Diagnostic accuracy of serum HE4, CA125 and ROMA in patients with ovarian cancer: a meta-analysis

Fragmentomics features of ovarian cancer

Ovarian cancer (OC) is a major cause of cancer mortality in women worldwide. Due to the occult onset of OC, its nonspecific clinical symptoms in the early phase, and a lack of effective early diagnostic tools, most OC patients are diagnosed at an advanced stage. In this study, shallow whole-genome sequencing was utilized to characterize fragmentomics features of circulating tumor DNA (ctDNA) in OC patients. By applying a machine learning model, multiclass fragmentomics data achieved a mean area under the curve (AUC) of 0.97 (95% CI 0.962-0.976) for diagnosing OC. OC scores derived from this model strongly correlated with the disease stage. Further comparative analysis of OC scores illustrated that the fragmentomics-based technology provided additional clinical benefits over the traditional serum biomarkers cancer antigen 125 (CA125) and the Risk of Ovarian Malignancy Algorithm (ROMA) index. In conclusion, fragmentomics features in ctDNA are potential biomarkers for the accurate diagnosis of OC.

Sensitivity and Specificity of Selected Biomarkers and Their Combinations in the Diagnosis of Ovarian Cancer

One of the greatest challenges in modern gynecological oncology is ovarian cancer. Despite the numerous studies currently being conducted, it is still sometimes detected at late clinical stages, where the prognosis is unfavorable. One significant contributing factor is the absence of sensitive and specific parameters that could aid in early diagnosis. An ideal screening test, in view of the low incidence of ovarian cancer, should have a sensitivity of greater than 75% and a specificity of at least 99.6%. To enhance sensitivity and specificity, diagnostic panels are being created by combining individual markers. The drive to develop better screening tests for ovarian cancer focuses on modern diagnostic methods based on molecular testing, which in turn aims to find increasingly effective biomarkers. Currently, researchers' efforts are focused on the search for a complementary parameter to those most commonly used that would satisfactorily enhance the sensitivity and specificity of assays. Several biomarkers, including microRNA molecules, autoantibodies, cDNA, adipocytokines, and galectins, are currently being investigated by researchers. This article reviews recent studies comparing the sensitivity and specificity of selected parameters used alone and in combination to increase detection of ovarian cancer at an early stage.

Early Ovarian Cancer Detection in the Age of Fallopian Tube Precursors: A Systematic Review

OBJECTIVE

To determine biomarkers other than CA 125 that could be used in identifying early-stage ovarian cancer.

DATA SOURCES

Ovid MEDLINE ALL, EMBASE, Web of Science Core Collection, ScienceDirect, Clinicaltrials.gov , and CAB Direct were searched for English-language studies between January 2008 and April 2023 for the concepts of high-grade serous ovarian cancer, testing, and prevention or early diagnosis.

METHODS OF STUDY SELECTION

The 5,523 related articles were uploaded to Covidence. Screening by two independent reviewers of the article abstracts led to the identification of 245 peer-reviewed primary research articles for full-text review. Full-text review by those reviewers led to the identification of 131 peer-reviewed primary research articles used for this review.

TABULATION, INTEGRATION, AND RESULTS

Of 131 studies, only 55 reported sensitivity, specificity, or area under the curve (AUC), with 36 of the studies reporting at least one biomarker with a specificity of 80% or greater specificity or 0.9 or greater AUC.

CONCLUSION

These findings suggest that although many types of biomarkers are being tested in ovarian cancer, most have similar or worse detection rates compared with CA 125 and have the same limitations of poor detection rates in early-stage disease. However, 27.5% of articles (36/131) reported biomarkers with better sensitivity and an AUC greater than 0.9 compared with CA 125 alone and deserve further exploration.

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.

High-Grade Serous Ovarian Cancer-A Risk Factor Puzzle and Screening Fugitive

High-grade serous ovarian cancer (HGSOC) is the most lethal tumor of the female genital tract. Despite extensive studies and the identification of some precursor lesions like serous tubal intraepithelial cancer (STIC) or the deviated mutational status of the patients (BRCA germinal mutation), the pathophysiology of HGSOC and the existence of particular risk factors is still a puzzle. Moreover, a lack of screening programs results in delayed diagnosis, which is accompanied by a secondary chemo-resistance of the tumor and usually results in a high recurrence rate after the primary therapy. Therefore, there is an urgent need to identify the substantial risk factors for both predisposed and low-risk populations of women, as well as to create an economically and clinically justified screening program. This paper reviews the classic and novel risk factors for HGSOC and methods of diagnosis and prediction, including serum biomarkers, the liquid biopsy of circulating tumor cells or circulating tumor DNA, epigenetic markers, exosomes, and genomic and proteomic biomarkers. The novel future complex approach to ovarian cancer diagnosis should be devised based on these findings, and the general outcome of such an approach is proposed and discussed in the paper.

Improved Prediction of Ovarian Cancer Using Ensemble Classifier and Shaply Explainable AI

The importance of detecting and preventing ovarian cancer is of utmost significance for women's overall health and wellness. Referred to as the "silent killer," ovarian cancer exhibits inconspicuous symptoms during its initial phases, posing a challenge for timely identification. Identification of ovarian cancer during its advanced stages significantly diminishes the likelihood of effective treatment and survival. Regular screenings, such as pelvic exams, ultrasound, and blood tests for specific biomarkers, are essential tools for detecting the disease in its early, more treatable stages. This research makes use of the Soochow University ovarian cancer dataset, containing 50 features for the accurate detection of ovarian cancer. The proposed predictive model makes use of a stacked ensemble model, merging the strengths of bagging and boosting classifiers, and aims to enhance predictive accuracy and reliability. This combination harnesses the benefits of variance reduction and improved generalization, contributing to superior ovarian cancer prediction outcomes. The proposed model gives 96.87% accuracy, which is currently the highest model result obtained on this dataset so far using all features. Moreover, the outcomes are elucidated utilizing the explainable artificial intelligence method referred to as SHAPly. The excellence of the suggested model is demonstrated through a comparison of its performance with that of other cutting-edge models.

Design and Fabrication of a 3D-Printed Microfluidic Immunoarray for Ultrasensitive Multiplexed Protein Detection

Microfluidic technology has revolutionized device fabrication by merging principles of fluid dynamics with technologies from chemistry, physics, biology, material science, and microelectronics. Microfluidic systems manipulate small volumes of fluids to perform automated tasks with applications ranging from chemical syntheses to biomedical diagnostics. The advent of low-cost 3D printers has revolutionized the development of microfluidic systems. For measuring molecules, 3D printing offers cost-effective, time, and ease-of-designing benefits. In this paper, we present a comprehensive tutorial for design, optimization, and validation for creating a 3D-printed microfluidic immunoarray for ultrasensitive detection of multiple protein biomarkers. The target is the development of a point of care array to determine five protein biomarkers for aggressive cancers. The design phase involves defining dimensions of microchannels, reagent chambers, detection wells, and optimizing parameters and detection methods. In this study, the physical design of the array underwent multiple iterations to optimize key features, such as developing open detection wells for uniform signal distribution and a flap for covering wells during the assay. Then, full signal optimization for sensitivity and limit of detection (LOD) was performed, and calibration plots were generated to assess linear dynamic ranges and LODs. Varying characteristics among biomarkers highlighted the need for tailored assay conditions. Spike-recovery studies confirmed the assay's accuracy. Overall, this paper showcases the methodology, rigor, and innovation involved in designing a 3D-printed microfluidic immunoarray. Optimized parameters, calibration equations, and sensitivity and accuracy data contribute valuable metrics for future applications in biomarker analyses.

Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers

Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.

Current and Emerging Strategies for Tubo-Ovarian Cancer Diagnostics

Tubo-ovarian cancer is the most lethal gynaecological cancer. More than 75% of patients are diagnosed at an advanced stage, which is associated with poorer overall survival. Symptoms at presentation are vague and non-specific, contributing to late diagnosis. Multimodal risk models have improved the diagnostic accuracy of adnexal mass assessment based on patient risk factors, coupled with findings on imaging and serum-based biomarker tests. Newly developed ultrasonographic assessment algorithms have standardised documentation and enable stratification of care between local hospitals and cancer centres. So far, no screening test has proven to reduce ovarian cancer mortality in the general population. This review is an update on the evidence behind ovarian cancer diagnostic strategies.

Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and Shapley analysis

OBJECTIVE

Ovarian cancer is a significant health issue with lasting impacts on the community. Despite recent advances in surgical, chemotherapeutic and radiotherapeutic interventions, they have had only marginal impacts due to an inability to identify biomarkers at an early stage. Biomarker discovery is challenging, yet essential for improving drug discovery and clinical care. Machine learning (ML) techniques are invaluable for recognising complex patterns in biomarkers compared to conventional methods, yet they can lack physical insights into diagnosis. eXplainable Artificial Intelligence (XAI) is capable of providing deeper insights into the decision-making of complex ML algorithms increasing their applicability. We aim to introduce best practice for combining ML and XAI techniques for biomarker validation tasks.

METHODS

We focused on classification tasks and a game theoretic approach based on Shapley values to build and evaluate models and visualise results. We described the workflow and apply the pipeline in a case study using the CDAS PLCO Ovarian Biomarkers dataset to demonstrate the potential for accuracy and utility.

RESULTS

The case study results demonstrate the efficacy of the ML pipeline, its consistency, and advantages compared to conventional statistical approaches.

CONCLUSION

The resulting guidelines provide a general framework for practical application of XAI in medical research that can inform clinicians and validate and explain cancer biomarkers.

Positive Rate of Malignant Cells in Endometrial Cytology Samples of Ovarian Cancer, Fallopian Tube Cancer, and Primary Peritoneal Cancer Patients: A Systematic Review and Meta-Analysis

To estimate the feasibility of diagnosing ovarian cancer, fallopian tube cancer, and primary peritoneal cancer through endometrial cytology, we performed a systematic review and meta-analysis to calculate the pooled positive rate of malignant cells in endometrial cytology samples. We queried PubMed, EMBASE, Medline, and Cochrane Central Register of Controlled Trails from inception to November 12, 2020 for studies estimating positive rates of malignant cells in endometrial cytology samples from patients with ovarian cancer, fallopian tube cancer, and primary peritoneal cancer. The positive rates of the included studies were calculated as pooled positive rate through meta-analyses of proportion. Subgroup analysis based on different sampling methods was conducted. Seven retrospective studies involving 975 patients were included. Pooled positive rate of malignant cells in endometrial cytology specimens of ovarian cancer, fallopian tube cancer, and primary peritoneal cancer patients was 23% (95% CI: 16% - 34%). Statistical heterogeneity between the included studies was considerable (I2 = 89%, P < 0.01). The pooled positive rates of the group of brushes and the group of aspiration smears were 13% (95% CI: 10% - 17%, I2 = 0, P = 0.45) and 33% (95% CI: 25% - 42%, I2 = 80%, P < 0.01), respectively. Although endometrial cytology is not an ideal diagnostic tool for ovarian cancer, fallopian tube cancer, and primary peritoneal cancer, it is a convenient, painless, and easy-to-implement adjunct to other tools. Sampling method is one of the factors that affect the detection rate.

Diagnostic performance of CA125, HE4, ROMA, and CPH-I in identifying primary ovarian cancer

AIMS

To evaluate the ability of carbohydrate antigen 125 (CA125), human epididymis protein 4 (HE4), risk of ovarian malignancy algorithm (ROMA), and Copenhagen Index (CPH-I) to identify primary ovarian cancer (OC) from borderline and benign ovarian tumors (OTs) and explore ideal cutoff points.

METHODS

A total of 684 OTs containing 276 OC patients, 116 ovarian borderline OTs and 292 benign OTs patients who underwent surgery in our hospital were included. We retrospectively searched the results of CA125 and HE4 before patients' surgery from the hospital's electronic medical records system. ROMA and CPH-I were calculated according to their menopausal status and age, respectively. Diagnostic performance of these four were assessed by drawing receiver operating characteristic (ROC) curves.

RESULTS

CA125, HE4, ROMA, and CPH-I were all significantly higher in OC women compared with borderline OTs (p < 0.001), followed by benign OTs (p < 0.001). Area under the curves (AUCs) for distinguishing OC were 0.850 (0.818-0.882), 0.891 (0.865-0.916), 0.910 (0.888-0.933) and 0.906 (0.882-0.930), respectively, and the corresponding ideal cutoff values for CA125, HE4, ROMA, and CPH-I were 132.5, 68.6, 23.8, and 6.4, respectively. The difference between ROMA and CPH-I was not significant (p = 0.97), but both were higher than CA125 and HE4 (p < 0.05). HE4 showed a significantly higher AUC than CA125 (p < 0.05). For postmenopausal women, CA125 performed equivalently to ROMA (p = 0.73) and CPH-I (p = 0.91).

CONCLUSIONS

In identifying patients with OC, ROMA and CPH-I outperformed single tumor marker. The diagnostic performance of HE4 was significantly higher than that of CA125. CA125 was more suitable for postmenopausal women.

Evaluation of He4 Use in the Diagnosis of Ovarian Cancer: First and Second Recurrence, and an Analysis of HE4 Concentration during Second- and Third-Line Chemotherapy

HE4 is a commonly used tumor marker for ovarian cancer (OC) diagnosis. In our study, we aimed to assess its use in the diagnosis of subsequent OC recurrences and to evaluate its changes during recurrence diagnosis and the subsequent lines of chemotherapy treatment. This retrospective single center study was conducted on 188 patients treated for ovarian cancer recurrence at the Department of Gynecological Surgery and Gynecological Oncology. The sensitivity and specificity of HE4 for patient survival prediction were analyzed using Receiver Operating Characteristics (ROC) and area under the curve (AUC) with 95% confidence intervals (95% CI). Survival times to reach one of the endpoints (OS, PFS, TFI, PFS2, TFI2) were analyzed using Kaplan-Meier curves. Elevated HE4 levels at the time of first relapse diagnosis, and after the third and the last course of second-line chemotherapy, significantly influences the time from OC diagnosis until first disease recurrence (PFS2) (p = 0.005, p = 0.015 and p = 0.002, respectively). Additionally, elevated serum HE4 concentration at the time of OC diagnosis (p = 0.012), and its later recurrence (first (p < 0.001), and second recurrent diagnosis (p = 0.143)) significantly influences patient OS. Increased HE4 concentration at the end of chemotherapeutic treatment negatively affects overall patient survival ((p = 0.006 for second line chemotherapy and (p = 0.022) for elevated HE4 concentration after the last course of third-line chemotherapy). Our preliminary results show an encouraging diagnostic and prognostic role of HE4 in recurrent ovarian cancer. HE4 measurements at different treatment time points during the second- and third-line chemotherapy treatment seem to correlate with patient survival.

Circulating microRNAs as Reliable Tumor Biomarkers: Opportunities and Challenges Facing Clinical Application

MicroRNAs (miRNAs) are involved in the development of human malignancies, and cells have the ability to secrete these molecules into extracellular compartments. Thus, cell-free miRNAs (circulating miRNAs) can potentially be used as biomarkers to evaluate pathophysiological changes. Although circulating miRNAs have been proposed as potential noninvasive tumor biomarkers for diagnosis, prognosis, and response to therapy, their routine application in the clinic is far from being achieved. This review focuses on the recent progress regarding the value of circulating miRNAs as noninvasive biomarkers, with specific consideration of their relevant clinical applications. In addition, we provide an in-depth analysis of the technical challenges that impact the assessment of circulating miRNAs. We also highlight the significance of integrating circulating miRNAs with the standard laboratory biomarkers to boost sensitivity and specificity. The current status of circulating miRNAs in clinical trials as tumor biomarkers is also covered. These insights and general guidelines will assist researchers in experimental practice to ensure quality standards and repeatability, thus improving future studies on circulating miRNAs. SIGNIFICANCE STATEMENT: Our review will boost the knowledge behind the inconsistencies and contradictory results observed among studies investigating circulating miRNAs. It will also provide a solid platform for better-planned strategies and standardized techniques to optimize the assessment of circulating cell-free miRNAs.

Growth Differentiation Factor-15 as a candidate biomarker in gynecologic malignancies: A meta-analysis

Growth Differentiation Factor-15 (GDF-15), though emerged as a novel marker in gynecological cancers, is yet to be recognized in clinical diagnostics. Eligible studies were sorted from multiple online databases, namely PubMed, Cochrane, ClinicalTrials.gov, Google Scholar, Web of Science, Embase, Scopus, LILACS, Opengrey. From six studies, histopathologically diagnosed cases without prior treatment, and with diagnostic accuracy data for GDF-15 in gynecological cancers, were included. Our meta-analysis shows that GDF-15 has pooled diagnostic odds ratio of 12.74 at 80.5% sensitivity and 74.1% specificity, and an AUC of 0.84. Hence, GDF-15 is a potential marker to differentiate gynecological malignancy from non-malignant tumors.

Roles of Antimicrobial Peptides in Gynecological Cancers

Antimicrobial peptides (AMPs) are essential components of the mucosal barrier of the female reproductive tract (FRT) and are involved in many important physiological processes, including shaping the microbiota and maintaining normal reproduction and pregnancy. Gynecological cancers seriously threaten women's health and bring a heavy burden to society so that new strategies are needed to deal with these diseases. Recent studies have suggested that AMPs also have a complex yet intriguing relationship with gynecological cancers. The expression level of AMPs changes during tumor progression and they may act as promising biomarkers in cancer detection and prognosis prediction. Although AMPs have long been considered as host protective, they actually play a "double-edged sword" role in gynecological cancers, either tumorigenic or antitumor, depending on factors such as AMP and cancer types, as well as AMP concentrations. Moreover, AMPs are associated with chemoresistance and regulation of AMPs' expression may alter sensitivity of cancer cells to chemotherapy. However, more work is needed, especially on the identification of molecular mechanisms of AMPs in the FRT, as well as the clinical application of these AMPs in detection, diagnosis and treatment of gynecological malignancies.

Diagnostic Models Combining Clinical Information, Ultrasound and Biochemical Markers for Ovarian Cancer: Cochrane Systematic Review and Meta-Analysis

Background: Ovarian cancer (OC) is a diagnostic challenge, with the majority diagnosed at late stages. Existing systematic reviews of diagnostic models either use inappropriate meta-analytic methods or do not conduct statistical comparisons of models or stratify test performance by menopausal status. Methods: We searched CENTRAL, MEDLINE, EMBASE, CINAHL, CDSR, DARE, Health Technology Assessment Database and SCI Science Citation Index, trials registers, conference proceedings from 1991 to June 2019. Cochrane collaboration review methods included QUADAS-2 quality assessment and meta-analysis using hierarchical modelling. RMI, ROMA or ADNEX at any test positivity threshold were investigated. Histology or clinical follow-up was the reference standard. We excluded screening studies, studies restricted to pregnancy, recurrent or metastatic OC. 2 × 2 diagnostic tables were extracted separately for pre- and post-menopausal women. Results: We included 58 studies (30,121 patients, 9061 cases of ovarian cancer). Prevalence of OC ranged from 16 to 55% in studies. For premenopausal women, ROMA at a threshold of 13.1 (+/−2) and ADNEX at a threshold of 10% demonstrated significantly higher sensitivity compared to RMI I at 200 (p < 0.0001) 77.8 (72.5, 82.4), 94.9 (92.5, 96.6), and 57.1% (50.6 to 63.4) but lower specificity (p < 0.002), 92.5 (90.0, 94.4), 84.3 (81.3, 86.8), and 78.2 (75.8, 80.4). For postmenopausal women, ROMA at a threshold of 27.7 (+/−2) and AdNEX at a threshold of 10% demonstrated significantly higher sensitivity compared to RMI I at a threshold of 200 (p < 0.001) 90.4 (87.4, 92.7), 97.6 (96.2, 98.5), and 78.7 (74.3, 82.5), specificity of ROMA was comparable, whilst ADneX was lower, 85.5 (81.3, 88.9), 81.3 (76.9, 85.0) (p = 0.155), compared to RMI 55.2 (51.2, 59.1) (p < 0.001). Conclusions: In pre-menopausal women, ROMA and ADNEX offer significantly higher sensitivity but significantly decreased specificity. In post-menopausal women, ROMA demonstrates significantly higher sensitivity and comparable specificity to RMI I, ADNEX has the highest sensitivity of all models, but with significantly reduced specificity. RMI I has poor sensitivity compared to ROMA or ADNEX. Choice between ROMA and ADNEX as a replacement test will depend on cost effectiveness and resource implications.

Menopausal status, ultrasound and biomarker tests in combination for the diagnosis of ovarian cancer in symptomatic women

BACKGROUND

Ovarian cancer (OC) has the highest case fatality rate of all gynaecological cancers. Diagnostic delays are caused by non-specific symptoms. Existing systematic reviews have not comprehensively covered tests in current practice, not estimated accuracy separately in pre- and postmenopausal women, or used inappropriate meta-analytic methods.

OBJECTIVES

To establish the accuracy of combinations of menopausal status, ultrasound scan (USS) and biomarkers for the diagnosis of ovarian cancer in pre- and postmenopausal women and compare the accuracy of different test combinations.

SEARCH METHODS

We searched CENTRAL, MEDLINE (Ovid), Embase (Ovid), five other databases and three trial registries from 1991 to 2015 and MEDLINE (Ovid) and Embase (Ovid) form June 2015 to June 2019. We also searched conference proceedings from the European Society of Gynaecological Oncology, International Gynecologic Cancer Society, American Society of Clinical Oncology and Society of Gynecologic Oncology, ZETOC and Conference Proceedings Citation Index (Web of Knowledge). We searched reference lists of included studies and published systematic reviews.

SELECTION CRITERIA

We included cross-sectional diagnostic test accuracy studies evaluating single tests or comparing two or more tests, randomised trials comparing two or more tests, and studies validating multivariable models for the diagnosis of OC investigating test combinations, compared with a reference standard of histological confirmation or clinical follow-up in women with a pelvic mass (detected clinically or through USS) suspicious for OC.

DATA COLLECTION AND ANALYSIS

Two review authors independently extracted data and assessed quality using QUADAS-2. We used the bivariate hierarchical model to indirectly compare tests at commonly reported thresholds in pre- and postmenopausal women separately. We indirectly compared tests across all thresholds and estimated sensitivity at fixed specificities of 80% and 90% by fitting hierarchical summary receiver operating characteristic (HSROC) models in pre- and postmenopausal women separately.

MAIN RESULTS

We included 59 studies (32,059 women, 9545 cases of OC). Two tests evaluated the accuracy of a combination of menopausal status and USS findings (IOTA Logistic Regression Model 2 (LR2) and the Assessment of Different NEoplasias in the adneXa model (ADNEX)); one test evaluated the accuracy of a combination of menopausal status, USS findings and serum biomarker CA125 (Risk of Malignancy Index (RMI)); and one test evaluated the accuracy of a combination of menopausal status and two serum biomarkers (CA125 and HE4) (Risk of Ovarian Malignancy Algorithm (ROMA)). Most studies were at high or unclear risk of bias in participant, reference standard, and flow and timing domains. All studies were in hospital settings. Prevalence was 16% (RMI, ROMA), 22% (LR2) and 27% (ADNEX) in premenopausal women and 38% (RMI), 45% (ROMA), 52% (LR2) and 55% (ADNEX) in postmenopausal women. The prevalence of OC in the studies was considerably higher than would be expected in symptomatic women presenting in community-based settings, or in women referred from the community to hospital with a suspicion of OC. Studies were at high or unclear applicability because presenting features were not reported, or USS was performed by experienced ultrasonographers for RMI, LR2 and ADNEX. The higher sensitivity and lower specificity observed in postmenopausal compared to premenopausal women across all index tests and at all thresholds may reflect highly selected patient cohorts in the included studies. In premenopausal women, ROMA at a threshold of 13.1 (± 2), LR2 at a threshold to achieve a post-test probability of OC of 10% and ADNEX (post-test probability 10%) demonstrated a higher sensitivity (ROMA: 77.4%, 95% CI 72.7% to 81.5%; LR2: 83.3%, 95% CI 74.7% to 89.5%; ADNEX: 95.5%, 95% CI 91.0% to 97.8%) compared to RMI (57.2%, 95% CI 50.3% to 63.8%). The specificity of ROMA and ADNEX were lower in premenopausal women (ROMA: 84.3%, 95% CI 81.2% to 87.0%; ADNEX: 77.8%, 95% CI 67.4% to 85.5%) compared to RMI 92.5% (95% CI 90.3% to 94.2%). The specificity of LR2 was comparable to RMI (90.4%, 95% CI 84.6% to 94.1%). In postmenopausal women, ROMA at a threshold of 27.7 (± 2), LR2 (post-test probability 10%) and ADNEX (post-test probability 10%) demonstrated a higher sensitivity (ROMA: 90.3%, 95% CI 87.5% to 92.6%; LR2: 94.8%, 95% CI 92.3% to 96.6%; ADNEX: 97.6%, 95% CI 95.6% to 98.7%) compared to RMI (78.4%, 95% CI 74.6% to 81.7%). Specificity of ROMA at a threshold of 27.7 (± 2) (81.5, 95% CI 76.5% to 85.5%) was comparable to RMI (85.4%, 95% CI 82.0% to 88.2%), whereas for LR2 (post-test probability 10%) and ADNEX (post-test probability 10%) specificity was lower (LR2: 60.6%, 95% CI 50.5% to 69.9%; ADNEX: 55.0%, 95% CI 42.8% to 66.6%).

AUTHORS' CONCLUSIONS

In specialist healthcare settings in both premenopausal and postmenopausal women, RMI has poor sensitivity. In premenopausal women, ROMA, LR2 and ADNEX offer better sensitivity (fewer missed cancers), but for ROMA and ADNEX this is off-set by a decrease in specificity and increase in false positives. In postmenopausal women, ROMA demonstrates a higher sensitivity and comparable specificity to RMI. ADNEX has the highest sensitivity in postmenopausal women, but reduced specificity. The prevalence of OC in included studies is representative of a highly selected referred population, rather than a population in whom referral is being considered. The comparative accuracy of tests observed here may not be transferable to non-specialist settings. Ultimately health systems need to balance accuracy and resource implications to identify the most suitable test.

Early-Stage Detection of Ovarian Cancer Based on Clinical Data Using Machine Learning Approaches

One of the common types of cancer for women is ovarian cancer. Still, at present, there are no drug therapies that can properly cure this deadly disease. However, early-stage detection could boost the life expectancy of the patients. The main aim of this work is to apply machine learning models along with statistical methods to the clinical data obtained from 349 patient individuals to conduct predictive analytics for early diagnosis. In statistical analysis, Student’s t-test as well as log fold changes of two groups are used to find the significant blood biomarkers. Furthermore, a set of machine learning models including Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting Machine (XGBoost), Logistic Regression (LR), Gradient Boosting Machine (GBM) and Light Gradient Boosting Machine (LGBM) are used to build classification models to stratify benign-vs.-malignant ovarian cancer patients. Both of the analysis techniques recognized that the serumsamples carbohydrate antigen 125, carbohydrate antigen 19-9, carcinoembryonic antigen and human epididymis protein 4 are the top-most significant biomarkers as well as neutrophil ratio, thrombocytocrit, hematocrit blood samples, alanine aminotransferase, calcium, indirect bilirubin, uric acid, natriumas as general chemistry tests. Moreover, the results from predictive analysis suggest that the machine learning models can classify malignant patients from benign patients with accuracy as good as 91%. Since generally, early-stage detection is not available, machine learning detection could play a significant role in cancer diagnosis.

Tissue factor pathway inhibitor 2: A potential diagnostic marker for discriminating benign from malignant ovarian tumors

OBJECTIVES

Carbohydrate antigen 125 (CA125), CA19-9, carcinoembryonic antigen (CEA), human epididymis protein 4 (HE4), and the Risk of Ovarian Malignancy Algorithm (ROMA) are widely used as tumor markers and algorithms for the diagnosis of ovarian cancer (OC). Tissue factor pathway inhibitor 2 (TFPI2) has been developed as a potential serodiagnostic marker for OC in Japan. The aim of this study is to evaluate the diagnostic accuracy of the six markers alone and in combination to find the best marker for discriminating between benign and malignant ovarian tumors.

METHODS

Frozen serum samples collected from 484 patients were divided into three groups based on histopathological results: OC (n = 119), borderline ovarian tumors (BR) (n = 48), and benign ovarian tumors (BN) (n = 317). Diagnostic accuracy was calculated with an area under a receiver operating characteristic (AUC) curve.

RESULTS

TFPI2 achieved the highest discrimination between the OC + BR group versus the BN group (AUC 0.8076). ROMA values best discriminated patients with OC from those with BN (AUC, 0.8966), which was equivalent to TFPI2 (AUC, 0.8937). For discriminating the OC group from the BR + BN group, the highest AUC value was achieved by ROMA values (AUC, 0.8884), and TFPI2 also showed comparable diagnostic accuracy (AUC, 0.8845). Combining TFPI2 with ROMA had the highest AUC (0.8420-0.9357).

CONCLUSION

TFPI2 may be a clinically useful single marker comparable to conventional ROMA values for discriminating between benign and malignant ovarian tumors.

Biomarkers for Early Diagnosis of Ovarian Carcinoma

The leading cause of gynecological cancer-related morbidity and mortality is ovarian cancer (OC), which is dubbed a silent killer. Currently, OC is a target of intense biomarker research, because it is often not discovered until the disease is advanced. The goal of OC research is to develop effective tests using biomarkers that can detect the disease at the earliest stages, which would eventually decrease the mortality, thereby preventing recurrence. Therefore, there is a pressing need to revisit the existing biomarkers to recognize the potential biomarkers that can lead to efficient predictors for the OC diagnosis. This Perspective covers an update on the currently available biomarkers used in the triaging of OC to gain certain insights into the potential role of these biomarkers and their estimation that are crucial to the understanding of neoplasm progression, diagnostics, and therapy.

Current and Emerging Methods for Ovarian Cancer Screening and Diagnostics: A Comprehensive Review

With a 5-year survival rate of less than 50%, ovarian high-grade serous carcinoma (HGSC) is one of the most highly aggressive gynecological malignancies affecting women today. The high mortality rate of HGSC is largely attributable to delays in diagnosis, as most patients remain undiagnosed until the late stages of -disease. There are currently no recommended screening tests for ovarian cancer and there thus remains an urgent need for new diagnostic methods, particularly those that can detect the disease at early stages when clinical intervention remains effective. While diagnostics for ovarian cancer share many of the same technical hurdles as for other cancer types, the low prevalence of the disease in the general population, coupled with a notable lack of sensitive and specific biomarkers, have made the development of a clinically useful screening strategy particularly challenging. Here, we present a detailed review of the overall landscape of ovarian cancer diagnostics, with emphasis on emerging methods that employ novel protein, genetic, epigenetic and imaging-based biomarkers and/or advanced diagnostic technologies for the noninvasive detection of HGSC, particularly in women at high risk due to germline mutations such as BRCA1/2. Lastly, we discuss the translational potential of these approaches for achieving a clinically implementable solution for screening and diagnostics of early-stage ovarian cancer as a means of ultimately improving patient outcomes in both the general and high-risk populations.

Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery

BACKGROUND

Artificial intelligence (AI), including machine learning (ML) and deep learning, has the potential to revolutionize biomedical research. Defined as the ability to "mimic" human intelligence by machines executing trained algorithms, AI methods are deployed for biomarker discovery.

OBJECTIVE

We detail the advancements and challenges in the use of AI for biomarker discovery in ovarian and pancreatic cancer. We also provide an overview of associated regulatory and ethical considerations.

METHODS

We conducted a literature review using PubMed and Google Scholar to survey the published findings on the use of AI in ovarian cancer, pancreatic cancer, and cancer biomarkers.

RESULTS

Most AI models associated with ovarian and pancreatic cancer have yet to be applied in clinical settings, and imaging data in many studies are not publicly available. Low disease prevalence and asymptomatic disease limits data availability required for AI models. The FDA has yet to qualify imaging biomarkers as effective diagnostic tools for these cancers.

CONCLUSIONS

Challenges associated with data availability, quality, bias, as well as AI transparency and explainability, will likely persist. Explainable and trustworthy AI efforts will need to continue so that the research community can better understand and construct effective models for biomarker discovery in rare cancers.

Modified HE4, CA125, and ROMA cut-off values and predicted probability of ovarian tumor in Chinese patients

BACKGROUND

Most prior studies investigating the risk of ovarian malignancy algorithm (ROMA) with cancer antigen 125 (CA125) and human epididymis protein 4 (HE4) have involved Caucasian population or other populations. To date, there have been no unique calculations of predicted probability (PP) risk specifically for Chinese populations to help physicians in primary care settings.

METHODS

A group of 534 women with ovarian tumor diagnoses were enrolled and serum HE4 and CA125 were measured in each individual. Modified cut-off values were obtained by maximizing area under the curve (AUC) values and adjusted by using logistic regression with corresponding sensitivity (SN), specificity (SP), Youden index (YI), positive predictive value (PPV), and negative predictive value (NPV).

RESULTS

By utilizing the ideal PPV, NPV, and AUC values, in premenopausal women modified HE4, CA125, ROMA, and PP cut-off values were 73.87 pmol/L, 61.60 U/mL, 18.47%, and 0.168, respectively. The same test values for postmenopausal women were 120.90 pmol/L, 76.21 U/mL, 26.48%, and 0.485, respectively. The SN for HE4 with the modified cut-off value was significantly lower than that for CA125 (P=0.040) in premenopausal women and lower than that for ROMA (P=0.001) and PP (P=0.044) in postmenopausal women. The AUC values for CA125, ROMA, and PP were all significantly higher than that for HE4 (P=0.006, 0.007, and 0.002, respectively) in postmenopausal women.

CONCLUSIONS

The modified cut-off values for HE4, CA125, ROMA, and PP with ideal SN, SP, YI, NPV, PPV were useful of ruling out ovarian malignancy among both pre- and post-menopausal women. In premenopausal women modified HE4, CA125, ROMA, and PP cut-off values were 73.87 pmol/L, 61.60 U/mL, 18.47%, and 0.168, respectively and in postmenopausal women were 120.90 pmol/L, 76.21 U/mL, 26.48%, and 0.485, respectively.

Toward improvement of screening through mass spectrometry-based proteomics: ovarian cancer as a case study

Ovarian cancer is one of the leading causes of cancer related deaths affecting United States women. Early-stage detection of ovarian cancer has been linked to increased survival, however, current screening methods, such as biomarker testing, have proven to be ineffective in doing so. Therefore, further developments are necessary to be able to achieve positive patient prognosis. Ongoing efforts are being made in biomarker discovery towards clinical applications in screening for early-stage ovarian cancer. In this perspective, we discuss and provide examples for several workflows employing mass spectrometry-based proteomics towards protein biomarker discovery and characterization in the context of ovarian cancer; workflows include protein identification and characterization as well as intact protein profiling. We also discuss the opportunities to merge these workflows for a multiplexed approach for biomarkers. Lastly, we provide our insight as to future developments that may serve to enhance biomarker discovery workflows while also considering translational potential.

Biomarkers in Ovarian Pathology: From Screening to Diagnosis. Review of the Literature

Background: Ovarian cancer has a low incidence, but high mortality due to a habitual diagnosis in advanced cancer stages. Currently, used biomarkers have good sensitivity, but low specificity. Aim: To determine the usefulness of the biomarkers and algorithms used up to now in the screening, diagnosis, response to treatments and identification of recurrence in patients with ovarian masses. Methodology: Systematic search of publications in English in the Medline-PubMed database with the terms: “biomarkers”, “tumour”, “tumour biomarkers”, “marker”, “tumour marker”, “ovarian cancer”, “ovarian”, “Neoplasms”, “cancer”, CA-125 Antigen; Human Epididymis-specific Protein E4; Risk of Malignancy Index (RMI); Risk of Ovarian Malignancy Algorithm (ROMA); Ovarian Neoplasms. Original articles, clinical trials, reviews, systematic reviews and meta-analyses, published between January 2000 and November 2020, were selected to determine the usefulness (among others) of CA 125 and HE4 antigen in ovarian cancer. Results: Finally, 39 transcendental publications were selected to write this article to determine the usefulness of tumour markers and algorithms in ovarian cancer. Conclusions: The usefulness of the tumour markers antigen CA125 and antigen HE4 individually or as a basis for decision-making algorithms has low specificity; however, there is little evidence that confirms their usefulness as markers in ovarian cancer screening.

Diagnostic measures comparison for ovarian malignancy risk in Epithelial ovarian cancer patients: a meta-analysis

Epithelial ovarian cancer has become the most frequent cause of deaths among gynecologic malignancies. Our study elucidates the diagnostic performance of Risk of Ovarian Malignancy Algorithm (ROMA), Human epididymis secretory protein 4 (HE4) and cancer antigen (CA125). To compare the diagnostic accuracy of ROMA, HE-4 and CA125 in the early diagnosis and screening of Epithelial Ovarian Cancer. Literature search in electronic databases such as Medicine: MEDLINE (through PUBMED interface), EMBASE, Google Scholar, Science Direct and Cochrane library from January 2011 to August 2020. Studies that evaluated the diagnostic measures of ROMA, HE4 and CA125 by using Chemilumincence immunoassay or electrochemiluminescence immunoassay (CLIA or ECLIA) as index tests. Using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). We included 32 studies in our meta-analysis. We calculated AUC by SROC, pooled estimated like sensitivity, specificity, likelihood ratio, diagnostic odds ratio (DOR), Tau square, Cochran Q through random effect analysis and meta-regression. Data was retrieved from 32 studies. The number of studies included for HE4, CA125 and ROMA tests was 25, 26 and 22 respectively. The patients with EOC were taken as cases, and women with benign ovarian mass were taken as control, which was 2233/5682, 2315/5875 and 2281/5068 respectively for the markers or algorithm. The pooled estimates of the markers or algorithm were sensitivity: ROMA (postmenopausal) (0.88, 95% CI 0.86–0.89) > ROMA (premenopausal) 0.80, 95% CI 0.78–0.83 > CA-125(0.84, 95% CI 0.82–0.85) > HE4 (0.73, 95% CI 0.71–0.75) specificity: HE4 (0.90, 95% CI 0.89–0.91) > ROMA (postmenopausal) (0.83, 95% CI 0.81–0.84) > ROMA (premenopausal) (0.80, 95% CI 0.79–0.82) > CA125 (0.73, 95%CI 0.72–0.74), Diagnostic odd’s ratio ROMA (postmenopausal) 44.04, 95% CI 31.27–62.03, ROMA (premenopausal)-18.93, 95% CI 13.04–27.48, CA-125-13.44, 95% CI 9.97–18.13, HE4-41.03, 95% CI 27.96–60.21 AUC(SE): ROMA (postmenopausal) 0.94(0.01), ROMA (premenopausal)-0.88(0.01), HE4 0.91(0.01), CA125-0.86(0.02) through bivariate random effects model considering the heterogeneity. Our study found ROMA as the best marker to differentiate EOC from benign ovarian masses with greater diagnostic accuracy as compared to HE4 and CA125 in postmenopausal women. In premenopausal women, HE4 is a promising predictor of Epithelial ovarian cancer; however, its utilisation requires further exploration. Our study elucidates the diagnostic performance of ROMA, HE4 and CA125 in EOC. ROMA is a promising diagnostic marker of Epithelial ovarian cancers in postmenopausal women, while HE4 is the best diagnostic predictor of EOC in the premenopausal group. Our study had only EOC patients as cases and those with benign ovarian masses as controls. Further, we considered the studies estimated using the markers by the same index test: CLIA or ECLIA. The good number of studies with strict inclusion criteria reduced bias because of the pooling of studies with different analytical methods, especially for HE4. We did not consider the studies published in foreign languages. Since a few studies were available for HE4 and CA125 in the premenopausal and postmenopausal group separately, data were inadequate for sub-group analysis. Further, we did not assess these markers' diagnostic efficiency stratified by the stage and type of tumour due to insufficient studies.

ESGO/ISUOG/IOTA/ESGE Consensus Statement on preoperative diagnosis of ovarian tumors

The European Society of Gynaecological Oncology (ESGO), the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG), the International Ovarian Tumour Analysis (IOTA) group and the European Society for Gynaecological Endoscopy (ESGE) jointly developed clinically relevant and evidence-based statements on the preoperative diagnosis of ovarian tumors, including imaging techniques, biomarkers and prediction models. ESGO/ISUOG/IOTA/ESGE nominated a multidisciplinary international group, including expert practising clinicians and researchers who have demonstrated leadership and expertise in the preoperative diagnosis of ovarian tumors and management of patients with ovarian cancer (19 experts across Europe). A patient representative was also included in the group. To ensure that the statements were evidence-based, the current literature was reviewed and critically appraised. Preliminary statements were drafted based on the review of the relevant literature. During a conference call, the whole group discussed each preliminary statement and a first round of voting was carried out. Statements were removed when consensus among group members was not obtained. The voters had the opportunity to provide comments/suggestions with their votes. The statements were then revised accordingly. Another round of voting was carried out according to the same rules to allow the whole group to evaluate the revised version of the statements. The group achieved consensus on 18 statements. This Consensus Statement presents these ESGO/ISUOG/IOTA/ESGE statements on the preoperative diagnosis of ovarian tumors and the assessment of carcinomatosis, together with a summary of the evidence supporting each statement.

The diagnostic accuracy of human epididymis protein 4 (HE4) for discriminating between benign and malignant pelvic masses: a systematic review and meta-analysis

INTRODUCTION

Many women with benign pelvic masses, suspected of ovarian cancer, are unnecessarily referred for treatment at specialized centers. There is an unmet clinical need to improve diagnostic assessment in these patients. Our objective was to obtain summary estimates of the accuracy of human epididymis protein (HE4) for diagnosing ovarian cancer and to compare the performance of HE4 with that of cancer antigen 125 (CA125).

MATERIAL AND METHODS

We searched PubMed, Ovid and Scopus using search terms for "pelvic masses" and "HE4", to identify studies that evaluated HE4 for diagnosing malignant ovarian masses, in adult women presenting with a pelvic mass, suspected of ovarian cancer, and with diagnosis confirmed by histopathology. Screening, data extraction and Risk of Bias assessment with the QUADAS-2 tool were done independently by two authors. We performed a meta-analysis of the accuracy of HE4 and CA125 using a random-effects bivariate logit-normal model. A study protocol was registered at PROSPERO (CRD42020158073).

RESULTS

In the 17 eligible studies, which included 3404 patients, ovarian cancer prevalence ranged from 15% to 71%. Overall, the studies were heterogeneous. All studies seemed to have recruited patients in specialized settings. A meta-analysis of seven HE4 studies resulted in a mean sensitivity of 79.4% (95% confidence interval [CI] 74.1%-83.8%) and a mean specificity of 84.1% (95% CI 79.6%-87.8%), for cut-off values of 67-72 pmol/L. Based on eight studies, the mean sensitivity of CA125 was 81.4% (95% CI 74.6%-86.2%) and the mean specificity was 56.8% (95% CI 47.9%-65.4%), at a cut-off of 35 U/ml. Given a 40% ovarian cancer prevalence, the positive predictive value (PPV) for HE4 would be 76.9% (71.9%-81.2%) versus 55.6% (50.2%-60.9%) for CA125. The negative predictive value (NPV) would be 85.9 (82.8%-88.6%) and 81.9% (76.2%-86.4%), respectively. At a 15% prevalence, the NPV would be 95.8% (95% CI 94.4%-96.7%) for HE4 and 94.4% (95% CI 92.3%-96.0%) for CA125. The PPV would be 46.9% (40.4%-53.4%) and 24.9% (21.1%-29.2%), respectively.

CONCLUSIONS

HE4 had higher specificity and similar sensitivity compared with CA125. At high prevalence, PPV was also higher for HE4, but at low prevalence, it had a similar NPV to CA125. The field would benefit from studies conducted in general settings.

Guideline No. 403: Initial Investigation and Management of Adnexal Masses

OBJECTIVES

To aid primary care physicians, emergency medicine physicians, and gynaecologists in the initial investigation of adnexal masses, defined as lumps that appear near the uterus or in or around ovaries, fallopian tubes, or surrounding connective tissue, and to outline recommendations for identifying women who would benefit from a referral to a gynaecologic oncologist for further management.

INTENDED USERS

Gynaecologists, obstetricians, family physicians, general surgeons, emergency medicine specialists, radiologists, sonographers, nurses, medical learners, residents, and fellows.

TARGET POPULATION

Adult women 18 years of age and older presenting for the evaluation of an adnexal mass.

OPTIONS

Women with adnexal masses should be assessed for personal risk factors, history, and physical findings. Initial evaluation should also include imaging and laboratory testing to triage women for management of their care either by a gynaecologic oncologist or as per SOGC guideline no. 404 on the initial investigation and management of benign ovarian masses.

EVIDENCE

A search of PubMed, Cochrane Wiley, and the Cochrane systematic reviews was conducted in January 2018 for English-language materials involving human subjects published since 2000 using three sets of terms: (i) ovarian cancer, ovarian carcinoma, adnexal disease, ovarian neoplasm, adnexal mass, fallopian tube disease, fallopian tube neoplasm, ovarian cyst, and ovarian tumour; (ii) the above terms in combination with predict neoplasm staging, follow-up, and staging; and (iii) the above two sets of terms in combination with ultrasound, tumour marker, CA 125, CEA, CA19-9, HE4, multivariable-index-assay, risk-of-ovarian-malignancy-algorithm, risk-of-malignancy-index, diagnostic imaging, CT, MRI, and PET. Relevant evidence was selected for inclusion in descending order of quality of evidence as follows: meta-analyses, systematic reviews, guidelines, randomized controlled trials, prospective cohort studies, observational studies, non-systematic reviews, case series, and reports. Additional articles were identified through cross-referencing the identified reviews. The total number of studies identified was 2350, with 59 being included in this review.

VALIDATION METHODS

The content and recommendations were drafted and agreed upon by the authors. The Executive and Board of the Society of Gynecologic Oncology of Canada reviewed the content and submitted comments for consideration. The Board of Directors of the Society of Obstetricians and Gynaecologists of Canada approved the final draft for publication. The quality of evidence was rated using the criteria described in the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) methodology framework (Table A1 of Online Appendix A). See Table A2 of Online Appendix A for the interpretation of strong and weak recommendations. The summary of findings is available upon request.

BENEFITS, HARMS, COSTS

Adnexal masses are common, and guidelines on how to triage them and manage the care of patients presenting with adnexal masses will continue to guide the practice of primary care providers and gynaecologists. Ovarian cancer outcomes are improved when initial surgery is performed by a gynaecologic oncologist, likely as a result of complete surgical staging and optimal cytoreduction. Given these superior outcomes, guidelines to assist in the triage of adnexal masses and the referral and management of the care of patients with an adnexal mass are critical.

SUMMARY STATEMENTS (GRADE RATINGS IN PARENTHESES)

RECOMMENDATIONS (GRADE RATINGS IN PARENTHESES).

HE4, A New Potential Tumor Marker for Early Diagnosis and Predicting of Breast Cancer Progression

Background & Objective:

This study examined the potential of human epididymis protein 4 (HE4) as a marker in early diagnosis or as a prognostic factor for breast cancer (BC) patients.

Methods:

A total of 31 patients diagnosed with BC were enrolled in the study between 2008 and 2018. The mRNA and protein expression levels of HE4 were analyzed by immunohistochemistry (IHC) and real-time polymerase chain reaction (PCR) in the BC tissue and the non-tumoral adjacent tissue. Using ELISA technique, HE4 plasma levels were also measured in 43 BC patients compared to 43 healthy individuals. The correlation between HE4 expression and clinicopathological features was then investigated.

Results:

An increase in HE4 expression was observed at mRNA and protein levels in the BC group compared to the control group (P<0.01, P<0.0001, respectively). In addition, the relative expression of HE4 mRNA in BC patients showed a significant correlation with the differentiation grade of cancer cells (P<0.001). Plasma levels of HE4 was also associated with grade (P<0.0001), stage, and tumor size in BC patients (for both P<0.01). Patients with metastatic BC (P<0.01), lymphatic invasion, and lymph node involvement (for both P<0.05) showed significantly higher plasma levels of HE4 expression than patients without metastasis.

Conclusion:

According to our findings, upregulation of HE4 may be related to invasive BC phenotype. Measuring plasma levels of HE4 could be useful as a screening test in early diagnosis of BC.

ESGO/ISUOG/IOTA/ESGE Consensus Statement on pre-operative diagnosis of ovarian tumors

The European Society of Gynaecological Oncology (ESGO), the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG), the International Ovarian Tumour Analysis (IOTA) group, and the European Society for Gynaecological Endoscopy (ESGE) jointly developed clinically relevant and evidence-based statements on the pre-operative diagnosis of ovarian tumors, including imaging techniques, biomarkers, and prediction models. ESGO/ISUOG/IOTA/ESGE nominated a multidisciplinary international group, including expert practising clinicians and researchers who have demonstrated leadership and expertise in the pre-operative diagnosis of ovarian tumors and management of patients with ovarian cancer (19 experts across Europe). A patient representative was also included in the group. To ensure that the statements were evidence-based, the current literature was reviewed and critically appraised. Preliminary statements were drafted based on the review of the relevant literature. During a conference call, the whole group discussed each preliminary statement and a first round of voting was carried out. Statements were removed when a consensus among group members was not obtained. The voters had the opportunity to provide comments/suggestions with their votes. The statements were then revised accordingly. Another round of voting was carried out according to the same rules to allow the whole group to evaluate the revised version of the statements. The group achieved consensus on 18 statements. This Consensus Statement presents these ESGO/ISUOG/IOTA/ESGE statements on the pre-operative diagnosis of ovarian tumors and the assessment of carcinomatosis, together with a summary of the evidence supporting each statement.

ESGO/ISUOG/IOTA/ESGE Consensus Statement on preoperative diagnosis of ovarian tumours

The European Society of Gynaecological Oncology (ESGO), the International Society of Ultrasound in Obstetrics and Gynecology (ISUOG), the International Ovarian Tumour Analysis (IOTA) group and the European Society for Gynaecological Endoscopy (ESGE) jointly developed clinically relevant and evidence-based statements on the preoperative diagnosis of ovarian tumours, including imaging techniques, biomarkers and prediction models. ESGO/ISUOG/IOTA/ESGE nominated a multidisciplinary international group, including expert practising clinicians and researchers who have demonstrated leadership and expertise in the preoperative diagnosis of ovarian tumours and management of patients with ovarian cancer (19 experts across Europe). A patient representative was also included in the group. To ensure that the statements were evidence-based, the current literature was reviewed and critically appraised. Preliminary statements were drafted based on the review of the relevant literature. During a conference call, the whole group discussed each preliminary statement and a first round of voting was carried out. Statements were removed when a consensus among group members was not obtained. The voters had the opportunity to provide comments/suggestions with their votes. The statements were then revised accordingly. Another round of voting was carried out according to the same rules to allow the whole group to evaluate the revised version of the statements. The group achieved consensus on 18 statements. This Consensus Statement presents these ESGO/ISUOG/IOTA/ESGE statements on the preoperative diagnosis of ovarian tumours and the assessment of carcinomatosis, together with a summary of the evidence supporting each statement.

Early clearance of serum HE4 and CA125 in predicting platinum sensitivity and prognosis in epithelial ovarian cancer

Objectives

To assess the clinical value of early clearance of HE4 and CA125 for platinum sensitivity and prognosis in patients with ovarian cancer.

Method

HE4 and CA125 value including clinical data of 89 patients with ovarian cancer were collected. The clearance of HE4 and CA125 were assessed base on the platinum sensitivity, two-year PFS, PFS and OS.

Results

Sixteen patients were classified as platinum resistant and 73 as platinum sensitive according to the response to platinum-base chemotherapy. When HE4 clearance after 3rd cycle chemotherapy or CA125 clearance after 1st cycle chemotherapy, it gave the highest AUC of 0.788, with 100% of sensitivity and 57.5% of specificity respectively between platinum resistant and platinum sensitive group. In addition, 59 patients were classified as two-year PFS group and 30 as not achieved two-year PFS group according to obtaining two-year PFS or not. It gave the highest AUC of 0.730, with 83.3% of sensitivity and 62.7% of specificity respectively when HE4 clearance after 3rd cycle chemotherapy or CA125 clearance after 1st cycle. The prolonged PFS and OS were significantly associated by the clearance of HE4 after 3rd cycle chemotherapy (p< 0.0001, p< 0.0001) as well as CA125 after 1st cycle chemotherapy (p< 0.0001, p< 0.0001).

Conclusions

Our data suggested that the early clearance of HE4 and CA125 could predict platinum response and prognosis in patients with ovarian cancer. Monitoring the HE4 and CA125 during first-line chemotherapy might be helpful in predicting platinum sensitivity and risk to progress and relapse.

The Use of Platelet/Lymphocyte Ratio and Cancer Antigen 125 Combined with Magnetic Resonance Diffusion-Weighted Imaging in Diagnosis of Recurrent Ovarian Cancer and Neuropathic Pain

OBJECTIVE

This work aimed to analyze the value of serum platelet/lymphocyte ratio (PLR), carbohydrate antigen 125 (CA125), and diffusion-weighted imaging (DWI) in the diagnosis of recurrent ovarian cancer.

METHODS

Forty-three patients with suspected recurrence of ovarian cancer were deemed as research objects, and 5 healthy people were set as controls. PLR, DWI, and CA125 level before surgery were analyzed. Pearson correlation analysis was implemented to explore the correlation between CA125 and lymphocyte count, platelet count, and PLR. Neuropathic Pain Scale was adopted to analyze the analgesic effect of patients with recurrent ovarian cancer before treatment and 7 days after treatment. Moreover, the quality of life scores of patients with recurrent ovarian cancer were evaluated before treatment and 30 days after treatment.

RESULTS

The platelet and PLR values of patients with recurrent ovarian cancer were significantly higher relative to healthy people (P < 0.001), while the lymphocyte count was relatively lower (P = 0.002). There were considerable differences in PLR and CA125 levels in patients with recurrent ovarian cancer before and after treatment (P < 0.001). The single detection of CA125 was significantly better than that of DWI and PLR (P = 0.034). The sensitivity and specificity of CA125 detection alone was 77.3% and 81.8%, respectively, while those of the joint detection of DWI + PLR + CA125 was 92.4% and 79.9%, respectively. PLR was proved to be positively correlated with CA125 before and after treatment (r = 0.687, P < 0.001). Pain scores in all aspects of patients with recurrence of ovarian cancer after treatment were lower than that before treatment (P < 0.001), and the quality of life score was significantly higher than that before treatment (P < 0.001).

CONCLUSIONS

CA125 and PLR combined with DWI had the best diagnostic effect for patients with recurrent ovarian cancer. After treatment, the levels of PLR and CA125 were reduced and the quality of life of patients was improved.

Diagnostic accuracy of transvaginal ultrasound examination for assigning a specific diagnosis to adnexal masses: A meta-analysis

Transvaginal ultrasound (TVUS) is a standard imaging modality for differentiating patients with benign or malignant suspected adnexal mass. To date, numerous studies have assessed the diagnostic accuracy of TVUS in various settings but with variable results. Therefore, the purpose of the present study was to perform a meta-analysis to evaluate the diagnostic accuracy of TVUS for the differentiation of adnexal masses. An electronic search in the Medline, Scopus, Cochrane and Embase databases from inception till November 2019 was carried out. Meta-analysis was performed to obtain pooled sensitivity and specificity of TVUS to distinguish malignant from benign adnexal masses. The quality assessment of diagnostic accuracy studies-2 tool was used to assess the quality of trials. A total of 41 studies with 18,391 patients were included. The pooled sensitivity and specificity of TVUS was 92% (95% CI: 90-94%) and 89% (95% CI: 85-92%), respectively. The area under the receiver operating characteristic curve was 0.96 (95% CI: 0.84-1.00). There was considerable heterogeneity with a statistically significant chi-square test (P<0.001) and I² of 99%. Meta-regression results indicated that index test standards, patient selection bias and study design were potential sources of heterogeneity (P<0.05). The funnel plot was symmetrical and low publication bias was confirmed by an insignificant Deek's test (P=0.90). The present systematic review and meta-analysis indicated that TVUS is useful in differentiating between benign and malignant tumours among patients with suspected adnexal mass with high sensitivity and specificity.

Using machine learning to predict ovarian cancer

OBJECTIVE

Ovarian cancer (OC) is one of the most common types of cancer in women. Accurately prediction of benign ovarian tumors (BOT) and OC has important practical value.

METHODS

Our dataset consists of 349 Chinese patients with 49 variables including demographics, blood routine test, general chemistry, and tumor markers. Machine learning Minimum Redundancy - Maximum Relevance (MRMR) feature selection method was applied on the 235 patients' data (89 BOT and 146 OC) to select the most relevant features, with which a simple decision tree model was constructed. The model was tested on the rest of 114 patients (89 BOT and 25 OC). The results were compared with the predictions produced by using the risk of ovarian malignancy algorithm (ROMA) and logistic regression model.

RESULTS

Eight notable features were selected by MRMR, among which two were identified as the top features by the decision tree model: human epididymis protein 4 (HE4) and carcinoembryonic antigen (CEA). Particularly, CEA is a valuable marker for OC prediction in patients with low HE4. The model also yields better prediction result than ROMA.

CONCLUSION

Machine learning approaches were able to accurately classify BOT and OC. Our goal is to derive a simple predictive model which also carries a good performance. Using our approach, we obtained a model that consists of just two biomarkers, HE4 and CEA. The model is simple to interpret and outperforms the existing OC prediction methods. It demonstrates that the machine learning approach has good potential in predictive modeling for the complex diseases.

Clinical Usefulness of Cancer Antigen (CA) 125, Human Epididymis 4, and CA72-4 Levels and Risk of Ovarian Malignancy Algorithm Values for Diagnosing Ovarian Tumors in Korean Patients With and Without Endometriosis

BACKGROUND

Tumor markers are useful for detection and preoperative evaluation of ovarian tumors. We evaluated the clinical usefulness of cancer antigen (CA) 125, human epididymis 4 (HE4), and CA72-4 levels and Risk of Ovarian Malignancy Algorithm (ROMA) values for differential diagnosis of malignant and borderline tumors among suspected ovarian tumors, and the effects of endometriosis on these tumor markers.

METHODS

In a total of 266 patients (213, 14, and 39 with benign, borderline and malignant tumors, respectively), CA125, HE4, and CA72-4 levels were measured, and ROMA values were calculated. Medians of each marker were compared among the three groups. The area under the ROC curve (AUC), sensitivity, and specificity were calculated to analyze the diagnostic performance of each marker.

RESULTS

All markers were significantly higher in the malignant group than in the benign group. HE4 levels and ROMA values were significantly higher in the malignant group than in the borderline group. ROMA value had the highest AUC for distinguishing the malignant and borderline groups from the benign group in premenopausal (0.773) and postmenopausal (0.927) patients. CA125 level was significantly higher in patients with endometriosis than in those without (P<0.001), whereas HE4 and CA72-4 levels were not affected by endometriosis (P=0.128 and 0.271, respectively).

CONCLUSIONS

ROMA value is the best marker to distinguish malignant and borderline tumors from benign tumors in pre- and postmenopausal patients. HE4 and CA72-4 levels provide information on possible CA125 elevation due to endometriosis.

Increased Diagnostic Accuracy of Adnexal Tumors with A Combination of Established Algorithms and Biomarkers

Ovarian cancer is the most lethal gynecologic cancer. Pre-diagnostic testing lacks sensitivity and specificity, and surgery is often the only way to secure the diagnosis. Exploring new biomarkers is of great importance, but the rationale of combining validated well-established biomarkers and algorithms could be a more effective way forward. We hypothesized that we can improve differential diagnostics and reduce false positives by combining (a) risk of malignancy index (RMI) with serum HE4, (b) risk of ovarian malignancy algorithm (ROMA) with a transvaginal ultrasound score or (c) adding HE4 to CA125 in a simple algorithm. With logistic regression modeling, new algorithms were explored and validated using leave-one-out cross validation. The analyses were performed in an existing cohort prospectively collected prior to surgery, 2013-2016. A total of 445 benign tumors and 135 ovarian cancers were included. All presented models improved specificity at cut-off compared to the original algorithm, and goodness of fit was significant (p < 0.001). Our findings confirm that HE4 is a marker that improves specificity without hampering sensitivity or diagnostic accuracy in adnexal tumors. We provide in this study "easy-to-use" algorithms that could aid in the triage of women to the most appropriate level of care when presenting with an unknown ovarian cyst or suspicious ovarian cancer.

Ovarian Cancer Biomarkers: Moving Forward in Early Detection

Ovarian cancer is a silent cancer which rate survival mainly relays in early stage detection. The discovery of reliable ovarian cancer biomarkers plays a crucial role in the disease management and strongly impact in patient's prognosis and survival. Although having many limitations CA125 is a classical ovarian cancer biomarker, but current research using proteomic or metabolomic methodologies struggles to find alternative biomarkers, using non-invasive our relatively non-invasive sources such as urine, serum, plasma, tissue, ascites or exosomes. Metabolism and metabolites are key players in cancer biology and its importance in biomarkers discovery cannot be neglected. In this chapter we overview the state of art and the challenges facing the use and discovery of biomarkers and focus on ovarian cancer early detection.

Efficacy of HE4, CA125, Risk of Malignancy Index and Risk of Ovarian Malignancy Index to Detect Ovarian Cancer in Women with Presumed Benign Ovarian Tumours: A Prospective, Multicentre Trial

BACKGROUND

Presumed benign ovarian tumours (PBOT) are defined by the International Ovarian Tumour Analysis (IOTA) group, without suspected sonographic criteria of cancer, without ascites or metastasis. The aim is to evaluate the efficacy of human epididymis protein 4 (HE4), cancer antigen 125 (CA125), the risk of malignancy index (RMI) and the risk of ovarian malignancy index (ROMA) to predict ovarian cancer in women with PBOT.

METHODS

It is a prospective, observational, multicentre, laboratory-based study including women with PBOT in four hospitals from 11 May 2015 through 12 May 2016. Preoperative CA125 and HE4 plasma levels were measured for all women. The primary endpoint was the specificity of CA125 and HE4 for diagnosing ovarian cancer. The main secondary endpoints were specificity and likelihood ratio of RMI, ROMA and tumours markers.

RESULTS

Two hundred and fifty patients were initially enrolled and 221 patients were finally analysed, including 209 benign ovarian tumours (94.6%) and 12 malignant ovarian tumours (5.4%). The malignant group had significantly higher mean values of HE4, CA125, RMI and ROMA compared to the benign group (p < 0.001). Specificity was significantly higher using a combination of HE4 and CA125 (99.5%) compared to either HE4 or CA125 alone (90.4% and 91.4%, respectively, p < 0.001). Moreover, the positive likelihood ratio for combination HE4 and CA125 was significantly higher (104.5; 95% CI 13.6-800.0) compared to HE4 alone (5.81; 95% CI 2.83-11.90) or CA125 alone (6.97; 95% CI 3.91-12.41).

CONCLUSIONS

The combination of HE4 and CA125 represents the best tool to predict the risk of ovarian cancer in patients with a PBOT.

Serum CA-125 levels in preeclampsia: A systematic review and meta-analysis

BACKGROUND

Preeclampsia is a leading cause of perinatal morbidity, although an optimal screening model is still under investigation. The aim of the present meta-analysis is to accumulate current evidence and evaluate the diagnostic accuracy of CA-125 in preeclampsia.

MATERIALS AND METHODS

Medline, Scopus, CENTRAL, Clinicaltrials.gov and Google Scholar were systematically searched. All studies reporting serum CA-125 among preeclamptic and healthy pregnant women were selected.

RESULTS

Nine studies involving 977 women were included. Meta-analysis revealed significant differences among patients with preeclampsia and control pregnant women (MD 15.86 IU/mL, 95% CI, 9.03-22.69). Patients with severe preeclampsia had significantly higher levels of CA-125 compared to patients with mild preeclampsia (MD 13.21 IU/mL, 95% CI, 1.94-24.49). Meta-regression analysis revealed that gestational age <34 weeks could positively affect this association.

CONCLUSIONS

The present meta-analysis suggests that serum CA-125 levels are increased in preeclamptic women during the third trimester of pregnancy. This association should be interpreted with caution as there are concerns for significant selection bias. Future studies are needed to corroborate these findings and investigate the diagnostic accuracy of this biomarker during early pregnancy.

Identification of clinical trait-related lncRNA and mRNA biomarkers with weighted gene co-expression network analysis as useful tool for personalized medicine in ovarian cancer

RELEVANCE

The pathogenesis and biomarkers of ovarian cancer (OC) remain not well-known in diagnosis, effective therapy, and prognostic assessment in OC personalized medicine. The novel identified lncRNA and mRNA biomarkers from gene co-expression modules associated with clinical traits provide new insight for effective treatment of ovarian cancer.

PURPOSE

Long non-coding RNAs (lncRNAs) are relevant to tumorigenesis via multiple mechanisms. This study aimed to investigate cancer-specific lncRNAs and mRNAs, and their related networks in OCs.

METHODS

This study comprehensively analyzed lncRNAs and mRNAs with associated competing endogenous RNA (ceRNA) network and lncRNA-RNA binding protein-mRNA network in the OC tissues in the Cancer Genome Atlas, including 2562 cancer-specific lncRNAs (n = 352 OC tissues) and 5000 mRNAs (n = 359 OC tissues). The weighted gene co-expression network analysis (WGCNA) was used to construct the co-expression gene modules and their relationship with clinical traits. The statistically significant difference of identified lncRNAs and mRNAs was confirmed with qRT-PCR in OC cells.

RESULTS

An lncRNA-based co-expression module was significantly correlated with patient age at initial pathologic diagnosis, lymphatic invasion, tissues source site, and vascular invasion, and identified 16 lncRNAs (ACTA2-AS1, CARD8-AS1, HCP5, HHIP-AS1, HOTAIRM1, ITGB2-AS1, LINC00324, LINC00605, LINC01503, LINC01547, MIR31HG, MIR155HG, OTUD6B-AS1, PSMG3-AS1, SH3PXD2A-AS1, and ZBED5-AS1) that were significantly related to overall survival in OC patients. An mRNA-based co-expression module was significantly correlated with patient age at initial pathologic diagnosis, lymphatic invasion, tumor residual disease, and vascular invasion; and identified 21 hub-mRNA molecules and 11 mRNAs (FBN3, TCF7L1, SBK1, TRO, TUBB2B, PLCG1, KIAA1549, PHC1, DNMT3A, LAMA1, and C10orf82) that were closely linked with OC patients' overall survival. Moreover, the prognostic model of five-gene signature (OTUD6B-AS1, PSMG3-AS1, ZBED5-AS1, SBK1, and PLCG1) was constructed to predict risk score in OC patients. Furthermore, starBase bioinformatics constructed the lncRNA-miRNA-mRNA and lncRNA-RNA binding protein-mRNA networks in OCs.

CONCLUSION

These new findings showed that lncRNA-related networks in OCs are a useful resource for identification of biomarkers in OCs.

Significance of Serum Human Epididymis Protein 4 and Cancer Antigen 125 in Distinguishing Type I and Type II Epithelial Ovarian Cancers

OBJECTIVE

The objective of this study was to assess the effectiveness of human epididymis protein 4 (HE4) and cancer antigen 125 (CA125), and risk of ovarian malignancy algorithm (ROMA) in identifying type I and type II epithelial ovarian cancers (EOCs).

METHODS

A cross-sectional diagnostic study was conducted of 499 Thai women older than 18 years who had clinically diagnosed pelvic masses and underwent elective surgery at our hospital between July 2012 and July 2014. Preoperative serum HE4 and CA125 levels were measured, and postoperative pathologic slides were reviewed.

RESULTS

Of 499 Thai women enrolled in this study, 357 were noncancerous (NC), 79 had type I EOCs (EOCs-I) and 63 had type II (EOCs-II). Risk of ovarian malignancy algorithm revealed significantly better performance than HE4 and CA125 in discriminating between NC and EOCs-I (receiver operating characteristic curve and area under the curve [ROC-AUC]: ROMA, 0.86; HE4, 0.80; and CA125, 0.77), and NC and EOCs-II (ROC-AUC: ROMA, 0.97; HE4, 0.95; and CA125, 0.93). In differentiation between EOCs-I and EOCs-II (setting EOC-II as reference), ROMA yielded a better performance than HE4 and CA125 (ROC-AUC: ROMA, 0.83; HE4, 0.82; and CA125, 0.77); however, CA125 and HE4 showed higher sensitivity (CA125, 77.8%; HE4, 76.2%), whereas ROMA had the highest specificity (79.8%).

CONCLUSIONS

In women who present with pelvic masses, ROMA performed very well in distinguishing between noncancer lesions and EOCs but the combined HE4 and CA125 test was more effective in predicting EOC types.

Usefulness of HE4 protein in differentiation of pelvic masses in woman

Introduction

HE4 protein (human epididymis protein-4), which is the fourth subfraction of human epithelial protein, is a glycoprotein widely used as a tumor marker in ovarian cancer. If was first discovered in the epididymal epithelium and recognized as a protease inhibitor contributing to sperm maturation. The plasma HE4 concentration may also be increased in gynecological pathologies other than ovarian cancer.

Material and methods

The study was conducted between 2016 and 2017 among patients hospitalized in the Academic Department of Gynaecology. A total of 191 women were examined. Depending on the type of pathology which was the reason for hospitalization, 4 groups of patients were identified in the study. The first of these included 30 patients with ovarian cancer, the second 33 patients with benign ovarian lesion, the third 50 patients with endometrial cancer, and the fourth 28 patients with leiomyomas.

Results

The highest concentration of HE4 protein was found in women with ovarian cancer, and it was statistically significantly higher compared to all other groups. Lower HE4 protein concentration than in women with ovarian cancer was reported in women with endometrial cancer, but it was statistically significantly higher compared to patients with uterine fibroids.

Conclusions

This marker may have significant clinical value in the differentiation of benign ovarian pathology from ovarian cancer. The study confirms the validity of using HE4 results in the assessment of potential malignancy of ovarian and endometrial lesions.

Diagnostic Performance of Serum Human Epididymis Protein 4 (HE4) for Prediction of Malignancy in Ovarian Masses

Background:

Early diagnosis of ovarian cancer is essential for long term disease control and mortality reduction. This has been achieved using tumor markers like cancer antigen 125 (CA-125) which is elevated in malignant as well as non-malignant conditions. This dilemma led to efforts towards development of newer markers like serum human epididymis secretory protein E4 (HE4). Present study aimed to evaluate role of HE4 in diagnosing ovarian cancers and comparing it with CA-125.

Methods:

Serum samples from 67 patients with ovarian cancer, 42 with benign ovarian masses and 26 healthy controls were collected preoperatively and tested for serum HE4 levels and CA-125 levels. Diagnostic performance of both tumor markers (HE4/CA-125) to diagnose malignancy in ovarian masses was calculated and compared to each other.

Results:

Mean CA-125 and HE4 levels were significantly higher in patients with ovarian cancer than in those with benign disease (p<0.001) or healthy controls (p< 0.001). Serum HE4 levels significantly increased in epithelial ovarian cancers when compared to non-epithelial ovarian cancers (p<0.01). Using benign control as comparison, receiver operating characteristic curve (ROC) was generated to predict a cut-off value for diagnosing malignancy for serum HE4 and CA-125. Compared to CA-125, HE4 had a similar sensitivity (83.6% vs. 85.10%) and higher specificity (100% vs. 90.48%); combination of serum HE4 and CA-125 improved the sensitivity to detect ovarian cancer to 92.54%. Sensitivity of HE4 to detect early stage ovarian cancer was superior to CA-125 (92.61% vs. 63.41%).

Conclusion:

Serum HE4, a novel tumor marker, discriminated epithelial ovarian cancer from benign ovarian masses. HE4 levels were related to the stage and histological types with the lowest levels in mucinous epithelial ovarian cancer and non-epithelial malignancy. Measuring serum HE4 levels alongwith CA-125 may provide higher accuracy for detecting epithelial ovarian cancer particularly in the early stages.