This study aimed to explore a discriminant method for cervical squamous epithelial cells based on computer image analysis to establish a foundation for artificial intelligence diagnosis of cervical cancer. A total of 1682 cells were captured from 53 Papanicolaou smears, and computer image analysis was used to test the chromatics and geometric structural parameters of the above cells. Stepwise discriminant analysis was used to establish the discriminant function, and the discriminant effects of the function were evaluated. The chromaticity and geometric features of the cell images had significantly different among low-grade cells (LGC), atypical squamous cells of undetermined significance, and high-grade cells (HGC), as well as between subclassifications within LGC and HGC (P < .05). Stepwise discriminant analysis was used to create functions for classifying cells into the categories LGC, atypical squamous cells of undetermined significance, and HGC, as well as subclassifications within LGC and HGC. Functions combined with chromatics and geometry features have a good discriminant effect on cervical squamous cells. The discriminant coincidence rates indicate that this method can be an appropriate reference approach for the classification and diagnosis of cervical squamous epithelial cells, and its potential applications are presented in a tentative study on automated image analysis systems for cytological fields.

Global cervical cancer incidence and mortality rank fourth in women with malignant tumors, which have been reduced by population-based cervical cancer screening. The previously used Papanicolaou cytological testing of cervical smears was replaced by the Bethesda classification, which facilitated the early detection of the pre-cancerous lesions, used together with other methods such as colposcopy, testing of high-risk genotypes of human papillomavirus (hrHPV) and molecular techniques. The recently introduced terminology as ASC (atypical squamous cells), ASC-US (ASC, undetermined significance), ASC-H (ASC, cannot exclude HSIL), as well LSIL and HSIL (low- and high-grade squamous intraepithelial lesions) is widely used in Hungary, which guides the treatments of the patients by the clinicians. The detection of HPV is incorporated into the management of the patients as well. It is, however, highly important to update the domestic cervical cancer screening, especially with the introduction of the first-line, primary HPV screening in Hungary, in agreement with the opinions of other groups.

The Aptima human papillomavirus (HPV) test (APTIMA) detects E6-E7 mRNA in abnormal cells in the uterine cervix. To investigate the accuracy of APTIMA for cervical cancer screening in Japan, 423 subjects, mostly referrals with abnormal cytology or being followed up for cervical intraepithelial neoplasia (CIN)1, were screened using two HPV tests, hybrid capture 2 (HC2) and APTIMA, and by the Pap test. Colposcopy was conducted in all subjects with a positive result in either test type. HPV genotyping was performed by Genosearch-31. A result of atypical squamous cells-undetermined significance (ASC-US) or worse on the HC2 test (ASC-US-HC2), and low-grade squamous intraepithelial lesion (LSIL) or worse (LSIL+) on the Pap test, was regarded as positive. APTIMA (97.5%) was more sensitive than LSIL+ (85.1%) for detecting CIN2 or worse (CIN2+) (McNemar test; p = .0003), and more sensitive (98.6%) than ASC-US-HC2 (92.7%) for detecting CIN3+. APTIMA and HC2 had similar sensitivities. HPV genotyping revealed that CIN2/3 with high-risk HPV (HR-HPV) was overlooked in five cases by ASC-US-HC2, and in four cases by HC2, while no such lesions were missed by APTIMA. Thus, APTIMA might be superior to HC2 for primary HPV screening in Japan. One cancer case positive for HPV67 (potentially high risk, [pHR]) was overlooked by Pap test and both HPV tests, suggesting a need for a new HPV test able to detect pHR-HPV types.

Objective: To evaluate the performance of Hybribio human papillomavirus (HPV) typing test kit for high risk HPV-DNA typing detection in screening of cervical precancer lesions. Methods: A total of 9 914 women were recruited in Henan, Shanxi, and Guangdong provinces from June to July 2017. All women underwent HPV DNA test. The women who diagnosed as HPV positive and cytological examination ≥ atypical squamous cells of undetermined significance (ASCUS) or HPV negative and cytological examination≥low-grade squamous intraepithelial lesions (LSIL) underwent colposcopy biopsy and pathological examination. Using the pathological diagnosis as the gold standard, the sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and 95% confidence interval (CI) of high-risk HPV and HPV16/18 tests were calculated. Results: The mean age of 9 914 subjects was (45.0±9.3) years old. Among them, 1 302 subjects were detected as high risk HPV positive, including 211 of HPV16 positive and 64 of HPV18 positive. According to the pathological gold standard of cervical intraepithelial neoplasia grade 2 (CIN2) or worse, the sensitivity and specificity of high risk-HPV and HPV 16/18 for triaging ASCUS women were 90.6% (95%CI: 75.8%-96.8%) and 78.0% (95%CI: 74.5%-81.2%) as well as 56.3% (95%CI: 39.3%-71.8%) and 95.7% (95%CI: 93.8%-97.1%), respectively. The sensitivity and specificity of high risk-HPV and HPV 16/18 for cervical precancer lesions screening were 95.1% (95%CI: 88.1%-98.1%) and 87.6% (95%CI: 86.9%-88.2%) as well as 65.9% (95%CI: 55.1%-75.2%) and 97.8% (95%CI: 97.5%-98.1%), respectively. Conclusions: The Hybribio HPV test kit has a relative high sensitivity and specificity for cervical precancer lesions screening and ASCUS triaging. It is reliable for HPV DNA detection and cervical cancer screening.

Persistent high-risk human papillomavirus (HR-HPV) infections play a major role in the development of invasive cervical cancer (CC), and screening for such infections is in many countries the primary method of detecting and preventing CC. HPV typing can be used for triage and risk stratification of women with atypical squamous cells of undetermined significance (ASC-US)/low-grade cervical lesions (LSIL), though the current clinical practice in Mexico is to diagnose CC or its preceding conditions mainly via histology and HR-HPV detection. Additional information regarding these HPV infections, such as viral load and co-infecting agents, might also be useful for diagnosing, predicting, and evaluating the possible consequences of the infection and of its prevention by vaccination. The goal of this follow-up hospital case study was to determine if HPV types, multiple HPV infections, and viral loads were associated with infection persistence and the cervical lesion grade. A total of 294 cervical cytology samples drawn from patients with gynecological alterations were used in this study. HPV types were identified by real-time PCR DNA analysis. A subset of HPV-positive patients was reevaluated to identify persistent infections. We identified HPV types 16, 18, and 39 as the most prevalent. One hundred five of the patients (59%) were infected with more than one type of HPV. The types of HPV associated with multiple HPV infections were 16, 18, and 39. In the follow-up samples, 38% of patients had not cleared the initially detected HPV infection, and these were considered persistent. We found here an association between multiple HPV infections and high viral loads with and infection persistence. Our findings suggest there are benefits in ascertaining viral load and multiple HPV infections status of HR-HPV infections for predicting the risk of persistence, a requirement for developing CC. These findings contribute to our understanding of HPV epidemiology and may allow screening programs to better assess the cancer-developing risks associated with individual HR-HPV infections.

The standard screening test for detecting cervical lesions and cancers is a Papanicolaou (Pap) smear. While squamous cell abnormalities remain the most common positive Pap test result, cytologic findings of glandular cell abnormalities have become more frequent in recent decades. The 2014 Bethesda System for reporting cervical cytology includes the classification "atypical glandular cells" (AGC). AGC have morphological abnormalities that fall outside the range of reactive changes, but are insufficient for a diagnosis of invasive adenocarcinoma. In several histologic follow-up studies, most AGC cases were found to represent a benign condition. In the current study, we evaluate the significance of AGC cytology findings by analyzing the histologic follow-up results of a large number of patients with AGC. Most patients with AGC in this study were found to have a significant lesion on follow-up (63.9%), with negative histologic results in only 36.1% of patients. Among patients with significant lesions, the most common result was low-grade squamous intraepithelial lesion (26.6%), followed by high-grade squamous intraepithelial lesion (23.2%). This provides further evidence to support the Chilean Clinical Guidelines for Cervical Cancer, which recommends diagnostic follow-up studies in all women with AGC to minimize the chance of undetected serious cervical disease.

The 2014 Bethesda System diagnostic criteria for atypical glandular cells (AGC) aid in the classification of atypical cells in cervical cytology. Anyway, AGC diagnosis remains challenging, due to low frequencies of this finding (approximately 0.5%-1% of Pap test results), abundance of AGC mimics, and significant interobserver variability. We developed an algorithm based on nuclear areas parameter that can help to differentiate AGC from Normal and Reactive glandular cells. Nuclear areas and perimeters were measured on 16 Pap smears with AGC and 18 with Reactive glandular cells of women aged between 30 and 77. Glandular cells from nonpathological Pap smears were used as controls. For each case, the means, medians, standard deviations, and the minimum and maximum values of both nuclear areas and perimeters of the cells of interest were calculated. The nuclear area analysis showed a 100% specificity in discriminating Normal from Altered cells (either Reactive or AGC), whereas the nuclear perimeter analysis showed a lower specificity (87.5%). Both nuclear area and perimeter variability analysis resulted in high specificity values in distinguishing Reactive cells from AGC. Therefore, a stepwise two-step algorithm using nuclear areas to discriminate Normal from Altered cells, and nuclear area variability to distinguish Reactive from AGC, allowed us to reliably classify the cells into these three categories. The morphometric analysis of nuclear area is a valuable and reliable aid in AGC diagnosis and standardization, easily integrable into common automatic algorithms.