Cervical cancer is the fourth most common malignancy in females worldwide, and a leading cause of death in the United Kingdom (UK). The human papillomavirus (HPV) is the strongest risk factor for developing cervical intraepithelial neoplasia and cancer. Across the UK, the national HPV immunisation programme, introduced in 2008, has been successful in protecting against HPV-related infections. Furthermore, the National Health Service (NHS) implemented the cytology-based cervical cancer screening service to all females aged 25 to 64, which has observed a decline in cervical cancer incidence. In the UK, there has been an overall decline in age-appropriate coverage since April 2010. In 2019, the COVID-19 pandemic disrupted NHS cancer screening and immunisation programmes, leading to a 6.8% decreased uptake of cervical cancer screening from the previous year. Engagement with screening has also been associated with social deprivation. In England, incidence rates of cervical cancer were reported to be 65% higher in the most deprived areas compared to the least, with lifestyle factors such as cigarette consumption contributing to 21% of cervical cancer cases. In this article, we provide an update on the epidemiology of cervical cancer, and HPV pathogenesis and transmission, along with the current prevention programmes within the NHS.
Shear wave elastography (SWE) has shown promise in distinguishing lymph node malignancies. However, the diagnostic accuracies of various SWE parameters that quantify tissue stiffness are yet to be demonstrated. To evaluate the pooled diagnostic accuracy of different SWE parameters for differentiating lymph node malignancies, we conducted a systematic screening of four databases using the PRISMA guidelines. Lymph node biopsy was adopted as the reference standard. Emax (maximum stiffness), Emean (mean stiffness), Emin (minimum stiffness), and Esd (standard deviation) SWE parameters were subjected to separate meta-analyses. A sub-group analysis comparing the use of Emax in cervical (including thyroid) and axillary lymph node malignancies was also conducted. Sixteen studies were included in this meta-analysis. Emax and Esd demonstrated the highest pooled sensitivity (0.78 (95% CI: 0.69–0.87); 0.78 (95% CI: 0.68–0.87)), while Emean demonstrated the highest pooled specificity (0.93 (95% CI: 0.88–0.98)). From the sub-group analysis, the diagnostic performance did not differ significantly in cervical and axillary LN malignancies. In conclusion, SWE is a promising adjunct imaging technique to conventional ultrasonography in the diagnosis of lymph node malignancy. SWE parameters of Emax and Esd have been identified as better choices of parameters for screening clinical purposes.
e13556 Background: Immune checkpoint inhibitors (ICI) are used to manage patients with both small cell (SCLC) and non-small cell (NSCLC) lung cancer. However, ICI response rates are often low, and identifying patients that will benefit from ICIs can be challenging. The value of biomarkers used to predict ICI response, such as PD-L1, Combined Positive Score (CPS) or tumor mutational burden (TMB) have been debated. Furthermore, the resources needed to assess these biomarkers may not be available in many centres. Developing more accurate and accessible tools that predict ICI responses could enable a precision medicine approach that improves patient outcomes. This study aimed to use a novel machine-learning (ML) algorithm to predict response to different ICI therapies in patients with lung cancer based on clinically available data. Methods: 334 eligible records were cleaned and reprocessed from textual to categorical data using one hot encoding. Complete datasets were available for 161 patients. Differences in the data distribution were handled using the Synthetic Minority Oversampling Technique. Six ML algorithms were trained, including Linear regression, Support Vector Classifier, XGBoost Classifier, Random Forest, Decision Tree, and Gaussian Naive Bayes Classifier. These algorithms used 80% of the training data, were tested on 20% of validation data and used the Grid Search Cross-Validation technique for hyperparameter optimization. Results: For the 161 patients included in the final analysis, the mean age was 68 years and 48% were female. 9% of patients had SCLC and 80% had NSCLC. Patients receiving Pembrolizumab, Nivolumab and Atezolizumab comprised 62%, 11% and 25%, respectively. The artificial intelligence (AI) algorithm predicted and stratified ICI response better than PD-L1 levels. Of the ML algorithms, XGBoost Classifier predicted response with the most accuracy, 64% (0.61 F1 score). This model found that good performance status (0-1), female gender and adenocarcinoma sub-type predicted response to ICI. On the other hand, M1, N2 staging, male gender, squamous cell carcinoma sub-type and receiving Atezolizumab were predictive of disease progression. Conclusions: This study developed multiple novel ML models to predict responses to ICIs in lung cancer. XGBoost Classifier used clinically available data to show that the type of ICI a patient receives, their histopathology sub-type and their TMN staging impact ICI response. Future work will aim to improve accuracy and predict ICI toxicity by including data from multiple centres, different cancer types and additional clinical variables. [Table: see text]
Endometrial cancer (EC) and cervical cancer (CC) are common malignancies in women in clinical practice. More uncommon non-ovarian malignancies, such as vulval cancer (VC), are also becoming more prevalent in women of all ages. Currently, there are few comprehensive reviews on the management of these conditions, despite the recent advances in the use of immunotherapy in the management of other forms of cancer. The treatment modalities for EC, CC and VC vary; however, platinum-based chemotherapy, surgical resection and radiotherapy are the main forms of treatment. In more advanced or recurrent disease, there is a limited number of efficacious treatments, with many clinicians relying on adjuvant chemotherapy despite the increased rationale for the use of immunotherapy. With the development of the novel adoptive T-cell therapy, intra-tumoural oncolytic viral therapy and cancer vaccines, the landscape of gynaecological cancer management is changing, and it is likely that treatment efficacy and outcomes will improve dramatically. This review aims to summarise the current management of endometrial, cervical and vulval cancer and to evaluate the novel therapies under development, as well as the future of the management of non-ovarian gynaecological malignancies.
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