Early diagnosis of symptomatic ovarian cancer in primary care in the UK: opportunities and challenges

Emerging nanotechnologies and their role in early ovarian cancer detection, diagnosis and interventions

Ovarian cancer presents a significant public health challenge, often being diagnosed at advanced stages due to the limitations of current detection methods. This systematic review addresses the urgent need for innovative approaches to enhance early detection and diagnosis of ovarian cancer. We systematically evaluate recent advancements in nanotechnology, focusing specifically on their novel applications and potential in comparison to traditional diagnostic modalities. Our analysis encompasses a wide range of studies investigating nanoparticles, biosensors, advanced imaging techniques, and biomarker detection platforms, with an emphasis on evaluating key performance indicators such as detection rates, turnaround times, and the accuracy of distinguishing cancerous from non-cancerous tissues. Our findings indicate that nanotechnology-based approaches have the potential to significantly improve early detection capabilities for ovarian cancer. Notably, studies on nanoparticle-based imaging techniques and biosensors consistently demonstrate high sensitivity and specificity for identifying ovarian cancer biomarkers, with detection rates exceeding 90% reported for early-stage cancers in several instances. This review underscores the promise of emerging nanotechnologies to transform the landscape of early detection and diagnosis, offering a pathway toward earlier diagnoses, enhanced therapeutic interventions, and improved patient outcomes. We advocate for future research dedicated to the translational efforts required to move these technologies from bench to bedside, ensuring their effectiveness is validated across diverse clinical populations.

Inflammatory Indices and CA 125: A New Approach to Distinguish Ovarian Carcinoma and Borderline Tumors in Suspicious Ovarian Neoplasms from a Retrospective Observational Multicentric Study

Background and Objectives: This study aimed to evaluate the diagnostic potential of systemic inflammatory indices such as Systemic Inflammation Response Index (SIRI) and Systemic Inflammatory Response (SIR). These were assessed in combination with CA 125 to distinguish ovarian carcinoma (OC) from borderline ovarian tumors (BOT) in patients with suspicious adnexal masses. Materials and Methods: A retrospective multicenter observational study including patients undergoing surgery for suspected ovarian neoplasms was conducted. Inclusion criteria required preoperative blood sampling for inflammatory markers and CA 125. SIR-125 and SIRI-125 were developed by combining SIR and SIRI with CA 125 levels. Diagnostic performance was assessed using ROC curve analysis and linear regression models. Results: A total of 63 patients (42 BOT, 21 OC) were analyzed. OC patients exhibited significantly higher SIR-125 and SIRI-125 values (p < 0.001). ROC analysis demonstrated good diagnostic accuracy, with AUCs of 0.83 (SIR-125) and 0.82 (SIRI-125). SIR-125 showed higher specificity (0.83), while SIRI-125 had superior sensitivity (0.86). Conclusions: SIR-125 and SIRI-125 enhance diagnostic differentiation between OC and BOT, providing a simple, cost-effective preoperative tool. Future prospective studies are needed to validate these findings in broader patient populations.

Ovarian cancer and its management through advanced drug delivery system

Ovarian cancer is the deadliest gynecological cancer, often being diagnosed at a late-stage when the disease is already advanced. This makes it known as the ''silent killer'' due to its high mortality rate among women. The ovary itself is composed of three main types of cells epithelial cells, germ cells, and stromal cells. Over 90% of all occurrences of ovarian cancer are epithelial ovarian carcinoma. Ovarian cancer is responsible for 2.5% of cancer in women. The primary signs include stomach bloating, trouble eating or feeling full rapidly, fatigue, and discomfort during intercourse. The management of ovarian cancer is improved by advanced drug delivery systems because they increase therapeutic targeting, reduce side effects, and overcome drug resistance. Accurate distribution to cancer cells is made possible by methods such as ligand-functionalized nanomedicines, dual-targeted nano-drugs, drug conjugates, antibody-drug conjugates and peptide/folate drug conjugates. These technologies enhance individualized and successful ovarian cancer treatment outcomes by maximizing chemotherapy efficacy, extending drug release, and lowering toxicity.

Impact of COVID-19 on Gynecologic Oncology Wait Times: A Mystery Caller Study

OBJECTIVE

Despite increasing wait times for oncologic care in the US, research has yet to examine the impact of COVID-19 on wait times to first appointments for gynecologic oncology patients. We sought to audit mean wait times, during and after the height of the pandemic, for an outpatient appointment with a gynecologic oncologist in the US.

METHODS

Office phone numbers were identified from the searchable Society for Gynecologic Oncology specialist patient-facing database. Using a "mystery caller" study approach, each unique phone number was called in 2020 and 2023. The caller asked for the soonest appointment available for her mother, who was recently found to have a 10 cm pelvic mass. The date of the soonest appointment and physician and office demographics were collected.

RESULTS

A total of 222 gynecologic oncology practices were called across 45 states and the District of Columbia. There was no difference in wait time post-COVID-19, highlighting an undescribed resilience in the face of unprecedented healthcare system stress. However, we also identified three major barriers to appointment scheduling including incorrect contact information in patient-facing databases, unanswered phones, and mandatory physician referrals prior to appointment scheduling.

CONCLUSIONS

Understanding factors influencing appointment wait times is essential to mitigating harm in oncologic care. Ours is the first nationwide audit of COVID-19's impact on barriers to gynecologic oncology care. While we highlight a surprising lack of increase in wait times between 2020 and 2023, we also identify actionable barriers to care such as updating public patient-facing information online.

The Integrated Bioinformatic Approach Reveals the Prognostic Significance of LRP1 Expression in Ovarian Cancer

A hyperactive tumour microenvironment (TME) drives unrestricted cancer cell survival, drug resistance, and metastasis in ovarian carcinoma (OC). However, therapeutic targets within the TME for OC remain elusive, and efficient methods to quantify TME activity are still limited. Herein, we employed an integrated bioinformatics approach to determine which immune-related genes (IRGs) modulate the TME and further assess their potential theragnostic (therapeutic + diagnostic) significance in OC progression. Using a robust approach, we developed a predictive risk model to retrospectively examine the clinicopathological parameters of OC patients from The Cancer Genome Atlas (TCGA) database. The validity of the prognostic model was confirmed with data from the International Cancer Genome Consortium (ICGC) cohort. Our approach identified nine IRGs, AKT2, FGF7, FOS, IL27RA, LRP1, OBP2A, PAEP, PDGFRA, and PI3, that form a prognostic model in OC progression, distinguishing patients with significantly better clinical outcomes in the low-risk group. We validated this model as an independent prognostic indicator and demonstrated enhanced prognostic significance when used alongside clinical nomograms for accurate prediction. Elevated LRP1 expression, which indicates poor prognosis in bladder cancer (BLCA), OC, low-grade gliomas (LGG), and glioblastoma (GBM), was also associated with immune infiltration in several other cancers. Significant correlations with immune checkpoint genes (ICGs) highlight the potential importance of LRP1 as a biomarker and therapeutic target. Furthermore, gene set enrichment analysis highlighted LRP1's involvement in metabolism-related pathways, supporting its prognostic and therapeutic relevance also in BLCA, OC, low-grade gliomas (LGG), GBM, kidney cancer, OC, BLCA, kidney renal clear cell carcinoma (KIRC), stomach adenocarcinoma (STAD), and stomach and oesophageal carcinoma (STES). Our study has generated a novel signature of nine IRGs within the TME across cancers, that could serve as potential prognostic predictors and provide a valuable resource to improve the prognosis of OC.

Comparative Review on Cancer Pathology from Aberrant Histone Chaperone Activity

Histone chaperones are integral to chromatin dynamics, facilitating the assembly and disassembly of nucleosomes, thereby playing a crucial role in regulating gene expression and maintaining genomic stability. Moreover, they prevent aberrant histone interactions prior to chromatin assembly. Disruption in histone chaperone function may result in genomic instability, which is implicated in pathogenesis. This review aims to elucidate the role of histone chaperones in cancer pathologies and explore their potential as therapeutic targets. Histone chaperones have been found to be dysregulated in various cancers, with alterations in expression levels, mutations, or aberrant interactions leading to tumorigenesis and cancer progression. In addition, this review intends to highlight the molecular mechanisms of interactions between histone chaperones and oncogenic factors, underscoring their roles in cancer cell survival and proliferation. The dysregulation of histone chaperones is significantly correlated with cancer development, establishing them as active contributors to cancer pathology and viable targets for therapeutic intervention. This review advocates for continued research into histone chaperone-targeted therapies, which hold potential for precision medicine in oncology. Future advancements in understanding chaperone functions and interactions are anticipated to lead to novel cancer treatments, enhancing patient care and outcomes.

Using online search activity for earlier detection of gynaecological malignancy

BACKGROUND

Ovarian cancer is the most lethal and endometrial cancer the most common gynaecological cancer in the UK, yet neither have a screening program in place to facilitate early disease detection. The aim is to evaluate whether online search data can be used to differentiate between individuals with malignant and benign gynaecological diagnoses.

METHODS

This is a prospective cohort study evaluating online search data in symptomatic individuals (Google user) referred from primary care (GP) with a suspected cancer to a London Hospital (UK) between December 2020 and June 2022. Informed written consent was obtained and online search data was extracted via Google takeout and anonymised. A health filter was applied to extract health-related terms for 24 months prior to GP referral. A predictive model (outcome: malignancy) was developed using (1) search queries (terms model) and (2) categorised search queries (categories model). Area under the ROC curve (AUC) was used to evaluate model performance. 844 women were approached, 652 were eligible to participate and 392 were recruited. Of those recruited, 108 did not complete enrollment, 12 withdrew and 37 were excluded as they did not track Google searches or had an empty search history, leaving a cohort of 235.

RESULTS

The cohort had a median age of 53 years old (range 20-81) and a malignancy rate of 26.0%. There was a difference in online search data between those with a benign and malignant diagnosis, noted as early as 360 days in advance of GP referral, when search queries were used directly, but only 60 days in advance, when queries were divided into health categories. A model using online search data from patients (n = 153) who performed health-related search and corrected for sample size, achieved its highest sample-corrected AUC of 0.82, 60 days prior to GP referral.

CONCLUSIONS

Online search data appears to be different between individuals with malignant and benign gynaecological conditions, with a signal observed in advance of GP referral date. Online search data needs to be evaluated in a larger dataset to determine its value as an early disease detection tool and whether its use leads to improved clinical outcomes.

Diagnostic Accuracy of Biomarkers and International Ovarian Tumor Analysis Simple Rules in Diagnosis of Ovarian Cancer

This study investigated whether combining International Ovarian Tumor Analysis (IOTA) Simple Rules with tumor biomarkers would improve the diagnostic accuracy for early detection of adnexal malignancies. Receiver operating characteristic curve analysis of suspected adnexal tumors was performed in 226 women admitted for surgery at the University Clinical Center of Kosovo. Primary outcome was the diagnostic accuracy of the combination of adnexal mass biomarkers and IOTA Simple Rules. IOTA Simple Rules combined with biomarker indications increased the diagnostic accuracy of classifying adnexal masses. Data analysis of individual measures showed that ferritin had the lowest rate of sensitivity.

Deep learning in ovarian cancer diagnosis: a comprehensive review of various imaging modalities

Ovarian cancer poses a major worldwide health issue, marked by high death rates and a deficiency in reliable diagnostic methods. The precise and prompt detection of ovarian cancer holds great importance in advancing patient outcomes and determining suitable treatment plans. Medical imaging techniques are vital in diagnosing ovarian cancer, but achieving accurate diagnoses remains challenging. Deep learning (DL), particularly convolutional neural networks (CNNs), has emerged as a promising solution to improve the accuracy of ovarian cancer detection. This systematic review explores the role of DL in improving the diagnostic accuracy for ovarian cancer. The methodology involved the establishment of research questions, inclusion and exclusion criteria, and a comprehensive search strategy across relevant databases. The selected studies focused on DL techniques applied to ovarian cancer diagnosis using medical imaging modalities, as well as tumour differentiation and radiomics. Data extraction, analysis, and synthesis were performed to summarize the characteristics and findings of the selected studies. The review emphasizes the potential of DL in enhancing the diagnosis of ovarian cancer by accelerating the diagnostic process and offering more precise and efficient solutions. DL models have demonstrated their effectiveness in categorizing ovarian tissues and achieving comparable diagnostic performance to that of experienced radiologists. The integration of DL into ovarian cancer diagnosis holds the promise of improving patient outcomes, refining treatment approaches, and supporting well-informed decision-making. Nevertheless, additional research and validation are necessary to ensure the dependability and applicability of DL models in everyday clinical settings.

Clinicopathologic Impact of NANOG, ZEB1, and EpCAM Biomarkers on Prognosis of Serous Ovarian Carcinoma

OBJECTIVES

Serous ovarian carcinoma (SOC) is a biologically heterogeneous with different genomic and molecular profiles, beside clinical response to the chemotherapy with subsequent in obstacles in starting unified, acceptable treatments and so we assess immunoexpression of Nanog, ZEB1, and EpCAM in SOC.

METHODS

In this study, the immunoexpression of Nanog, ZEB1, and EpCAM was studied in 60 cases of SOC. Overall survival (OS), disease-free survival (DFS) data and response to chemotherapy were  analyzed.

RESULTS

NANOG was immunostained in 65% of the cases with a significant association with tumor grade, lymph node metastasis, and FIGO stage (p < 0.001 for each). ZEB1 showed moderate- high expression in 58.3% of the cases with significant up-regulation of ZEB1 expression with SOC grade, nodal metastasis, and SOC FIGO stage (p<0.001). EpCAM revealed high expression in 60% of the cases with significant association with higher grade, nodal metastasis, and advanced stage (p < 0.001 for each). Up-regulation of Nanog was significantly associated with response to chemotherapy, relapse, shorter OS and DFS (p < 0.001 for each). ZEB1 overexpression exhibited a significant association with response to chemotherapy (p= 0.012), relapse, shorter OS and DFS (p<0.001 for each). Moreover, the high EpCAM had a significant association with response to chemotherapy (p= 0.043), relapse (p < 0.001) shorter OS (p=0.006) and DFS (p< 0.001).

CONCLUSIONS

Up-regulation of Nanog and ZEB-1 and EpCAM perhaps promote an aggressive SOC with a high risk of relapse and unfavorable response to standard chemotherapy regimen.