European trends in ovarian cancer mortality, 1990-2020 and predictions to 2025

European cancer mortality predictions for the year 2025 with focus on breast cancer

BACKGROUND

We predicted the number of cancer deaths and rates for 2025 in the European Union (EU), its five most populous countries, and the UK, focusing on breast cancer.

MATERIALS AND METHODS

We derived population data and death certificates for all cancers and major sites for the EU, France, Germany, Italy, Poland, Spain, and the UK since 1970, from the World Health Organization and United Nations databases. Estimates for 2025 were computed by linear regression on recent trends identified through Poisson joinpoint regression, considering the slope of the most recent trend segment. Deaths averted from 1989 to 2025 were calculated by applying the 1988 peak rate to subsequent population data.

RESULTS

We estimated 1 280 000 cancer deaths in the EU in 2025, corresponding to age-standardised rates (ASRs) of 120.9/100 000 males (-3.5% versus 2020) and 79.1/100 000 females (-1.2%). In the UK, we predicted 173 000 cancer deaths and ASRs of 101.2/100 000 males (-10.1%) and 82.1/100 000 females (-6.3%). In the EU, favourable trends are predicted for major neoplasms, except pancreatic cancer, in males (+2.0%) and females (+3.0%), and lung (+3.8%) and bladder (+1.9%) cancers among females. Breast cancer mortality showed favourable trends in all countries. Substantial decreases were predicted for EU females aged 50-69 years (-9.8%) and 70-79 years (-12.4%). Between 1989 and 2025, we estimated about 6.8 million averted cancer deaths in the EU, including over 373 000 breast cancer deaths. Corresponding numbers for the UK were 1 500 000 and 197 000.

CONCLUSION

EU breast cancer rates have fallen by 30% since 1990, due to advances in prevention, treatment, and early detection. Contrasting trends in lung cancer among males and females reflect differing tobacco smoking patterns. Female lung cancer mortality is still increasing in the EU, though less than in the previous decade. Persistent unfavourable pancreatic cancer trends can be related to the increasing prevalence of obesity and limited therapeutic advances, requiring continued attention.

A pilot study evaluating the feasibility of enriching and detecting circulating tumour cells from peripheral and ovarian veins in rare epithelial ovarian carcinomas

INTRODUCTION

Studies on circulating tumour cells (CTCs) in rare epithelial ovarian carcinomas (EOC) are limited, despite their potential as a minimally invasive biomarker for monitoring cancer progression and predicting outcomes. This pilot study aimed to assess the feasibility of enriching and detecting CTCs from both peripheral and ovarian vein blood samples in rare EOC subtypes.

MATERIALS AND METHODS

Blood samples were collected from the peripheral and ovarian veins of 20 patients with rare EOC. Among the 20 patients, 12 had early-stage disease (I-II), while 8 had advanced disease (III-IV). CTCs were enriched using the Parsortix® system and immunophenotyped via immunofluorescence targeting epithelial markers (EpCAM/pan-cytokeratin) and Hoechst for positive selection, and CD45 for negative selection. CTC status (positive versus negative) was correlated with clinicopathological data.

RESULTS

CTCs were successfully detected in 45 % (1-19 CTCs) of baseline peripheral samples and 55 % (1-4776 CTCs) of ovarian vein samples. CTC doublets and clusters were detected in ovarian vein samples (3/11), but not in peripheral samples (0/20). A higher proportion of deaths were observed in CTC+ patients compared to CTC- patients (p = 0.0088).

CONCLUSION

Here we demonstrate the feasibility of enriching and detecting CTCs from both peripheral and ovarian vein blood in patients with rare EOC. The higher CTC yield in ovarian vein blood suggests that tumour-draining blood may play a role in improving CTC detection. This pilot study paves the way for larger studies to investigate the prognostic utility of CTCs and refine their clinical value in these rare understudied EOC.

FCM-NPOA: A hybrid Fuzzy C-means clustering with nomadic people optimizer for ovarian cancer detection

Ovarian cancer is a highly prevalent cancer among women; However, it remains difficult to find effective pharmacological solutions to treat this deadly disease. However, early detection can significantly increase life expectancy. To address this issue, a predictive model for early diagnosis of ovarian cancer was developed by applying statistical techniques and machine learning models to clinical data from 349 patients. A hybrid evolutionary deep learning model was proposed by integrating genetic and histopathological imaging modalities within a multimodal fusion framework. Machine learning pipelines have been built using feature selection and dilution approaches to identify the most relevant genes for disease classification. A comparison was performed between the UNeT and transformer models for semantic segmentation, leading to the development of an optimized fuzzy C-means clustering algorithm (FCM-NPOA-PM-UI) for the classification of gynecological abdominopelvic tumors. Performing better than individual classifiers and other machine learning methods, the suggested ensemble model achieved an average accuracy of 98.96%, precision of 97.44%, and F1 score of 98.7%. With average Dice scores of 0.98 and 0.97 for positive tumors and 0.99 and 0.98 for malignant tumors, the Transformer model performed better in segmentation than the UNeT model. Additionally, we observed a 92.8% increase in accuracy when combining five machine learning models with biomarker data: random forest, logistic regression, SVM, decision tree, and CNN. These results demonstrate that the hybrid model significantly improves the accuracy and efficiency of ovarian cancer detection and classification, offering superior performance compared to traditional methods and individual classifiers.

Multiple perception contrastive learning for automated ovarian tumor classification in CT images

Ovarian cancer is among the most common malignant tumours in women worldwide, and early identification is essential for enhancing patient survival chances. The development of automated and trustworthy diagnostic techniques is necessary because traditional CT picture processing mostly depends on the subjective assessment of radiologists, which can result in variability. Deep learning approaches in medical image analysis have advanced significantly, particularly showing considerable promise in the automatic categorisation of ovarian tumours. This research presents an automated diagnostic approach for ovarian tumour CT images utilising supervised contrastive learning and a Multiple Perception Encoder (MP Encoder). The approach incorporates T-Pro technology to augment data diversity and simulates semantic perturbations to increase the model's generalisation capability. The incorporation of Multi-Scale Perception Module (MSP Module) and Multi-Attention Module (MA Module) enhances the model's sensitivity to the intricate morphology and subtle characteristics of ovarian tumours, resulting in improved classification accuracy and robustness, ultimately achieving an average classification accuracy of 98.43%. Experimental results indicate the method's exceptional efficacy in ovarian tumour classification, particularly in cases involving tumours with intricate morphology or worse picture quality, thereby markedly enhancing classification accuracy. This advanced deep learning framework proficiently tackles the complexities of ovarian tumour CT image interpretation, offering clinicians enhanced diagnostic support and aiding in the optimisation of early detection and treatment strategies for ovarian cancer.

Impact of cancer during reproductive age on the probability of livebirth after cancer: a register-based cohort study among Danish women aged 18-39 with and without cancer

PURPOSE

This register-based study investigates the probability of a livebirth after cancer during the female reproductive age.

METHODS

The study population, derived from the DANAC II cohort, included women aged 18-39 diagnosed with cancer between 1978 and 2016, matched with 60 undiagnosed women each from the general population. Primary outcome was a livebirth after cancer with follow-up until death, emigration, or end of follow-up. Hazard ratios (HR) were calculated using multivariable Cox regression analyses.

RESULTS

The population included 21,596 women with cancer and 1,295,760 without. The 20-year cumulative incidence of livebirth after cancer/study entry was lower among women with cancer (0.22 [95% CI 0.22-0.22]) compared to those without (0.34 [95% CI 0.34-0.34]). The HR of a livebirth after cancer was 0.61 [95% CI 0.59-0.63]; highest at age 18-25 (HR = 0.72 [95% CI 0.68-0.76]); and lowest at age 33-39 (HR = 0.50 [95% CI 0.47-0.54]). Nullipara women had higher HR of a livebirth than those with children (HR = 0.72 [95% CI 0.69-0.75] vs. HR = 0.48 [95% CI 0.46-0.51]). HR was lowest for women with breast, gynecological, central-nerve-system cancer, and leukemia. Women with/without cancer were comparable in assisted reproductive technology initiation after cancer/study entry, but HR was higher among nullipara than in those with prior children.

CONCLUSIONS

Cancer during reproductive years significantly and negatively impacts HR of a livebirth after cancer, particularly as age at diagnosis increases. Low HR of livebirth is observed in specific cancer groups.

IMPLICATIONS FOR CANCER SURVIVORS

Results underscore the importance of oncofertility counseling at diagnosis, referral to fertility specialist before treatment, and follow-up after cancer, focusing on cancer type, parity status, and age at diagnosis.

Addition of synthetic polymers to a conventional cryoprotectant solution in the vitrification of bovine ovarian tissue

Some synthetic polymers can be used at low concentrations to reduce the toxicity of conventional cryoprotectant agents. In this study we investigated whether the addition of synthetic polymers to a conventional cryoprotectant solution would improve the cryopreservation of bovine ovarian tissue. Freshly collected ovaries from ten adult crossbred cows were incised using a scalpel in the frontal section. From each cow, ovarian cortical slices of 1 mm thickness were divided into 30 fragments of 3 × 3 mm, of which 10 served as fresh controls, 10 were vitrified with conventional cryoprotectant agents (2.93 M glycerol, 27 % w/v; 4.35 M ethylene glycol, 27 % w/v), and 10 were vitrified using the same cryoprotectant agents in addition to synthetic polymers (0.2 % PVP K-12, 0.2 % SuperCool X-1000 ™ w/v and 0.4 % SuperCool Z-1000 ™ w/v). After warming, histology was used to assess follicular quantity and integrity, while in vitro culture of mechanically isolated follicles encapsulated in an alginate matrix was performed for 15 days to assess their growth and hormonal production. Vitrified ovarian tissues presented abnormal morphology, a higher percentage of atretic follicles, and their isolated follicles had lower survival rates and lower frequency of antrum formation during in vitro culture compared to those from fresh tissue. At the end of culture, the follicles that had been cryopreserved produced less estradiol and progesterone than the fresh ones. The addition of synthetic polymers during tissue vitrification did not modify any of these parameters. We conclude that, under the conditions of this study, the use of this combination of synthetic polymers for tissue vitrification did not enhance the preservation of the morphological or functional integrity of bovine ovarian follicles.

Past and present: a bibliometric study on the treatment of recurrent ovarian cancer

Background

Ovarian cancer (OC) is a gynecological malignancy with a high mortality rate worldwide. The unfavorable prognosis of OC is mainly attributed to the recurrent propensity. Recently, mortality from OC has exhibited a downward trend. These favorable patterns are likely to be driven by advancements in novel therapeutic regimens. However, there is a lack of visualize analysis of the application of these new drugs on women with recurrent OC (ROC). Therefore, we aimed to provide a bibliometric analysis of the evolving paradigms in the ROC treatment.

Methods

Documents on ROC treatment were systematically collected from the MEDLINE database and Web of Science Core Collection (WOSCC). The retrieved documents were exported in the plain text file format, and files were named and saved to the paths specified by the Java application. Microsoft Excel (version 2010), Citespace (6.2.R4) and VOSviewer (1.6.19) were used for data analysis, and included the following: 1) annual publication trend; 2) contributions of countries, institutions and authors; 3) co-citation of journals and references; and 4) co-occurrence of keywords.

Results

A total of 914 documents published in the MEDLINE and 9,980 ones in WOSCC were retrieved. There has been an upward trend in the productivity of publications on ROC treatment on by years. The United States was the leading contributor in this field, and the University of Texas System stood out as the most productive institution. Giovanni Scambia and Maurie Markman were the research leaders in the field of ROC treatment. The journal Gynecologic Oncology had the highest citation frequency. The reference entitled with "Niraparib Maintenance Therapy in Platinum-Sensitive, Recurrent Ovarian Cancer" got highest centrality of 0.14 in the co-citation network. Keyword analysis revealed that the focus of current ROC treatment was on platinum-based anticancer drugs, paclitaxel, angiogenesis inhibitors (AIs), immune checkpoint inhibitors (ICIs) and poly (ADP-ribose) polymerase inhibitors (PARPis).

Conclusion

Scholars from a multitude of countries have been instrumental in the advancement of ROC treatment. The research hotspots and trend in the field of predominantly originated from leading international journals and specialized periodicals focused on gynecologic oncology. Maintenance therapy using AIs or (and) PARPis has emerged as a significant complement to platinum-based chemotherapy for patients with ROC.

Explainable AI for Interpretation of Ovarian Tumor Classification Using Enhanced ResNet50

Deep learning architectures like ResNet and Inception have produced accurate predictions for classifying benign and malignant tumors in the healthcare domain. This enables healthcare institutions to make data-driven decisions and potentially enable early detection of malignancy by employing computer-vision-based deep learning algorithms. These CNN algorithms, in addition to requiring huge amounts of data, can identify higher- and lower-level features that are significant while classifying tumors into benign or malignant. However, the existing literature is limited in terms of the explainability of the resultant classification, and identifying the exact features that are of importance, which is essential in the decision-making process for healthcare practitioners. Thus, the motivation of this work is to implement a custom classifier on the ovarian tumor dataset, which exhibits high classification performance and subsequently interpret the classification results qualitatively, using various Explainable AI methods, to identify which pixels or regions of interest are given highest importance by the model for classification. The dataset comprises CT scanned images of ovarian tumors taken from to the axial, saggital and coronal planes. State-of-the-art architectures, including a modified ResNet50 derived from the standard pre-trained ResNet50, are implemented in the paper. When compared to the existing state-of-the-art techniques, the proposed modified ResNet50 exhibited a classification accuracy of 97.5 % on the test dataset without increasing the the complexity of the architecture. The results then were carried for interpretation using several explainable AI techniques. The results show that the shape and localized nature of the tumors play important roles for qualitatively determining the ability of the tumor to metastasize and thereafter to be classified as benign or malignant.

Dual-Specificity Tyrosine Phosphorylation-Regulated Kinase 3 Expression and Its Correlation with Prognosis and Growth of Serous Ovarian Cancer: Correlation of DYRK3 with Ovarian Cancer Survival

Background

Epithelial ovarian cancer, primarily serous ovarian cancer (SOC), stands as a predominant cause of cancer-related mortality among women globally, emphasizing the urgent need for comprehensive research into its molecular underpinnings. Within this context, the dual-specificity tyrosine phosphorylation-regulated kinase 3 (DYRK3) has emerged as a potential key player with implications for prognosis and tumor progression.

Methods

This study conducted a meticulous retrospective analysis of 254 SOC cases from our medical center to unravel the prognostic significance of DYRK3. Survival analyses underscored DYRK3 as an independent adverse prognostic factor in SOC, with a hazard ratio of 2.60 (95% CI 1.67-4.07, P < 0.001). Experimental investigations involved DYRK3 knockdown in serous ovarian cancer cell lines (CAOV3 and OVCAR-3) through a shRNA strategy, revealing substantial decreases in cell growth and invasion capabilities. Bioinformatics analyses further hinted at DYRK3's involvement in modulating the tumor immune microenvironment. In vivo experiments with DYRK3-knockdown cell lines validated these findings, demonstrating a notable restriction in the growth of ovarian cancer xenografts.

Results

Our findings collectively illuminate DYRK3 as a pivotal tumor-promoting oncogene in SOC. Beyond its adverse prognostic implications, DYRK3 knockdown exhibited promising therapeutic potential by impeding cancer progression and potentially influencing the tumor immune microenvironment.

Conclusions

This study establishes a compelling foundation for further research into DYRK3's intricate role and therapeutic potential in ovarian cancer treatment. As we unravel the complexities surrounding DYRK3, our work not only contributes to the understanding of SOC pathogenesis but also unveils new prospects for targeted therapeutic interventions, holding promise for improved outcomes in ovarian cancer management.

An Empirical Evaluation of a Novel Ensemble Deep Neural Network Model and Explainable AI for Accurate Segmentation and Classification of Ovarian Tumors Using CT Images

Ovarian cancer is one of the leading causes of death worldwide among the female population. Early diagnosis is crucial for patient treatment. In this work, our main objective is to accurately detect and classify ovarian cancer. To achieve this, two datasets are considered: CT scan images of patients with cancer and those without, and biomarker (clinical parameters) data from all patients. We propose an ensemble deep neural network model and an ensemble machine learning model for the automatic binary classification of ovarian CT scan images and biomarker data. The proposed model incorporates four convolutional neural network models: VGG16, ResNet 152, Inception V3, and DenseNet 101, with transformers applied for feature extraction. These extracted features are fed into our proposed ensemble multi-layer perceptron model for classification. Preprocessing and CNN tuning techniques such as hyperparameter optimization, data augmentation, and fine-tuning are utilized during model training. Our ensemble model outperforms single classifiers and machine learning algorithms, achieving a mean accuracy of 98.96%, a precision of 97.44%, and an F1-score of 98.7%. We compared these results with those obtained using features extracted by the UNet model, followed by classification with our ensemble model. The transformer demonstrated superior performance in feature extraction over the UNet, with a mean Dice score and mean Jaccard score of 0.98 and 0.97, respectively, and standard deviations of 0.04 and 0.06 for benign tumors and 0.99 and 0.98 with standard deviations of 0.01 for malignant tumors. For the biomarker data, the combination of five machine learning models-KNN, logistic regression, SVM, decision tree, and random forest-resulted in an improved accuracy of 92.8% compared to single classifiers.