Risk-Based Colposcopy for Cervical Precancer Detection: A Cross-Sectional Multicenter Study in China

A Glimmer of Hope for Patients with a T3 Transformation Zone: miRNAs Are Potential Biomarkers for Cervical Dysplasia

BACKGROUND

This pilot study assesses the potential use of miRNAs in the triage of colposcopy patients with type 3 (nonvisible) cervical transformation zone (TZ). Type 3 TZ is a constitutional finding associated with many problems and controversies in colposcopy patient management. Here, we present miRNAs as a potential biomarker for the detection of CIN3 in these cases.

MATERIALS AND METHODS

Cervical mucosa samples (CMS) were collected from patients presenting with T3 transformation zone during routine workup using the Cytobrush. Depending on the histological and cytological result, as well as the result of the routinely performed HPV PCR, patients were divided into three groups: patients with a high-grade intraepithelial lesion (CIN3) and a positive high-risk HPV test (CIN3 group), patients without an intraepithelial lesion and a positive high-risk HPV test (HPV group), and healthy controls (N = no intraepithelial lesion and negative HPV test). The cervical mucus samples included in the study were tested for their expression levels of distinct miRNAs using qPCR.

RESULTS

All investigated miRNAs were consistently detectable in every sample. The CMSs of histologically graded CIN 3 showed consistently high expression levels of all eight miRNAs, whereas the CMSs from healthy patients (N) show generally lower expression levels. However, CMSs from patients of the HPV group represented a very heterogeneous group.

CONCLUSIONS

The data presented here can provide a solid basis for future research into a triage test for patients with a T3 transformation zone on the basis of commonly used clinical equipment.

Detection of cervical high-grade squamous intraepithelial lesions and assessing diagnostic performance of colposcopy among women with oncogenic HPV

BACKGROUND

HPV screening tests may improve cervical cancer risk stratification and better guide decisions about follow-up with colposcopy/biopsy. This study aimed to estimate the risk of cervical intraepithelial neoplasia grade 2 or worse (CIN2+) among women with oncogenic HPV types and evaluate the performance of colposcopy in the diagnosis of histologic CIN2 + at Putuo Hospital, Shanghai, China.

METHODS

This cross-sectional survey was conducted from February 2020 to December 2022 among women who were referred to colposcopy. Women with high-risk (HR) HPV-positive, cytology testing and colposcopy-directed biopsy were included.

RESULTS

Univariate and multivariate analysis indicated that high-grade colposcopic impression ((OR, 17.61%, 95%CI: 11.54-26.85%) was associated with the highest risk for detecting CIN2+, followed by HSIL + cytology (OR, 6.90%, 95%CI: 3.56-13.37%) and HPV16/18 positive (OR, 2.91%, 95%CI: 2.12-3.99%). Overall, CIN2 + was detected in 14.6% of 2007 women. HPV16/18 had higher CIN2 + risks than other HR-HPV genotypes (30.1% vs. 10.2%, P<0.001). Among women with low-grade cytology, 24.1% had CIN2+, and the risks for HPV16/18 (58.2%) were higher than for other HR-HPV(16.8%). For those with high-grade cytology, there was no significant difference between HPV groups ( 75.0% vs. 72.9%, P > 0.05). The diagnostic performance of colposcopy in diagnosis of CIN2 + by senior and junior colposcopists was comparable.

CONCLUSIONS

The results indicated that referral to colposcopy is recommended in managing women with HR-HPV positive, and colposcopic impressions provide key clues for identification CIN2+.

Developing a predictive nomogram for colposcopists: a retrospective, multicenter study of cervical precancer identification in China

BACKGROUND

Colposcopic examination with biopsy is the standard procedure for referrals with abnormal cervical cancer screening results; however, the decision to biopsy is controvertible. Having a predictive model may help to improve high-grade squamous intraepithelial lesion or worse (HSIL+) predictions which could reduce unnecessary testing and protecting women from unnecessary harm.

METHODS

This retrospective multicenter study involved 5,854 patients identified through colposcopy databases. Cases were randomly assigned to a training set for development or to an internal validation set for performance assessment and comparability testing. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to reduce the number of candidate predictors and select statistically significant factors. Multivariable logistic regression was then used to establish a predictive model which generates risk scores for developing HSIL+. The predictive model is presented as a nomogram and was assessed for discriminability, and with calibration and decision curves. The model was externally validated with 472 consecutive patients and compared to 422 other patients from two additional hospitals.

RESULTS

The final predictive model included age, cytology results, human papillomavirus status, transformation zone types, colposcopic impressions, and size of lesion area. The model had good overall discrimination when predicting HSIL + risk, which was internally validated (Area Under the Curve [AUC] of 0.92 (95%CI 0.90-0.94)). External validation found an AUC of 0.91 (95%CI 0.88-0.94) across the consecutive sample, and 0.88 (95%CI 0.84-0.93) across the comparative sample. Calibration suggested good coherence between predicted and observed probabilities. Decision curve analysis also suggested this model would be clinically useful.

CONCLUSION

We developed and validated a nomogram which incorporates multiple clinically relevant variables to better identify HSIL + cases during colposcopic examination. This model may help clinicians determining next steps and in particular, around the need to refer patients for colposcopy-guided biopsies.

Evaluating the Feasibility of Machine-Learning-Based Predictive Models for Precancerous Cervical Lesions in Patients Referred for Colposcopy

Background: Colposcopy plays an essential role in cervical cancer control, but its performance remains unsatisfactory. This study evaluates the feasibility of machine learning (ML) models for predicting high-grade squamous intraepithelial lesions or worse (HSIL+) in patients referred for colposcopy by combining colposcopic findings with demographic and screening results. Methods: In total, 7485 patients who underwent colposcopy examination in seven hospitals in mainland China were used to train, internally validate, and externally validate six commonly used ML models, including logistic regression, decision tree, naïve bayes, support vector machine, random forest, and extreme gradient boosting. Nine variables, including age, gravidity, parity, menopause status, cytological results, high-risk human papillomavirus (HR-HPV) infection type, HR-HPV multi-infection, transformation zone (TZ) type, and colposcopic impression, were used for model construction. Results: Colposcopic impression, HR-HPV results, and cytology results were the top three variables that determined model performance among all included variables. In the internal validation set, six ML models that integrated demographics, screening results, and colposcopic impression showed significant improvements in the area under the curve (AUC) (0.067 to 0.099) and sensitivity (11.55% to 14.88%) compared with colposcopists. Greater increases in AUC (0.087 to 0.119) and sensitivity (17.17% to 22.08%) were observed in the six models with the external validation set. Conclusions: By incorporating demographics, screening results, and colposcopic impressions, ML improved the AUC and sensitivity for detecting HSIL+ in patients referred for colposcopy. Such models could transform the subjective experience into objective judgments to help clinicians make decisions at the time of colposcopy examinations.