Risk-stratified management of cervical high-grade squamous intraepithelial lesion based on machine learning.

The concordance rate between conization and colposcopy-directed biopsy (CDB) proven cervical high-grade squamous intraepithelial lesion (HSIL) were 64-85%. We aimed to identify the risk factors associated with pathological upgrading or downgrading after conization in patients with cervical HSIL and to provide risk-stratified management based on a machine learning predictive model. This retrospective study included patients who visited the Obstetrics and Gynecology Hospital of Fudan University from January 1 to December 31, 2019, were diagnosed with cervical HSIL by CDB, and subsequently underwent conization. A wide variety of data were collected from the medical records, including demographic data, laboratory findings, colposcopy descriptions, and pathological results. The patients were categorized into three groups according to their postconization pathological results: low-grade squamous intraepithelial lesion (LSIL) or below (downgrading group), HSIL (HSIL group), and cervical cancer (upgrading group). Univariate and multivariate analyses were performed to identify the independent risk factors for pathological changes in patients with cervical HSIL. Machine learning prediction models were established, evaluated, and subsequently verified using external testing data. In total, 1585 patients were included, of whom 65 (4.1%) were upgraded to cervical cancer after conization, 1147 (72.4%) remained having HSIL, and 373 (23.5%) were downgraded to LSIL or below. Multivariate analysis showed a 2% decrease in the incidence of pathological downgrade for each additional year of age and a 1% increase in lesion size. Patients with cytology > LSIL (odds ratio [OR] = 0.33; 95% confidence interval [CI], 0.21-0.52), human papillomavirus (HPV) infection (OR = 0.33; 95% CI, 0.14-0.81), HPV 33 infection (OR = 0.37; 95% CI, 0.18-0.78), coarse punctate vessels on colposcopy examination (OR = 0.14; 95% CI, 0.06-0.32), HSIL lesions in the endocervical canal (OR = 0.48; 95% CI, 0.30-0.76), and HSIL impression (OR = 0.02; 95% CI, 0.01-0.03) were less likely to experience pathological downgrading after conization than their counterparts. The independent risk factors for pathological upgrading to cervical cancer after conization included the following: age (OR = 1.08; 95% CI, 1.04-1.12), HPV 16 infection (OR = 4.07; 95% CI, 1.70-9.78), the presence of coarse punctate vessels during colposcopy examination (OR = 2.21; 95% CI, 1.08-4.50), atypical vessels (OR = 6.87; 95% CI, 2.81-16.83), and HSIL lesions in the endocervical canal (OR = 2.91; 95% CI, 1.46-5.77). Among the six machine learning prediction models, the back propagation (BP) neural network model demonstrated the highest and most uniform predictive performance in the downgrading, HSIL, and upgrading groups, with areas under the curve (AUCs) of 0.90, 0.84, and 0.69; sensitivities of 0.74, 0.84, and 0.42; specificities of 0.90, 0.71, and 0.95; and accuracies of 0.74, 0.84, and 0.95, respectively. In the external testing set, the BP neural network model showed a higher predictive performance than the logistic regression model, with an overall AUC of 0.91. Therefore, a web-based prediction tool was developed in this study. BP neural network prediction model has excellent predictive performance and can be used for the risk stratification of patients with CDB-diagnosed HSIL.

Anal intraepithelial neoplasia screening in women from the largest center for lower genital tract disease in China.

This study aimed to provide comprehensive clinical screening data for anal intraepithelial neoplasia (AIN). This study included 312 patients who underwent high-resolution anoscopy (HRA) examinations between January 1, 2020 and April 15, 2024. Clinical data, including demographic information, clinical history, cytology/high-risk human papilloma virus (hrHPV) results, and HRA records, were analyzed. The median age of all patients was 42 years (interquartile range: 33-52 years). Approximately 26.3% reported a history of VIN2/3+, 13.5% had a history of VaIN2/3+, 29.8% had a history of CIN2/3+, 44.6% had persistent cervical HPV16 infection, and 12.5% had immune suppression. Among the 312 patients, 14.4% were diagnosed with AIN2/3, 25.0% with AIN1 and 60.6% were normal. Anal cytological abnormalities were found in 41.3% of all patients, with a significantly higher rate in AIN2/3 patients than in ≤AIN1, 71.1% versus 36.3%, p < 0.001. The hrHPV positivity rate was 89.7%, with HPV16 being the most prevalent. The complete agreement rate for HRA impressions was 79.5%. Multi-variable analysis revealed immune suppression (odds ratio [OR]: 3.47, 95% confidence interval [CI]: 1.42-8.5) and VIN2/3+ (OR: 2.82, 95% CI: 1.27-6.28) were independent risk factors for AIN2/3. Abnormal cytology results (OR: 3.3, 95% CI: 1.52-7.17) and anal HPV16 infection (OR: 3.2, 95% CI: 1.26-8.12) demonstrated similar ORs for AIN2/3. Early screening for AIN2/3+ is crucial in Chinese women with lower genital tract precancerous and cancerous lesions, particularly in those with VIN2/3+ and immune suppression.