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The microbiome is able to modulate immune responses, alter the physiology of the human organism, and increase the risk of viral infections and development of diseases such as cancer. In this review, we address changes in the cervical microbiota as potential biomarkers to identify the risk of cervical intraepithelial neoplasia (CIN) development and invasive cervical cancer in the context of human papillomavirus (HPV) infection. Current approaches for clinical diagnostics and the manipulation of microbiota with the use of probiotics and through microbiota transplantation are also discussed.

Background The World Health Organization (WHO) called for global action towards the elimination of cervical cancer. One of the main strategies is to screen 70% of women at the age between 35 and 45 years and 90% of women managed appropriately by 2030. So far, approximately 85% of cervical cancers occur in low- and middle-income countries (LMICs). The colposcopy-guided biopsy is crucial for detecting cervical intraepithelial neoplasia (CIN) and becomes the main bottleneck limiting screening performance. Unprecedented advances in artificial intelligence (AI) enable the synergy of deep learning and digital colposcopy, which offers opportunities for automatic image-based diagnosis. To this end, we discuss the main challenges of traditional colposcopy and the solutions applying AI-guided digital colposcopy as an auxiliary diagnostic tool in low- and middle- income countries (LMICs). Main body Existing challenges for the application of colposcopy in LMICs include strong dependence on the subjective experience of operators, substantial inter- and intra-operator variabilities, shortage of experienced colposcopists, consummate colposcopy training courses, and uniform diagnostic standard and strict quality control that are hard to be followed by colposcopists with limited diagnostic ability, resulting in discrepant reporting and documentation of colposcopy impressions. Organized colposcopy training courses should be viewed as an effective way to enhance the diagnostic ability of colposcopists, but implementing these courses in practice may not always be feasible to improve the overall diagnostic performance in a short period of time. Fortunately, AI has the potential to address colposcopic bottleneck, which could assist colposcopists in colposcopy imaging judgment, detection of underlying CINs, and guidance of biopsy sites. The automated workflow of colposcopy examination could create a novel cervical cancer screening model, reduce potentially false negatives and false positives, and improve the accuracy of colposcopy diagnosis and cervical biopsy. Conclusion We believe that a practical and accurate AI-guided digital colposcopy has the potential to strengthen the diagnostic ability in guiding cervical biopsy, thereby improves cervical cancer screening performance in LMICs and accelerates the process of global cervical cancer elimination eventually.

Background Female-specific cancers, particularly breast, cervical, ovarian, and uterine cancers, account for nearly 40% of all cancers in women. This study aimed to analyze the global epidemiological trends of these cancers from 1990 to 2021, offering insights into their evolving patterns and providing valuable information for health policymakers to allocate healthcare resources more effectively. Methods Data from the Global Burden of Disease Study 2021 (GBD 2021) were used to comprehensively assess the global incidence, mortality, and disability-adjusted life years (DALYs) of female-specific cancers. Age-standardized rates facilitated cross-regional comparisons, accounting for differences in population size and demographics. The socio-demographic index (SDI) was employed to categorize regions and evaluate correlations between cancer burden and economic level. In addition, risk factors attributable to female-specific cancer deaths and DALYs were assessed based on the comparative risk assessment model of the GBD project. Results From 1990 to 2021, the global burden of female-specific cancers increased at varying rates. In 2021, breast cancer accounted for 2.08 million incident cases, 0.66 million deaths, and 20.25 million DALYs globally. In comparison, cervical, ovarian, and uterine cancers had lower burdens, with 0.67 million, 0.30 million, and 0.47 million incident cases, respectively. Age-standardized rates of breast, ovarian, and uterine cancers showed positive correlations with SDI, while cervical cancer exhibited a negative correlation. Attributable risk factors for breast cancer-associated deaths in 2021 included dietary risks, high body-mass index (BMI), high fasting plasma glucose, alcohol use, tobacco use, and low physical activity. Additional risk factors were unsafe sex and tobacco use for cervical cancer, high BMI and occupational risks for ovarian cancer, and high BMI for uterine cancer. Conclusions The burden of female-specific cancers has increased in recent decades, with significant demographic and regional discrepancies. These findings highlight the urgent need for targeted public health interventions to mitigate the global impact of these cancers.

Background Colposcopy 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. Methods Anonymized 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. Results The 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  < 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%; all p  < 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%; all p  > 0.05). The CAIADS also demonstrated a superior ability in predicting biopsy sites, with a median mean-intersection-over-union (mIoU) of 0.758. Conclusions The CAIADS has potential in assisting beginners and for improving the diagnostic quality of colposcopy and biopsy in the detection of cervical precancer/cancer.

Cervical cancer is the third most common malignant tumor in women worldwide in terms of both incidence and mortality. The field of cervical cancer treatment is rapidly evolving, and various combination therapies are being explored to enhance the efficacy of immune checkpoint inhibitors (ICI) and provide new treatment options for patients at different disease stages. Clinical trials involving immune checkpoint inhibitors are now being conducted following a phase 3 trial with cemiplimab, an ICI, which demonstrated a significant improvement in prognosis in advanced or metastatic cervical cancer patients. These trials include monotherapy and combination therapy with other immune therapies, chemotherapy, or radiation therapy. Furthermore, other approaches for controlling tumors via the immune system, such as therapeutic vaccination for specific tumor antigens or immune cell therapy including chimeric antigen receptor (CAR)-T cell therapy and tumor-infiltrating lymphocytes are being investigated. Ongoing trials will continue to illuminate the optimal strategies for combining these therapies and addressing challenges associated with immune checkpoint failure in cervical cancer. Herein, we conducted a review of articles related to immunotherapy for cervical cancer and describe current treatment strategies for cervical cancer via immunotherapy.

Cervical cancer is the leading cause of cancer deaths among Sub-Saharan African women. This systematic review aimed to identify information sources and their relation to cervical cancer knowledge, literacy, screening, and attitudes. Peer-reviewed literature was searched on 2 March 2022, and updated on 24 January 2023, in four databases—CINAHL Plus, Embase, PubMed, and Web of Science. Eligible studies included those that were empirical, published after 2002, included rural women, and reported on information sources and preferences. The quality of the selected articles was assessed using the Mixed Methods Appraisal Tool. Data extraction was conducted on an Excel spreadsheet, and a narrative synthesis was used to summarize findings from 33 studies. Healthcare workers were the most cited information sources, followed by mass media, social networks, print media, churches, community leaders, the Internet, and teachers. Community leaders were preferred, while healthcare workers were the most credible sources among rural women. There was generally low cervical cancer knowledge, literacy, and screening uptake, yet high prevalence of negative attitudes toward cervical cancer and its screening; these outcomes were worse in rural areas. A content analysis revealed a positive association of health information sources with cervical cancer literacy, knowledge, screening, and positive screening attitudes. Disparities in cervical cancer prevention exist between rural and urban Sub-Saharan African women.

Laparoscopic/robotic radical hysterectomy has been historically considered oncologically equivalent to open radical hysterectomy for patients with early cervical cancer. However, a recent prospective randomized trial (Laparoscopic Approach to Cervical Cancer, LACC) has demonstrated significant inferiority of the minimally invasive approach. The aim of this study is to evaluate the oncologic outcomes of combined laparoscopic-vaginal radical hysterectomy. Between August 1994 and December 2018, patients with invasive cervical cancer were treated using minimally-invasive surgery at the Universities of Jena, Charité Berlin (Campus CCM and CBF) and Cologne and Asklepios Clinic Hamburg. 389 patients with inclusion criteria identical to the LACC trial were identified. In contrast to the laparoscopic/robotic technique used in the LACC trial, all patients in our cohort underwent a combined transvaginal-laparoscopic approach without the use of any uterine manipulator. A total of 1952 consecutive patients with cervical cancer were included in the analysis. Initial International Federation of Gynecology and Obstetrics (FIGO) stage was IA1 lymphovascular space invasion (LVSI+), IA2 and IB1/IIA1 in 32 (8%), 43 (11%), and 314 (81%) patients, respectively, and histology was squamous cell in 263 (68%), adenocarcinoma in 117 (30%), and adenosquamous in 9 (2%) patients. Lymphovascular invasion was confirmed in 106 (27%) patients. The median number of lymph nodes was 24 (range 2–86). Lymph nodes were tumor-free in 379 (97%) patients. Following radical hysterectomy, 71 (18%) patients underwent adjuvant chemoradiation or radiation. After a median follow-up of 99 (range 1–288) months, the 3-, 4.5-, and 10-year disease-free survival rates were 96.8%, 95.8%, and 93.1 %, and the 3-, 4.5-, and 10-year overall survival rates were 98.5%, 97.8%, and 95.8%, respectively. Recurrence location was loco-regional in 50% of cases with recurrence (n=10). Interestingly, 9/20 recurrences occurred more than 39 months after surgery. The combined laparoscopic-vaginal technique for radical hysterectomy with avoidance of spillage and manipulation of tumor cells provides excellent oncologic outcome for patients with early cervical cancer. Our retrospective data suggest that laparoscopic-vaginal surgery may be oncologically safe and should be validated in further randomized trials.

Background Cervical cancer screening participation remains insufficient in most countries. Our aim was to evaluate whether offering a HPV self-sampling kit, either mailed directly to the woman’s home or using timely opt-in procedures for ordering the kit, increased screening participation compared with a standard second reminder. Methods In this randomized, controlled effectiveness trial, 9791 Danish women aged 30–64 who were due to receive the second reminder were equally randomized to either: 1) direct mailing of a second reminder and a self-sampling kit (directly mailed group); 2) mailing of a second reminder that offered a self-sampling kit to be ordered by e-mail, text message, phone, or webpage (opt-in group); or 3) mailing of a second reminder to attend regular cytology screening (control group). In an intention-to-treat analysis, we estimated the participation rate at 180 days post intervention, by returning a self-sample or attending regular cytology screening. We calculated the proportion of women with a positive HPV self-sample who attended for cervical cytology triage at the general practitioner within 90 days. Results Participation was significantly higher in the directly mailed group (38.0%) and in the opt-in group (30.9%) than in the control group (25.2%) (participation difference (PD): 12.8%, 95% CI: 10.6–15.0% and PD: 5.7%, 95% CI: 3.5–7.9%, respectively). Within 90 days, 107 women (90.7%, 95% CI: 83.9–95.3%) with a HPV-positive self-sample attended follow-up. Conclusions Offering the opportunity of HPV self-sampling as an alternative to regular cytology screening increased participation; the direct mailing strategy was the most effective invitation strategy. A high compliance with follow-up was seen. Trial registration Current Controlled Trials NCT02680262 . Registered 10 February 2016.

As one of the most common female cancers, cervical cancer often develops years after a prolonged and reversible pre-cancerous stage. Traditional classification algorithms used for detection of cervical cancer often require cell segmentation and feature extraction techniques, while convolutional neural network (CNN) models demand a large dataset to mitigate over-fitting and poor generalization problems. To this end, this study aims to develop deep learning models for automated cervical cancer detection that do not rely on segmentation methods or custom features. Due to limited data availability, transfer learning was employed with pre-trained CNN models to directly operate on Pap smear images for a seven-class classification task. Thorough evaluation and comparison of 13 pre-trained deep CNN models were performed using the publicly available Herlev dataset and the Keras package in Google Collaboratory. In terms of accuracy and performance, DenseNet-201 is the best-performing model. The pre-trained CNN models studied in this paper produced good experimental results and required little computing time. Graphical Abstract

Cancer arising from the uterine cervix is the fourth most common cause of cancer death among women worldwide. Almost 90% of cervical cancer mortality has occurred in low- and middle-income countries. One of the major aetiologies contributing to cervical cancer is the persistent infection by the cancer-causing types of the human papillomavirus. The disease is preventable if the premalignant lesion is detected early and managed effectively. In this review, we outlined the standard guidelines that have been introduced and implemented worldwide for decades, including the cytology, the HPV detection and genotyping, and the immunostaining of surrogate markers. In addition, the staging system used to classify the premalignancy and malignancy of the uterine cervix, as well as the safety and efficacy of the various treatment modalities in clinical trials for cervical cancers, are also discussed. In this millennial world, the advancements in computer-aided technology, including robotic modules and artificial intelligence (AI), are also incorporated into the screening, diagnostic, and treatment platforms. These innovations reduce the dependence on specialists and technologists, as well as the work burden and time incurred for sample processing. However, concerns over the practicality of these advancements remain, due to the high cost, lack of flexibility, and the judgment of a trained professional that is currently not replaceable by a machine.