Early Diagnosis Of Ovarian Cancer Research Articles

Electrochemical Sensor for the Detection and Accurate Early Diagnosis of Ovarian Cancer.

Ovarian cancer (OC) has the highest mortality rate among malignant tumors, primarily because it is difficult to diagnose early. Exosomes, a type of extracellular vesicle rich in parental information, have garnered significant attention in the field of cancer diagnosis and treatment. They play an important regulatory role in the occurrence, development, and metastasis of OC. Consequently, exosomes have emerged as noninvasive biomarkers for early cancer detection. Therefore, identifying cancer-derived exosomes may offer a novel biomarker for the early detection of OC. In this study, we developed a metal-organic frameworks assembled "double hook"-type aptamer electrochemical sensor, which enables accurate early diagnosis of OC. Under optimal experimental conditions, electrochemical impedance spectroscopy technology demonstrated a good linear relationship within the concentration range of 31-3.1 × 106 particles per microliter, with a detection limit as low as 12 particles per microliter. The universal exosome detection platform is constructed, and this platform can not only differentiate between high-grade serous ovarian cancer (HGSOC) patients and healthy individuals but also distinguish between HGSOC patients and nonhigh-grade serous OC (non-HGSOC). Consequently, it provides a novel strategy for the early diagnosis of OC and holds great significance in clinical differential diagnosis.

Innovative approach towards early prediction of ovarian cancer: Machine learning- enabled XAI techniques

Globally, ovarian cancer affects women disproportionately, causing significant morbidity and mortality rates. The early diagnosis of ovarian cancer is necessary for enhancing patient health and survival rates. This research article explores the utilization of Machine Learning (ML) techniques alongside eXplainable Artificial Intelligence (XAI) methodologies to aid in the early detection of ovarian cancer. ML techniques have recently gained popularity in developing predictive models to detect early-stage ovarian cancer. These predictions are made using XAI in a transparent and understandable way for healthcare professionals and patients. The primary aim of this study is to evaluate the effectiveness of various ovarian cancer prediction methodologies. This includes assessing K Nearest Neighbors, Support Vector Machines, Decision trees, and ensemble learning techniques such as Max Voting, Boosting, Bagging, and Stacking. A dataset of 349 patients with known ovarian cancer status was collected from Kaggle. The dataset included a comprehensive range of clinical features such as age, family history, tumor markers, and imaging characteristics. Preprocessing techniques were applied to enhance input data, including feature scaling and dimensionality reduction. A Minimum Redundancy Maximum Relevance (MRMR) algorithm was used to select the features in the model. Our experimental results demonstrate that in Support Vector Machines, we found 85 % base model accuracy and 89 % accuracy after stacking several ensemble learning techniques. With the help of XAI, complex ML algorithms can be given more profound insights into their decision-making, improving their applicability. This paper aims to introduce the best practices for integrating ML and artificial intelligence in biomarker evaluation. Building and evaluating Shapley values-based classifiers and visualizing results were the focus of our investigation. The study contributes to the field of oncology and women's health by offering a promising approach to the early diagnosis of ovarian cancer.

Stretchable Photonic Crystal-Assisted Glycoprotein Identification for Ovarian Cancer Diagnosis.

Photonic crystals with specific wavelengths can realize surface-enhanced excitation and emission intensities of fluorophores and enhance the fluorescence signals of fluorescent molecules. Herein, stretchable photonic crystals with good mechanochromic properties provide continuously adjustable forbidden wavelengths by stretching to change the lattice spacing, with reflectance peaks blue-shifted up to 110 nm to match indicators of different wavelengths and produce differentiated optical enhancement effects. Glycoproteins are significantly identified as clinical markers. However, the wide participation of glycoproteins in various life processes poses enormous complexity and critical challenges for rapid, facile, high-throughput, and accurate clinical analysis or health assessment. In this work, we proposed a stretchable photonic crystal-assisted glycoprotein identification approach for early ovarian cancer diagnosis. Stretchable photonic crystals can provide rich optical information to efficiently identify glycoproteins in complex matrices. A double-indicator fluorescence sensor was designed to respond to the protein trunk and oligosaccharide segment of glycoproteins separately for improved recognition accuracy. Seven typical glycoproteins could be discriminated from proteins, saccharides, or mixture interferents. Clinical ovarian cancer samples for early, intermediate, and advanced ovarian cancer and healthy subjects were verified with 100% accuracy. This strategy of stretchable photonic crystal-assisted glycoprotein identification provides an effective method for accurate, rapid ovarian cancer diagnosis and timely clinical treatment.

Development and validation of an interpretable model integrating multimodal information for improving ovarian cancer diagnosis

Ovarian cancer, a group of heterogeneous diseases, presents with extensive characteristics with the highest mortality among gynecological malignancies. Accurate and early diagnosis of ovarian cancer is of great significance. Here, we present OvcaFinder, an interpretable model constructed from ultrasound images-based deep learning (DL) predictions, Ovarian–Adnexal Reporting and Data System scores from radiologists, and routine clinical variables. OvcaFinder outperforms the clinical model and the DL model with area under the curves (AUCs) of 0.978, and 0.947 in the internal and external test datasets, respectively. OvcaFinder assistance led to improved AUCs of radiologists and inter-reader agreement. The average AUCs were improved from 0.927 to 0.977 and from 0.904 to 0.941, and the false positive rates were decreased by 13.4% and 8.3% in the internal and external test datasets, respectively. This highlights the potential of OvcaFinder to improve the diagnostic accuracy, and consistency of radiologists in identifying ovarian cancer.

A novel ratiometric electrochemical immunosensor for the detection of cancer antigen 125 based on three-dimensional carbon nanomaterial and MOFs

Accurate and sensitive detection of cancer antigen 125 (CA125) is important in early ovarian cancer diagnosis and monitoring. In the present work, a novel electrochemical ratiometric immunosensor for the determination of CA125 based on three-dimensional reduced graphene oxide/multi-walled carbon nanotube carboxylic acid- thionine (3DrGO/MWCNTs-Thi) and UIO-66-NH2/ferrocenecarboxylic acid (UiO-66-Fc) was constructed. 3DrGO/MWCNTs-Thi of immobilized primary antibody (Ab1) and UiO-66-Fc of labeled second antibody (Ab2) were used as capture probes and signaling probes for CA125, respectively. As the concentration of CA125 increases, the oxidation peak currents (IThi and IFc) generated by the two materials were changed in opposite directions, and therefore the ratio of the currents (IThi/IFc) was negatively correlated with the concentration of CA125. Thanks to the amplification of high loading of signaling molecules by the framework nanocomposites and the ratiometric strategy, the constructed electrochemical immunosensor exhibits outstanding accuracy, stability, and sensitivity. Under optimal conditions, the detection range spanned from 0.01 U mL−1 to 80 U mL−1, with a calculated limit of detection (LOD) of 0.0089 U mL−1. The immunosensor was successfully applied to real serum sample tests, indicating its potential for clinical detection applications.

Abstract 1065: Two-step method for early detection of ovarian cancer with high specificity via platelet RNA

Abstract Owing to lack of disease-specific symptoms and screening methods, most of patients with ovarian cancer are diagnosed at an advanced stage. Recent technology based on platelet RNA aids in detection of tumors at the early stages. Also, for population screening of low-prevalence diseases like cancer, accurate detection with high specificity is essential to improve diagnostic methods. Thus, we proposed platelet RNA-based early ovarian cancer detection with great specificity. It is a two-step method for detecting ovarian tumors with more than 99% specificity using exon-exon junction features and sampling invariant normalization and finding their malignancy with more than 99% negative predictive value using exon-exon junction and hematology parameters. First, we downloaded public platelet transcriptome data of gynecological cancer including ovarian cancer (n=151), benign tumor (n=30), and healthy counterparts (n=218) from GEO (GSE158508, 89843, and 183635). Second, we prospectively enrolled patients with gynecological cancer (n=31), benign tumors (n=20), and healthy women (n=18) at Seoul National University Hospital (SNUH) and Boaz Medical Center and obtained platelet transcriptome data (FMH data). Feature selection and model development were conducted using public training set (n=243), public validation set (n=156), and FMH training/validation set (n=47). The batch-invariant normalization was performed, and 198 splice junctions were selected as features from training/validation set for the bootstrap aggregation of the SVM model diagnosing the existence of an ovarian tumor. The model performance was assessed from a separate test set only composed of FMH data (n=22). Our model that demonstrated 96.8% sensitivity, 100.0% specificity, with a predetermined cut-off value. Next, FMH training/validation set was used for model development of following malignancy test. 10 splice junctions and 19 hematology parameters were selected and SVM models were separately developed for splice junctions and hematology parameters using 5-fold cross validation. The predictions were combined using a logical OR operation to obtain the final prediction result. The result showed its diagnostic performance with 100% sensitivity or NPV, and 50.0% specificity in the test data set with a predetermined cut-off value. We proposed a two-step method to operationally improve the existing protocol for ovarian cancer screening. Our model initially excludes most healthy individuals who do not have ovarian tumors and excludes patients with benign ovarian tumors who do not necessitate medical testing. To conclude, our model allows physicians to identify a subset of ovarian tumor patients with a higher risk of malignancy even at early stages; it exhibits potential in enhancing early diagnosis of ovarian cancer, leading to increase of treatment effectiveness and ultimately augmenting the survival of patients with ovarian cancer. Citation Format: Eunyong Ahn, Se Ik Kim, Sungmin Park, Sarah Kim, Yeochan Kim, Eunchong Huang, Suyeon Lee, Dong Won Hwang, Heeyeon Kim, HyunA Jo, Untack Cho, Juwon Lee, TaeJin Ahn, Yong-Sang Song. Two-step method for early detection of ovarian cancer with high specificity via platelet RNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1065.

Ovarian cancer awareness of women in Turkey: A cross-sectional study.

Early diagnosis of ovarian cancer (OC) increases survival rates; however, due to low awareness levels, women may be diagnosed with OC at the advanced stage. The aim of this cross-sectional study is to reveal the OC awareness of Turkish women and affecting factors. Participants were invited to study via social media tools between February-June 2022. Data was collected with Personal Information Form and the "OC Awareness Scale" from 446 women. 81% of the participants did not recall OC symptoms, 80.8% recognized OC risk factors. The most frequently recalled and recognized OC symptom is pelvic pain (19.8%; 55.8%, respectively). The most frequently recalled and recognized OC risk factors were smoking (43.1%, 67.9%, respectively) and family history (39%, 58.7%, respectively). 2% of the participants felt very confident in recognizing the signs, 72.9% would seek help within 1-2 days when they recognized the signs of OC. The awareness of OC was higher among women who had advanced age, higher education, family history and were in menopause. Turkish women have low level of awareness and knowledge about OC symptoms and risk factors. There is an urgent need for an OC awareness campaign that takes into account the socio-demographic characteristics of women. The results of the study may also guide strategies to prevent OC.

Association of common glutathione transferase polymorphisms with ovarian cancer risk and chemoresistance

Ovarian cancer is regarded as the most lethal gynecological cancer with a five-year survival below 45%. It represents the seventh most common cancer among women. Due to the limited availability of biomarkers and reliable screening methods for early diagnosis of ovarian cancer, much research is being conducted to explore and understand the factors that may increase the risk of developing this kind of cancer. When surgery and chemotherapy treatments have been fully utilized, the development of chemoresistance becomes a critical factor in the progression of the disease. Glutathione transferases (GSTs) are a group of enzymes that play a role in the process of detoxification. Genes that code for GSTs proteins exhibit polymorphism, which can lead to either total or partial loss of enzymatic function. Cytosolic GST activity is composed of many different isoenzymes that facilitate interactions between glutathione and hazardous chemicals, including cancerogenes, anticancer drugs and byproducts of oxidative stress. The scope of this review is to clarify the association of common GST polymorphisms with ovarian cancer risk and chemoresistance.

Diagnostic biomarkers in ovarian cancer: advances beyond CA125 and HE4.

Ovarian cancer (OC) is the most lethal gynaecologic malignancy, attributed to its insidious growth, non-specific symptoms and late presentation. Unfortunately, current screening modalities are inadequate at detecting OC and many lack the appropriate specificity and sensitivity that is desired from a screening test. Nearly 70% of cases are diagnosed at stage III or IV with poor 5-year overall survival. Therefore, the development of a sensitive and specific biomarker for early diagnosis and screening for OC is of utmost importance. Currently, diagnosis is guided by CA125, the patient's menopausal status and imaging features on ultrasound scan. However, emerging evidence suggests that a combination of CA125 and HE4 (another serum biomarker) and patient characteristics in a multivariate index assay may provide a higher specificity and sensitivity than either CA125 and HE4 alone in the early detection of OC. Other attempts at combining various serum biomarkers into one multivariate index assay such as OVA1, ROMA and Overa have all shown promise. However, significant barriers exist before these biomarkers can be implemented in clinical practice. This article aims to provide an up-to-date review of potential biomarkers for screening and early diagnosis of OC which may have the potential to transform its diagnostic landscape.

The Role of Cancer Stem Cell Markers in Ovarian Cancer.

Ovarian cancer is the most lethal gynaecological cancer and the eighth most common female cancer. The early diagnosis of ovarian cancer remains a clinical problem despite the significant development of technology. Nearly 70% of patients with ovarian cancer are diagnosed with stages III-IV metastatic disease. Reliable diagnostic and prognostic biomarkers are currently lacking. Ovarian cancer recurrence and resistance to chemotherapy pose vital problems and translate into poor outcomes. Cancer stem cells appear to be responsible for tumour recurrence resulting from chemotherapeutic resistance. These cells are also crucial for tumour initiation due to the ability to self-renew, differentiate, avoid immune destruction, and promote inflammation and angiogenesis. Studies have confirmed an association between CSC occurrence and resistance to chemotherapy, subsequent metastases, and cancer relapses. Therefore, the elimination of CSCs appears important for overcoming drug resistance and improving prognoses. This review focuses on the expression of selected ovarian CSC markers, including CD133, CD44, CD24, CD117, and aldehyde dehydrogenase 1, which show potential prognostic significance. Some markers expressed on the surface of CSCs correlate with clinical features and can be used for the diagnosis and prognosis of ovarian cancer. However, due to the heterogeneity and plasticity of CSCs, the determination of specific CSC phenotypes is difficult.

CT Findings of Stage IA Ovarian Cancer: Comparison between Borderline Tumor and Stage IC Ovarian Cancer.

Ovarian cancer is a common gynecological malignant tumor in women. Most patients have reached the advanced stage when they visit the hospital. In order to diagnose ovarian cancer at an early stage, treat it at an early stage, and improve the survival rate of patients, this study has used the imaging computed tomography (CT) method to diagnose stage IA ovarian cancer. The purpose of this work was to study CT features of stage IA ovarian cancer, and compare the borderline tumor and stage IC ovarian cancer at the same time so as to improve the CT diagnosis of early ovarian cancer. We retrospectively collected clinical and CT data of patients with stage I ovarian cancer and borderline ovarian tumor admitted to Nantong Tumor Hospital from 2013 to 2021. Altogether, 23 cases of patients (borderline ovarian tumor, 9 cases; stage IA ovarian cancer, 5 cases; stage IC ovarian cancer, 9 cases) were involved. CT characteristics of these patients were analyzed in terms of the tumor diameter, cystic solid structure, solid component, septation, enhancement, peritoneal thickening, ascites, and abdominal lymph nodes. CT features of stage IA ovarian cancer included large tumor size (average diameter: 15 cm), cystic solid structure (4/5; 80%), septation (4/5; 80%), and enhanced cystic wall, septum, or solid components of the tumor on contrast-enhanced CT (5/5; 100%) no peritoneal thickening (0/5; 0%), no ascites (0/5; 0%), and no abdominal lymph node enlargement (0/5; 0%). The tumor structure did not differ significantly between stage IA and IC ovarian cancers (p > 0.05), while intraperitoneal ascites did (χ2 = 0.031; p < 0.05). Stage IA and borderline ovarian tumors did not differ significantly in ovarian tumor structure (p > 0.05). CT features of stage IA ovarian cancer included large tumor size, cystic solid structure, septation, and enhanced cystic wall and solid parts in the tumors. No pelvic or abdominal metastasis was observed.

EP30.01: Transvaginal colour Doppler ultrasound in early diagnosis of ovarian cancer: our experience since 1999

EP30.01: Transvaginal colour Doppler ultrasound in early diagnosis of ovarian cancer: our experience since 1999

Two new isostructural lanthanide metal-organic frameworks based on 2,5-furandicarboxylic acid for highly efficient sensing of lysophosphatidic acid

Ovarian cancer (OC) is a serious health concern that is highly prevailing in women. In this regard, lysophosphatidic acid (LPA) is used as a biomarker for OC, and hence its early detection can help in a better treatment plan and cure of the disease. Thus, two new lanthanide metal-organic frameworks [Eu2(FDA)3(DMF)2(MeOH)2]n(Eu-MOF) and [Tb2(FDA)3(DMF)2(MeOH)2]n(Tb-MOF) were synthesized by using 2,5-furandicarboxylic acid as ligand under solvothermal method, which were in turn applied for the detection of LPA. Single-crystal X-ray diffraction indicated that both the Eu-MOF and Tb-MOF were isomorphic with tfz-d topology of {43}2{46.618.84} based on binuclear second building units to form 3D framework. Meanwhile, Eu-MOF realized highly selective detection for LPA through a strong fluorescence quenching with KSV = 1.17 × 104 M−1, effectively identified LPA compared to other major components of plasma. These findings conclude that Eu-MOF can be counted as a potential contender as a fluorescent sensor for the detection of LPA which can in turn help the early diagnosis of OC.

The importance of the regularity of preventive examinations in the early diagnosis of ovarian cancer

Objective. To show the importance of regularity of preventive examinations in early diagnostics of ovarian cancer. Malignant ovarian tumors occupy the third place in the frequency of occurrence among the gynecological tumors after cancer of the uterine body and cervix but they leave them behind with regard to the mortality rate. Detection of the disease at late stages leads to only 17 % five-year survival rate in Europe and Russia.&#x0D; Materials and methods. A prospective study was conducted on the basis of the Gynecological Unit of Perm Regional Oncological Dispensary. It enclosed 50 patients aged 45 to 77 years, operated for ovarian cancer with various stages of the disease. The following methods were used: collection of complaints and anamnesis, study of outpatient cards and medical histories, assessment of morphological conclusions.&#x0D; Results. According to the anamnesis, all patients represented a risk group for ovarian cancer. The annual gynecological preventive examination was carried out in 40 % of patients, ultrasound examination of the pelvic organs was performed in 28 % of cases. The smallest group,10 % of patients, was a group of patients with stage I of the disease and asymptomatic course of pathology, who underwent preventive examinations and ultrasound in 100 % of cases. The largest number of patients had stage III 40 %, annual preventive examinations were carried out in 20 % of women of this group without ultrasound examination of the pelvic organs. In 64 % of cases, a late examination by a doctor was detected a year or more after the symptoms of the disease occurred. Patients with stages III and IV of the disease were postmenopausal, were unemployed and lived in rural areas.&#x0D; Conclusions. The absence of symptoms at an early stage of ovarian cancer and late onset of treatment with the complaints available actualizes the need for annual preventive gynecological examinations with mandatory ultrasound verification of the diagnosis to identify the disease in time.

Circulating microRNAs for Early Diagnosis of Ovarian Cancer: A Systematic Review and Meta-Analysis.

In this study, we conducted a systematic review and meta-analysis to summarize and evaluate the global research potential of different circulating miRNAs as an early diagnostic biomarker for OC. A systematic literature search for relevant studies was conducted in June 2020 and followed up in November 2021. The search was conducted in English databases (PubMed, ScienceDirect). The primary search resulted in a total of 1887 articles, which were screened according to the prior established inclusion and exclusion criteria. We identified 44 relevant studies, of which 22 were eligible for the quantitative meta-analysis. Statistical analysis was performed using the Meta-package in Rstudio. Standardized mean differences (SMD) of relative levels between control subjects and OC patients were used to evaluate the differential expression. All studies were quality evaluated using a Newcastle-Ottawa Scale. Based on the meta-analysis, nine miRNAs were identified as dysregulated in OC patients compared to controls. Nine were upregulated in OC patients compared to controls (miR-21, -125, -141, -145, -205, -328, -200a, -200b, -200c). Furthermore, miR-26, -93, -106 and -200a were analyzed, but did not present an overall significant difference between OC patients and controls. These observations should be considered when performing future studies of circulating miRNAs in relation to OC: sufficient size of clinical cohorts, development of consensus guidelines for circulating miRNA measurements, and coverage of previously reported miRNAs.

Affinity-based electrochemical biosensor with antifouling properties for detection of lysophosphatidic acid, a promising early-stage ovarian cancer biomarker

Electrochemical techniques are considered to be highly sensitive, capable of fast response and can be easily miniaturized, properties which can aid with regard to the fabrication of compact point-of-care medical devices; however, the main challenge in developing such a tool is overcoming a ubiquitous, problematic phenomenon known as non-specific adsorption (NSA). NSA is due to the fouling of non-target molecules in the blood on the recognition surface of the device. To overcome NSA, we have developed an affinity-based electrochemical biosensor using medical-grade stainless steel electrodes and following a unique and novel strategy using silane-based interfacial chemistry to detect lysophosphatidic acid (LPA), a highly promising biomarker, which was found to be elevated in 90 % of stage I OC patients and gradually increases as the disease progresses to later stages. The biorecognition surface was developed using the affinity-based gelsolin-actin system, which was previously investigated by our group to detect LPA using fluorescence spectroscopy. We demonstrate the capability of this label-free biosensor to detect LPA in goat serum with a detection limit of 0.7 µM as a proof-of-concept for the early diagnosis of ovarian cancer.

A Dual Biomarker TK1 Protein and CA125 or HE4-Based Algorithm as a Better Diagnostic Tool than ROMA Index in Early Detection of Ovarian Cancer

The early detection of ovarian cancer is presently not effective, and it is crucial to establish biomarkers for the early diagnosis of ovarian cancer to improve the survival of patients. The aim of this study was to investigate the role of thymidine kinase 1 (TK1) in combination with CA 125 or HE4 to serve as a potential diagnostic biomarkers for ovarian cancer. In this study, a set of 198 serum samples consisting of 134 ovarian tumor patients and 64 healthy age-matched controls were analyzed. The TK1 protein levels in serum samples were determined using the AroCell TK 210 ELISA. A combination of TK1 protein with CA 125 or HE4 showed better performance than either of them alone in the differentiation of early stage ovarian cancer from the healthy control group, but also a significantly better performance than the ROMA index. However, this was not observed using a TK1 activity test in combination with the other markers. Furthermore, the combination of TK1 protein and CA 125 or HE4 could differentiate early stage disease (stage I, II) more efficiently from advanced-stage (stage III, IV) disease (p < 0.0001). The combination of TK1 protein with CA 125 or HE4 increased the potential of detecting ovarian cancer at early stages.

Label-free electrochemical bioplatform based on Au-modified magnetic Fe3O4/α-Fe2O3 hetero-nanorods for sensitive quantification of ovarian cancer tumor marker

Due to challenges in the ultrasensitive detection of human epididymis protein 4 (HE4), a vital tumor marker for the early diagnosis of ovarian cancer (OC), it is necessary to establish efficient methods for HE4 determination. Recently, electrochemical bioplatforms combined with nanomaterials have shown outstanding performance in the analysis of biological molecules. The surface modification of conductive materials is essential for signal amplification and detection sensitivity. Herein, we developed a novel label-free HE4 electrochemical bioplatform by introducing magnetic Fe3O4/α-Fe2O3 hetero-nanorods (Fe3O4/α-Fe2O3 NRs) as a substrate for signal amplification and DNA aptamer (Apt) probe immobilization in combination with magnetic self-assembly and magnetic separation. Differential pulse voltammetry (DPV) results verified that the platform had excellent specificity for HE4 under optimized conditions and was positively linearly correlated with the logarithm of HE4 concentration (R2 = 0.996) with a linear range of 0.1 pg mL−1 to 1 ng mL−1 and detection limit (LOD) of 27.5 fg mL−1. Moreover, the bioplatform demonstrated promising results for the detection of HE4 in spiked serum samples.

A label-free dual immunosensor for the simultaneous electrochemical determination of CA125 and HE4 biomarkers for the early diagnosis of ovarian cancer.

The blood levels of cancer antigen 125 (CA125) and human epididymal secretory protein 4 (HE4) are measured in the diagnosis and progression monitoring of ovarian cancer (OC), and the Risk of Ovarian Malignancy Algorithm (ROMA) score% values are calculated for cancer risk assessment. For the first time, disposable dual screen-printed carbon electrodes modified with reduced graphene oxide, polythionine, and gold nanoparticles were used to fabricate label-free electrochemical dual CA125-HE4 immunosensors for the sensitive, fast, and practical simultaneous determination of CA125 and HE4. DPV and SWV methods were used to simultaneously determine antigens in two different linear ranges (1-100pgmL-1 and 1-50ngmL-1). High sensitivity, low LOD, and LOQ were obtained for two linear ranges with a correlation coefficient above 0.99. The application stability of the dual CA125-HE4 immunosensors was determined as 60days, and the storage stability was determined as 16weeks. The dual immunosensors exhibited high selectivity in eight different antigen mixtures. The reusability of the dual immunosensors has been tested up to 9 cycles. ROMA score% values for pre-menopausal and post-menopausal status were calculated using the concentration of CA125 and HE4 in the blood serum and assessing OC risk. The disposable dual immunosensors can be used in point-of-care tests for rapid and practical simultaneous determination of CA125 and HE4 with high selectivity, sensitivity, and repeatability.

Особенности ранней диагностики новообразований яичников

Aim: to identify clinical and anamnestic risk factors for ovarian masses (OM), as well as to clarify the significance of various diagnostic criteria in the early diagnosis of ovarian cancer (OC). Patients and Methods: a retrospective analysis of 50 cases of patients with OM was conducted. The following groups were formed: the main group (50 patients with OM) and the control group — practically healthy female subjects (32 patients). Retrospectively, depending on the results concerning histology of the removed surgical material, the main group was divided into 2 subgroups: subgroup 1 — 37 patients with benign OM, subgroup 2 — 13 patients with borderline and malignant OM. The patterns of the anamnesis, the level of the Ca-125 tumor marker, the values of the Risk of Malignancy Index (RMI), and the tumor histology structure were assessed. Results: during the study, it was found that in patients with OM, diseases of the gastrointestinal tract (GIT), urinary system (24%), pelvic inflammatory disease (PID), as well as menstrual disorders of various types and uterine fibroids (p&lt;0.05) are more common. The incidence of gynecologic cancer in the family history is also significantly higher in patients with OM vs. the control group (36% and 3%, respectively, p&lt;0.05). Elevated Ca-125 values were noted both in the group with benign ovarian masses and in the group with borderline and malignant ovarian tumors.it has been established that the main risk factors for the OC development are diseases of GIT (Odds Ratio (OR) 2,09) and urinary system (OR 1,53), genetic predisposition to cancer of the reproductive organs (OR 1,86), menstrual disorder (OR 1,86), uterine fibroids (OR 1,74) and a history of PID (OR 1,83). Conclusion: despite a large number of studies devoted to the problem of OC early diagnosis, there is still no reliable criterion with high sensitivity and specificity. Female patients with postmenopause and adverse ultrasound criteria should have high oncological alarm of doctors, even with low values of the Ca-125 oncological marker. KEYWORDS: ovarian tumors, neoplasms, ovarian cancer, risk factors, Ca-125, malignancy index RMI. FOR CITATION: Smirnova A.V., Malyshkina A.I., Khrushkova E.P. Characteristics in early diagnosis of ovarian masses. Russian Journal of Woman and Child Health. 2023;6(2):105–111 (in Russ.). DOI: 10.32364/2618-8430-2023-6-2-105-111.