Journal of Lower Genital Tract Disease最新文献

Objective: To evaluate the effectiveness and complication rate of vestibulectomy for vulvodynia.

Methods: A retrospective cohort study in a teaching and university hospital analyzing patients with vulvodynia with insufficient response to conservative treatment who underwent a vestibulectomy. Data from 114 consecutive vestibulectomy procedures done between September 2009 and October 2018 were retrospectively analyzed. All procedures were performed by the same surgeon.The primary outcome was difference in pain scale (6-point Q-tip test, Nociceptive Rating Scale) between preoperative consultation, postoperative visit, and last follow-up consultation. The secondary outcome was surgical complications, such as wound dehiscence and hematoma.

Results: Complete data were available for 80 patients. There was a significant reduction in median pain scores of between 65% and 80% on all 6 evaluated vestibular points during Q-tip tests. The median follow-up was 21 months, ranging from 1 to 92 months (interquartile range [IQR]). Overall, 75% of patients needed no further treatment at the end of the follow-up period. In 22.6% (18/80), a limited wound dehiscence was noted. No other complications were reported nor were there any cases of worsening of the complaints.

Conclusion/discussion: In this retrospective cohort study, a significant pain reduction occurred after vestibulectomy in patients who were not responding to conservative treatment. The complication rate of this surgical procedure is low. Vestibulectomy seems to be an effective technique for management of vulvodynia.

Objective: Persistent infection with high-risk human papillomavirus (HPV) is a key contributor to cervical intraepithelial neoplasia (CIN), but the relation between high-risk HPV genotypes and the location of CIN lesions remains unclear. The aims of this study were to investigate the most frequent biopsy site of CIN lesions in women with different HPV infection and to analyze the biopsy times, CIN frequency, and the clustering of CIN frequency based on 12-o'clock sites and cervical quadrant locations.

Materials and method: We conducted a retrospective study of HPV detection and genotyping at the virology department of our hospital. Colposcopy exams were performed by specialists according to a standardized protocol, and all visually abnormal areas were further biopsied. Pearson chi-squared tests and cluster analyses were implemented to analyze the data.

Results: Among 1,381 women enrolled in this study, 933 cases infected with HPV. HPV16, HPV58, and HPV18 were the most common genotypes. The most frequent biopsy site was the 6 o'clock position. The highest frequency of high-grade CIN findings in single-genotype HPV groups was the 6 o'clock position and that for multiple-genotype HPV group was the 12 o'clock location. All CIN clusters were found in the 6 and 12 o'clock biopsy sites, except in the HPV18 group. Quadrant 2 and 4 were clustered in most groups.

Conclusions: The 6 and 12 o'clock sites in cervical quadrant 2 and 4 should be targeted during cervical biopsy procedures. These findings can provide clinicians with specific recommendations on the optimal site for CIN biopsy when considering the HPV genotype.

Objective: A deep learning classifier that improves the accuracy of colposcopic impression.

Methods: Colposcopy images taken 56 seconds after acetic acid application were processed by a cervix detection algorithm to identify the cervical region. We optimized models based on the SegFormer architecture to classify each cervix as high-grade or negative/low-grade. The data were split into histologically stratified, random training, validation, and test subsets (80%-10%-10%). We replicated a 10-fold experiment to align with a prior study utilizing expert reviewer analysis of the same images. To evaluate the model's robustness across different cameras, we retrained it after dividing the dataset by camera type. Subsequently, we retrained the model on a new, histologically stratified random data split and integrated the results with patients' age and referral data to train a Gradient Boosted Tree model for final classification. Model accuracy was assessed by the receiver operating characteristic area under the curve (AUC), Youden's index (YI), sensitivity, and specificity compared to the histology.

Results: Out of 5,485 colposcopy images, 4,946 with histology and a visible cervix were used. The model's average performance in the 10-fold experiment was AUC = 0.75, YI = 0.37 (sensitivity = 63%, specificity = 74%), outperforming the experts' average YI of 0.16. Transferability across camera types was effective, with AUC = 0.70, YI = 0.33. Integrating image-based predictions with referral data improved outcomes to AUC = 0.81 and YI = 0.46. The use of model predictions alongside the original colposcopic impression boosted overall performance.

Conclusions: Deep learning cervical image classification demonstrated robustness and outperformed experts. Further improved by including additional patient information, it shows potential for clinical utility complementing colposcopy.