Necessity for subsequent surgery in women of child-bearing age with positive margins after conization

Development of a machine learning-based model for predicting positive margins in high-grade squamous intraepithelial lesion (HSIL) treatment by Cold Knife Conization(CKC): a single-center retrospective study

OBJECTIVES

This study aims to analyze factors associated with positive surgical margins following cold knife conization (CKC) in patients with cervical high-grade squamous intraepithelial lesion (HSIL) and to develop a machine-learning-based risk prediction model.

METHOD

We conducted a retrospective analysis of 3,343 patients who underwent CKC for HSIL at our institution. Logistic regression was employed to examine the relationship between demographic and pathological characteristics and the occurrence of positive surgical margins. Various machine learning methods were then applied to construct and evaluate the performance of the risk prediction model.

RESULTS

The overall rate of positive surgical margins was 12.9%. Independent risk factors identified included glandular involvement (OR = 1.716, 95% CI: 1.345-2.189), transformation zone III (OR = 2.838, 95% CI: 2.258-3.568), HPV16/18 infection (OR = 2.863, 95% CI: 2.247-3.648), multiple HR-HPV infections (OR = 1.930, 95% CI: 1.537-2.425), TCT ≥ ASC-H (OR = 3.251, 95% CI: 2.584-4.091), and lesions covering ≥ 3 quadrants (OR = 3.264, 95% CI: 2.593-4.110). Logistic regression demonstrated the best prediction performance, with an accuracy of 74.7%, sensitivity of 76.7%, specificity of 74.4%, and AUC of 0.826.

CONCLUSION

Independent risk factors for positive margins after CKC include HPV16/18 infection, multiple HR-HPV infections, glandular involvement, extensive lesion coverage, high TCT grades, and involvement of transformation zone III. The logistic regression model provides a robust and clinically valuable tool for predicting the risk of positive margins, guiding clinical decisions and patient management post-CKC.

The Effect of Cervical Cold-Knife Conization (CKC) on HPV Infection in Patients with High-Grade Cervical Intraepithelial Neoplasia: A Retrospective Study

Purpose

Investigation of HPV infection treatment in women undergoing cervical cold-knife conization for advanced cervical intraepithelial neoplasia.

Patients and Methods

A retrospective analysis was conducted on patients who underwent cervical cold-knife conization for cervical intraepithelial neoplasia grade II-III at Beijing Obstetrics and Gynecology Hospital from January 2017 to December 2018. The HPV infection status of the patients at 6 months, 1 year, and 2 years after surgery was collected. We use chi square analysis and binary logistic regression to evaluate various factors such as age, number of pregnancies, number of cesarean sections, number of vaginal deliveries, HPV type, size of surgical specimens (diameter and height), and the influence of specimen edge on HPV infection.

Results

A total of 334 patients were included in the analysis. The patients are mainly infected with HPV 16/58/52. Age is a influencing factor for HPV recovery 12 months after CKC surgery (P=0.002). Based on the diagnosis of HPV one year after CKC, the recovery rate of HPV58 patients is significantly lower than HPV16. Age is a influencing factor for the recovery of HPV infection (P<0.05).

Conclusion

The treatment of HPV infection by CKC is related to the patient's age and HPV subtype but not to number of pregnancies, number of pregnancies, number of vaginal deliveries, size of surgical specimens, and marginal conditions. The rate of HPV negative conversion is relatively high 24 months after the patient does not undergo surgery, but there is currently a lack of data on cervical lesions that match HPV results.

Extensive lesions and a positive cone margin are strong predictors of residual disease in subsequent hysterectomy following conization for squamous intraepithelial lesion grade 2 or 3 study design

BACKGROUND

This study aimed to determine the predictive factors for post-conization of residual disease in subsequent hysterectomy for cervical intraepithelial neoplasia grade 2 or 3.

METHODS

This retrospective study included 267 patients with histologically confirmed cervical intraepithelial neoplasia grade 2 or 3 who underwent hysterectomy within 7 months after conization. Clinical data (e.g., age, menopausal status, gravidity, parity, type of transformation zone, conization method) as well as pathological data pertaining to conization and hysterectomy were collected from medical records. A logistic regression model was used to analyze the relationship between the variables and risk of residual lesions in hysterectomy samples.

RESULTS

Overall, 70 (26.2%) patients had residual lesions in their hysterectomy specimens. Univariate analyses revealed that age ≥ 50 years (p=0.019), endocervical gland involvement(p=0.013), positive margin(p < 0.001), and involvement of 3-4 quadrants(p < 0.001) were risk factors for residual lesions. Conversely, postmenopausal status, gravidity ≥ 3, parity ≥ 2, loop electrosurgical excision procedure, and type III transformation zone were not risk factors for residual lesions. A positive margin(p < 0.001) and multiple-quadrant involvement(p < 0.001) were identified as independent risk factors for residual lesions on multivariate analysis.

CONCLUSIONS

Multiple-quadrant involvement and a positive cone margin were reliable predictive factors for residual disease. Total hysterectomy or repeated cervical conization should be considered for patients with these two risk factors. The identification of high-risk patients with extensive lesions by colposcopic examination before conization is indispensable, as it will enable surgeons to perform conization with consideration of risk factors and possibly improve the approach used for the excisional procedure. For high-risk patients, colposcope-guided cold-knife conization is preferred when resources permit.

Practical Model for Residual/Recurrent Cervical Intraepithelial Lesions in Patients with Negative Margins after Cold-Knife Conization

Objective: This study aimed to identify reliable risk factors for residual/recurrent cervical intraepithelial lesions in patients with negative margins after cold-knife conization. Methods: A total of 2352 women with HSILs (high-grade squamous intraepithelial lesions) with negative margins who underwent cold-knife conization between January 2014 and December 2020 were included; in total, 1411 women were assigned to the development cohort, and 941 women were assigned to the validation cohort. Multivariate logistic regression was used to build four predictive models based on the different combinations of follow-up data (Model A: preoperative factors; Model B: first-follow-up data; Model C: second-follow-up data; Model D: data from both follow-ups). The accuracy, sensitivity, specificity, false-positive rate (FPR), false-negative rate (FNR), and area under the receiver operating characteristic curve (AUC) were evaluated on the validation cohort. The predictive power of risk factors was further validated using six machine learning algorithms. Results: Model D demonstrated the highest AUC of 0.91 (95% CI, 0.87 to 0.96) in the validation cohort, whereas Models A, B, and C achieved AUCs of 0.69 (95% CI, 0.59 to 0.78), 0.88 (95% CI, 0.80 to 0.95), and 0.89 (95% CI, 0.81 to 0.97) respectively. The six machine learning methods achieved consistent results. Kaplan-Meier (KM) survival curves demonstrated that our models could effectively stratify patients with all models (p < 0.05 for all models). Conclusion: Our model, which is based on preoperative and follow-up factors, can serve as a complementary screening procedure for the early detection or prediction of recurrence after cold-knife conization in HSIL patients.

Construction and Evaluation of a Clinical Prediction Scoring System for Positive Cervical Margins Under Colposcopy

Introduction

Currently, the commonly used surgical methods for cervical lesions include loop electrosurgical excision procedure (LEEP) and cold knife conization (CKC). However, the positive rate of surgical margins after LEEP is relatively high, which leads to disease recurrence and places further demand on clinical treatment. This study investigated factors related to positive margins after LEEP and established a scoring system to enhance preoperative risk assessment and surgical selection.

Materials and Methods

A retrospective analysis of the clinical data of 411 patients undergoing LEEP surgery for cervical lesions in the First Affiliated Hospital of University of Science and Technology of China (USTC), from January 2016 to March 2021, was performed. Cases were divided into a negative margin group (349 cases) and a positive margin group according to postoperative pathology. In the positive group (62 cases), single-factor and multi-factor analyses screened influencing factors; a logistic and additive scoring system was established; furthermore, a ROC curve was used to evaluate scoring effectiveness.

Results

The positive rate of resection margins after LEEP was 15.1%. Univariate analysis indicated a relationship to patient age, menopause, preoperative ThinPrep Cytology Test (TCT) results, lesion quadrant number under colposcopy, cervical biopsy, and the result of endocervical curettage (ECC). Multivariate analysis showed that age >35 y, menopause, preoperative TCT being high-grade squamous intraepithelial lesion (HSIL), four quadrants being involved under colposcopy, and ECC being HSIL were all independent influencing factors of positive margins after LEEP (P < 0.05). These were included with the above factors to establish a logistic and additive scoring system. When the logistic score was 17, the sensitivity and specificity of predicting positive margins after LEEP were 80.6 and 61.6%, respectively. When the additive score was 6, the sensitivity and specificity were 74.2 and 66.2%, respectively. Both scoring systems had good predictability (area under the curve AUC >0.75).

Conclusions

This study quantified factors influencing positive margins after LEEP and established a scoring system for evaluating patients before surgery to provide a basis for individualized treatment and selection of surgical methods.