Peng Xue

Pooled performance of urinary human papillomavirus (HPV) testing for the presence of cervical HPV

Abstract Background Human papillomavirus (HPV) testing is the preferred method for cervical cancer screening. Noninvasive urinary HPV testing offers an attractive alternative to improve screening coverage among underscreened populations. However, few studies have assessed its performance in detecting cervical HPV via systematic review and meta‐analysis. Methods This study systematically searched PubMed, Embase, Web of Science Core Collection, and the Cochrane Library from inception until September 2023. The aim was to assess the performance of urinary HPV testing for detecting cervical HPV against two distinct reference standards: cervical HPV infection and histologically confirmed cervical intraepithelial neoplasia grade 2 or worse (CIN2+) or grade 3 or worse (CIN3+). The study also pooled results of vaginal self‐sampling and participants' attitudes toward urine sampling. This study was registered in PROSPERO (CRD42023462218). Results A total of 2292 records were initially retrieved, and four previous reviews were examined. The meta‐analysis was conducted in two primary parts on the basis of the reference standard. First, with cervical HPV infection as the reference standard, the analysis included 65 studies (17,766 individuals). Compared to cervical samples, urine samples had a lower detection rate for high‐risk HPV (46% vs. 53%; p  = .110), with a pooled sensitivity of 76% (95% CI, 72%–80%) and specificity of 90% (95% CI, 87%–92%). Second, with CIN2+ as the reference standard, the analysis included 31 studies (15,054 individuals). In a direct comparison with paired data from 28 of these studies, urine samples demonstrated lower sensitivity than cervical samples (79% [95% CI, 72%–84%] vs. 93% [95% CI, 89%–96%]) but slightly higher specificity (58% [95% CI, 50%–65%] vs. 50% [95% CI, 42%–58%]). Additionally, with CIN2+ as the reference standard, this analysis of 21 studies (8974 individuals) showed that vaginal self‐samples had higher sensitivity (89% vs. 79%) but lower specificity (43% vs. 49%) compared to urine samples. Similar results were observed for CIN3+. Questionnaires or interviews conducted in 13 studies with 2426 participants revealed a greater preference for urine sampling. Conclusions Urinary HPV testing exhibits lower sensitivity than both cervical professional sampling and vaginal self‐sampling. Although its noninvasive nature supports its potential utility as a complementary strategy in resource‐limited or culturally conservative populations, it is not currently suitable for general population screening where standard methods are available because of its suboptimal performance and lack of standardized protocols for sampling and testing. Furthermore, standardization of sampling and testing protocols is required before routine implementation.

The Infection Status and Distribution of hrHPV in Cervical Lesions Among Overweight Women Referred for Colposcopy: A Cross-Sectional Study

Introduction Among opportunistically screened population with above normal-weight, screening-related information remains limited. This study aimed to evaluate the infection status and distribution of high-risk (hr) human papillomavirus (HPV) on precancerous grades and cancer among overweight women compared with normal-weight women, and further explored the association between clinical characteristics and both HPV infection and cervical lesions. Methods The reporting of this cross-sectional retrospective study conforms to STROBE guidelines. This study was conducted in the Affiliated Cancer Hospital of Xinjiang Medical University. A total of 720 out of 1146 women with complete medical records of demographic and clinical characteristics were enrolled on the colposcopy clinic. HrHPV infection status, cytology abnormality rates, detection rates of cervical intraepithelial neoplasia (CIN) grades and cancer, and clinical performance of triage tests were evaluated by Body Mass Index (BMI) levels, which were classified into two groups as overweight women (BMI ≥24 kg/m 2 ) and normal-weight women (BMI <24 kg/m2). Results The hrHPV infection rate of overweight women (73.0%) was not significantly lower than that of normal-weight women (78.6%) by the GenoArray test ( P = 0.09) and by HC2 test (68.5% vs 71.0%, P = 0.53). The positive rates of most frequent hrHPV subtypes of overweight women vs normal-weight women were HPV16 (31.1% vs 39.1%, P = 0.03). The detection rates of CIN lesions were lower among overweight vs normal-weight (28.3% vs 37.4%, P = 0.01), while the detections rate of cancer was slightly higher but not significant (7.2% vs 4.6%, P = 0.14). The clinical performance of different screening strategies were similar between overweight and normal weight women. Conclusions The HPV16 prevalence and the detection rate of cervical precancerous lesions was lower in overweight women than normal-weight women, indicating that targeted management strategies should be given to overweight women to decrease the underdiagnosis.

Deep Learning-Assisted System Improves Practical Effects in Cervical Cytopathology Diagnosis: A Comparative Study of Reading Modes

Deep learning (DL) has significantly improved the diagnostic accuracy and efficiency of cytopathologists. However, current DL-assisted reading modes have yet to be fully evaluated, and there is limited evidence regarding cytopathologists' preferences and experiences. This study employs a randomized, controlled, 4-way crossover design to assess the effectiveness of 4 distinct cytopatholog reading modes. This study included retrospectively collected 1620 cervical slides between 2021 and 2022. These slides were read by 108 certified cytopathologists with varying expertise using the 4 reading modes: unassisted, concurrent, second, and triage mode. A questionnaire survey was conducted to gather the cytopathologists' adoption of each mode, including mode score and their confidence and preferences. Compared with unassisted, all DL-assisted modes improved the cytopathologists' diagnostic performance. The unassisted mode had a sensitivity of 66.2% and a specificity of 72.5%. The second, concurrent, and triage modes all improved on these metrics: sensitivity increased by 20.2%, 17.3%, and 16.7%, respectively, whereas specificity increased by 13.2%, 10.8%, and 22.3%, respectively. The median reading time per slide was prolonged in second mode from 200 to 235 seconds but substantially reduced to 130 seconds in triage mode, and 53 seconds in concurrent mode (P 90% of cytopathologists agreed that the DL-assisted mode was useful in improving diagnostic confidence, and they preferred the concurrent mode, whereas the triage mode achieved the highest performance in the mode score. The second mode stands out for its heightened sensitivity, the triage mode boasts superior specificity, and the concurrent mode leads in efficiency when assisting cytopathologists. These findings provide useful information for choosing appropriate DL-assisted modes in cytopathological practice.

Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection

Deep learning (DL) enabled liquid-based cytology has potential for cervical cancer screening or triage. Here, we develop a DL model using whole cytology slides from 17,397 women and test it on 10,826 additional cases through a three-stage process. The DL model achieves robust performance across nine hospitals. In a multi-reader, multi-case study, it outperforms cytopathologists' sensitivity by 9%. Reading time significantly decreases with DL assistance (218s vs 30s; p  0.999), yet it has reduced specificity (0.831 vs 0.901; p < 0.0001). Notably, hospital-based opportunistic screening shows that junior cytopathologists with DL assistance significantly improve both their sensitivity and specificity (0.857 vs 0.657, 0.840 vs 0.737; both p < 0.0001). When triaging human papillomavirus-positive cases, DL assistance exhibits better performance than junior cytopathologists alone. These findings support using the DL model as an assistance tool in cervical screening and case triage.

AI-Based Identification Method for Cervical Transformation Zone Within Digital Colposcopy: Development and Multicenter Validation Study

Background In low- and middle-income countries, cervical cancer remains a leading cause of death and morbidity for women. Early detection and treatment of precancerous lesions are critical in cervical cancer prevention, and colposcopy is a primary diagnostic tool for identifying cervical lesions and guiding biopsies. The transformation zone (TZ) is where a stratified squamous epithelium develops from the metaplasia of simple columnar epithelium and is the most common site of precancerous lesions. However, inexperienced colposcopists may find it challenging to accurately identify the type and location of the TZ during a colposcopy examination. Objective This study aims to present an artificial intelligence (AI) method for identifying the TZ to enhance colposcopy examination and evaluate its potential clinical application. Methods The study retrospectively collected data from 3616 women who underwent colposcopy at 6 tertiary hospitals in China between 2019 and 2021. A dataset from 4 hospitals was collected for model conduction. An independent dataset was collected from the other 2 geographic hospitals to validate model performance. There is no overlap between the training and validation datasets. Anonymized digital records, including each colposcopy image, baseline clinical characteristics, colposcopic findings, and pathological outcomes, were collected. The classification model was proposed as a lightweight neural network with multiscale feature enhancement capabilities and designed to classify the 3 types of TZ. The pretrained FastSAM model was first implemented to identify the location of the new squamocolumnar junction for segmenting the TZ. Overall accuracy, average precision, and recall were evaluated for the classification and segmentation models. The classification performance on the external validation was assessed by sensitivity and specificity. Results The optimal TZ classification model performed with 83.97% classification accuracy on the test set, which achieved average precision of 91.84%, 89.06%, and 95.62% for types 1, 2, and 3, respectively. The recall and mean average precision of the TZ segmentation model were 0.78 and 0.75, respectively. The proposed model demonstrated outstanding performance in predicting 3 types of the TZ, achieving the sensitivity with 95% CIs for TZ1, TZ2, and TZ3 of 0.78 (0.74-0.81), 0.81 (0.78-0.82), and 0.8 (0.74-0.87), respectively, with specificity with 95% CIs of 0.94 (0.92-0.96), 0.83 (0.81-0.86), and 0.91 (0.89-0.92), based on a comprehensive external dataset of 1335 cases from 2 of the 6 hospitals. Conclusions Our proposed AI-based identification system classified the type of cervical TZs and delineated their location on multicenter, colposcopic, high-resolution images. The findings of this study have shown its potential to predict TZ types and specific regions accurately. It was developed as a valuable assistant to encourage precise colposcopic examination in clinical practice.

The Distribution of Cervical Transformation Zone and Its Impact on Colposcopic Diagnosis: A Multicenter Study in China

Objective The value of the transformation zone (TZ) is often overlooked in clinical settings. This study aims to assess TZ distribution, associated factors, and its impact on colposcopic diagnosis. Methods χ2 tests were used to analyze demographics, clinical history, and tissue samples to examine the differences in TZ distribution. Factors affecting the TZ were explored using logistic regression, and diagnostic indicators were calculated. Results A total of 5,302 individual datasets were finally included. TZ1, TZ2, and TZ3 accounted for 31.6%, 38.5%, and 30.0%, respectively. Age is the most important factor that influences the location of the TZ. The proportion of TZ3 steadily increased with age, comprising over 55% in women over 50. The colposcopic diagnostic performance shows that high-grade squamous intraepithelial lesion or worse (HSIL+) sensitivity of TZ3 (58.1%, 95% confidence interval [CI] = 52.9–63.4) is significantly lower than that of TZ1 (69.8%, 95% CI = 65.5–74.1) and TZ2 (73.2%, 95% CI = 69.7–76.8). The HSIL+ specificity of TZ3 (96.3, 95% CI = 95.3–97.4) was higher than that of TZ1 (96.3, 95% CI = 95.2–97.3) and TZ2 (92.5, 95% CI = 91.1–93.9). The HSIL+ positive predictive value (81.3%, 95% CI = 76.4–86.2) and negative predictive value (89.3%, 95% CI = 87.6–90.9) for TZ3 are high, with no significant differences when compared with TZ1 and TZ2. Conclusions Age predominantly influences TZ location, with TZ3 being most frequently found in women over 50. While TZ3 poses a higher risk of missed diagnosis during colposcopy, it remains clinically valuable in identifying diseased and nondiseased status. Increasing colposcopists' awareness of TZ importance is needed in clinical practice.

Distribution and diagnostic value of single and multiple high‐risk HPV infections in detection of cervical intraepithelial neoplasia: A retrospective multicenter study in China

AbstractThe risk associated with single and multiple human papillomavirus (HPV) infections in cervical intraepithelial neoplasia (CIN) remains uncertain. This study aims to explore the distribution and diagnostic significance of the number of high‐risk HPV (hr‐HPV) infections in detecting CIN, addressing a crucial gap in our understanding. This comprehensive multicenter, retrospective study meticulously analyzed the distribution of single and multiple hr‐HPV, the risk of CIN2+, the relationship with CIN, and the impact on the diagnostic performance of colposcopy using demographic information, clinical histories, and tissue samples. The composition of a single infection was predominantly HPV16, 52, 58, 18, and 51, while HPV16 and 33 were identified as the primary causes of CIN2+. The primary instances of dual infection were mainly observed in combinations such as HPV16/18, HPV16/52, and HPV16/58, while HPV16/33 was identified as the primary cause of CIN2+. The incidence of hr‐HPV infections shows a dose–response relationship with the risk of CIN (p for trend &lt;0.001). Compared to single hr‐HPV, multiple hr‐HPV infections were associated with increased risks of CIN1 (1.44, 95% confidence interval [CI]: 1.20–1.72), CIN2 (1.70, 95% CI: 1.38–2.09), and CIN3 (1.08, 95% CI: 0.86–1.37). The colposcopy‐based specificity of single hr‐HPV (93.4, 95% CI: 92.4–94.4) and multiple hr‐HPV (92.9, 95% CI: 90.8–94.6) was significantly lower than negative (97.9, 95% CI: 97.0–98.5) in detecting high‐grade squamous intraepithelial lesion or worse (HSIL+). However, the sensitivity of single hr‐HPV (73.5, 95% CI: 70.8–76.0) and multiple hr‐HPV (71.8, 95% CI: 67.0–76.2) was higher than negative (62.0, 95% CI: 51.0–71.9) in detecting HSIL+. We found that multiple hr‐HPV infections increase the risk of developing CIN lesions compared to a single infection. Colposcopy for HSIL+ detection showed high sensitivity and low specificity for hr‐HPV infection. Apart from HPV16, this study also found that HPV33 is a major pathogenic genotype.

Genotype, cervical intraepithelial neoplasia, and type‐specific cervical intraepithelial neoplasia distributions in hrHPV+ cases referred to colposcopy: A multicenter study of Chinese mainland women

AbstractTo investigate age and type‐specific prevalences of high‐risk human papillomavirus (hrHPV) and cervical intraepithelial neoplasia (CIN) in hrHPV+ women referred to colposcopy. This is a retrospective, multicenter study. Participants were women referred to one of seven colposcopy clinics in China after testing positive for hrHPV. Patient characteristics, hrHPV genotyping, colposcopic impressions, and histological diagnoses were abstracted from electronic records. Main outcomes were age‐related type‐specific prevalences associated with hrHPV and CIN, and colposcopic accuracy. Among 4419 hrHPV+ women referred to colposcopy, HPV 16, 52, and 58 were the most common genotypes. HPV 16 prevalence was 39.96%, decreasing from 42.57% in the youngest group to 30.81% in the eldest group. CIN3+ prevalence was 15.00% and increased with age. As lesion severity increases, HPV16 prevalence increased while the prevalence of HPV 52 and 58 decreased. No age‐based trend was identified with HPV16 prevalence among CIN2+, and HPV16‐related CIN2+ was less common in women aged 60 and above (44.26%) compared to those younger than 60 years (59.61%). Colposcopy was 0.73 sensitive at detecting CIN2+ (95% confidence interval[CI]: 0.71, 0.75), with higher sensitivity (0.77) observed in HPV16+ women (95% CI: 0.74, 0.80) compared to HPV16− women (0.68, 95% CI: 0.64, 0.71). Distributions of hrHPV genotypes, CIN, and type‐specific CIN in Chinese mainland hrHPV+ women referred to colposcopy were investigated for the first time. Distributions were found to be age‐dependent and colposcopic performance appears related to HPV genotypes. These findings could be used to improve the management of women referred to colposcopy.

Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies

AbstractBackgroundColposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colposcopic Artificial Intelligence Auxiliary Diagnostic System (CAIADS) for grading colposcopic impressions and guiding biopsies.MethodsAnonymized digital records of 19,435 patients were obtained from six hospitals across China. These records included colposcopic images, clinical information, and pathological results (gold standard). The data were randomly assigned (7:1:2) to a training and a tuning set for developing CAIADS and to a validation set for evaluating performance.ResultsThe agreement between CAIADS-graded colposcopic impressions and pathology findings was higher than that of colposcopies interpreted by colposcopists (82.2% versus 65.9%, kappa 0.750 versus 0.516,p &lt; 0.001). For detecting pathological high-grade squamous intraepithelial lesion or worse (HSIL+), CAIADS showed higher sensitivity than the use of colposcopies interpreted by colposcopists at either biopsy threshold (low-grade or worse 90.5%, 95% CI 88.9–91.4% versus 83.5%, 81.5–85.3%; high-grade or worse 71.9%, 69.5–74.2% versus 60.4%, 57.9–62.9%; allp &lt; 0.001), whereas the specificities were similar (low-grade or worse 51.8%, 49.8–53.8% versus 52.0%, 50.0–54.1%; high-grade or worse 93.9%, 92.9–94.9% versus 94.9%, 93.9–95.7%; allp &gt; 0.05). The CAIADS also demonstrated a superior ability in predicting biopsy sites, with a median mean-intersection-over-union (mIoU) of 0.758.ConclusionsThe CAIADS has potential in assisting beginners and for improving the diagnostic quality of colposcopy and biopsy in the detection of cervical precancer/cancer.

Improving colposcopic accuracy for cervical precancer detection: a retrospective multicenter study in China

Abstract Background Colposcopy alone can result in misidentification of high-grade squamous intraepithelial or worse lesions (HSIL +), especially for women with Type 3 transformation zone (TZ) lesions, where colposcopic assessment is particularly imprecise. This study aimed to improve HSIL + case identification by supplementing referral screening results to colposcopic findings. Methods This is an observational multicenter study of 2,417 women, referred to colposcopy after receiving cervical cancer screening results. Logistic regression analysis was conducted under uni- and multivariate models to identify factors which could be used to improve HSIL + case identification. Histological diagnosis was established as the gold standard and is used to assess accuracy, sensitivity, and specificity, as well as to incrementally improve colposcopy. Results Multivariate analysis highlighted age, TZ types, referral screening, and colposcopists’ skills as independent factors. Across this sample population, diagnostic accuracies for detecting HSIL + increased from 72.9% (95%CI 71.1–74.7%) for colposcopy alone to 82.1% (95%CI 80.6–83.6%) after supplementing colposcopy with screening results. A significant increase in colposcopic accuracy was observed across all subgroups. Although, the highest increase was observed in women with a TZ3 lesion, and for those diagnosed by junior colposcopists. Conclusion It appears possible to supplement colposcopic examinations with screening results to improve HSIL + detection, especially for women with TZ3 lesions. It may also be possible to improve junior colposcopists’ diagnoses although, further psychological research is necessary. We need to understand how levels of uncertainty influence diagnostic decisions and what the concept of “experience” actually is and what it means for colposcopic practice.

Assessing artificial intelligence enabled liquid‐based cytology for triaging HPV‐positive women: a population‐based cross‐sectional study

AbstractIntroductionCytology‐based triaging is commonly used to manage the care of women with positive human papillomavirus (HPV) results, but it suffers from subjectivity and a lack of sensitivity and reproducibility. The diagnostic performance of an artificial intelligence‐enabled liquid‐based cytology (AI‐LBC) triage approach remains unclear. Here, we compared the clinical performance of AI‐LBC, human cytologists and HPV16/18 genotyping at triaging HPV‐positive women.Material and methodsHPV‐positive women were triaged using AI‐LBC, human cytologists and HPV16/18 genotyping. Histologically confirmed cervical intraepithelial neoplasia grade 2/3 or higher (CIN2+/CIN3+) were accepted as thresholds for clinical performance assessments.ResultsOf the 3514 women included, 13.9% (n = 489) were HPV‐positive. The sensitivity of AI‐LBC was comparable to that of cytologists (86.49% vs 83.78%, P = 0.744) but substantially higher than HPV16/18 typing at detecting CIN2+ (86.49% vs 54.05%, P = 0.002). While the specificity of AI‐LBC was significantly lower than HPV16/18 typing (51.33% vs 87.17%, P &lt; 0.001), it was significantly higher than cytologists at detecting CIN2+ (51.33% vs 40.93%, P &lt; 0.001). AI‐LBC reduced referrals to colposcopy by approximately 10%, compared with cytologists (51.53% vs 60.94%, P = 0.003). Similar patterns were also observed for CIN3+.ConclusionsAI‐LBC has equivalent sensitivity and higher specificity compared with cytologists, with more efficient colposcopy referrals for HPV‐positive women. AI‐LBC could be particularly useful in regions where experienced cytologists are few in number. Further investigations are needed to determine triaging performance through prospective designs.

Diagnostic value of high‐risk HPV other than type 16/18 in high‐grade cervical neoplasia among cytology‐negative women: A multicenter retrospective study

AbstractBackgroundHuman papillomavirus (HPV) is a necessary cause of cervical cancer, and a tool more sensitive than cytology for the early screening of cervical precancers. The two most carcinogenic genotypes HPV 16/18 have been reported in the majority of studies. High‐risk HPVs other than HPV 16/18 (non‐16/18‐hrHPVs) cause approximately a quarter of cervical cancers, and we aimed to analyze the genotype‐specific prevalence, risk and diagnostic efficiency of non‐16/18‐hrHPVs in cervical carcinogenesis among Chinese cytology‐negative women.MethodsA total of 7043 females who had abnormal cervical testing results from January 2018 to October 2021 were enrolled, among them 3091 were cytology‐negative. Descriptive statistics was used to estimate the HPV genotype‐specific prevalence, and multivariable logistic regression was used to estimate the genotype‐specific non‐16/18 hrHPVs risk of cervical carcinogenesis. The evaluation of diagnostic value among HPV genotypes included the possibility of predicting cervical intraepithelial neoplasia grade 2/3 or worse (CIN2+/CIN3+) and the diagnostic efficiency measured by increased referral rate and referral numbers of colposcopies per additional CIN2+/CIN3+ detected.ResultsAmong HPV‐positive cytology‐negative women, the five dominant genotypes for CIN2+/CIN3+ were HPV 31/33/35/52/58. HPV 52/58/33 had comparatively high sensitivity and specificity in predicting CIN2+/CIN3+, while the referral strategy of multiple HPV58 required 26 colposcopies to detect 1 CIN3+, compared with 14, 12, and 8 required by multiple HPV52, 31, and 33, respectively.ConclusionsHPV31/33/35/52/58 infections are significant risk factors for cervical lesions, and multiple HPV 31/33/52 infections should be included in the previously recommended HPV16/18 genotyping triage for colposcopy in China, as the benefits of disease prevention may outweigh the disadvantages of increasing requirements for colposcopy services.