Evidence map›Paper›PMID 39739926›Full record

ArticleCancer medicine2025

A Clinical Prediction Model for Pathologic Upgrade to Invasive Carcinoma Following Conization of Cervical High-Grade Squamous Intraepithelial Lesions.

Qiao Liu, Jing Yang, Hui Cheng, Chuqiang Shu, Yi Tang, Jing Zhao

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Article in Cancer medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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3 citing papers in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Qiao LiuHunan Provincial Maternal and Child Health Care Hospital, University of South China, Changsha, Hunan, People's Republic of China.
Jing YangHunan Provincial Maternal and Child Health Care Hospital, University of South China, Changsha, Hunan, People's Republic of China.
Hui ChengHunan Provincial Maternal and Child Health Care Hospital, University of South China, Changsha, Hunan, People's Republic of China.
Chuqiang ShuHunan Provincial Maternal and Child Health Care Hospital, University of South China, Changsha, Hunan, People's Republic of China.
Yi TangHunan Provincial Maternal and Child Health Care Hospital, University of South China, Changsha, Hunan, People's Republic of China.
Jing ZhaoHunan Provincial Maternal and Child Health Care Hospital, University of South China, Changsha, Hunan, People's Republic of China.ORCID https://orcid.org/0009-0008-4104-206X

Funding

Clinical Medical Research Center for Cervical Cancer Prevention and Treatment of Hunan Province 2021SK4021Hunan Provincial Maternal and Child Health Care Hospital's 2023 Sharp New Cultivation Program Projects 2023RX27Natural Science Foundation of Hunan Province Department Joint Fund Project 2023JJ60013Natural Science Foundation of Hunan Province Medical and health industry joint fund project 2024JJ9335
6 · The paper itself

Abstract

objectiveTo explore the risk factors associated with the pathological progression to invasive carcinoma following the conization of cervical high-grade squamous intraepithelial lesions (HSIL) and to construct a risk prediction model to guide preoperative risk assessment and optimize the selection of surgical approaches.

methodsA retrospective analysis was conducted on the clinical data of 3337 patients who underwent cervical conization for HSIL at Hunan Provincial Maternal and Child Health Care Hospital from December 2016 to March 2022. The patients were categorized into the pathological progression group (398 cases) and the nonprogression group (2939 cases) based on postconization pathology results. Statistical significance factors were selected by least absolute shrinkage and selection operator regression and then multivariate logistic regression was utilized to build predictive models, which were presented as a nomogram and evaluated for discriminability, calibration, and decision curves. The Bootstrap method was utilized for internal validation. A total of 277 patients were enrolled from April 2022 to October 2022 for external validation.

resultsThe percentage of pathologic upgrades to invasive carcinoma following cervical conization was 11.9%. The predictive model included age, contact bleeding symptoms, HPV16/18 infection, HSIL cytology, cervical biopsy pathology diagnosis level, suspicious stromal infiltration in the biopsy pathology diagnosis, and endocervical curettage HSIL. The model demonstrated good overall discrimination in predicting the risk of HSIL progression to early invasive cancer, and internal validation confirmed its reliability (C-index = 0.787). Area under the curve analysis indicated good model discriminability across external datasets. The decision curve analysis also suggested that this model is clinically useful.

conclusionWe developed and validated a nomogram incorporating multiple clinically relevant variables to better identify cases of HSIL progressing to early cervical cancer, providing a basis for individualized treatment and surgical approach selection.

Indexed as

ConizationNomogramsUterine Cervical NeoplasmsAdultDisease ProgressionFemaleHumansMiddle AgedNeoplasm GradingNeoplasm InvasivenessPapillomavirus InfectionsRetrospective StudiesRisk AssessmentRisk FactorsSquamous Intraepithelial LesionsSquamous Intraepithelial Lesions of the Cervixcervical cancerHSILnomogrampathological progressionpredictive models

Identifiers

PMID39739926
PMCPMC11683552

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