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Based on Lasso-Logistic regression for construction of a risk prediction model for persistence and progression of low-grade cervical intraepithelial neoplasia
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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
Modern Preventive Medicine | 2026, 53(4) : 760 - 768
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Modern Preventive Medicine | 2026, 53(4): 760-768
• Clinical Medicine and Prevention •
Based on Lasso-Logistic regression for construction of a risk prediction model for persistence and progression of low-grade cervical intraepithelial neoplasia
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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
Published: 2026-02-25 doi: 10.20043/j.cnki.MPM.202505243
Outline
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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
Year 2026 volume 53 Issue 4
30
16
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doi: 10.20043/j.cnki.MPM.202505243
  • Receive Date:2025-05-19
  • Online Date:2026-11-02
  • Published:2026-02-25
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  • Received:2025-05-19
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    Department of Epidemiology, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi 030001, China
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表12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
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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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