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Article(id=1324078056234681310, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077868950631142, articleNumber=null, orderNo=null, doi=10.20043/j.cnki.MPM.202505243, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1747584000000, receivedDateStr=2025-05-19, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1793614408787, onlineDateStr=2026-11-02, pubDate=1771948800000, pubDateStr=2026-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793614408787, onlineIssueDateStr=2026-11-02, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793614408787, creator=13701087609, updateTime=1793614408787, updator=13701087609, issue=Issue{id=1324077868950631142, tenantId=1146029695717560320, journalId=1227665162245664772, year='2026', volume='53', issue='4', pageStart='577', pageEnd='768', issueExtLink='null', onlineDate='null', pubDate='1771948800000', pubDateStr='2026-02-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1793614364134, creator='13701087609', updateTime=1793614446224, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1324078213328142371, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077868950631142, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1324078213332336676, tenantId=1146029695717560320, journalId=1227665162245664772, issueId=1324077868950631142, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=760, endPage=768, ext={EN=ArticleExt(id=1324078056461173727, articleId=1324078056234681310, tenantId=1146029695717560320, journalId=1227665162245664772, language=EN, title=Based on Lasso-Logistic regression for construction of a risk prediction model for persistence and progression of low-grade cervical intraepithelial neoplasia, columnId=1228016569138213037, journalTitle=Modern Preventive Medicine, columnName=Clinical Medicine and Prevention, runingTitle=null, highlight=null, articleAbstract=
Objective

Toexplore the factors influencing the persistence and progression of low-grade cervical intraepithelial neoplasia (CIN1), and to construct and validate a Nomogram prediction model therefor.

Methods

A total of 564 patients with histopathologically confirmed CIN1 were enrolled from a community-based cohort of married women in Jiexiu City, Shanxi Province, between June and December 2022. All patients underwent pathological re-examination at 12 months, and their CIN1 lesions showed three outcomes: regression, persistence or progression. The study population was divided into a training set and a validation set at a ratio of 7:3. Lasso regression was used to screen predictor variables associated with the risk of CIN1 persistence and progression, and a Nomogram model was constructed using multivariate Logistic regression analysis.The model was evaluated using the concordance index(C-index), Hosmer-Lemeshow (H-L) test, calibration curves, and receiver operating characteristic (ROC) curves.

Results

After applying the inclusion and exclusion criteria, a total of 479 participants were enrolled in this study, with 335 assigned to the training set and 144 to the validation set. Lasso-Logistic regression identified serum folate level, HR-HPV infection, genital cleaning frequency, underwear change frequency, and gynecological history as variables associated with CIN1 persistence and progression. The internally validated C-index of the constructed Nomogram was 0.767, indicating good predictive performance.The H-L test showed a good fit of the model (χ2=1.447, P=0.485). The calibration curves of both the training and validation sets were close to the ideal diagonal, indicating good calibration. The areas under the ROC curves for the training and validation sets were 0.784 (95% CI:0.734-0.835) and 0.815 (95% CI:0.736-0.895), respectively, demonstrating good discriminative ability.

Conclusion

Serum folate level, HR-HPV infection, genital cleaning frequency, underwear change frequency, and gynecological history are significant predictive variables for persistence and progression of CIN1.The constructed Nomogram can intuitively predict the incidence of CIN1 persistent or progression in patients, providing a theoretical reference for the prevention, treatment, and individualized management of early cervical lesions.

, authors=Nan TIAN1, De-cheng LI1, Yu-shu PENG1, Xin-lin FENG1, Rui ZHANG1, Zhi-yuan DAI1, Jin-tao WANG1, Wei-hong ZHAO2, Zhi-qiang TIAN3, Ling DING1, authorsList=Nan TIAN, De-cheng LI, Yu-shu PENG, Xin-lin FENG, Rui ZHANG, Zhi-yuan DAI, Jin-tao WANG, Wei-hong ZHAO, Zhi-qiang TIAN, Ling DING, authorCompany=null, correspAuthors=Ling DING, 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=1324078057857876972, articleId=1324078056234681310, tenantId=1146029695717560320, journalId=1227665162245664772, language=CN, title=基于Lasso-Logistic回归构建低级别宫颈上皮内瘤变持续和进展的风险预测模型, columnId=1228016570119680182, journalTitle=现代预防医学, columnName=临床与预防, runingTitle=null, highlight=null, articleAbstract=
目的

探讨低级别宫颈上皮内瘤变持续和进展的影响因素，构建并验证其Nomogram预测模型。

方法

以2022年6月—12月山西省介休市建立的已婚妇女社区队列中经组织病理学确诊的564例低级别宫颈上皮内瘤变患者（CIN1）为研究对象。随访12个月后再行病理学检查，出现CIN1消退、持续和进展，7∶3分为训练集和验证集，采用Lasso回归筛选与CIN1持续和进展风险有关的预测变量，使用多因素logistic回归构建Nomogram模型，利用C指数、H‐L检验、校准曲线、受试者工作特征（ROC）曲线对模型进行评价。

结果

本研究按纳排标准最终纳入479例研究对象，训练集335例，验证集144例。Lasso-Logistic回归筛选出血清叶酸、HR-HPV感染、清洗阴部频率、换洗内裤频率及妇科病史与CIN1持续和进展相关。在此基础上构建Nomogram模型，内部验证一致性指数为0.767，提示模型有较好预测效能。H-L检验结果显示模型的拟合程度较好（χ2=1.447, P=0.485）。训练集与验证集校准曲线均接近理想对角线，模型校准度较好；ROC曲线下面积分别为0.784（95% CI:0.734~0.835）、0.815（95% CI:0.736~0.895），显示区分度良好。

结论

血清叶酸、HR-HPV感染、清洗阴部频率、换洗内裤频率及妇科病史是CIN1持续和进展的重要预测变量，据此建立的Nomogram模型可直观预测CIN1患者持续进展的发生率，为早期宫颈病变的防治和个体化治疗提供一定理论参考。

, authors=田楠1, 李德铖1, 彭煜舒1, 冯新琳1, 张瑞1, 戴志远1, 王金桃1, 赵卫红2, 田志强3, 丁玲1, authorsList=田楠, 李德铖, 彭煜舒, 冯新琳, 张瑞, 戴志远, 王金桃, 赵卫红, 田志强, 丁玲, authorCompany=null, correspAuthors=丁玲, authorNote=

田楠（2000—），女，硕士在读，研究方向：肿瘤流行病学

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丁玲，E-mail:
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注：A：系数路径图；B：十倍交叉验证图。

, figureFileSmall=INbdpS35knUSa6zIzZ4sOw==, figureFileBig=W/EZ3dKzUtH6zL0nlZPZ0Q==, tableContent=null), ArticleFig(id=1324078065151770671, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=EN, label=Fig.2, caption=Nomogram model visualization of CIN1 persistence and progression, figureFileSmall=ttpW1eCdy+aTzAE5CKzG+Q==, figureFileBig=dKDcc4s2CfL/lNf5EQswuQ==, tableContent=null), ArticleFig(id=1324078065218879536, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=CN, label=图2, caption=CIN1持续和进展Nomogram模型可视化, figureFileSmall=ttpW1eCdy+aTzAE5CKzG+Q==, figureFileBig=dKDcc4s2CfL/lNf5EQswuQ==, tableContent=null), ArticleFig(id=1324078065294377009, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=EN, label=Fig.3, caption=Calibration curves for the Nomogram model, figureFileSmall=lypQu1u+KnOOTXsKycdIKA==, figureFileBig=WAG4wvjzjFfVoE+IxjUxvw==, tableContent=null), ArticleFig(id=1324078065365680178, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=CN, label=图3, caption=Nomogram模型校准曲线

注：A：训练集；B：验证集。

, figureFileSmall=lypQu1u+KnOOTXsKycdIKA==, figureFileBig=WAG4wvjzjFfVoE+IxjUxvw==, tableContent=null), ArticleFig(id=1324078065428594739, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=EN, label=Fig.4, caption=ROC curve for Nomogram model, figureFileSmall=LQCo+XjVEg/JXxTiP1UD/g==, figureFileBig=891xKmOeQwf8wdYNz3OvfQ==, tableContent=null), ArticleFig(id=1324078065508286516, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=CN, label=图4, caption=Nomogram模型ROC曲线

注：A：训练集；B：验证集。

, figureFileSmall=LQCo+XjVEg/JXxTiP1UD/g==, figureFileBig=891xKmOeQwf8wdYNz3OvfQ==, tableContent=null), ArticleFig(id=1324078065583783989, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=EN, label=Table 1, caption=

Comparison of demographic characteristics between patients in the excluded group and the complete follow-up group (n, %)

, figureFileSmall=null, figureFileBig=null, tableContent=
因素排除组（n, %）完成随访组（n, %）χ2P
年龄(岁)1.5860.208
<5037(43.53)244(50.94)
≥5048(56.47)235(49.06)
婚姻状况0.3780.538
已婚80(94.12)461(96.24)
其他5(5.88)18(3.76)
文化程度3.6830.159
小学及以下24(28.23)92(19.20)
初中到高中49(57.65)317(66.18)
大学及以上12(14.12)70(14.62)
职业2.1150.146
非农民34(40.00)153(31.94)
农民51(60.00)326(68.06)
人均年收入(万元)0.3170.573
≤250(58.82)266(55.53)
>235(41.18)213(44.47)
), ArticleFig(id=1324078065667670070, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=CN, label=表1, caption=

排除组与完成随访组患者人口学特征比较（n, %）

, figureFileSmall=null, figureFileBig=null, tableContent=
因素排除组（n, %）完成随访组（n, %）χ2P
年龄(岁)1.5860.208
<5037(43.53)244(50.94)
≥5048(56.47)235(49.06)
婚姻状况0.3780.538
已婚80(94.12)461(96.24)
其他5(5.88)18(3.76)
文化程度3.6830.159
小学及以下24(28.23)92(19.20)
初中到高中49(57.65)317(66.18)
大学及以上12(14.12)70(14.62)
职业2.1150.146
非农民34(40.00)153(31.94)
农民51(60.00)326(68.06)
人均年收入(万元)0.3170.573
≤250(58.82)266(55.53)
>235(41.18)213(44.47)
), ArticleFig(id=1324078065747361847, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=EN, label=Table 2, caption=

Comparison of baseline characteristics of study subjects in training and validation sets(n, %)

, figureFileSmall=null, figureFileBig=null, tableContent=
因素训练集（n, %）验证集（n, %）χ2P
年龄(岁)0.1350.713
<50155(46.27)64(44.44)
≥50180(53.73)80(55.56)
婚姻状况0.1290.720
已婚319(95.23)136(94.44)
其他16(4.78)8(5.56)
文化程度2.1260.346
小学及以下64(19.10)32(22.22)
初中到高中223(66.57)86(59.72)
大学及以上48(14.33)26(18.06)
职业0.0780.780
非农民230(68.66)97(67.36)
农民105(31.34)47(32.64)
吸烟1.1020.294
是9(2.69)1(0.69)
否326(97.31)143(99.31)
人均年收入(万元)0.1560.693
≤2188(56.12)78(54.17)
>2147(43.88)66(45.83)
清洗阴部频率(次/周)0.7270.394
≥3268(80.00)120(83.33)
<367(20.00)24(16.67)
换洗内裤频率(次/周)0.1230.726
≥3157(46.87)70(48.61)
<3178(53.13)74(51.39)
性生活后清洗0.0410.839
是253(75.52)110(76.39)
否82(24.48)34(23.61)
首次性生活年龄(岁)1.7160.190
≤2076(22.69)25(17.36)
>20259(77.31)119(82.64)
产次(次)0.0510.821
>392(27.46)41(28.47)
≤3243(72.54)103(71.53)
绝经1.6520.199
是167(49.85)81(56.25)
否168(50.15)63(43.75)
妇科病史0.0210.883
有142(42.39)60(41.67)
无193(57.61)84(58.33)
肿瘤家族史0.4300.512
有47(14.03)17(11.81)
无288(85.97)127(88.19)
HR-HPV感染0.1190.731
是104(31.04)47(32.64)
否231(68.96)97(67.36)
血清叶酸(nmol/L)0.7600.859
≤13.0568(20.30)29(20.14)
13.06~16.7885(25.37)36(25.00)
16.79~21.79114(34.03)45(31.25)
>21.7968(20.30)34(23.61)
), ArticleFig(id=1324078065843830840, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=CN, label=表2, caption=

训练集与验证集研究对象基线特征比较（n, %）

, figureFileSmall=null, figureFileBig=null, tableContent=
因素训练集（n, %）验证集（n, %）χ2P
年龄(岁)0.1350.713
<50155(46.27)64(44.44)
≥50180(53.73)80(55.56)
婚姻状况0.1290.720
已婚319(95.23)136(94.44)
其他16(4.78)8(5.56)
文化程度2.1260.346
小学及以下64(19.10)32(22.22)
初中到高中223(66.57)86(59.72)
大学及以上48(14.33)26(18.06)
职业0.0780.780
非农民230(68.66)97(67.36)
农民105(31.34)47(32.64)
吸烟1.1020.294
是9(2.69)1(0.69)
否326(97.31)143(99.31)
人均年收入(万元)0.1560.693
≤2188(56.12)78(54.17)
>2147(43.88)66(45.83)
清洗阴部频率(次/周)0.7270.394
≥3268(80.00)120(83.33)
<367(20.00)24(16.67)
换洗内裤频率(次/周)0.1230.726
≥3157(46.87)70(48.61)
<3178(53.13)74(51.39)
性生活后清洗0.0410.839
是253(75.52)110(76.39)
否82(24.48)34(23.61)
首次性生活年龄(岁)1.7160.190
≤2076(22.69)25(17.36)
>20259(77.31)119(82.64)
产次(次)0.0510.821
>392(27.46)41(28.47)
≤3243(72.54)103(71.53)
绝经1.6520.199
是167(49.85)81(56.25)
否168(50.15)63(43.75)
妇科病史0.0210.883
有142(42.39)60(41.67)
无193(57.61)84(58.33)
肿瘤家族史0.4300.512
有47(14.03)17(11.81)
无288(85.97)127(88.19)
HR-HPV感染0.1190.731
是104(31.04)47(32.64)
否231(68.96)97(67.36)
血清叶酸(nmol/L)0.7600.859
≤13.0568(20.30)29(20.14)
13.06~16.7885(25.37)36(25.00)
16.79~21.79114(34.03)45(31.25)
>21.7968(20.30)34(23.61)
), ArticleFig(id=1324078065936105529, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=EN, label=Table 3, caption=

General demographic characteristics of the training group and analysis of associated factors(n, %)

, figureFileSmall=null, figureFileBig=null, tableContent=
因素消退组（n, %）持续和进展组（n, %）χ2P
年龄(岁)5.4760.019
<5097(41.99)58(55.77)
≥50134(58.01)46(44.23)
婚姻状况0.7200.396
已婚222(96.10)97(93.27)
其他9(3.90)7(6.73)
文化程度1.6220.445
小学及以下40(17.31)24(23.08)
初中到高中158(68.40)65(62.50)
大学及以上33(14.29)15(14.42)
职业0.0230.879
非农民158(68.40)72(69.23)
农民73(31.60)32(30.77)
吸烟0.0001.000
是6(2.60)3(2.88)
否225(97.40)101(97.12)
人均年收入(万元)0.0230.880
≤2129(55.84)59(56.73)
>2102(44.16)45(43.27)
清洗阴部频率(次/周)5.8600.015
≥3193(83.55)75(72.12)
<338(16.45)29(27.88)
换洗内裤频率(次/周)6.4600.011
≥3119(51.52)38(36.54)
<3112(48.48)66(63.46)
性生活后清洗3.1450.076
是168(72.73)85(81.73)
否63(27.27)19(18.27)
首次性生活年龄(岁)0.2020.653
≤2054(23.38)22(21.15)
>20177(76.62)82(78.85)
产次(次)1.4560.228
>368(29.44)24(23.08)
≤3163(70.56)80(76.92)
绝经1.3090.253
是120(51.95)47(45.19)
否111(48.05)57(54.81)
妇科病史4.5400.033
有89(38.53)53(50.96)
无142(61.47)51(49.04)
肿瘤家族史2.4370.119
有37(16.02)10(9.62)
无194(83.98)94(90.38)
HR-HPV感染7.4770.006
是61(26.41)43(41.35)
否170(73.59)61(58.65)
血清叶酸(nmol/L)52.162<0.001
≤13.0526(11.26)42(40.38)
13.06~16.7852(22.51)33(31.73)
16.79~21.7994(40.69)20(19.23)
>21.7959(25.54)9(8.65)
), ArticleFig(id=1324078066019991610, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=CN, label=表3, caption=

训练集一般人口学特征以及相关因素分析（n, %）

, figureFileSmall=null, figureFileBig=null, tableContent=
因素消退组（n, %）持续和进展组（n, %）χ2P
年龄(岁)5.4760.019
<5097(41.99)58(55.77)
≥50134(58.01)46(44.23)
婚姻状况0.7200.396
已婚222(96.10)97(93.27)
其他9(3.90)7(6.73)
文化程度1.6220.445
小学及以下40(17.31)24(23.08)
初中到高中158(68.40)65(62.50)
大学及以上33(14.29)15(14.42)
职业0.0230.879
非农民158(68.40)72(69.23)
农民73(31.60)32(30.77)
吸烟0.0001.000
是6(2.60)3(2.88)
否225(97.40)101(97.12)
人均年收入(万元)0.0230.880
≤2129(55.84)59(56.73)
>2102(44.16)45(43.27)
清洗阴部频率(次/周)5.8600.015
≥3193(83.55)75(72.12)
<338(16.45)29(27.88)
换洗内裤频率(次/周)6.4600.011
≥3119(51.52)38(36.54)
<3112(48.48)66(63.46)
性生活后清洗3.1450.076
是168(72.73)85(81.73)
否63(27.27)19(18.27)
首次性生活年龄(岁)0.2020.653
≤2054(23.38)22(21.15)
>20177(76.62)82(78.85)
产次(次)1.4560.228
>368(29.44)24(23.08)
≤3163(70.56)80(76.92)
绝经1.3090.253
是120(51.95)47(45.19)
否111(48.05)57(54.81)
妇科病史4.5400.033
有89(38.53)53(50.96)
无142(61.47)51(49.04)
肿瘤家族史2.4370.119
有37(16.02)10(9.62)
无194(83.98)94(90.38)
HR-HPV感染7.4770.006
是61(26.41)43(41.35)
否170(73.59)61(58.65)
血清叶酸(nmol/L)52.162<0.001
≤13.0526(11.26)42(40.38)
13.06~16.7852(22.51)33(31.73)
16.79~21.7994(40.69)20(19.23)
>21.7959(25.54)9(8.65)
), ArticleFig(id=1324078066095489083, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=EN, label=Table 4, caption=

Multifactor logistic regression model results

, figureFileSmall=null, figureFileBig=null, tableContent=
因素β Wald值OR(95%CI)P
年龄(岁)
<501
≥50-0.5490.2813.8010.578(0.333~1.003)0.051
清洗阴部频率(次/周)
<31
≥3-0.9490.3547.1920.387(0.194~0.775)0.007
换洗内裤频率(次/周)
<31
≥3-0.8750.3038.3240.417(0.230~0.755)0.004
HR-HPV感染
否1
是0.7170.2876.2422.049(1.167~3.597)0.012
妇科病史
否1
是0.5560.2744.1131.743(1.019~2.983)0.043
血清叶酸(nmol/L)
>21.791
16.79~21.790.4120.4550.8211.510(0.619~3.681)0.365
13.06~16.781.4020.4469.8714.064(1.695~9.746)0.002
≤13.052.4080.46127.26311.111(4.500~27.434)<0.001
), ArticleFig(id=1324078066175180860, tenantId=1146029695717560320, journalId=1227665162245664772, articleId=1324078056234681310, language=CN, label=表4, caption=

多因素logistic回归模型结果

, figureFileSmall=null, figureFileBig=null, tableContent=
因素β Wald值OR(95%CI)P
年龄(岁)
<501
≥50-0.5490.2813.8010.578(0.333~1.003)0.051
清洗阴部频率(次/周)
<31
≥3-0.9490.3547.1920.387(0.194~0.775)0.007
换洗内裤频率(次/周)
<31
≥3-0.8750.3038.3240.417(0.230~0.755)0.004
HR-HPV感染
否1
是0.7170.2876.2422.049(1.167~3.597)0.012
妇科病史
否1
是0.5560.2744.1131.743(1.019~2.983)0.043
血清叶酸(nmol/L)
>21.791
16.79~21.790.4120.4550.8211.510(0.619~3.681)0.365
13.06~16.781.4020.4469.8714.064(1.695~9.746)0.002
≤13.052.4080.46127.26311.111(4.500~27.434)<0.001
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基于Lasso-Logistic回归构建低级别宫颈上皮内瘤变持续和进展的风险预测模型
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田楠 1 , 李德铖 1 , 彭煜舒 1 , 冯新琳 1 , 张瑞 1 , 戴志远 1 , 王金桃 1 , 赵卫红 2 , 田志强 3 , 丁玲 1
现代预防医学 | 临床与预防 2026,53(4): 760-768
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现代预防医学 |临床与预防 2026 , 53 (4) : 760 -768
基于Lasso-Logistic回归构建低级别宫颈上皮内瘤变持续和进展的风险预测模型
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田楠1, 李德铖1, 彭煜舒1, 冯新琳1, 张瑞1, 戴志远1, 王金桃1, 赵卫红2, 田志强3, 丁玲1
作者信息
  • 1.山西医科大学公共卫生学院流行病学教研室，山西 太原 030001
  • 2.山西医科大学第二临床医学院妇产科
  • 3.山西白求恩医院（山西医学科学院
通讯作者:
丁玲，E-mail:
作者简介:

田楠（2000—），女，硕士在读，研究方向：肿瘤流行病学

Based on Lasso-Logistic regression for construction of a risk prediction model for persistence and progression of low-grade cervical intraepithelial neoplasia
Nan TIAN1, De-cheng LI1, Yu-shu PENG1, Xin-lin FENG1, Rui ZHANG1, Zhi-yuan DAI1, Jin-tao WANG1, Wei-hong ZHAO2, Zhi-qiang TIAN3, Ling DING1
Affiliations
  • Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, China
出版时间: 2026-02-25 doi: 10.20043/j.cnki.MPM.202505243
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摘要
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目的

探讨低级别宫颈上皮内瘤变持续和进展的影响因素，构建并验证其Nomogram预测模型。

方法

以2022年6月—12月山西省介休市建立的已婚妇女社区队列中经组织病理学确诊的564例低级别宫颈上皮内瘤变患者（CIN1）为研究对象。随访12个月后再行病理学检查，出现CIN1消退、持续和进展，7∶3分为训练集和验证集，采用Lasso回归筛选与CIN1持续和进展风险有关的预测变量，使用多因素logistic回归构建Nomogram模型，利用C指数、H‐L检验、校准曲线、受试者工作特征（ROC）曲线对模型进行评价。

结果

本研究按纳排标准最终纳入479例研究对象，训练集335例，验证集144例。Lasso-Logistic回归筛选出血清叶酸、HR-HPV感染、清洗阴部频率、换洗内裤频率及妇科病史与CIN1持续和进展相关。在此基础上构建Nomogram模型，内部验证一致性指数为0.767，提示模型有较好预测效能。H-L检验结果显示模型的拟合程度较好（χ2=1.447, P=0.485）。训练集与验证集校准曲线均接近理想对角线，模型校准度较好；ROC曲线下面积分别为0.784（95% CI:0.734~0.835）、0.815（95% CI:0.736~0.895），显示区分度良好。

结论

血清叶酸、HR-HPV感染、清洗阴部频率、换洗内裤频率及妇科病史是CIN1持续和进展的重要预测变量，据此建立的Nomogram模型可直观预测CIN1患者持续进展的发生率，为早期宫颈病变的防治和个体化治疗提供一定理论参考。

宫颈上皮内瘤变  /  Lasso回归  /  风险预测模型  /  Nomogram
Objective

Toexplore the factors influencing the persistence and progression of low-grade cervical intraepithelial neoplasia (CIN1), and to construct and validate a Nomogram prediction model therefor.

Methods

A total of 564 patients with histopathologically confirmed CIN1 were enrolled from a community-based cohort of married women in Jiexiu City, Shanxi Province, between June and December 2022. All patients underwent pathological re-examination at 12 months, and their CIN1 lesions showed three outcomes: regression, persistence or progression. The study population was divided into a training set and a validation set at a ratio of 7:3. Lasso regression was used to screen predictor variables associated with the risk of CIN1 persistence and progression, and a Nomogram model was constructed using multivariate Logistic regression analysis.The model was evaluated using the concordance index(C-index), Hosmer-Lemeshow (H-L) test, calibration curves, and receiver operating characteristic (ROC) curves.

Results

After applying the inclusion and exclusion criteria, a total of 479 participants were enrolled in this study, with 335 assigned to the training set and 144 to the validation set. Lasso-Logistic regression identified serum folate level, HR-HPV infection, genital cleaning frequency, underwear change frequency, and gynecological history as variables associated with CIN1 persistence and progression. The internally validated C-index of the constructed Nomogram was 0.767, indicating good predictive performance.The H-L test showed a good fit of the model (χ2=1.447, P=0.485). The calibration curves of both the training and validation sets were close to the ideal diagonal, indicating good calibration. The areas under the ROC curves for the training and validation sets were 0.784 (95% CI:0.734-0.835) and 0.815 (95% CI:0.736-0.895), respectively, demonstrating good discriminative ability.

Conclusion

Serum folate level, HR-HPV infection, genital cleaning frequency, underwear change frequency, and gynecological history are significant predictive variables for persistence and progression of CIN1.The constructed Nomogram can intuitively predict the incidence of CIN1 persistent or progression in patients, providing a theoretical reference for the prevention, treatment, and individualized management of early cervical lesions.

Cervical intraepithelial neoplasia  /  Lasso  /  Risk prediction models  /  Nomogram
田楠, 李德铖, 彭煜舒, 冯新琳, 张瑞, 戴志远, 王金桃, 赵卫红, 田志强, 丁玲. 基于Lasso-Logistic回归构建低级别宫颈上皮内瘤变持续和进展的风险预测模型. 现代预防医学, 2026 , 53 (4) : 760 -768 . DOI: 10.20043/j.cnki.MPM.202505243
Nan TIAN, De-cheng LI, Yu-shu PENG, Xin-lin FENG, Rui ZHANG, Zhi-yuan DAI, Jin-tao WANG, Wei-hong ZHAO, Zhi-qiang TIAN, Ling DING. Based on Lasso-Logistic regression for construction of a risk prediction model for persistence and progression of low-grade cervical intraepithelial neoplasia[J]. Modern Preventive Medicine, 2026 , 53 (4) : 760 -768 . DOI: 10.20043/j.cnki.MPM.202505243
正文
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宫颈癌是全球女性第四大常见肿瘤[1]，我国发病率和死亡率呈上升趋势[2]，山西省为高发地区[3]。宫颈癌的发生经历宫颈上皮内瘤变（cervical intraepithelial neoplasia, CIN）的多阶段过程，为早发现、早诊断提供了契机[4]。研究显示，50%~80%低级别CIN（CIN1）1~3年内可自然消退，合并高危型人乳头瘤病毒（High-risk human papillomavirus, HR-HPV）者4.67%进展为CIN2、3[5-7]，浸润癌风险显著升高[8-9]。最新中国专家共识提出对可能存在高级别宫颈上皮内瘤变风险的低级别病变应慎重处理[10]。因此，对CIN1患者实施个性化分层管理，识别持续进展风险尤为重要。HR-HPV是CIN和宫颈病变的主要病因[11]，但HR-HPV阴性CIN1患者仍可能持续和进展[12]，提示除HPV外，还有其他因素参与宫颈病变。在宫颈癌防治领域，聚焦可改变因素阻断宫颈病变进展，对公共卫生实践更具指导价值。既往研究多通过ROC曲线分析HPV E6/E7 mRNA及阴道微生态等生物学指标对CIN进展的预测价值[13-14]，忽视了可人为干预的生活行为因素。叶酸作为关键营养素与宫颈病变密切相关，国内外及本课题组前期研究表明，低血清叶酸（Serum folate, SF）可增加宫颈病变恶性进展风险[15-16]，阴道清洗频率、换洗内裤频率会影响CIN1转归[17]，这些因素是公共卫生干预的重要靶点。当前缺乏整合生物学与生活行为因素，精准预测CIN1持续进展的模型，难以满足个体化防治需求。本研究采用巢式病例对照设计，选择2022年6月—12月山西省介休市已婚女性社区队列中的CIN1患者为研究对象，问卷收集生活行为因素等相关信息，检测HR-HPV、血清叶酸，经12个月随访行病理学检查后分为CIN1消退组、持续和进展组。通过Lasso-Logistic回归构建Nomogram模型，量化可改变影响因素对宫颈病变进展的作用，为CIN1患者早期风险评估、个性化干预及公共卫生精准防控提供科学依据与理论支撑。
1　材料与方法
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1.1　研究对象
选择2022年6月—12月山西省介休市已婚妇女社区队列中，经组织病理学诊断为CIN1的患者为研究对象，12个月后再行病理学检查，出现CIN1消退、持续和进展。纳入标准：（1）籍贯及居住地均为山西，且在当地连续稳定居住1年以上；（2）经病理学诊断为CIN1的新发病例。排除标准：（1）子宫切除术、阴道及宫颈病变治疗史者；（2）妊娠期和哺乳期妇女；（3）其他恶性肿瘤病史者；（4）三个月内B族维生素服用者；（5）失访及资料信息不完整者。最终479例CIN1患者纳入研究。本研究获得山西医科大学伦理委员会审查（批准号: 2021SLL013）。
1.2　样本量估算
采用Riley精确法，结合Lasso-Logistic回归建模策略，使用R软件“pmsampsize”包，在预期结局发生率19%、Cox-Snell R2=0.164、目标收缩因子0.9、6个参数条件下，计算得出至少需要299例样本，此时变量事件数≈ 9.5，可满足模型判别能力与校准度估计精度要求。本研究实际纳入479例研究对象，病例组148例，对照组331例，满足样本量估算要求。
1.3　基线资料收集与实验室检测
基线进行问卷调查和生物样本收集与指标检测。受试者签订知情同意书后，在专职调查员的监督下，通过问卷星由调查员面对面协助完成问卷填写，内容包括人口学特征、生活习惯、生殖情况、既往病史等。宫颈脱落细胞由妇科医生收集，采用HPV-DNA分型试剂盒、全自动荧光PCR分析仪及扩增仪行HPV分型检测；空腹抽取5 ml肘静脉血，经Access免疫测定系统及配套的叶酸检测试剂盒，采用化学发光免疫分析法测定血清叶酸。
1.4　CIN1消退、持续和进展的判定
研究对象进入队列时进行HPV分型检测、液基细胞学检测和阴道镜下组织病理学检查。12个月后随访，对研究对象再行病理学检查，结果为正常宫颈判定为消退，仍为CIN1判定为持续，CIN2、3或宫颈癌判定为进展。
1.5　统计学方法
采用SPSS 26.0和R 4.4.2整理数据。血清叶酸按四分位数法分为四组，分别是≤13.05、13.06~16.78、16.79~21.79及>21.79 nmol/L。计量资料正态分布以表示，组间比较采用独立样本t检验；非正态分布以M（Q1, Q3）表示，组间比较采用Mann-Whitney U检验。计数资料用频数（%）表示，组间比较采用χ2检验。数据集按7∶3随机分为训练集与验证集。基于文献与临床共识确定初始候选变量，训练集单因素分析筛选纳入Lasso回归的变量，采用“glmnet”包执行Lasso回归，十折交叉验证取lambda.min筛选预测变量。采用“rms”包进行多因素logistic回归构建风险预测模型，并用Nomogram可视化。拆分样本法（Split-sample）进行内部验证，采用Bootstrap 1 000次重抽样计算验证集的一致性指数（Consistency index）、Hosmer-Lemeshow（H-L）检验、校准曲线、受试者工作特征曲线（ROC）对模型进行评价，检验水准α=0.05。
2　结果
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2.1　基本情况
本研究共纳入564例符合标准的CIN1患者，年龄为51（43，57），血清叶酸为16.70（12.89，21.14）。随访12个月后出现失访（n=52），资料缺失（n=33），按照纳入排除标准予以排除。分析显示，排除组与完整随访组在年龄[52（44，57）、50（43，57）（P=0.542）]、血清叶酸[15.53（13.51，28.46）、17.09（12.78，20.92）（P=0.389）]及其他人口学特征上的差异无统计学意义（P>0.05），见表1。最终479例纳入后续分析。
2.2　研究对象基线特征可比性
研究最终纳入479例研究对象，CIN1消退组331例，持续和进展组148例。CIN1消退组与持续和进展组的年龄分别是51（45, 57）和48.5（41, 56）（P=0.021）；血清叶酸分别是18.54（13.88, 21.21）和15.66（11.36, 20.70）（P<0.001）。按7∶3随机分为训练集（335例）与验证集（144例），两组在婚姻状况等基线资料上的差异无统计学意义（P>0.05），见表2。
2.3　训练集CIN1持续和进展的单因素分析
训练集中CIN1消退组231例，持续和进展组104例。单因素分析显示，CIN1消退组与持续和进展组在年龄[51（45，57）、48（40，56）（P=0.011）]、血清叶酸[18.54（13.54，21.21）、15.73（11.45，20.95）（P=0.001）]、清洗阴部频率、换洗内裤频率、妇科病史及HR-HPV感染方面差异有统计学意义（P<0.05）。而两组在婚姻状况、产次等方面的差异无统计学意义（P>0.05），见表3。
2.4　Lasso回归筛选预测变量
本研究将单因素分析中有意义的变量纳入Lasso回归进行筛选，经10折交叉验证，以交叉验证误差值最小时的λ值（lambda.min=0.001 8）为最优解，根据非零系数获得年龄、血清叶酸、清洗阴部频率、换洗内裤频率、HR-HPV感染、妇科病史6个预测变量，筛选过程见图1。
2.5　多因素logistic回归模型构建
以Lasso回归筛选的6个变量为自变量，CIN1是否消退为因变量，构建多因素logistic回归模型，见表4，清洗阴部频率和换洗内裤频率为保护因素，HR-HPV感染和妇科病史为危险因素，血清叶酸≤13.05 nmol/L，13.06~16.78 nmol/L为危险因素。
2.6　列线图可视化模型
基于多因素logistic回归结果，以血清叶酸、HR-HPV、清洗阴部频率、换洗内裤频率、妇科病史为预测因子，可视化Nomogram模型，量化CIN1持续和进展的概率，见图2。例如血清叶酸≤13.05 nmol/L，感染HR-HPV，清洗阴部频率<3次/周，换洗内裤频率<3次/周且有妇科疾病的CIN1患者，其持续或进展的概率为88.09%。
2.7　Nomogram预测模型的验证
Split-sample法进行内部验证，Bootstrap 1 000次重复抽样得到验证集的C-index为0.767；H-L拟合优度检验（χ2=1.447, P=0.485）表明模型的拟合程度较好；训练集与验证集校准曲线均接近理想对角线，校准度较好，见图3；ROC曲线分析结果显示，训练集与验证集的AUC分别为0.784（95% CI:0.734~0.835）、0.815（95% CI:0.736~0.895），说明模型的区分度好，见图4。
3　讨论
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CIN1的自然消退率高，但临床中过于频繁的筛查和阴道镜检查存在潜在危害[18]，且增加患者的经济负担，因此寻找CIN1持续进展的相关因素，建立精准的风险预测模型，指导CIN1治疗及随访，对预防宫颈病变持续进展具有重要意义。
HR-HPV感染会引发宫颈上皮癌前病变，甚至宫颈癌[19]，但HPV感染仅是宫颈癌变危险因素之一，众多危险因素相互作用会进一步增加癌变的风险[20]。本研究发现低血清叶酸与不良的卫生习惯会增加CIN1患者持续发展的风险，因此，我们关注HPV感染的同时，生活中应注意保持良好的生活习惯，适当补充叶酸，加强早期干预与随访，提高预防意识。
叶酸是人体必需的B族维生素，参与众多物质合成代谢，是细胞生长和组织修复的必需物质[21]，其无法由人体自身合成，叶酸缺乏会导致DNA合成障碍，细胞形态异常及生理功能受损，与癌症发生发展直接相关[22]。本研究发现血清叶酸为≤13.05 nmol/L，13.06~16.78 nmol/L是CIN1患者发生持续进展的危险因素。课题组前期研究亦发现，低血清叶酸是宫颈癌和癌前病变的危险因素，其浓度升高与CIN发生风险降低相关，且低叶酸浓度会增加CIN1进展到CIN2、3的风险[16-17]。因此充足膳食摄取或科学补充对维持健康至关重要，全球多个国家已实施叶酸强化计划以降低疾病风险。研究提出，补充叶酸有助于CIN1消退，适量补充叶酸与维生素B12可抑制宫颈癌发生发展[23]。课题组前期细胞学实验亦发现，补充叶酸可抑制C33A和CaSki细胞增殖、迁移及侵袭，促进其凋亡[24]，提示叶酸有望应用于宫颈癌防治，适当补充或能降低宫颈病变进展风险，可作为一级预防潜在策略。
Alexandra M Holdcroft等[25]研究表明，不良卫生习惯可能破坏阴道微环境，促进有害病原微生物生长，进而引发组织病理改变。存在妇科炎症、宫颈疾病等妇科疾病会显著增加女性患CIN2、3的风险[26]。本研究亦发现清洗阴道频率及换洗内裤频率高是CIN1持续和进展的保护因素，妇科病史则为危险因素。提示女性应提高健康意识，注意阴部清洁、勤换内裤，养成良好的卫生习惯，阻止早期宫颈病变恶化。
本研究存在一定局限性。第一，虽控制了宫颈病变相关重要因素，但仍无法完全排除未测量和未知混杂因素；第二，部分数据依赖受试者回忆，存在回忆偏倚，未来可结合电子健康档案提升数据准确性；第三，研究对象仅覆盖山西地区女性，结果外推需结合目标人群特征；第四，未对预测模型进行外部验证，后续应扩大样本范围，开展多中心研究以验证模型外部适用性，提升模型泛化能力。
综上所述，本研究经Lasso-Logistic回归分析确定，血清叶酸、HR-HPV感染、清洗阴部频率、换洗内裤频率及妇科病史为CIN1持续和进展的预测变量，构建的Nomogram预测模型经内部验证，表现出良好的区分度、校准度。该模型可识别CIN1高持续进展风险患者，指导其补充叶酸，改善卫生习惯并及时随访，为CIN1患者早期风险评估、个性化干预及公共卫生精准防控提供新方向。后续将开展前瞻性多中心研究以明确模型预测价值，为提升女性生殖健康水平、制定合理防治策略提供依据。
基金
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  • 国家自然科学基金(81703313)
  • 山西省自然科学研究面上项目(20210302123308)
  • 山西省高等教育“百亿工程”科技引导专项(BYBLD002)
文献
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2026年第53卷第4期
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doi: 10.20043/j.cnki.MPM.202505243
  • 接收时间：2025-05-19
  • 首发时间：2026-11-02
  • 出版时间：2026-02-25
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  • 收稿日期：2025-05-19
基金
国家自然科学基金(81703313)
山西省自然科学研究面上项目(20210302123308)
山西省高等教育“百亿工程”科技引导专项(BYBLD002)
作者信息
    1.山西医科大学公共卫生学院流行病学教研室，山西 太原 030001
    2.山西医科大学第二临床医学院妇产科
    3.山西白求恩医院（山西医学科学院

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2种不同金属材料的力学参数
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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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