Sensitivity and specificity values of high-risk HPV DNA, p16/ki-67 and HPV mRNA in young women with atypical squamous cells of undetermined significance (ASCUS) or low-grade squamous intraepithelial lesion (LSIL)

Predicting cervical intraepithelial neoplasia and determining the follow-up period in high-risk human papillomavirus patients

Purpose

Despite strong efforts to promote human papillomavirus (HPV) vaccine and cervical cancer screening, cervical cancer remains a threat to women's reproductive health. Some high-risk HPV types play a crucial role in the progression of cervical cancer and precancerous lesions. Therefore, HPV screening has become an important means to prevent, diagnose, and triage cervical cancer. This study aims to leverage artificial intelligence to predict individual risks of cervical intraepithelial neoplasia (CIN) in women with high-risk HPV infection and to recommend the appropriate triage strategy and follow-up period according to the risk level.

Materials and methods

A total of 475 cases were collected in this study. The sources were from the Department of Gynecology and Obstetrics in a tertiary hospital, a case report on HPV from the PubMed website, and clinical data of cervical cancer patients from The Cancer Genome Atlas (TCGA) database. Through in-depth study of the interaction between high-risk HPV and its risk factors, the risk factor relationship diagram structure was constructed. A Classification of Lesion Stages (CLS) algorithm was designed to predict cervical lesion stages. The risk levels of patients were analyzed based on all risk factors, and follow-up periods were formulated for each risk level.

Results

Our proposed CLS algorithm predicted the probability of occurrence of CIN3-the precancerous lesion stage of cervical cancer. This prediction was based on patients' HPV-16 and -18 infection status, age, presence of persistent infection, and HPV type. Follow-up periods of 3-6 months, 6-12 months, and 3- to 5-year intervals were suggested for high-risk, medium-risk, and low-risk patients, respectively.

Conclusion

A lesion prediction model was constructed to determine the probabilities of occurrence of CIN by analyzing individual data, such as patient lifestyle, physical assessments, and patient complaints, in order to identify high-risk patients. Furthermore, the potential implications of the calculated features were mined to devise prevention strategies.

Correlation between human papillomavirus viral load and cervical lesions classification: A review of current research

Cervical cancer is the fourth largest malignant tumor among women in the world. Human papillomavirus (HPV) infection can lead to cervical intraepithelial neoplasia (CIN) and cervical cancer. Active papillomavirus infection occurs when the infected basal cells replicate and fill a certain area. Persistent HPV infection can lead to squamous intraepithelial lesions, which are divided into CIN1, CIN2, and CIN3 according to how much epithelium is impacted. Different types of HPV have different possibilities of causing cervical cancer, and high-risk HPV is the main cause of cervical cancer. Research showed that viral load may be an indicator of the progression of cervical precancerous lesions, but this association does not seem to be universal. This article aims to summarize different genotypes, multiple infections, especially viral load, in cervical precancerous lesions, to guide early intervention.

Non-Invasive Differential Diagnosis of Cervical Neoplastic Lesions by the Lipid Profile Analysis of Cervical Scrapings

Cervical cancer is one of the most common cancers in women with pronounced stages of precancerous lesions. Accurate differential diagnosis of such lesions is one of the primary challenges of medical specialists, which is vital to improving patient survival. The aim of this study was to develop and test an algorithm for the differential diagnosis of cervical lesions based on lipid levels in scrapings from the cervical epithelium and cervicovaginal canal. The lipid composition of the samples was analyzed by high-performance chromato-mass spectrometry. Lipid markers were selected using the Mann–Whitney test with a cutoff value of 0.05 and by projections to latent structures discriminant analysis, where a projection threshold of one was chosen. The final selection of variables for binomial logistic regressions was carried out using the Akaike information criterion. As a result, a final neoplasia classification method, based on 20 logistic regression sub-models, has an accuracy of 79% for discrimination NILM/cervicitis/LSIL/HSIL/cancer. The model has a sensitivity of 83% and a specificity of 88% for discrimination of several lesions (HSIL and cancer). This allows us to discuss the prospective viability of further validation of the developed non-invasive method of differential diagnosis.

The Role of p16/Ki-67 Immunostaining, hTERC Amplification and Fibronectin in Predicting Cervical Cancer Progression: A Systematic Review

Human papillomaviruses (HPVs) are common sexually transmitted infectious agents responsible for several anogenital and head and neck cancers. Cervical cancer (CC) is the fourth leading cause of death in women with cancer. The progression of a persistent HPV infection to cancer takes 15–20 years and can be preventable through screening. Cervical cytology (Pap smear) is the standard screening test for CC and precancerous lesions. For ASC-US and ASC-H lesions, a combination of Pap smear and HR-HPV analysis is recommended as a triage step before colposcopy. However, these tests cannot predict progression to CC. For this purpose, we summarized current scientific data on the role of p16/Ki-67 immunohistostaining, telomerase and fibronectin in predicting progression to CC. p16 and p16/Ki-67 dual staining (DS) were more specific than HR-HPV DNA testing for the detection of CIN2+/CIN3+ in women with ASC-US and LSIL. Similarly, hTERC FISH analysis significantly improved the specificity and positive predictive value of HPV DNA testing in differentiating CIN2+ from CIN2 cytological samples. In conclusion, p16 IHC, p16/Ki-67 DS and hTERC FISH amplification are all valid adjunctive biomarkers which significantly increase the sensitivity and specificity of cervical dysplasia diagnosis, especially when combined with HPV DNA testing. However, considering the global socioeconomic background, we can postulate that p16 and p16/ Ki-67 IHC can be used as a next step after positive cytology for ASC-US or LSIL specimens in low-income countries, instead of HPV DNA testing. Alternatively, if HPV DNA testing is covered by insurance, p16 or p16/Ki-67 DS and HPV DNA co-testing can be performed. In middle- and high-income countries, hTERC amplification can be performed as an adjunctive test to HPV DNA testing in women with ASC-US and LSIL.

Diagnostic performance of p16/Ki-67 dual immunostaining at different number of positive cells in cervical smears in women referred for colposcopy

Background

The aim of the study was to evaluate the diagnostic accuracy of p16/Ki-67 dual immunostaining (p16/ Ki-67 DS) in cervical cytology and the number of positive p16/Ki-67 cells to diagnose high grade cervical intraepithelial neoplasia (CIN2+) in colposcopy population.

Subjects and methods

We performed an analysis on a subset cohort of 174 women enrolled within a large-scale randomised controlled human papillomavirus (HPV) self-sampling project organised as part of the population-based Cervical Cancer Screening Programme ZORA in Slovenia. This subset cohort of patients was invited to the colposcopy clinic, underwent p16/Ki-67 DS cervical cytology and had the number of p16/Ki-67 positive cells determined.

Results

Among analysed women, 42/174 (24.1%) had histologically confirmed CIN2+. The risk for CIN2+ was increasing with the number of positive cells (p < 0.001). The sensitivity of p16/Ki-67 DS for detection of CIN2+ was 88.1%, specificity was 65.2%, positive predictive value was 44.6% and negative predictive value was 94.5%.

Conclusions

Dual p16/Ki-67 immunostaining for the detection of CIN2+ has shown high sensitivity and high negative predictive value in our study, which is comparable to available published data. The number of p16/Ki-67 positive cells was significantly associated with the probability of CIN2+ detection. We observed a statistically significant and clinically relevant increase in specificity if the cut-off for a positive test was shifted from one cell to three cells.