British Journal of Hospital Medicine

Prevention and Diagnosis of Cervical Cancers in the UK and Europe: What Have We Learned in the Last 10 Years and What Is Next?

This review presents an outline of the state of the knowledge on the prevention and diagnosis of cervical cancers over the last decade. Screening and prevention methods are considered, such as cervical self-sampling, Human Papillomavirus (HPV) vaccination and the use of digital technologies and artificial intelligence (AI) in diagnostics and screening, as are some success stories. However, despite the successes stemming from the UK and Europe–such as that no new cases of cervical cancer have been detected in a cohort of Scottish women vaccinated against HPV between 12–13 years old–cervical screening rates are falling and social, cultural and economic barriers to HPV vaccination uptake remain. There are several clear pathways that could reduce not only incidence rates of cervical cancer, but also non-cervical HPV related cancers such as oropharyngeal and anal cancers.

Squamous cell carcinoma of the ovary arising from a mature cystic teratoma associated with hypercalcaemia

We present a rare case of a 40-year-old nulliparous lady, with no past medical or surgical history, who was diagnosed with metastatic squamous cell carcinoma of the right ovary that originated from a mature cystic ovarian teratoma. Our patient underwent debulking total abdominal hysterectomy, bilateral salpingo-oophorectomy and partial omentectomy followed by postoperative carboplatin and paclitaxel chemotherapy. Rapid disease progression ensued, complicated by severe parathyroid hormone-related protein-induced hypercalcaemia resistant to medical therapy. The patient was treated in a palliative manner and died five months after her diagnosis.

Clinicopathological Determinants of Lymph Node Metastasis in Early-Stage Cervical Cancer: A Retrospective Cohort Study

Aims/Background Accurate identification of lymph node metastasis is critical for optimising surgical strategies in early-stage cervical cancer. This study aimed to analyse multiple clinicopathological factors which are potentially associated with lymph node metastasis to guide personalised lymphadenectomy decisions. Methods This retrospective cohort study included 266 patients with early-stage cervical cancer (International Federation of Gynecology and Obstetrics [FIGO] stage IA1 to IIA2) who underwent surgical treatment at Sir Run Run Shaw Hospital, School of Medicine, Zhejiang University, between 1 December 2014 and 31 December 2019. Patients were followed up every 3 months for the first 2 years, every 6 months for the next 3 years, and annually thereafter. The presence of lymph node metastasis was included as the primary outcome, while the associated factors as secondary outcomes. The univariate and multivariate logistic regression were performed to identify risk factors associated with lymph node metastasis. Results The mean age of the study participants (n = 266) was 44.26 years (standard deviation [SD] = 10.19), and the median follow-up duration was 48.7 months (range 12–72 months). Lymph node metastasis was observed in 15.41% of patients. The metastatic rates increased with advancing FIGO stage: IA1 and IA2 (0%), IB1 (13.44%), IB2 (15.00%), IIA1 (23.33%), and IIA2 (66.67%). Univariate analysis identified FIGO stage (p < 0.001), depth of stromal invasion (p < 0.001), tumour size (p = 0.017), parametrial invasion (p < 0.001), and lymphovascular space invasion (LVSI) (p < 0.001) as significantly associated risk factors for lymph node metastasis. Multivariate analysis identified tumour size ≥4 cm (adjusted odds ratio [OR]: 3.857; 95% confidence interval [CI]: 1.530–9.728; p = 0.004), FIGO stage II (adjusted OR: 8.247; 95% CI: 3.171–21.455; p < 0.001), LVSI (adjusted OR: 2.974; 95% CI: 1.344–6.632; p = 0.008), and parametrial invasion (adjusted OR: 5.585; 95% CI: 1.900–16.415; p = 0.002) as independent risk factors for nodal metastasis. Conclusion This study identifies several key clinicopathological factors associated with lymph node metastasis in early-stage cervical cancer. These findings underscore the importance of meticulous preoperative risk assessment and offer an evidence-based foundation for tailored surgical planning to improve patient outcomes.

Prediction of Cervical Cancer Lymph Node Metastasis via a Multimodal Transfer Learning Approach

Aims/Background In the treatment of patients with cervical cancer, lymph node metastasis (LNM) is an important indicator for stratified treatment and prognosis of cervical cancer. This study aimed to develop and validate a multimodal model based on contrast-enhanced multiphase computed tomography (CT) images and clinical variables to accurately predict LNM in patients with cervical cancer. Methods This study included 233 multiphase contrast-enhanced CT images of patients with pathologically confirmed cervical malignancies treated at the Affiliated Dongyang Hospital of Wenzhou Medical University. A three-dimensional MedicalNet pre-trained model was used to extract features. Minimum redundancy-maximum correlation, and least absolute shrinkage and selection operator regression were used to screen the features that were ultimately combined with clinical candidate predictors to build the prediction model. The area under the curve (AUC) was used to assess the predictive efficacy of the model. Results The results indicate that the deep transfer learning model exhibited high diagnostic performance within the internal validation set, with an AUC of 0.82, accuracy of 0.88, sensitivity of 0.83, and specificity of 0.89. Conclusion We constructed a comprehensive, multiparameter model based on the concept of deep transfer learning, by pre-training the model with contrast-enhanced multiphase CT images and an array of clinical variables, for predicting LNM in patients with cervical cancer, which could aid the clinical stratification of these patients via a noninvasive manner.

Utilising deep learning networks to classify ZEB2 expression images in cervical cancer

Aims/Background Cervical cancer continues to be a significant cause of cancer-related deaths among women, especially in low-resource settings where screening and follow-up care are lacking. The transcription factor zinc finger E-box-binding homeobox 2 (ZEB2) has been identified as a potential marker for tumour aggressiveness and cancer progression in cervical cancer tissues. Methods This study presents a hybrid deep learning system developed to classify cervical cancer images based on ZEB2 expression. The system integrates multiple convolutional neural network models—EfficientNet, DenseNet, and InceptionNet—using ensemble voting. We utilised the gradient-weighted class activation mapping (Grad-CAM) visualisation technique to improve the interpretability of the decisions made by the convolutional neural networks. The dataset consisted of 649 annotated images, which were divided into training, validation, and testing sets. Results The hybrid model exhibited a high classification accuracy of 94.4% on the test set. The Grad-CAM visualisations offered insights into the model's decision-making process, emphasising the image regions crucial for classifying ZEB2 expression levels. Conclusion The proposed hybrid deep learning model presents an effective and interpretable method for the classification of cervical cancer based on ZEB2 expression. This approach holds the potential to substantially aid in early diagnosis, thereby potentially enhancing patient outcomes and mitigating healthcare costs. Future endeavours will concentrate on enhancing the model's accuracy and investigating its applicability to other cancer types.

Metastatic breast cancer in the uterine cervix

Advances in systemic treatment for recurrent metastatic cervical cancer

Cervical cancer remains a leading cause of cancer-related mortality among women worldwide, particularly in underdeveloped nations. Despite advances in standard therapies, patients with recurrent metastatic cervical cancer face a poor prognosis and limited treatment options. This study aims to evaluate the efficacy and safety of emerging therapeutic approaches in managing this challenging condition, including immunotherapy, targeted medicines, and chemotherapy. Our review of recent literature and clinical trials highlights the significant progress made in the systematic treatment of metastatic cervical cancer. While each treatment modality has its strengths and limitations, the collective data suggest a trend towards improved patient outcomes with the adoption of these novel therapies. However, the heterogeneity of patient responses underscores the need for personalised treatment strategies. In conclusion, improvements in medical technology and the adoption of tailored treatment approaches have led to promising advancements in the management of recurrent metastatic cervical cancer. To further enhance patient care, there is an urgent need for more extensive clinical trial data and the development of more efficient personalised treatment plans. This study aims to contribute to this effort by comprehensively analysing current therapeutic strategies and identifying areas for future research.

A rare case of an androgen-secreting mucinous borderline ovarian tumour in a perimenopausal woman

A case of rapidly progressive diffuse scleroderma following treated cervical cancer