Evidence map›Paper›PMID 41799691›Full record

ArticleInternational journal of women's health2026

High-Risk Clinical Characteristics and Risk Model for Unexpected Cervical Cancer Diagnosed After Surgery in Patients with LSIL/HSIL Biopsy.

Guilong Pan, Wan Wang, Zhen Sun, Ran Chu, Jing Xue, Na Li

Abstract read
In one paragraph

Article in International journal of women's health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Guilong Pan *Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.
Wan Wang *Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.ORCID 0009-0000-7231-7021
Zhen Sun *Department of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.
Ran ChuDepartment of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.ORCID 0000-0002-1133-5186
Jing XueDepartment of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.
Na LiDepartment of Gynecology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, People's Republic of China.ORCID 0000-0001-8455-7209

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Women with colposcopy-directed biopsy results of low-grade or high-grade squamous intraepithelial lesion (LSIL/HSIL) who are subsequently found to have cervical cancer after surgery likely represent missed preoperative malignancy. This study aims to identify risk factors associated with these missed diagnoses and to develop a preoperative risk prediction model. Methods: Patients with LSIL/HSIL on colposcopy-directed biopsy who underwent cervical conization or hysterectomy at a single center were retrospectively included. Postoperative cervical cancer was the adverse outcome. Baseline characteristics were compared using chi-square tests. Predictors were identified by univariate and multivariable logistic regression to build a logistic regression model. Internal validation applied machine learning with five-fold cross-validation. Performance was evaluated by receiver operating characteristic (ROC) curve (AUC) and accuracy, with discrimination, calibration, and clinical utility assessed using ROC curves, calibration plots, and decision curve analysis (DCA). Results: A total of 1271 patients were included, and 79 were diagnosed with cervical cancer after surgery. Risk factors significantly associated with postoperative cervical cancer diagnosis included age (OR, 1.0; 95% CI, 1.0-1.0, p=0.045), gravidity (OR, 1.2; 95% CI, 1.1-1.3, p <0.001), human papillomavirus 16 or 18 infection (OR, 2.6; 95% CI, 1.4-4.6, p=0.002), colposcope biopsy site numbers ≥4 (OR, 0.2; 95% CI, 0.1-0.3, p <0.001), ThinPrep Cytologic Test indicates high-grade squamous intraepithelial lesion (OR, 3.1; 95% CI, 1.1-8.5, p=0.028), and neutrophil-to-lymphocyte ratio (OR, 1.1; 95% CI, 1.0-1.3, p=0.015). In five-fold cross-validation, the machine learning algorithms yielded an AUC of 0.72-0.81 and an accuracy of 0.93-0.94 for the model. ROC curves, calibration curves, and DCA analysis demonstrate the model's excellent predictive capability and performance. Conclusion: This study developed and internally validated a clinical prediction model to estimate the preoperative risk of occult cervical cancer in patients with LSIL/HSIL on colposcopy-directed biopsy. This model may support risk-stratified surgical decision-making and help reduce missed diagnoses.

Indexed as

cervical cancercolposcopymissed diagnosispredictive model

Identifiers

PMID41799691
PMCPMC12960049

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.