidentally discovered gallstones: results from a large cohort study, refAbstract=null), Reference(id=1322608154735657242, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, doi=null, pmid=null, pmcid=null, year=2023, volume=9, issue=1, pageStart=30, pageEnd=41, url=null, language=null, rfNumber=[23], rfOrder=26, authorNames=Chou TS, Lin CL, Chen LW, journalName=Obes Sci Pract, refType=null, unstructuredReference=Chou TS, Lin CL, Chen LW, et al. Waist-to-height ratio for the prediction of gallstone disease among different obesity indicators [J]. Obes Sci Pract, 2023, 9 (1): 30-41., articleTitle=Waist-to-height ratio for the prediction of gallstone disease among different obesity indicators, refAbstract=null), Reference(id=1322608154806960411, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, doi=null, pmid=null, pmcid=null, year=2022, volume=20, issue=3, pageStart=e529, pageEnd=e537, url=null, language=null, rfNumber=[24], rfOrder=27, authorNames=Yuan S, Gill D, Giovannucci EL, journalName=Clinical Gastroenterology and Hepatology, refType=null, unstructuredReference=Yuan S, Gill D, Giovannucci EL, et al. Obesity, type 2 diabetes, lifestyle factors, and risk of gallstone disease: a mendelian randomization investigation [J]. Clinical Gastroenterology and Hepatology, 2022, 20 (3): e529-e537., articleTitle=Obesity, type 2 diabetes, lifestyle factors, and risk of gallstone disease: a mendelian randomization investigation, refAbstract=null), Reference(id=1322608154974732572, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, doi=null, pmid=null, pmcid=null, year=2024, volume=32, issue=11, pageStart=1147, pageEnd=1154, url=null, language=null, rfNumber=[25], rfOrder=28, authorNames=贾丽刚, 费翔, 经翔, journalName=中国医学影像学杂志, refType=null, unstructuredReference=贾丽刚,费翔,经翔, 等. 超声造影鉴别诊断胆囊息肉样病变性质的多中心研究 [J]. 中国医学影像学杂志, 2024, 32 (11): 1147-1154., articleTitle=超声造影鉴别诊断胆囊息肉样病变性质的多中心研究, refAbstract=null), Reference(id=1322608155054424349, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, doi=null, pmid=null, pmcid=null, year=2024, volume=32, issue=11, pageStart=1147, pageEnd=1154, url=null, language=null, rfNumber=[25], rfOrder=29, authorNames=Jia LG, Fei X, Jing X, journalName=Chinese Journal of Medical Imaging, refType=null, unstructuredReference=Jia LG, Fei X, Jing X, et al. Contrast-Enhanced ultrasound in the differential diagnosis of gallbladder polypoid lesions: a multicenter study [J]. Chinese Journal of Medical Imaging, 2024, 32 (11): 1147-1154. (In Chinese), articleTitle=Contrast-Enhanced ultrasound in the differential diagnosis of gallbladder polypoid lesions: a multicenter study, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322608137488679045, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, xref=1., ext=[AuthorCompanyExt(id=1322608137501261958, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, companyId=1322608137488679045, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China), AuthorCompanyExt(id=1322608137505456263, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, companyId=1322608137488679045, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.四川大学华西公共卫生学院/华西第四医院流行病与卫生统计系,四川 成都 610041)]), AuthorCompany(id=1322608137580953736, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, xref=2., ext=[AuthorCompanyExt(id=1322608137593536649, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, companyId=1322608137580953736, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.四川大学华西医院,普通外科,胆道外科)])], figs=[ArticleFig(id=1322608147089441004, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, language=EN, label=Figure 1, caption=Workflow of differential clinical factors exploration, model construction, and validation, figureFileSmall=Uy+5dyKQsXHEPN4UjQUJuw==, figureFileBig=qQ4vDoGmXwVZst7i1aIHGA==, tableContent=null), ArticleFig(id=1322608147420791021, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, language=CN, label=图1, caption=差异临床因素探寻、模型构建与验证流程图, figureFileSmall=Uy+5dyKQsXHEPN4UjQUJuw==, figureFileBig=qQ4vDoGmXwVZst7i1aIHGA==, tableContent=null), ArticleFig(id=1322608148947517678, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, language=EN, label=Figure 2, caption=Regularization path of LASSO regression, figureFileSmall=ipbL9go/3cfFhKVmNJd7gA==, figureFileBig=hvNDt6CxOmYN55QgLuCQsg==, tableContent=null), ArticleFig(id=1322608149039792368, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, language=CN, label=图2, caption=LASSO回归正则化路径图, figureFileSmall=ipbL9go/3cfFhKVmNJd7gA==, figureFileBig=hvNDt6CxOmYN55QgLuCQsg==, tableContent=null), ArticleFig(id=1322608149136261362, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, language=EN, label=Figure 3, caption=SHAP analysis of feature contributions in the XGBoost model, figureFileSmall=+JK211DqpcztdNnZmuyqVQ==, figureFileBig=OHyEXlC8y+xXct9tTrb5OA==, tableContent=null), ArticleFig(id=1322608149241118963, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, language=CN, label=图3, caption=XGBoost模型特征贡献SHAP分析

注:TG为甘油三酯;TC为总胆固醇;UA为血尿酸;G为球蛋白;UN为血尿素;G为球蛋白。

, figureFileSmall=+JK211DqpcztdNnZmuyqVQ==, figureFileBig=OHyEXlC8y+xXct9tTrb5OA==, tableContent=null), ArticleFig(id=1322608149379530996, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, language=EN, label=Figure 4, caption=Nomogram of the predictive model, figureFileSmall=mNhIEr6R8XLvLc5EQlZdZw==, figureFileBig=G7ICW5idYxZiMhnJTyO0uA==, tableContent=null), ArticleFig(id=1322608149517943029, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, language=CN, label=图4, caption=预测模型指标列线图

注:TG为甘油三酯;UA为血尿酸。

, figureFileSmall=mNhIEr6R8XLvLc5EQlZdZw==, figureFileBig=G7ICW5idYxZiMhnJTyO0uA==, tableContent=null), ArticleFig(id=1322608149631189239, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, language=EN, label=Figure 5, caption=Calibration curves and ROC curves of the differential diagnostic model in training, internal validation, and external validation cohorts, figureFileSmall=6oCG56Z0ws5tDs+/EN5HNw==, figureFileBig=vmyH5PzYZ+MZIPJv/xUz7Q==, tableContent=null), ArticleFig(id=1322608149773795576, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, language=CN, label=图5, caption=鉴别诊断模型在训练集、内部验证集与外部验证集人群校准曲线和ROC曲线图

注:图A训练集校准曲线;图B训练集ROC曲线;图C内部验证集校准曲线;图D内部验证集ROC曲线;图E外部验证集校准曲线;图F外部验证集ROC曲线。

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Baseline characteristics of subjects

, figureFileSmall=null, figureFileBig=null, tableContent=
指标分类/水平GSD (%)胆囊息肉 (%)χ2/Z值P值
性别男性3 523(47.49)10 927(64.70)663.06<0.001
女性3 896(52.51)5 961(35.30)
年龄(岁)≤401 328(17.90)4 659(27.59)626.42<0.001
41~604 521(60.94)10 461(61.94)
>601 570(21.16)1 768(10.47)
BMI(kg/m2)<18.595(1.28)302(1.79)447.83<0.001
18.5~23.92 573(34.68)7 723(45.73)
24~27.93 359(45.28)7 108(42.09)
≥281 392(18.76)1 755(10.39)
腰臀比例正常2 307(31.10)8 456(50.07)751.46<0.001
中心肥胖5 112(68.90)8 432(49.93)
血糖水平正常5 660(76.29)14 604(86.48)415.88<0.001
糖耐量受损898(12.10)1 350(7.99)
糖尿病861(11.61)934(5.53)
脂肪肝未患4 434(59.77)12 242(72.49)386.86<0.001
患有2 985(40.23)4 646(27.51)
肾结石未患7 118(95.94)16 161(95.70)0.720.396
患有301(4.06)727(4.30)
高血压未患4 953(66.76)2 739(75.43)195.23<0.001
患有2 466(33.24)4 149(24.57)
ALT(IU/L)≤42.006 339(85.44)14 673(86.88)9.020.003
≥42.011 080(14.56)2 215(13.12)
AST(IU/L)≤42.006 926(93.35)16 082(95.23)35.36<0.001
≥42.01493(6.65)806(4.77)
GGT(IU/L)≤50.005 786(77.99)13 869(82.12)56.67<0.001
≥50.011 633(22.01)3 019(17.88)
肌酐(μ mol/L)≤592 142(28.87)3 400(20.13)234.96<0.001
59.01~104.005 080(68.47)13 114(77.65)
≥104.01197(2.6)374(2.21)
甘油三酯(mmol/L)<0.48(0.11)13(0.08)109.08<0.001
0.4~1.724 426(59.66)11 250(66.62)
>1.732 985(40.23)5 625(33.31)
总胆固醇(mmol/L)<3.178(1.05)141(0.83)33.30<0.001
3.1-5.65 462(73.62)13 009(77.03)
≥5.71 879(25.33)3 738(22.13)
HDL-C(mmol/L)<0.9289(3.90)594(3.52)18.710.058
0.9~2.06 895(92.94)15 570(92.19)
>2235(3.17)724(4.29)
LDL-C(mmol/L)<2.071 191(16.05)2 849(16.87)12.280.002
2.07~3.104 416(59.52)10 253(60.71)
>3.101 812(24.42)3 786(22.42)
ALP(IU/L)≤114.006 155(82.96)14 797(87.62)93.52<0.001
≥114.011 264(17.04)2 091(12.38)
血尿素(mmol/L)≤8.307 164(96.56)16 401(97.12)5.150.023
≥8.31255(3.44)487(2.88)
血尿酸(pmol/L)≤4286 197(83.53)13 919(82.42)4.370.036
>4281 222(16.47)2 969(17.58)
球蛋白(g/L)<25148(1.99)447(2.65)28.21<0.001
25.01~38.007 175(96.71)16 320(96.64)
>38.0196(1.29)121(0.72)
白球比<1.2296(3.99)344(2.04)77.53<0.001
1.21~2.507 123(96.01)16 542(97.95)
≥2.510(0)2(0.01)
甲胎蛋白(ng/L)≤10.507 166(96.59)16 385(97.02)3.050.081
≥10.51253(3.41)503(2.98)
癌胚抗原(ng/L)≤5.207 383(99.51)16 825(99.63)1.330.248
≥5.2136(0.49)63(0.37)
), ArticleFig(id=1322608150356803836, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, language=CN, label=表1, caption=

研究对象基线特征

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指标分类/水平GSD (%)胆囊息肉 (%)χ2/Z值P值
性别男性3 523(47.49)10 927(64.70)663.06<0.001
女性3 896(52.51)5 961(35.30)
年龄(岁)≤401 328(17.90)4 659(27.59)626.42<0.001
41~604 521(60.94)10 461(61.94)
>601 570(21.16)1 768(10.47)
BMI(kg/m2)<18.595(1.28)302(1.79)447.83<0.001
18.5~23.92 573(34.68)7 723(45.73)
24~27.93 359(45.28)7 108(42.09)
≥281 392(18.76)1 755(10.39)
腰臀比例正常2 307(31.10)8 456(50.07)751.46<0.001
中心肥胖5 112(68.90)8 432(49.93)
血糖水平正常5 660(76.29)14 604(86.48)415.88<0.001
糖耐量受损898(12.10)1 350(7.99)
糖尿病861(11.61)934(5.53)
脂肪肝未患4 434(59.77)12 242(72.49)386.86<0.001
患有2 985(40.23)4 646(27.51)
肾结石未患7 118(95.94)16 161(95.70)0.720.396
患有301(4.06)727(4.30)
高血压未患4 953(66.76)2 739(75.43)195.23<0.001
患有2 466(33.24)4 149(24.57)
ALT(IU/L)≤42.006 339(85.44)14 673(86.88)9.020.003
≥42.011 080(14.56)2 215(13.12)
AST(IU/L)≤42.006 926(93.35)16 082(95.23)35.36<0.001
≥42.01493(6.65)806(4.77)
GGT(IU/L)≤50.005 786(77.99)13 869(82.12)56.67<0.001
≥50.011 633(22.01)3 019(17.88)
肌酐(μ mol/L)≤592 142(28.87)3 400(20.13)234.96<0.001
59.01~104.005 080(68.47)13 114(77.65)
≥104.01197(2.6)374(2.21)
甘油三酯(mmol/L)<0.48(0.11)13(0.08)109.08<0.001
0.4~1.724 426(59.66)11 250(66.62)
>1.732 985(40.23)5 625(33.31)
总胆固醇(mmol/L)<3.178(1.05)141(0.83)33.30<0.001
3.1-5.65 462(73.62)13 009(77.03)
≥5.71 879(25.33)3 738(22.13)
HDL-C(mmol/L)<0.9289(3.90)594(3.52)18.710.058
0.9~2.06 895(92.94)15 570(92.19)
>2235(3.17)724(4.29)
LDL-C(mmol/L)<2.071 191(16.05)2 849(16.87)12.280.002
2.07~3.104 416(59.52)10 253(60.71)
>3.101 812(24.42)3 786(22.42)
ALP(IU/L)≤114.006 155(82.96)14 797(87.62)93.52<0.001
≥114.011 264(17.04)2 091(12.38)
血尿素(mmol/L)≤8.307 164(96.56)16 401(97.12)5.150.023
≥8.31255(3.44)487(2.88)
血尿酸(pmol/L)≤4286 197(83.53)13 919(82.42)4.370.036
>4281 222(16.47)2 969(17.58)
球蛋白(g/L)<25148(1.99)447(2.65)28.21<0.001
25.01~38.007 175(96.71)16 320(96.64)
>38.0196(1.29)121(0.72)
白球比<1.2296(3.99)344(2.04)77.53<0.001
1.21~2.507 123(96.01)16 542(97.95)
≥2.510(0)2(0.01)
甲胎蛋白(ng/L)≤10.507 166(96.59)16 385(97.02)3.050.081
≥10.51253(3.41)503(2.98)
癌胚抗原(ng/L)≤5.207 383(99.51)16 825(99.63)1.330.248
≥5.2136(0.49)63(0.37)
), ArticleFig(id=1322608150457467133, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, language=EN, label=Table 2, caption=

Multivariable logistic regression analysis

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指标分类/水平胆结石(%)GP(%)OR值 (95% CI)P值
性别男性3 523(47.49)10 927(64.70)参考值
女性3 896(52.51)5 961(35.30)2.61(2.43~2.79)<0.001
年龄(岁)≤401 328(17.90)4 659(27.59)参考值
41~604 521(60.94)10 461(61.94)1.30(1.21~1.41)<0.001
>601 570(21.16)1 768(10.47)2.53(2.28~2.80)<0.001
BMI (kg/m2)<18.595(1.28)302(1.79)参考值
18.5~23.92 573(34.68)7 723(45.73)0.92(0.72~1.17)0.477
24~27.93 359(45.28)7 108(42.09)1.15(0.90~1.48)0.264
≥281 392(18.72)1 755(10.39)1.53(1.18~1.99)<0.001
腰臀比例正常2 307(31.10)8 456(50.07)参考值
中心肥胖5 112(68.90)8 432(49.93)1.23(1.15~1.32)<0.001
GGT (IU/L)≤50.005 786(77.99)13 869(82.12)参考值
>50.001 633(33.01)3 010(17.88)1.27(1.17~1.38)<0.001
甘油三酯 (mmol/L)<0.48(0.11)13(0.08)参考值
0.4-1.724 426(59.66)11 250(66.62)0.52(0.21~1.28)0.154
>1.722 985(40.23)5 625(33.31)0.58(0.23~1.44)0.238
HDL-C (mmol/L)<0.9289(3.90)594(3.52)参考值
0.9~2.06 895(92.94)15 570(92.19)0.98(0.84~1.14)0.763
> 2.0235(3.17)724(4.29)0.67(0.54~0.84)<0.001
LDL-C (mmol/L)<2.071 191(16.05)2 849(16.87)参考值
2.07~3.104 416(59.52)10 253(60.71)0.90(0.83~0.98)0.016
>3.101 812(24.42)3 786(22.42)0.84(0.76~0.92)<0.001
AST(IU/L)≤42.006 926(93.35)16 082(95.23)参考值
>42.00493(6.65)806(4.77)1.12(0.99~1.28)0.071
ALP(IU/L)≤114.006 155(82.96))14 797(87.62)参考值
>114.001 264(17.04)2 091(12.38)1.14(1.05~1.24)0.002
血尿素(pmol/L)≤428.006 197(83.53)13 919(82.42)参考值
>428.001 222(16.47)2 969(17.58)1.06(0.97~1.15)0.188
血糖水平正常5 660(76.29)14 604(86.48)参考值
糖耐量受损898(12.10)1 350(7.99)1.34(1.22~1.47)<0.001
糖尿病861(11.61)934(5.53)1.71(1.53~1.90)<0.001
脂肪肝未患4 434(59.77)12 242(72.49)参考值
患有2 985(40.23)4 646(27.51)1.47(1.36~1.58)<0.001
高血压未患4 953(66.76)2 739(75.43)参考值
患有2 466(33.24)4 149(24.57)1.06(0.99~1.13)0.121
), ArticleFig(id=1322608150767845630, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1322608133260820597, language=CN, label=表2, caption=

多因素logistic回归分析

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指标分类/水平胆结石(%)GP(%)OR值 (95% CI)P值
性别男性3 523(47.49)10 927(64.70)参考值
女性3 896(52.51)5 961(35.30)2.61(2.43~2.79)<0.001
年龄(岁)≤401 328(17.90)4 659(27.59)参考值
41~604 521(60.94)10 461(61.94)1.30(1.21~1.41)<0.001
>601 570(21.16)1 768(10.47)2.53(2.28~2.80)<0.001
BMI (kg/m2)<18.595(1.28)302(1.79)参考值
18.5~23.92 573(34.68)7 723(45.73)0.92(0.72~1.17)0.477
24~27.93 359(45.28)7 108(42.09)1.15(0.90~1.48)0.264
≥281 392(18.72)1 755(10.39)1.53(1.18~1.99)<0.001
腰臀比例正常2 307(31.10)8 456(50.07)参考值
中心肥胖5 112(68.90)8 432(49.93)1.23(1.15~1.32)<0.001
GGT (IU/L)≤50.005 786(77.99)13 869(82.12)参考值
>50.001 633(33.01)3 010(17.88)1.27(1.17~1.38)<0.001
甘油三酯 (mmol/L)<0.48(0.11)13(0.08)参考值
0.4-1.724 426(59.66)11 250(66.62)0.52(0.21~1.28)0.154
>1.722 985(40.23)5 625(33.31)0.58(0.23~1.44)0.238
HDL-C (mmol/L)<0.9289(3.90)594(3.52)参考值
0.9~2.06 895(92.94)15 570(92.19)0.98(0.84~1.14)0.763
> 2.0235(3.17)724(4.29)0.67(0.54~0.84)<0.001
LDL-C (mmol/L)<2.071 191(16.05)2 849(16.87)参考值
2.07~3.104 416(59.52)10 253(60.71)0.90(0.83~0.98)0.016
>3.101 812(24.42)3 786(22.42)0.84(0.76~0.92)<0.001
AST(IU/L)≤42.006 926(93.35)16 082(95.23)参考值
>42.00493(6.65)806(4.77)1.12(0.99~1.28)0.071
ALP(IU/L)≤114.006 155(82.96))14 797(87.62)参考值
>114.001 264(17.04)2 091(12.38)1.14(1.05~1.24)0.002
血尿素(pmol/L)≤428.006 197(83.53)13 919(82.42)参考值
>428.001 222(16.47)2 969(17.58)1.06(0.97~1.15)0.188
血糖水平正常5 660(76.29)14 604(86.48)参考值
糖耐量受损898(12.10)1 350(7.99)1.34(1.22~1.47)<0.001
糖尿病861(11.61)934(5.53)1.71(1.53~1.90)<0.001
脂肪肝未患4 434(59.77)12 242(72.49)参考值
患有2 985(40.23)4 646(27.51)1.47(1.36~1.58)<0.001
高血压未患4 953(66.76)2 739(75.43)参考值
患有2 466(33.24)4 149(24.57)1.06(0.99~1.13)0.121
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基于多维度临床数据的胆结石与胆囊息肉鉴别预测模型构建与多中心验证研究
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徐逸婷 1 , 何梦琳 1 , 王欣 1 , 李鸿钰 1 , 江果恒 1 , 谢林君 1 , 江奕 1 , 罗菁 1 , 白轩 1 , 陈梓洁 1 , 张翠华 1 , 杨红 1 , 于文倩 1 , 高敏 2 , 卢炯 2 , 王新 1
现代预防医学 | 流行病与统计方法 2025,52(21): 3900-3908
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现代预防医学 |流行病与统计方法 2025 , 52 (21) : 3900 -3908
基于多维度临床数据的胆结石与胆囊息肉鉴别预测模型构建与多中心验证研究
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徐逸婷(2002-),女,本科在读,研究方向:胆结石和胆管癌病因流行病学研究

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徐逸婷1, 何梦琳1, 王欣1, 李鸿钰1, 江果恒1, 谢林君1, 江奕1, 罗菁1, 白轩1, 陈梓洁1, 张翠华1, 杨红1, 于文倩1, 高敏2, 卢炯2, 王新1
作者信息
  • 1.四川大学华西公共卫生学院/华西第四医院流行病与卫生统计系,四川 成都 610041
  • 2.四川大学华西医院,普通外科,胆道外科
通讯作者:
王新,E-mail:
作者简介:

徐逸婷(2002-),女,本科在读,研究方向:胆结石和胆管癌病因流行病学研究

Development and multicenter validation of a predictive model for gallstone disease and gallbladder polyps based on multidimensional clinical data
Yi-ting XU1, Meng-lin HE1, Xin WANG1, Hong-yu LI1, Guo-heng JIANG1, Lin-jun XIE1, Yi JIANG1, Jing LUO1, Xuan BAI1, Zi-jie CHEN1, Cui-hua ZHANG1, Hong YANG1, Wen-qian YU1, Min GAO2, Jiong LU2, Xin WANG1
Affiliations
  • Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan 610041, China
出版时间: 2025-11-10 doi: 10.20043/j.cnki.MPM.202504174
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目的

胆结石(gallstone disease)与胆囊息肉(gallbladder polyps)的临床表现及超声影像存在重叠,但转归和管理策略差异显著,误诊易导致过度或延误干预。本研究旨在构建并验证一个基于多维度临床指标的鉴别诊断模型,以提升对胆结石与胆囊息肉的鉴别诊断能力。

方法

本研究纳入2015—2020年重庆市开州区某医院体检人群共371 518例,从中选取被诊断为胆结石或胆囊息肉的患者24 307例,并按8:2的比例划分为训练集(19 445例)与内部验证集(4 862例)。通过LASSO回归、XGBoost算法对变量进行筛选,基于logistic回归构建鉴别诊断模型。以校准曲线、受试者工作曲线(receiver operating characteristic curve,ROC)下面积、决策曲线等指标分别评估模型的标定能力、区分能力及临床适用性。同时,选取同期在重庆市两江新区某医院参加健康体检的胆结石和胆囊息肉患者共14 341例,对模型的鉴别诊断能力进行外部验证。

结果

研究发现,女性、高龄、高BMI水平、中心肥胖、高γ-谷氨酰转肽酶、谷丙转氧酶水平、高血糖水平以及脂肪肝患者更易被诊断为胆结石;而高水平高密度脂蛋白胆固醇、高水平低密度脂蛋白胆固醇人群更倾向于被诊断为胆囊息肉。基于上述因素构建的诊断模型,其准确性评价结果显示:预测标定能力良好,预测区分ROC曲线下面积达69.4%(95% CI:67.8% ~ 70.9%),决策曲线也表明该模型具有较好的临床适用性。此外,外部验证结果进一步证实了该模型的鉴别诊断能力。本项目还建立了在线网站(https://yitingxu129.shinyapps.io/logisticmodel/),旨在有效辅助鉴别胆结石与胆囊息肉。

结论

本研究表明,性别、年龄、BMI以及血生化指标等因素在胆结石和胆囊息肉的鉴别诊断中具有潜在价值。基于这些因素构建的鉴别诊断模型,具备良好的预测效果和临床适用性。未来,该模型有望辅助临床影像科医生对胆结石与胆囊息肉进行鉴别诊断,提高诊断准确性,降低疾病负担。

胆结石  /  胆囊息肉  /  鉴别诊断  /  预测模型
Objective

The clinical symptoms and ultrasound images of gallstone disease (GSD) and gallbladder polyps (GP) overlap, yet they have distinct prognoses and management strategies. Misdiagnosis can lead to excessive or delayed intervention. This study aims to develop and validate a differential diagnostic model based on multidimensional clinical indicators to enhance the differentiation between GSD and GP.

Methods

We included 371 518 individuals from health check-ups at a hospital in Kaizhou District, Chongqing, from 2015 to 2020, selecting 24 307 GSD or GP patients. These were split into a training set (19 445 cases) and an internal validation set (4 862 cases) at an 8:2 ratio. Variables were selected using Lasso regression and XGBoost algorithm, and a diagnostic model was built with Logistic regression. The model’s calibration, discrimination, and clinical applicability were evaluated using the calibration curve, the area under the ROC curve (AUC), and decision curve analysis, respectively.For external validation, 14 341 GSD and GP patients undergoing health checkups during the same period at a hospital in the Liangjiang New Area, Chongqing, were included to assess the model’s discriminative performance.

Results

Women, older individuals, those with high BMI, central obesity, elevated GGT and ALT levels, high blood glucose levels, and fatty liver were more likely to be diagnosed with GSD. Individuals with high HDL-C and LDL-C levels were more likely to have GP. The model demonstrated good calibration and discrimination, with an ROC AUC of 69.4% (95% CI: 67.8%-70.9%). Decision curve analysis further indicated that the model had good clinical applicability, and external validation further confirmed its discriminatory performance. An online tool (https://yitingxu129.shinyapps.io/logisticmodel/) was created to aid in differentiating GSD and GP.

Conclusion

This study shows that gender, age, BMI, and blood biochemistry indicators are valuable in differentiating GSD and GP.The developed model has good predictive performance and clinical applicability and is expected to assist radiologists in making accurate differential diagnoses of GSD and GP, reduce misdiagnoses, and lower the disease burden.

Gallstone disease  /  Gallbladder polyps  /  Differential diagnosis  /  Predictive model
徐逸婷, 何梦琳, 王欣, 李鸿钰, 江果恒, 谢林君, 江奕, 罗菁, 白轩, 陈梓洁, 张翠华, 杨红, 于文倩, 高敏, 卢炯, 王新. 基于多维度临床数据的胆结石与胆囊息肉鉴别预测模型构建与多中心验证研究. 现代预防医学, 2025 , 52 (21) : 3900 -3908 . DOI: 10.20043/j.cnki.MPM.202504174
Yi-ting XU, Meng-lin HE, Xin WANG, Hong-yu LI, Guo-heng JIANG, Lin-jun XIE, Yi JIANG, Jing LUO, Xuan BAI, Zi-jie CHEN, Cui-hua ZHANG, Hong YANG, Wen-qian YU, Min GAO, Jiong LU, Xin WANG. Development and multicenter validation of a predictive model for gallstone disease and gallbladder polyps based on multidimensional clinical data[J]. Modern Preventive Medicine, 2025 , 52 (21) : 3900 -3908 . DOI: 10.20043/j.cnki.MPM.202504174
胆结石(gallstone disease)和胆囊息肉(gallbladder polyps)是常见的胆道系统疾病,临床表现具有一定重叠性,但两者的疾病转归和临床管理策略存在显著差异[1-2]。胆结石患者常面临胆囊炎、胆管梗阻等并发症风险,而胆囊息肉患者需警惕恶变倾向,尤其是直径≥10mm的病变[3]。目前,腹部超声检查是进行胆结石和胆囊息肉诊断的主要手段[4-6]。然而,超声检查对部分非典型病例的鉴别仍存在困难,误诊可能导致过度治疗或延误干预[7-9]。这种诊断困境不仅增加了患者的医疗负担,还可能影响患者的预后和生活质量。因此,开发一种可靠的鉴别诊断工具,以补充传统影像学检查的不足,具有重要的临床价值和应用意义。尽管近年来对胆结石和胆囊息肉的研究逐渐增多,但针对两者差异因素分析及鉴别诊断预测模型的研究仍相对较少。本研究旨在利用全国多中心健康体检人群队列,识别胆结石和胆囊息肉的差异临床因素,并建立鉴别诊断模型,以实现两者的精准鉴别诊断。通过整合多维度临床指标,本研究构建的预测模型不仅有望提高诊断准确性,还可为临床医生提供一种高效、便捷的辅助诊断工具,尤其适用于超声诊断不典型的疑难病例。这一创新性研究将为优化胆道疾病的临床管理提供新的思路和方法,推动胆道疾病精准诊疗的发展。
本研究纳入了2015年1月—2020年12月重庆市开州区某医院参加健康体检的研究对象,排除既往未患胆结石或胆囊息肉的健康人群、胆囊切除术的患者、同时患有胆结石和胆囊息肉的患者以及未进行超声检查和无明确诊断的研究对象,最终纳入24 307名研究对象。研究人群按照80%和20%比例划分为建模数据集和验证数据集。选择同期在重庆市两江新区某医院参加健康体检,并被诊断为胆结石或胆囊息肉的患者14 341人作为鉴别诊断模型的外部验证队列,见图1。所有参与者均在研究开始前签署了知情同意书,且本研究已获得四川大学华西公共卫生学院(四川大学华西第四医院)伦理委员会的批准(批准号:Gwll2021055)。
胆结石和胆囊息肉的诊断由当地医院具有至少三年工作经验的专业超声医师通过腹部超声影像进行判断。对于诊断不明确的病例,由至少二名专家进行综合判断。具体诊断标准为:胆囊腔内出现高回声区域,且该区域随体位变化而移动、伴有声影且无血流信号的,诊断为胆结石;而回声区域不随体位变化、无声影且有血流信号的,诊断为胆囊息肉[5]。
根据以往文献报道的与胆结石或胆囊息肉关联的指标,结合本项目可获取的数据资源,拟纳入以下变量用于构建鉴别诊断模型:年龄[8]、性别[10]、身体质量指数(body mass index, BMI)[11]、腰臀比例[11]、血糖水平[12]、高血压[13]、脂肪肝[14]、肾结石[14]、高密度脂蛋白胆固醇(high-density lipoprotein cholesterol, HDL-C)[15-16]、低密度脂蛋白胆固醇(low-density lipoprotein cholesterol, LDL-C)[15-16]、总胆固醇[16]、甘油三酯[16]、谷丙转氨酶(alanine aminotransferase, ALT)[17]、谷草转氨酶(aspartate aminotransferase, AST)[17]、γ-谷氨酰转肽酶(gamma-glutamyl transpeptidase, GGT)[17]、肌酐[18]、碱性磷酸酶(alkaline phosphatase, ALP)[17]、白球比[17]、球蛋白[17]、血尿素[19]、血尿酸[19]、甲胎蛋白[20]及癌胚抗原[21]。
分类变量采用样本量和其所占百分比进行描述,组间差异分析采用χ2检验或者秩和检验进行分析。应用LASSO回归正则化和XGBoost梯度提升决策树模型进行胆结石与胆囊息肉差异因素重要性评估。综合LASSO回归和XGBoost的结果,筛选出表现良好的变量纳入预测模型。采用多因素logistic回归分析建立胆结石与胆囊息肉的鉴别诊断模型,用列线图展示预测模型,通过可视化方式展示各变量对预测模型的贡献程度,为临床应用提供直观的参考。实现超声检查的辅助诊断。预测模型的预测效果分别采用标定能力、区分能力和预测准确性进行评价。标定能力用于评价鉴别诊断模型预测的胆囊息肉发病人数和实际发病人数的比例,具体结果采用标准曲线进行展示,较好标定能力其标准曲线应与45度对角线接近或重叠。预测模型区分能力采用受试者工作曲线(receiver operating characteristic curve,ROC)下面积 (under the receiver operating curve, AUC)进行评价,其波动于0.5~1.0之间,分值越高代表模型的区分能力越强。使用SPSS Statistics 27.0(IBM Corp., Armonk, New York, United States)和R语言4.4.0版本(R Core Team,2024)进行统计分析,双侧检验水准α=0.05。数据清理与整理主要依赖 dplyr、stringr、reshape2 和 magrittr 等 R 包。特征选择方面,采用 glmnet 包实现 LASSO 回归;XGBoost 建模则基于 xgboost 包,并结合 caret 包进行交叉验证与参数调优。SHAPforxgboost 包用于解释XGBoost 模型的特征重要性。Logistic回归模型的构建通过 rms 和 Hmisc 包完成。模型评估方面,使用pROC 包绘制ROC并计算 AUC 值;ggbeeswarm 用于展示 SHAP 值的分布;rms 与 ggplot2 包联合用于绘制列线图、校准曲线及决策曲线图。采用shiny、rsconnect、rms、Hmisc、dyplyr及tidyr搭建在线模型。
表1描述了胆结石和胆囊息肉两组间的基线特征情况。结果显示,与胆囊息肉相比,胆结石组女性、糖尿病、脂肪肝、高血压患病比例较高,年龄、BMI、腰臀比例、ALT、AST、GGT、甘油三酯、总胆固醇、LDL-C、ALP、血尿素、球蛋白较高,而HDL-C、血尿酸、白球比水平较低。但是胆结石与胆囊息肉相比,肾结石、甲胎蛋白、癌胚抗原等指标差异没有统计学意义。
选择与胆结石与胆囊息肉差异存在统计学意义的因素,采用LASSO回归正则化、XGBoost梯度提升决策树模型进行特征变量筛选。由图2所示,LASSO回归正则化系数和XGBoost特征重要性SHAP值为衡量对模型贡献度的指标其中,LASSO回归提示男性、高龄、高BMI水平、中心肥胖、高HDL-C、甘油三酯、AST、GGT、白球比、高血糖水平等指标对模型贡献度高;XGBoost显示除上述指标外,年龄41~60岁、ALP≥114.01 IU/L、LDL-C>3.1 mmol/L、LDL-C为2.07~3.1 mmol/L、高血压、脂肪肝、血尿酸≥428.01 pmol/L等指标也呈现了较好的贡献,但白球比为 1.21~2.50效果不佳,见图3。综合LASSO回归与XGBoost结果,最终选用性别、年龄、BMI、腰臀比例、血糖水平、脂肪肝、高血压、GGT、甘油三酯、AST、ALP、HDL-C、LDL-C、血尿酸,纳入多因素logistic回归模型构建预测模型,对胆结石和胆囊息肉进行鉴别诊断。
多因素logistic回归模型结果显示,女性(OR=2.61,95% CI: 2.43~2.79)、年龄41~60岁(OR=1.30, 95% CI:1.21~1.41)、>60岁(OR=2.53, 95% CI:2.28~2.80)、BMI≥28 kg/m2 (OR=1.53, 95% CI: 1.18~1.99)、中心肥胖(OR=1.23, 95% CI: 1.15~1.32)、GGT≥50.01 IU/L(OR=1.27,95% CI: 1.17~1.38)、ALP≥114.01 IU/L(OR=1.14,95% CI: 1.05~1.24)、糖耐量受损(OR=1.34, 95% CI:1.22~1.47)、糖尿病(OR=1.71, 95% CI: 1.53~1.90)、脂肪肝(OR=1.47, 95% CI: 1.36~1.58)人群更倾向于被诊断为胆结石,而HDL-C>2 mmol/L(OR= 0.67, 95% CI:0.54~0.84)、LDL-C≥2.07 mmol/L(OR= 0.84, 95% CI:0.76~0.92)的患者更倾向于被诊断为胆囊息肉。基于上述因素,构建了胆结石和胆囊息肉鉴别诊断模型,并同步建立了在线应用模型(https://yitingxu129.shinyapps.io/logisticmodel/)。见表2、图4。
在标定能力评价方面,研究结果显示,无论在训练集、内部验证集和外部验证集数据中,校准曲线均与45度对角线呈现几乎重合状态,即该模型预测准确性较高,见图5A、C、E。在区分能力评价方面,训练集人群中预测模型的AUC值为68.3%(95% CI: 67.5%~69.1%),见图5B,内部验证集的AUC值为69.4%(95% CI: 67.8%~70.9%),见图5D,提示模型拥有较好的区分度。金山医院外部验证集AUC为69.0%(95% CI: 68.1%~70.0%),见图5F,验证了模型具有较好的人群区分能力。DCA结果显示,使用本模型进行治疗决策的净收益大于不治疗或全面治疗的净收益,显示本模型具有较好的临床应用价值,见图6。
本研究通过分析大规模健康体检人群数据,构建并验证了一个基于多维度临床指标的预测模型,用于鉴别胆结石和胆囊息肉。研究结果表明,性别、BMI、腰臀比例、高血脂水平、高血糖水平、脂肪肝等在胆结石和胆囊息肉人群中差异存在统计学意义。这些指标的变化可能反映了胆结石与胆囊息肉在代谢异常及肝功能损伤方面的潜在病理生理机制。基于这些指标构建的预测模型在内部和外部验证中均表现出较好的区分度和校准能力,为临床医生提供了一种便捷的辅助诊断工具。
研究结果显示,女性患胆结石的风险高于男性,这一趋势与既往研究一致[22],这可能与雌激素水平调控胆固醇代谢及胆汁成分变化有关。胆结石组的BMI和腰臀比例高于胆囊息肉组和对照组,表明肥胖及内脏脂肪堆积在胆结石发生中的促进作用强于胆囊息肉[23]。在脂质代谢方面,胆结石患者的TG和HDL-C水平较对照组有所升高,符合脂质代谢紊乱在胆结石形成中的关键作用[16]。甘油三酯、HDL-C、LDL-C在模型中呈现了良好的贡献度,提示脂质代谢异常并非胆结石所特有,但其程度与模式可作为两者的鉴别依据。此外,胆结石患者中高血糖水平、脂肪肝的比例升高,提示胆结石可能与胰岛素抵抗及代谢综合征的共病相关,而胆囊息肉组血糖水平变化相对较小,提示其与糖代谢异常的关联性较弱,仍需进一步探讨其临床意义[24]。肝功能指标方面,胆结石患者GGT水平升高,提示胆道梗阻可能引发继发性肝损伤,而胆囊息肉患者的肝酶水平未见有统计学意义的异常,进一步支持两者在胆道病变机制上的差异[10]。
在临床实践中,胆囊息肉与胆结石主要依靠超声检查鉴别。胆囊息肉通常表现为固定的强回声、无声影,部分病例可检测到血流信号,而胆结石则随体位变化并伴有声影、无血流信号[5]。然而,部分病例超声表现不典型,如胆囊壁胆结石或小胆结石移动幅度极小,部分胆囊息肉合并胆囊炎也可表现胆囊壁增厚[9],增加了诊断难度。此外,部分胆结石的声影仅在特定角度可见,易被误诊为胆囊息肉[7],部分胆囊息肉患者无血流信号亦可能被误诊为胆结石[25]。超声诊断依赖医生经验,当前对超声特征量化界定的研究仍较有限,易导致误诊。
胆结石与胆囊息肉在风险及治疗策略上差异显著。胆囊息肉包括胆固醇息肉、腺瘤性息肉等,直径≥10 mm者需警惕恶变[2];胆结石可能引发胆囊炎或胆总管梗阻[5]。胆囊息肉治疗关注恶变风险,直径<10 mm者建议随访,≥10 mm或有恶变倾向者建议手术[4];胆结石治疗依据症状决定,伴症状或并发症者建议手术,无症状者可观察或使用熊去氧胆酸治疗[1]。误诊可能导致不当干预,如胆囊息肉误诊为胆结石可能延误恶变监测,而胆结石误诊为胆囊息肉可能错失炎症或梗阻管理,影响预后[4]。
目前,针对胆结石与胆囊息肉间的鉴别诊断尚无已开发的预测模型。本研究结合多维度临床指标,揭示了两者在代谢异常、炎症状态及肝功能方面的关键差异,创新性地通过整合性别、BMI、腰臀比例、血脂、血糖、肝功能指标等具有统计学意义的变量,构建了一套具有较高鉴别效能的临床预测模型,并引入外部队列进行验证。该模型在一定程度上弥补了超声诊断依赖经验、部分病例影像特征不典型导致误诊的不足,提供了一种基于客观数据的辅助诊断工具,有助于提高胆囊息肉与胆结石的鉴别准确性,对超声难以诊断的患者作辅助鉴别诊断,同时为疾病的病理机制研究、精准预防提供新的思路。
然而,本研究仍存在一定的局限性。首先,本研究为横断面研究,无法阐明各个指标与胆结石和胆囊息肉的因果关联,部分指标差异的临床意义与机制尚不完全明确,对胆结石与胆囊息肉发病机制差异及潜在共病关系的深入探讨仍需进一步的研究证据支持;其次,本研究仅基于特定地区的中心队列数据,难以全面反映不同人群的特征,未来需开展跨地域跨人群的多中心前瞻性研究,以验证模型的普适性。最后,本研究构建的模型纳入的指标集中在体格检查、代谢、炎症及肝功能方面,未涵盖家族史、炎症因子、氧化应激标志物等生物标志物,未纳入膳食与健康行为等数据,缺乏带回声、声影及血流信号标签的胆结石与胆囊息肉超声影像学数据,可能在一定程度上影响模型的预测效果。
本研究发现性别、BMI、腰臀比例、血脂、血糖和肝功能等指标在胆结石和胆囊息肉鉴别诊断中发挥潜在应用价值,并集合以上因素构建并验证了一个基于多维度临床指标的胆结石与胆囊息肉鉴别诊断模型。高模型可为临床医生提供辅助诊断支持,提高鉴别准确性,降低误诊风险。未来需进一步优化模型性能,结合更多实时数据以实现个体化风险预测。尽管研究存在局限性,如横断面设计和地域限制,但其为胆道疾病精准诊疗提供了新思路。

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2025年第52卷第21期
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doi: 10.20043/j.cnki.MPM.202504174
  • 接收时间:2025-04-13
  • 首发时间:2026-10-29
  • 出版时间:2025-11-10
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  • 收稿日期:2025-04-13
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    1.四川大学华西公共卫生学院/华西第四医院流行病与卫生统计系,四川 成都 610041
    2.四川大学华西医院,普通外科,胆道外科

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2种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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