A novel diagnostic index combining HE4, CA125 and age may improve triage of women with suspected ovarian cancer - An international multicenter study in women with an ovarian mass

Evaluation of serum CA125, HE4 and CA724 and the risk of ovarian malignancy algorithm score in the diagnosis of high-grade serous ovarian cancer

AIM

To develop a new algorithm for the detection of high-grade serous ovarian cancer (HGSOC).

METHODS

Patients diagnosed with HGSOC, borderline ovarian tumours (BOTs) or benign ovarian masses (BOMs) were enrolled between February 2019 and December 2020. Patients with BOTs or BOMs were grouped as non-HGSOC. The cases were divided randomly into a training cohort (two-thirds of cases) and a validation cohort (one-third of cases). Logistic regression was used to find risk factors for HGSOC and to create a new algorithm in the training cohort. Receiver operating characteristic curves were used to compare the diagnostic value of tumour biomarkers. Sensitivity and specificity of tumour markers and the new algorithm were calculated in the training cohort and validation cohort.

RESULTS

This study found significant differences in age; BRCA1/2 mutation status; CA125, CA724 and HE4 levels; and Risk of Ovarian Malignancy Algorithm score between the two groups.Logistic regression analysis showed that CA125 and BRCA1/2 were risk factors for HGSOC. A new algorithm combining CA125 and BRCA1/2 increased the specificity of CA125 for diagnosis of HGSOC. The new algorithm had sensitivity of 81.08% and specificity of 93.10% in the training cohort.

CONCLUSION

The new algorithm using CA125 and BRCA1/2 helped to distinguish between patients with HGSOC and patients with non-HGSOC.

Screening and prevention of ovarian cancer

Ovarian cancer remains the most lethal gynaecological malignancy with 314 000 cases and 207 000 deaths annually worldwide. Ovarian cancer cases and deaths are predicted to increase in Australia by 42% and 55% respectively by 2040. Earlier detection and significant downstaging of ovarian cancer have been demonstrated with multimodal screening in the largest randomised controlled trial of ovarian cancer screening in women at average population risk. However, none of the randomised trials have demonstrated a mortality benefit. Therefore, ovarian cancer screening is not currently recommended in women at average population risk. More frequent surveillance for ovarian cancer every three to four months in women at high risk has shown good performance characteristics and significant downstaging, but there is no available information on a survival benefit. Population testing offers an emerging novel strategy to identify women at high risk who can benefit from ovarian cancer prevention. Novel multicancer early detection biomarker, longitudinal multiple marker strategies, and new biomarkers are being investigated and evaluated for ovarian cancer screening. Risk-reducing salpingo-oophorectomy (RRSO) decreases ovarian cancer incidence and mortality and is recommended for women at over a 4-5% lifetime risk of ovarian cancer. Pre-menopausal women without contraindications to hormone replacement therapy (HRT) undergoing RRSO should be offered HRT until 51 years of age to minimise the detrimental consequences of premature menopause. Currently risk-reducing early salpingectomy and delayed oophorectomy (RRESDO) should only be offered to women at increased risk of ovarian cancer within the context of a research trial. Pre-menopausal early salpingectomy is associated with fewer menopausal symptoms and better sexual function than bilateral salpingo-oophorectomy. A Sectioning and Extensively Examining the Fimbria (SEE-FIM) protocol should be used for histopathological assessment in women at high risk of ovarian cancer who are undergoing surgical prevention. Opportunistic salpingectomy may be offered at routine gynaecological surgery to all women who have completed their family. Long term prospective opportunistic salpingectomy studies are needed to determine the effect size of ovarian cancer risk reduction and the impact on menopause.

Derivation and validation of a nomogram based on clinical characteristics to diagnose endometriosis associated ovarian cancer preoperatively

PURPOSE

The preoperative diagnosis of endometriosis associated ovarian cancer (EAOC) remains challenging for lack of effective diagnostic biomarker. We aimed to study clinical characteristics and develop a nomogram for diagnosing EAOC before surgery.

METHODS

A total of 87 patients with EAOC and 348 patients with ovarian endometrioma (OEM) were enrolled in our study. Least absolute shrinkage and selection operator (LASSO) regression and Logistic regression were utilized to select variables and construct the prediction model. The performance of the model was assessed using receiver operating characteristic (ROC) analyses and calibration plots, while decision curve analyses (DCAs) were conducted to assess clinical value. Bootstrap resampling was used to evaluated the stability of the model in the derivation set.

RESULTS

The EAOC patients were older compared to the OEM patients (46.41 ± 9.62 vs. 36.49 ± 8.09 year, P < 0.001) and proportion of postmenopausal women was higher in EAOC group than in the OEM group (34.5 vs. 1.5%, P < 0.001). Our prediction model, which included age at diagnosis, tumor size, cancer antigen (CA) 19-9 and risk of ovarian malignancy algorithm (ROMA), demonstrated an area under the curve (AUC) of 0.858 (95% confidence interval (CI): 0.795-0.920) in the derivation set (N = 304) and an AUC of 0.870 (95% CI: 0.779-0.961) in the validation set (N = 131). The model fitted both the derivation (Hosmer-Lemeshow test (HL) chi-square = 12.600, P = 0.247) and the validation (HL chi-square = 8.210, P = 0.608) sets well.

CONCLUSION

Compared to patients with OEM, those with EAOC exhibited distinct clinical characteristics. Our four-variable prediction model demonstrated excellent performance in both the derivation and validation sets, suggesting its potential to assist with preoperative diagnosis of EAOC.

Comparison of the risk of ovarian malignancy algorithm and Copenhagen Index for the preoperative assessment of Japanese women with ovarian tumors

OBJECTIVE

To compare the risk of ovarian malignancy algorithm (ROMA) and Copenhagen Index (CPH-I) in their ability to distinguish epithelial ovarian cancer (EOC) and malignant ovarian tumors (MLOT) from benign ovarian tumors (BeOT) in Japanese women.

METHODS

Patients with pathologically diagnosed ovarian tumors were included in this study. The study validated the diagnostic performance of ROMA and CPH-I.

RESULTS

Among the 463 Japanese women included in this study, 312 had BeOT, 99 had EOC, and 52 had other MLOT. The receiver-operator characteristic (ROC) area under the curve (AUCs) of ROMA (0.89) and CPH-I (0.89) for distinguishing EOC from BeOT were significantly higher than that of CA125 (0.82) (CA 125 vs. ROMA; p = 0.002, vs. CPH-I; p < 0.001). The ROC-AUCs of ROMA (0.82) and CPH-I (0.81) for distinguishing MLOT from BeOT were significantly higher than that of CA125 (0.75) (CA 125 vs. ROMA: p = 0.003, vs. CPH-I: p < 0.001). The sensitivity (SN)/specificity (SP) of ROMA and CPH-I for distinguishing EOC from BeOT at standard cut-off points were 69%/90%, and 69%/90%, respectively, those for distinguishing MLOT from BeOT were 54%/90%, and 55%/90%, respectively.

CONCLUSION

ROMA and CPH-I performed comparably well and better than CA125 in distinguishing EOC from BeOT in Japanese women. ROMA and CHP-I should be used with caution in practical situations, where all histological possibilities for must be considered, because the SNs of ROMA and CPH-I were only 54% and 55%.

Development of prediction model to estimate future risk of ovarian lesions: A multi-center retrospective study

Background

To develop the preoperative prediction of ovarian lesions using regression-based statistics analyses and machine learning methods based on multiple serological biomarkers in China.

Methods

1137 patients with ovarian lesions in Zhujiang Hospital and 518 patients in others hospital in China were randomly assigned to training, test and external validation cohorts. Five machine learning classifiers, including Random Forest (RF), Extreme Gradient Boosting (XGB), Support Vector Classifier (SVC), K-nearest Neighbor (KN), Multi-Layer Perceptron (MLP) and the Lasso-Logistics prediction model (LLRM) were used to derive diagnostic information from 23 predictors.

Results

The RF model had a high diagnostic value (AUC = 0.968) in predicting benign and malignant ovarian disease. Age and MLR were also potential diagnostic indicators for predicting ovarian disease except tumor indicators. The RF model well distinguished borderline ovarian tumors (AUC = 0.742). The RFM had a high predictive power to identify ovarian serous adenocarcinoma (AUC = 0.943) and ovarian endometriosis cysts (AUC = 0.914).

Conclusions

The RF models can effectively predict adnexal lesions, promising to be adjuncts to the preoperative prediction of ovarian cancer.

Are we ready for translational research based on material and data from the Danish CancerBiobank and can we gain new knowledge from biobank registration?

Bio-and GenomeBank, Denmark (RBGB) is a nationwide infra-structure. Danish CancerBiobank (DCB) is a biobank in RBGB. The aim is to describe the degree of biological material collected and stored in DCB for patients diagnosed with primary ovarian cancer registered in The Danish Gynecologic Cancer Database (DGCD). Furthermore, to investigate the concordance between predicted organ of disease registered in RBGB at time of sampling (presumed diagnosis) with final diagnosis for patient. Data extraction from DGCD and DCB. Biological materials are present for 1.347 (62%) of 2.172 patients with primary ovarian cancer (OC). The median age of OC patients were 68 years (range: 18-90 years). Median age of patients with biological material in DCB was 67 years and for patients without biological material in DCB 69 years (p ≤ 0.0001). The histological subtypes for the 1347 OC patients with biological material were 911 (68%) serous adenocarcinoma, 97 (7%) endometrioid adenocarcinoma, 80 (6%) mucinous adenocarcinoma, 58 (4%) clear cell carcinoma, and for 201 (15%) no information were registered. For 327 patients (24%), the presumed diagnosis was hematological with a final diagnosis of OC. Using clinical data and biological material including pre-analytical data regarding the biological material the possibility for translational research is optimal. Furthermore, information registered through daily working procedures may propose the need for additional biomarkers to aid clinicians to stratify patients to treatment in correct fast-track packages.

Advantages and Limitations of Ultrasound as a Screening Test for Ovarian Cancer

Ovarian cancer (OC) is the seventh most common malignancy diagnosed among women, the eighth leading cause of cancer mortality globally, and the most common cause of death among all gynecological cancers. Even though recent advances in technology have allowed for more accurate radiological and laboratory diagnostic tests, approximately 60% of OC cases are diagnosed at an advanced stage. Given the high mortality rate of advanced stages of OC, early diagnosis remains the main prognostic factor. Our aim is to focus on the sonographic challenges in ovarian cancer screening and to highlight the importance of sonographic evaluation, the crucial role of the operator΄s experience, possible limitations in visibility, emphasizing the importance and the necessity of quality assurance protocols that health workers have to follow and finally increasing the positive predictive value. We also analyzed how ultrasound can be combined with biomarkers (ex. CA-125) so as to increase the sensitivity of early-stage OC detection or, in addition to the gold standard examination, the CT (Computed tomography) scan in OC follow-up. Improvements in the performance and consistency of ultrasound screening could reduce the need for repeated examinations and, mainly, ensure diagnostic accuracy. Finally, we refer to new very promising techniques such as liquid biopsies. Future attempts in order to improve screening should focus on the identification of features that are unique to OC and that are present in early-stage tumors.

Diagnostic accuracy of the Copenhagen Index in ovarian malignancy: A meta-analysis

OBJECTIVE

To assess the diagnostic value of the Copenhagen index for ovarian malignancy.

METHODS

PubMed, Web of Science, the Cochrane Library, Embase, CBM, CNKI, and WanFang databases were searched throughout June 2021. Statistical analyses were performed using Stata 12, Meta-DiSc, and RevMan 5.3. The pooled sensitivity, specificity, and diagnostic odds ratio were calculated, the summary receiver operating characteristic curve was drawn, and the area under the curve was calculated.

RESULTS

Ten articles, including 11 studies with a total of 5266 patients, were included. The pooled sensitivity, specificity, and diagnostic odds ratio were 0.82 [95% CI (0.80-0.83)], 0.88 [95% CI (0.87-0.89)], and 57.31 [95% CI (32.84-100.02)], respectively. The area under the summary receiver operating characteristics curve and the Q index were 0.9545 and 0.8966, respectively.

CONCLUSION

Our systematic review shows that the sensitivity and specificity of the Copenhagen index are high enough for it to be used in a clinical setting to provide accurate ovarian cancer diagnosis without considering menopausal status.

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.

Value of the Copenhagen index in the diagnosis of malignant adnexal tumors: A meta-analysis

Ovarian cancer is a common gynecological malignant tumor. Early diagnosis is important for the prognosis of patients with ovarian cancer. To evaluate the accuracy of the Copenhagen Index (CPH-I) in detecting malignant adnexal tumors and to compare the diagnostic accuracy of CPH-I and the Risk of Ovarian Malignancy Algorithm (ROMA). PubMed, Web of Science, and Cochrane Library databases were used to retrieve eligible studies. The overall sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the (summary receiver operating characteristic) curve (AUC) were 0.81, 0.88, 6.61, 0.22, 30.43, and 0.91, respectively, in diagnosing malignant adnexal tumors. In addition, using ROMA, the sensitivity, specificity, and AUC were 0.83, 0.85, and 0.90, respectively. CPH-I showed high accuracy in diagnosing malignant adnexal tumors, which could be an alternative method with similar efficacy to ROMA. CPH-I was more advantageous in diagnosing adnexal tumors in postmenopausal women.

Copenhagen index (CPH-I) is more favorable than CA125, HE4, and risk of ovarian malignancy algorithm (ROMA): Nomogram prediction models with clinical-ultrasonographic feature for diagnosing ovarian neoplasms

Background

We aimed to analyze the benign and malignant identification efficiency of CA125, HE4, risk of ovarian malignancy algorithm (ROMA), Copenhagen Index (CPH-I) in ovarian neoplasms and establish a nomogram to improve the preoperative evaluation value of ovarian neoplasms.

Methods

A total of 3,042 patients with ovarian neoplasms were retrospectively classified according to postoperative pathological diagnosis [benign, n = 2389; epithelial ovarian cancer (EOC), n = 653]. The patients were randomly divided into training and test cohorts at a ratio of 7:3. Using CA125, HE4, ROMA, and CPH-I, Receiver operating characteristic (ROC) curves corresponding to different truncation values were calculated and compared, and optimal truncation values were selected. Clinical and imaging risk factors were calculated using univariate regression, and significant variables were selected for multivariate regression analysis combined with ROMA and CPH-I. Nomograms were constructed to predict the occurrence of EOC, and the accuracy was assessed by external validation.

Results

When the cutoff value of CA125, HE4, ROMA, and CPH-I was 100 U/ml, 70 pmol/L, 12.5/14.4% (premenopausal/postmenopausal) and 5%, respectively, the AUC was 0.674, 0.721, 0.750 and 0.769, respectively. From univariate regression, the clinical risk factors were older age, menopausal status, higher birth rate, hypertension, and diabetes; imaging risk factors were multilocular tumors, solid nodules, bilateral tumors, larger tumor diameter, and ascites. The AUC of the nomogram containing ROMA and CPH-I was 0.8914 and 0.9114, respectively, which was better than the prediction accuracies of CA125, HE4, ROMA, and CPH-I alone. The nomogram with CPH-I was significantly better than that with ROMA (P < 0.001), and a nomogram decision curve analysis (DCA) containing CPH-I seemed to have better clinical benefits than ROMA. For external validation of this nomogram containing ROMA and CPH-I, the C-indices were 0.889 and 0.900, and the calibration curves were close to 45°, showing good agreement with the predicted values.

Conclusion

We conclude that CPH-I and ROMA have higher diagnostic values in the preoperative diagnosis of EOC than other single tumor markers like CA125 or HE4. A nomogram based on CPH-I and ROMA with clinical and ultrasonic indicators had a better diagnostic value, and the CPH-I nomogram had the highest diagnostic efficacy.

The Endometriotic Neoplasm Algorithm for Risk Assessment (e-NARA) Index Sheds Light on the Discrimination of Endometriosis-Associated Ovarian Cancer from Ovarian Endometrioma

BACKGROUND

Magnetic resonance (MR) relaxometry provides a noninvasive tool to discriminate endometriosis-associated ovarian cancer (EAOC) from ovarian endometrioma (OE) with high accuracy. However, this method has a limitation in discriminating malignancy in clinical use because the R2 value depends on the device manufacturer and repeated imaging is unrealistic. The current study aimed to reassess the diagnostic accuracy of MR relaxometry and investigate a more powerful tool to distinguish EAOC from OE.

METHODS

This retrospective study was conducted at our institution from December, 2012, to May, 2022. A total of 150 patients were included in this study. Patients with benign ovarian tumors (n = 108) mainly received laparoscopic surgery, and cases with suspected malignancy (n = 42) underwent laparotomy. Information from a chart review of the patients' medical records was collected.

RESULTS

A multiple regression analysis revealed that the age, the tumor diameter, and the R2 value were independent malignant predicting factors. The endometriotic neoplasm algorithm for risk assessment (e-NARA) index provided high accuracy (sensitivity, 85.7%; specificity, 87.0%) to discriminate EAOC from OE.

CONCLUSIONS

The e-NARA index is a reliable tool to assess the probability of malignant transformation of endometrioma.

Molecular Biomarkers for the Early Detection of Ovarian Cancer

Ovarian cancer is the deadliest gynecological cancer, leading to over 152,000 deaths each year. A late diagnosis is the primary factor causing a poor prognosis of ovarian cancer and often occurs due to a lack of specific symptoms and effective biomarkers for an early detection. Currently, cancer antigen 125 (CA125) is the most widely used biomarker for ovarian cancer detection, but this approach is limited by a low specificity. In recent years, multimarker panels have been developed by combining molecular biomarkers such as human epididymis secretory protein 4 (HE4), ultrasound results, or menopausal status to improve the diagnostic efficacy. The risk of ovarian malignancy algorithm (ROMA), the risk of malignancy index (RMI), and OVA1 assays have also been clinically used with improved sensitivity and specificity. Ongoing investigations into novel biomarkers such as autoantibodies, ctDNAs, miRNAs, and DNA methylation signatures continue to aim to provide earlier detection methods for ovarian cancer. This paper reviews recent advancements in molecular biomarkers for the early detection of ovarian cancer.

Biomarkers in Gynecologic Tumors

Gynecologic malignancies are one of the most frequent cancers amongst women. Biomarkers are crucial for the differential diagnosis of adnexal masses; however, their potential for diagnosis is limited. In the era of difficulty in ovarian cancer screening, novel biomarkers are defined, but CA125 still remains the most valuable one. Circulating tumor DNAs, DNA hypermethylation, metabolites, microRNAs, and kallikreins have recently turned out as ovarian cancer biomarkers and are being applied to clinical practice. For uterine cancer, genomic classification has now been described, it will be used as a prognostic tool. In this chapter, we describe ovarian, endometrial, and cervical cancer biomarkers in detail.

Defining Models to Classify between Benign and Malignant Adnexal Masses Using Routine Laboratory Parameters

Simple Summary

In patients with adnexal masses, classification into benign or malignant tumors is essential for optimal treatment planning, but remains challenging. In the search for new models applicable in a routine clinical setting, we compared classical single parameters to multiparameter predictive models.

Abstract

Discrimination between benign and malignant adnexal masses is essential for optimal treatment planning, but still remains challenging in a routine clinical setting. In this retrospective study, we aimed to compare albumin as a single parameter to calculate models by analyzing laboratory parameters of 1552 patients with an adnexal mass (epithelial ovarian cancer (EOC): n= 294; borderline tumor of the ovary (BTO): n = 66; benign adnexal mass: n = 1192) undergoing surgery. Models comprising classical laboratory parameters show better accuracies (AUCs 0.92–0.93; 95% CI 0.90–0.95) compared to the use of single markers, and could easily be implemented in clinical practice by containing only readily available markers. This has been incorporated into a nomogram.

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.

The Comparison of Three Predictive Indexes to Discriminate Malignant Ovarian Tumors from Benign Ovarian Endometrioma: The Characteristics and Efficacy

This study aimed to evaluate the prediction efficacy of malignant transformation of ovarian endometrioma (OE) using the Copenhagen Index (CPH-I), the risk of ovarian malignancy algorithm (ROMA), and the R2 predictive index. This retrospective study was conducted at the Department of Gynecology, Nara Medical University Hospital, from January 2008 to July 2021. A total of 171 patients were included in the study. In the current study, cases were divided into three cohorts: pre-menopausal, post-menopausal, and a combined cohort. Patients with benign ovarian tumor mainly received laparoscopic surgery, and patients with suspected malignant tumors underwent laparotomy. Information from a review chart of the patients’ medical records was collected. In the combined cohort, a multivariate analysis confirmed that the ROMA index, the R2 predictive index, and tumor laterality were extracted as independent factors for predicting malignant tumors (hazard ratio (HR): 222.14, 95% confidence interval (CI): 22.27−2215.50, p < 0.001; HR: 9.80, 95% CI: 2.90−33.13, p < 0.001; HR: 0.15, 95% CI: 0.03−0.75, p = 0.021, respectively). In the pre-menopausal cohort, a multivariate analysis confirmed that the CPH index and the R2 predictive index were extracted as independent factors for predicting malignant tumors (HR: 6.45, 95% CI: 1.47−28.22, p = 0.013; HR: 31.19, 95% CI: 8.48−114.74, p < 0.001, respectively). Moreover, the R2 predictive index was only extracted as an independent factor for predicting borderline tumors (HR: 45.00, 95% CI: 7.43−272.52, p < 0.001) in the combined cohort. In pre-menopausal cases or borderline cases, the R2 predictive index is useful; while, in post-menopausal cases, the ROMA index is better than the other indexes.

Biomarker-Based Models for Preoperative Assessment of Adnexal Mass: A Multicenter Validation Study

Ovarian cancer (OC) is the most lethal genital malignancy in women. We aimed to develop and validate new proteomic-based models for non-invasive diagnosis of OC. We also compared them to the modified Risk of Ovarian Malignancy Algorithm (ROMA-50), the Copenhagen Index (CPH-I) and our earlier Proteomic Model 2017. Biomarkers were assessed using bead-based multiplex technology (Luminex®) in 356 women (250 with malignant and 106 with benign ovarian tumors) from five European centers. The training cohort included 279 women from three centers, and the validation cohort 77 women from two other centers. Of six previously studied serum proteins (CA125, HE4, osteopontin [OPN], prolactin, leptin, and macrophage migration inhibitory factor [MIF]), four contributed significantly to the Proteomic Model 2021 (CA125, OPN, prolactin, MIF), while leptin and HE4 were omitted by the algorithm. The Proteomic Model 2021 revealed a c-index of 0.98 (95% CI 0.96, 0.99) in the training cohort; however, in the validation cohort it only achieved a c-index of 0.82 (95% CI 0.72, 0.91). Adding patient age to the Proteomic Model 2021 constituted the Combined Model 2021, with a c-index of 0.99 (95% CI 0.97, 1) in the training cohort and a c-index of 0.86 (95% CI 0.78, 0.95) in the validation cohort. The Full Combined Model 2021 (all six proteins with age) yielded a c-index of 0.98 (95% CI 0.97, 0.99) in the training cohort and a c-index of 0.89 (95% CI 0.81, 0.97) in the validation cohort. The validation of our previous Proteomic Model 2017, as well as the ROMA-50 and CPH-I revealed a c-index of 0.9 (95% CI 0.82, 0.97), 0.54 (95% CI 0.38, 0.69) and 0.92 (95% CI 0.85, 0.98), respectively. In postmenopausal women, the three newly developed models all achieved a specificity of 1.00, a positive predictive value (PPV) of 1.00, and a sensitivity of >0.9. Performance in women under 50 years of age (c-index below 0.6) or with normal CA125 (c-index close to 0.5) was poor. CA125 and OPN had the best discriminating power as single markers. In summary, the CPH-I, the two combined 2021 Models, and the Proteomic Model 2017 showed satisfactory diagnostic accuracies, with no clear superiority of either model. Notably, although combining values of only four proteins with age, the Combined Model 2021 performed comparably to the Full Combined Model 2021. The models confirmed their exceptional diagnostic performance in women aged ≥50. All models outperformed the ROMA-50.

Comparison of HE4, CA125, ROMA and CPH-I for Preoperative Assessment of Adnexal Tumors

(1) OBJECTIVE: To assess the performance of CA125, HE4, ROMA index and CPH-I index to preoperatively identify epithelial ovarian cancer (EOC) or metastatic cancer in the ovary (MCO). (2) METHODS: single center retrospective study, including women with a diagnosis of adnexal mass. We obtained the AUC, sensitivity, specificity and predictive values were of HE4, CA125, ROMA and CPH-I for the diagnosis of EOC and MCO. Subgroup analysis for women harboring adnexal masses with inconclusive diagnosis of malignancy by ultrasound features and Stage I EOC was performed. (3) RESULTS: 1071 patients were included, 852 (79.6%) presented benign/borderline tumors and 219 (20.4%) presented EOC/MCO. AUC for HE4 was higher than for CA125 (0.91 vs. 0.87). No differences were seen between AUC of ROMA and CPH-I, but they were both higher than HE4 AUC. None of the tumor markers alone achieved a sensitivity of 90%; HE4 was highly specific (93.5%). ROMA showed a sensitivity and specificity of 91.1% and 84.6% respectively, while CPH-I showed a sensitivity of 91.1% with 79.2% specificity. For patients with inconclusive diagnosis of malignancy by ultrasound features and with Stage I EOC, ROMA showed the best diagnostic performance (4) CONCLUSIONS: ROMA and CPH-I perform better than tumor markers alone to identify patients harboring EOC or MCO. They can be helpful to assess the risk of malignancy of adnexal masses, especially in cases where ultrasonographic diagnosis is challenging (stage I EOC, inconclusive diagnosis of malignancy by ultrasound features).

A combined strategy of TK1, HE4 and CA125 shows better diagnostic performance than risk of ovarian malignancy algorithm (ROMA) in ovarian carcinoma

BACKGROUND

The dual marker algorithm Risk of Ovarian Malignancy Algorithm (ROMA) has been widely used in the clinic for the identification of equivocal pelvic masses in ovarian carcinoma. To obtain higher diagnostic efficiency, we created a new diagnostic index, Risk of Ovarian Malignancy Index (ROMI), by combing thymidine kinase 1 (TK1), HE4 and CA125.

METHODS

335 patients with pelvic masses on imaging and 46 healthy controls were enrolled. Serum TK1 was analyzed before further study. ROMI and ROMA were evaluated for diagnostic efficiency.

RESULTS

The level of TK1 was elevated in malignant ovarian tumors compared to benign masses (p < 0.001) and healthy controls (p < 0.001). TK1 expression was positively correlated with stage, intrapelvic metastasis, lymphatic metastasis and distant metastasis (all p values < 0.001). The area under the receiver operating characteristic curve (AUC) of ROMI was higher than that of ROMA for both pre- and postmenopausal women. ROMI had better sensitivity, specificity, accuracy, and positive and negative predictive values than ROMA in diagnosis of all-stage or stage I + II ovarian carcinoma for both pre- and postmenopausal women.

CONCLUSIONS

TK1 is a potential biomarker in detection of ovarian carcinoma. ROMI shows better diagnostic performance than ROMA in distinguishing malignant ovarian tumors from benign masses.

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.

Consideration should be given to smoking, endometriosis, renal function (eGFR) and age when interpreting CA125 and HE4 in ovarian tumor diagnostics

OBJECTIVES

To evaluate the impact of different biologic, histopathologic and lifestyle factors on serum levels of human epididymis protein 4 (HE4) and Cancer antigen 125 (CA125) in the diagnostic work up of women with an ovarian cyst or pelvic tumor.

METHODS

The statistical evaluation was performed on a population of 445 women diagnosed with a benign ovarian disease, included in a large Swedish multicenter trial (ClinicalTrials.gov NCT03193671). Multivariable logistic regression analyses were performed to distinguish between the true negatives and false positives through adjusting for biologic, histopathologic and lifestyle factors on serum samples of CA125 and HE4 separately. The likelihood ratio test was used to determine statistical significance and Benjamini-Hochberg correction to adjust for multiple testing.

RESULTS

A total of 31% of the women had false positive CA125 but only 9% had false positive results of HE4. Smoking (OR 6.62 95% CI 2.93-15.12) and impaired renal function, measured by eGFR (OR 0.18 95% CI 0.08-0.39), were independently predictive of falsely elevated serum levels of HE4. Endometriosis was the only variable predictive of falsely elevated serum levels of CA125 (OR 7.96 95% CI 4.53-14.39). Age correlated with increased serum levels of HE4.

CONCLUSIONS

Smoking, renal failure, age and endometriosis are factors that independently should be considered when assessing serum levels of HE4 and CA125 in women with an ovarian cyst or pelvic mass to avoid false indications of malignant disease.

Copenhagen Index versus ROMA in preoperative ovarian malignancy risk stratification: Result from the first Vietnamese prospective cohort study

OBJECTIVES

This study aimed to evaluate the diagnostic performances of the Copenhagen Index (CPH-I) and Risk of Ovarian Malignancy Algorithm (ROMA) in the preoperative prediction of ovarian cancer.

METHODS

In a prospective cohort study, data were collected from 475 patients with ovarian masses diagnosed by gynecologic examination / ultrasound who were hospitalized at the Departments of Obstetrics and Gynecology, Hue University of Medicine and Pharmacy Hospital and Hue Central Hospital, Vietnam, between January 2018 and June 2020. ROMA and CPH-I were calculated based on measurements of serum carbohydrate antigen (CA-125) and human epididymis protein (HE4). The final diagnosis was based on clinical features, radiologic and histologic findings, and the International Federation of Gynecology and Obstetrics (FIGO) 2014 stages of ovarian cancer were recorded. Matching the values of ROMA and CPH-I to postoperative histopathology reports resulted in the preoperative prediction values.

RESULTS

Among the 475 women, 408 had benign tumors, 5 had borderline tumors and 62 had malignant tumors. The two indices showed similar discriminatory performances with no significant differences (p > 0.05). At an optimal cut-off, the sensitivities/specificities of ROMA and CPH-I for ovarian cancer diagnosis were 74.2% and 91.8%, 87.1% and 78.5%, respectively. The optimal cut-off for CPH-I was 1.89%. The areas under the ROC curves (AUCs) of ROMA and CPH-I were 0.882 (95% CI: 0.849-0.909) and 0.898 (95% CI: 0.867-0.924), respectively.

CONCLUSIONS

The introduction of the Copenhagen Index to help stratify the malignancy risk of ovarian tumors, irrespective of menopausal status, might be applied as a simple alternative with a similar efficacy to ROMA in clinical practice.

A novel diagnostic nomogram based on serological and ultrasound findings for preoperative prediction of malignancy in patients with ovarian masses

OBJECTIVE

To develop a novel diagnostic nomogram model to predict malignancy in patients with ovarian masses.

METHODS

In total, 1277 patients with ovarian masses were retrospectively analyzed. Receiver operating characteristic (ROC) analysis was performed to identify valuable predictive factors. Univariate and multivariate logistic regression analyses were used to identify risk factors for ovarian cancer. Subsequently, a predictive nomogram model was developed. The performance of the nomogram model was assessed by its calibration and discrimination in a validation cohort. Decision curve analysis (DCA) was applied to assess the clinical net benefit of the model.

RESULTS

Overall, 496 patients (38.8%) had ovarian cancer. Eighteen parameters were significantly different between the malignant and benign groups. Five parameters were identified as being most optimal for predicting malignancy, including age, carbohydrate antigen 125, fibrinogen-to-albumin ratio, monocyte-to-lymphocyte ratio, and ultrasound result. These parameters were incorporated to establish a nomogram model, and this model exhibited an area under the ROC curve (AUC) of 0.937 (95% confidence interval [CI], 0.920-0.954). The model was also well calibrated in the validation cohort and showed an AUC of 0.925 (95%CI, 0.896-0.953) at the cut-off point of 0.298. DCA confirmed that the nomogram model achieved the best clinical utility with almost the entire range of threshold probabilities. The model has demonstrated superior efficacy in predicting malignancy compared to currently available models, including the risk of ovarian malignancy algorithm, copenhagen index, and the risk of malignancy index. More importantly, the nomogram established here showed potential value in identification of early-stage ovarian cancer.

CONCLUSION

The cost-effective and easily accessible nomogram model exhibited favorable accuracy for preoperative prediction of malignancy in patients with ovarian masses, even at early stages.

CA125 and Ovarian Cancer: A Comprehensive Review

Simple Summary

CA125 has been the most promising biomarker for screening ovarian cancer; however, it still does not have an acceptable accuracy in population-based screening for ovarian cancer. In this review article, we have discussed the role of CA125 in diagnosis, evaluating response to treatment and prognosis of ovarian cancer and provided some suggestions in improving the clinical utility of this biomarker in the early diagnosis of aggressive ovarian cancers. These include using CA125 to screen individuals with symptoms who seek medical care rather than screening the general population, increasing the cutoff point for the CA125 level in the plasma and performing the test at point-of-care rather than laboratory testing. By these strategies, we would detect more aggressive ovarian cancer patients in stages that the tumour can be completely removed by surgery, which is the most important factor in redusing recurrence rate and improving the survival of the patients with ovarian cancer.

Abstract

Ovarian cancer is the second most lethal gynecological malignancy. The tumour biomarker CA125 has been used as the primary ovarian cancer marker for the past four decades. The focus on diagnosing ovarian cancer in stages I and II using CA125 as a diagnostic biomarker has not improved patients’ survival. Therefore, screening average-risk asymptomatic women with CA125 is not recommended by any professional society. The dualistic model of ovarian cancer carcinogenesis suggests that type II tumours are responsible for the majority of ovarian cancer mortality. However, type II tumours are rarely diagnosed in stages I and II. The recent shift of focus to the diagnosis of low volume type II ovarian cancer in its early stages of evolution provides a new and valuable target for screening. Type II ovarian cancers are usually diagnosed in advanced stages and have significantly higher CA125 levels than type I tumours. The detection of low volume type II carcinomas in stage IIIa/b is associated with a higher likelihood for optimal cytoreduction, the most robust prognostic indicator for ovarian cancer patients. The diagnosis of type II ovarian cancer in the early substages of stage III with CA125 may be possible using a higher cutoff point rather than the traditionally used 35 U/mL through the use of point-of-care CA125 assays in primary care facilities. Rapid point-of-care testing also has the potential for effective longitudinal screening and quick monitoring of ovarian cancer patients during and after treatment. This review covers the role of CA125 in the diagnosis and management of ovarian cancer and explores novel and more effective screening strategies with CA125.

A multiplex biomarker assay improves the diagnostic performance of HE4 and CA125 in ovarian tumor patients

OBJECTIVE

Survival in epithelial ovarian cancer (EOC) remains poor. Most patients are diagnosed in late stages. Early diagnosis increases the chance of survival. We used the proximity extension assay from Olink Proteomics to search for new protein biomarkers with the potential to improve the diagnostic performance of CA125 and HE4 in patients with ovarian tumors.

MATERIAL AND METHODS

Plasma samples were obtained from 180 women with ovarian tumors; 30 cases of benign tumor, 28 cases with borderline tumors, 25 early EOC cases (FIGO stage I) and 97 advanced EOC cases (FIGO stages II-IV). Proteins were measured using the Olink® Oncology II and Inflammation panels. For statistical analyses, patients were categorized into benign tumors versus cancer and benign tumors versus borderline + cancer, respectively.

RESULTS

We analyzed 177 biomarkers. Thirty-four proteins had ROC AUC > 0.7 for discrimination between benign tumors and cancer. Fifteen proteins had ROC AUC > 0.7 for discrimination between benign tumors and borderline tumors + cancer. HE4 ranked highest for both comparisons. A reference model with HE4, CA125 and age (AUC 0.838 for benign tumors vs. cancer and AUC 0.770 for benign tumors vs. borderline tumors + cancer) was compared to the reference model with the addition of each of the remaining proteins with AUC > 0.7. ITGAV was the only individual biomarker found to improve diagnostic performance of the reference model, to AUC 0.874 for benign tumors vs. cancer and AUC 0.818 for benign tumors vs. borderline tumors + cancer (p < 0.05). Cross-validation and LASSO regression was combined to select multiple biomarker combinations. The best performing model for discrimination between benign tumors and borderline tumors + cancer was a 6-biomarker combination (HE4, CA125, ITGAV, CXCL1, CEACAM1, IL-10RB) and age (AUC 0.868, sensitivity 0.86 and specificity 0.82, p = 0.016 for comparison with the reference model).

CONCLUSION

HE4 was the best performing individual biomarker for discrimination between benign ovarian tumors and EOC including borderline tumors. The addition of other carcinogenesis-related biomarkers in a multiplex biomarker panel can improve the diagnostic performance of the established biomarkers HE4 and CA125.

Base rate of ovarian cancer on algorithms in patients with a pelvic mass

Objective

Algorithms have been developed to identify ovarian cancer in women with a pelvic mass. The aim of this study was to determine how the base rates of ovarian cancer influence the case finding abilities of recently developed algorithms applicable to pelvic tumors. We used three ovarian cancer algorithms and the principle of Bayes’ theorem for risk estimation.

Methods

First, we evaluated the case finding abilities of the Risk of Malignancy Algorithm, the Rajavithi–Ovarian Predictive Score, and the Copenhagen Index in a prospectively collected sample at Oslo University Hospital of 227 postmenopausal women with a 74% base rate of ovarian cancer. Second, we examined the case finding abilities of the Risk of Malignancy Algorithm in three published studies with different base rates of ovarian cancer. We applied Bayes’ theorem in these examinations.

Results

In the Oslo sample, all three algorithms functioned poorly as case finders for ovarian cancer. When the base rate changed from 8.2% to 43.8% in the three studies using the Risk of Malignancy Algorithm, the proportion of false negative ovarian cancer diagnoses increased from 1.2% to 3.4%, and the number of false positive diagnosis increased from 4.6% to 14.2%.

Conclusion

This study demonstrated that the base rate of ovarian cancer in the samples tested was important for the case finding abilities of algorithms.

Diagnostic Value of Risk of Malignancy Algorithm (ROMA) in Adnexal Masses

Background

Differentiating malignancy from benign diseases is the key to successful management of adnexal masses. Risk of malignancy algorithm (ROMA) has been used for this purpose. We have prospectively studied the diagnostic value of ROMA in patients presented with adnexal masses.

Methods

We prospective calculated ROMA values prior to surgery for adnexal masses. The risk calculated was then correlated with the histological findings, and results were analyzed according to menopausal status. ROMA cutoff value was determined using ROC curve, and sensitivity, specificity and predictive values were calculated. Statistics were performed on SPSS software (version 20.0).

Results

There were 94 patients with adnexal masses included in the study, 65 (69.1%) had epithelial ovarian cancer and 29 (30.9%) were diagnosed benign on histopathology. In both pre- and postmenopausal patients, ROMA values were significantly higher in patients with malignancy compared to those with benign disease (p < 0.05). ROMA score was of a significant diagnostic value in both premenopausal (AUC = 0.914, Z = 10.81, p < 0.001) and postmenopausal patients (AUC = 0.975, Z = 21.51, p < 0.001). In premenopausal females, ROMA > 13.3% was able to discriminate malignant from benign patients with 97.06% sensitivity and 85.00% specificity. The positive and negative predictive values were 91.7% and 94.4%. Similarly, in postmenopausal females, ROMA value of > 76% achieved 87.10% sensitivity and 100.00% specificity in discriminating malignant from benign patients with 100% positive and 69.2% negative predictive value. The overall accuracy of ROMA in pre- and postmenopausal patients was 87.0% and 85%, respectively.

Conclusions

ROMA is a useful and accurate test for differentiating epithelial ovarian cancer from benign ovarian masses. Further studies are needed to compare performance of ROMA with the Risk of Malignancy Index (RMI), CA 125 and HE4. Such comparative studies will be helpful to the clinician in deciding the best diagnostic tool for women with adnexal masses.

The generally low sensitivity of CA 125 for FIGO stage I ovarian cancer diagnosis increases for endometrioid histotype

BACKGROUND

The serum marker CA 125 is still the most widely used biomarker for ovarian cancer (OC) diagnosis in gynecological and oncological setting, but its predictive role in early-stage OC is still debated. The aim of this study was to explore the value of CA 125 in distinguishing between early-stage OC and borderline ovarian tumor (BOT) and to evaluate the accuracy of CA 125 in the detection of early stage OC.

METHODS

A retrospective cohort study was performed at the University Hospital of Bologna (Italy) on 1296 consecutive women suffering from OC or BOT (diagnosed at histology) between 1988-2017. Patients for whom CA 125 level was determined preoperatively were included. The positive cut-off level used was >35 U/mL.

RESULTS

Of 910 patients, 192 (21.1%) were diagnosed with BOT and 718 (78.9%) with OC. The sensitivity of CA 125 for stage I OC was 54.4 (95% CI: 45.3-63.3) (51.5 for IA, 54.6 for IB, 58.3 for IC), but it increased to 78.0 (95% CI: 63.7-88.0) for stage II. Interestingly, in stage I OC, CA 125 presented a significantly higher sensitivity for the endometrioid histotype [72.4 (95% CI: 52.5-86.5) vs. 49.0 (95% CI: 38.6-59.4), P=0.026]. The positive likelihood ratio of CA 125 for early-stage OC compared to BOT was 1.29 (95% CI: 1.06-1.58).

CONCLUSIONS

Despite its limited sensitivity for early-stage OCs, CA 125 still represents a useful serum marker to early differentiate between OCs and BOTs. Its sensitivity for stage I OC increases in endometrioid histotype.

Comparison of Risk of Ovarian Malignancy Algorithm and cancer antigen 125 to discriminate between benign ovarian tumor and early-stage ovarian cancer according to imaging tumor subtypes

The present study aimed to compare the accuracy of the Risk of Ovarian Malignancy Algorithm (ROMA) and cancer antigen (CA)125 to discriminate between benign ovarian tumors and early-stage ovarian cancer according to imaging tumor subtypes associated with post-operative histopathological findings. A total of 1,207 patients who were assessed using the ROMA test due to suspected early-stage ovarian cancer and underwent surgery at Asan Medical Center (Seoul, Korea) between September 2014 and March 2018 were identified. A total of 981 patients who met the inclusion criteria were included in the retrospective analysis. Among the 981 subjects, 816 had benign tumors, 90 had malignant tumors and 75 had borderline tumors. Of the patients diagnosed with ovarian cancer or borderline tumor, 47.3% were judged as high-risk by the ROMA test and 58.2% had CA125 levels of >35 U/ml. The specificity and accuracy of ROMA were higher compared with those of CA125 in pre-menopausal females. However, the superiority of the ROMA test in the identification of malignant ovarian tumors compared with CA125 was only observed in patients with endometriotic-type tumors but not in any of the other tumor subtypes. In the endometriotic type of ovarian tumor, the superiority of the ROMA test compared to CA125 was confirmed in triage of ovarian tumor. However, the sensitivity and specificity of ROMA and CA125 were similar for the other tumor types. Therefore, future development of better tumor-specific biomarkers for triage of ovarian tumor is required.

Scoring systems for the evaluation of adnexal masses nature: current knowledge and clinical applications

Adnexal masses are a common finding in women, with 20% of them developing at least one pelvic mass during their lifetime. There are more than 30 different subtypes of adnexal tumours, with multiple different subcategories, and the correct characterisation of the pelvic masses is of paramount importance to guide the correct management. On that basis, different algorithms and scoring systems have been developed to guide the clinical assessment. The first scoring system implemented into the clinical practice was the Risk of Malignancy Index, which combines ultrasound evaluation, menopausal status, and serum CA-125 levels. Today, current guidelines regarding female patients with adnexal masses include the application of International Ovarian Tumours Analysis simple rules, logistic regression model 1 (LR1) and LR2, OVERA, cancer ovarii non-invasive assessment of treating strategy, and assessment of Different Neoplasias in the adnexa. In this scenario, the choice of the scoring system for the discrimination between benign and malignant ovarian tumours can be complex when approaching patients with adnexal masses. This review aims to summarise the available evidence regarding the different scoring systems to provide a complete overview of the topic.

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.

Serum HE4 and diagnosis of ovarian cancer in postmenopausal women with adnexal masses

BACKGROUND

Transvaginal ultrasound and serum CA125 are routinely used for differential diagnosis of pelvic adnexal mass. Use of human epididymis 4 was approved in the United States in 2011. However, there is scarcity of studies evaluating the additional value of human epididymis 4.

OBJECTIVE

The objective of the study was to evaluate the performance characteristics of transvaginal ultrasound, CA125, and human epididymis 4 for differential diagnosis of ovarian cancer in postmenopausal women with adnexal masses.

STUDY DESIGN

This was a cohort study nested within the screen arms of the multicenter randomized controlled trial, United Kingdom Collaborative Trial of Ovarian Cancer Screening, based in England, Wales, and Northern Ireland. In United Kingdom Collaborative Trial of Ovarian Cancer Screening, 48,230 women randomized to transvaginal ultrasound screening and 50,078 to multimodal screening (serum CA125 interpreted by Risk of Ovarian Cancer Algorithm with second line transvaginal ultrasound) underwent the first (prevalence) screen. Women with adnexal lesions and/or persistently elevated risk were clinically assessed and underwent surgery or follow-up for a median of 10.9 years. Banked samples taken within 6 months of transvaginal ultrasound from all clinically assessed women were assayed for human epididymis 4 and CA125. Area under the curve and sensitivity for diagnosing ovarian cancer of multiple penalized logistic regression models incorporating logCA125, log human epididymis 4, age, and simple ultrasound features of the adnexal mass were compared.

RESULTS

Of 1590 (158 multimodal, 1432 ultrasound) women with adnexal masses, 78 were diagnosed with ovarian cancer (48 invasive epithelial ovarian, 14 type I, 34 type II; 24 borderline epithelial; 6 nonepithelial) within 1 year of scan. The area under the curve (0.893 vs 0.896; P = .453) and sensitivity (74.4% vs 75.6% ;P = .564) at fixed specificity of 90% of the model incorporating age, ultrasound, and CA125 were similar to that also including human epididymis 4. Both models had high sensitivity for invasive epithelial ovarian (89.6%) and type II (>91%) cancers.

CONCLUSION

Our population cohort study suggests that human epididymis 4 adds little value to concurrent use of CA125 and transvaginal ultrasound in the differential diagnosis of adnexal masses in postmenopausal women.

[Comparison of serum cancer antigen 125, human epididymis protein 4, ROMA, and CPH-I for diagnosis of ovarian cancer in Chinese patients with ovarian mass]

OBJECTIVE

To compare the performance of serum cancer antigen 125 (CA125), human epididymis protein 4 (HE4), Risk of Ovarian Malignancy Algorithm (ROMA) and Copenhagen index (CPH-I) for differential diagnosis of benign and malignant diseases in patients with ovarian mass.

METHODS

We retrospectively analyzed the data of 719 women with pelvic mass, and the performance of preoperative serum levels of CA125 and HE4, ROMA and CPH-I for differential diagnosis of the masses was compared.

RESULTS

Of the 710 women analyzed, 531 were diagnosed with benign ovarian lesions, 44 with borderline ovarian tumors (BOTs), 119 with epithelial ovarian cancers (EOCs), and 25 with non-EOCs. In differentiating ovarian cancer (OC) and BOT from benign lesions, the area under the receiver-operator characteristic (ROC) curve (AUC) was 0.854 for HE4, 0.856 for ROMA, 0.854 for CPH-I, and 0.792 for CA125, demonstrating better diagnostic performance of HE4, ROMA, and CPH-I than CA125 alone; the diagnostic sensitivity was 56.9% for HE4, 70.2% for CA125, 69.1% for ROMA, and 63.8% for CPH-I; the specificity was the best with HE4 (94.4%) and CPH-I (94.7%). In sub-analysis of EOC vs benign lesions, the AUCs of HE4, ROMA, and CPH-I increased to 0.946, 0.947, and 0.943, respectively, all greater than that of CA125 (0.888). In other sub-analyses, HE4, ROMA, and CPH-I all showed greater AUCs than CA125 alone.

CONCLUSIONS

This study confirms the accuracy of HE4, ROMA, and CPH-I for differentiating malignant from benign ovarian mass, and all these 3 tests show better performance than CA125. Furthermore, HE4 and CPH-I is superior to ROMA and CA125 in terms of specificity, while CA125 and ROMA have better diagnostic sensitivities.

Multi-biomarker panel signature as the key to diagnosis of ovarian cancer

Early detection of ovarian cancer has been a challenge to manage the high mortality rate caused by this deadly disease. The trends in mortality have been reduced by the scientific contributions from the corners across the globe, however accounting for the fifth leading cause of gynecological mortality. The complexities in the clinical presentation, origin of tumor, and gene expression profiles had added to much difficulty in understanding and diagnosis of the disease. Stage 1 diagnosis of ovarian cancer improves the 5-year survival rate to around 92%. Cancer antigen-125 (CA-125) is the gold standard tumor marker found at abnormally high levels in the blood of many women in ovarian cancer. However, many non-cancerous conditions exhibit high levels of CA-125 and several women have normal CA-125 level in the early stage of ovarian cancer, suggesting CA-125 biomarker is not specific enough for the screening of early stage ovarian cancer. In addition, several other biomarkers, including HE4 have been added in the diagnostic field for higher sensitivity and specificity in the diagnosis and progression of ovarian cancer. HE4 is a prospective single serum biomarker which has been approved by the FDA to monitor the disease progression in epithelial ovarian cancer. However, owing to low sensitivity and specificity, combination of a panel of biomarkers has been proposed in the diagnosis of the disease. Based on extensive biomarkers research findings, here we discuss current trends in diagnostic approaches and updated potential several panels of cancer biomarkers for early detection of ovarian cancer. It has been recently reported that CA125 in combinations with two or more biomarkers have outperformed single biomarker assays for early detection of the disease. Moreover, CA-125 with CA 19–9, EGFR, G-CSF, Eotaxin, IL-2R, cVCAM, MIF improved the sensitivity with 98.2 % and specificity of 98.7% in early stage detection of ovarian cancer. Overall, this review demonstrates a panel of biomarkers signature as the potential tool for prototype development in future and other advanced approaches for early diagnosis of ovarian cancer to avoid false-diagnosis and excessive cost.

CPH-I and HE4 Are More Favorable Than CA125 in Differentiating Borderline Ovarian Tumors from Epithelial Ovarian Cancer at Early Stages

Aim

To evaluate the diagnosis value of serum human epididymis protein 4 (HE4), cancer antigen 125 (CA125), the Risk of Ovarian Malignancy Algorithm (ROMA), and Copenhagen Index (CPH-I) at early stages for differentiating borderline ovarian tumors from epithelial ovarian cancer.

Methods

We recruited 144 borderline ovarian tumors in FIGO stages I and II (BOT I+II), 108 epithelial ovarian cancers in FIGO stages I and II (EOC I+II), and 238 benign ovarian tumor patients with surgical treatment in the retrospective study. The concentration of HE4 and CA125 and the values of CPH-I and ROMA were assessed separately.

Results

The HE4 level and ROMA and CPH-I values of EOC I+II were all higher than that of BOT I+II and benign groups whether in all, pre-, or postmenopausal groups (P < 0.01). When distinguishing BOT I+II from EOC I+II, the AUC-ROC of CPH-I and HE4 were bigger than CA125 (P < 0.001), while the CPH-I has the highest sensitivities in all and postmenopausal groups (78.7%, 85.1%), and HE4 has the highest specificity and PPV (90.91%, 88.64%) in postmenopausal groups. Under pathological stratification, HE4, ROMA, and CPH-I of the serous EOC I+II were higher than that of BOT I+II (P < 0.001) and the AUC of the three indices were significantly bigger than CA125 (P < 0.001). However, the concentration of HE4 and CA125 and the values of CPH-I and ROMA have no significant difference between the two endometrioid subgroups. The index with the highest sensitivity and NPV among the four indices of different pathological subtype groups was CPH-I, and the index with the highest specificities and PPV was HE4.

Conclusion

CPH-I was more valuable than CA125 for differentiating BOT I+II from EOC I+II regardless of menopausal status, while HE4 might be better than CA125 for postmenopausal subgroups. HE4 and CPH-I were more favorable than CA125 for differentiating BOT I+II from EOC I+II in the case of unknown pathology or in serous type.

Knockdown of HE4 suppresses aggressive cell growth and malignant progression of ovarian cancer by inhibiting the JAK/STAT3 pathway

Human epididymis protein 4 (HE4) is well known to be a predictor of ovarian cancer clinically. HE4 is reported to play crucial roles in ovarian cancer progression and metastasis. The purpose of the present study was to explore its biological role and molecular mechanism in ovarian cancer. In our study, we found that expression levels of HE4 in tissues, serum and urine in ovarian cancer were upregulated compared to healthy and benign groups. HE4 expression was elevated in ovarian cancer cells. Knockdown of HE4 dampened cell proliferation and Ki67 expression, as well as enhanced apoptosis, caspase-3 activity and cleaved-caspase-3 expression. In addition, HE4 downregulation repressed invasion and migration capabilities of ovarian cancer cells. Western blot analyses showed that knockdown of HE4 reduced the levels of matrix metalloproteinases (MMP-2 and MMP-9) and inhibited epithelial to mesenchymal transition (EMT) in ovarian cancer cells. In vivo animal experiments revealed that HE4 downregulation constrained the growth of xenograft tumor. Mechanism research showed that knockdown HE4 inhibited the activity of JAK/STAT3 pathway in vitro and in vivo. In conclusion, our findings reported that knockdown of HE4 suppresses aggressive cell growth and malignant progression of ovarian cancer by inhibiting the JAK/STAT3 pathway, which provides valuable insights to contribute to develop novel HE4-targeted therapies.

Summary: Our findings reported that HE4 knockdown suppresses aggressive cell growth and malignant progression of ovarian cancer by inhibiting the JAK/STAT3 pathway, which could provide a valuable insight into developing novel HE4-targeted therapies.

Human Epididymis Protein 4 (HE4) Reference Limits in Polish Population of Healthy Women, Pregnant Women, and Women with Benign Ovarian Tumors

Objectives

Defining precisely the normal range of HE4 protein is crucial for the proper interpretation of tumor marker results and for a more efficient diagnosis of ovarian malignancy. The aim of our study was to evaluate a reference limit of HE4 protein in a population to promote and facilitate the common use of HE4 protein assays. We also tried to identify potential association of HE4 levels with other conditions such as smoking, age, BMI, and creatinine levels.

Methods

Blood samples were collected from 617 patients divided into three groups: healthy, pregnant, and with benign ovarian tumors. Serum HE4 concentrations were measured following a standard procedure. HE4 reference ranges for each group and association of HE4 levels with BMI, creatinine, and smoking were investigated.

Results

HE4 reference limit for healthy patients equals 85 pmol/l, which becomes 73 pmol/l and 93 pmol/l for pre and postmenopausal subgroups, respectively. There is a statistically significant correlation between HE4 serum level and smoking (p = 0.000001) and there is no correlation with creatinine. But if we take into account age and smoking, in multivariate analysis, there is a correlation. For pregnant, the upper limit values of normal HE4 levels are 55 pmol/l (median = 40 pmol/l), 80 pmol/l (median = 43 pmol/l), and 106 pmol/l (median = 53 pmol/l) for the first, second, and third trimesters, respectively.

Conclusions

HE4 protein value strongly depends on the patient's age and smoking. The serum concentration of HE4 marker increases with the duration of pregnancy. Understanding the normal range of HE4 protein enables the correct interpretation of marker measurements. This may result in an earlier and more effective diagnosis of ovarian cancer.

The Application Value of Serum HE4 in the Diagnosis of Lung Cancer

Background:

To investigate the clinical value of HE4 detection in the diagnosis of lung cancer and the clinical significance of combined detection with CEA, NSE and CYFRA21-1.

Methods:

90 cases of lung cancer, 30 cases of pulmonary tuberculosis, 30 cases of pneumonia and 30 cases of health physical examination were selected. The levels of serum HE4, CYFRA21-1, CEA and NSE were detected by electrochemiluminescence method. Statistical analysis was performed to observe the sensitivity and specificity.

Results:

The levels of serum HE4, CEA, NSE and CYFRA21-1 in lung cancer group were significantly higher than those in tuberculosis group and health physical examination group. There was no significant difference in the levels of HE4, CEA and NSE between the lung cancer group and the pneumonia group, the difference of CYFRA21-1 level was statistically significant (p<0.05).With health physical examination group as normal controls, the sensitivity and specificity of combined detection of HE4, CEA, NSE and CYFRA21-1 in the diagnosis of lung cancer were 82.2% and 90.0%,and the area under the curve (AUC) was 0.907, followed by HE4 (0.867), CYFRA21-1 (0.787), CEA (0.752) and NSE (0.747).

Conclusion:

HE4 can be used as a serological marker for the diagnosis of lung cancer. The combined detection of HE4, CEA, NSE and CYFRA21-1 can improve the diagnosis of lung cancer. Serum HE4 levels are highly specific in distinguishing between lung cancer patients and normal population, and are equivalent to CYFRA21-1; but they are less specific than CYFRA21-1 in distinguishing lung cancer patients from pneumonia patients.

Diagnostic Accuracy of the Risk of Ovarian Malignancy Algorithm in Clinical Practice at a Single Hospital in Korea

Background

The risk of ovarian malignancy algorithm (ROMA) is used for assessing ovarian cancer risk in women with a pelvic mass. Its diagnostic accuracy is variable. We investigated whether the clinically acceptable minimal sensitivity of >80.0% could be obtained with the suggested cutoff of 7.4%/25.3% for pre/postmenopausal women and with adjusted cutoffs set to a specificity of ≥75.0% or a sensitivity of 95.0%, in a hospital with a lower ovarian cancer (OC) prevalence than previously reported.

Methods

ROMA scores were calculated from measurements of human epididymis protein 4 and cancer antigen 125 in blood specimens from 443 patients with a pelvic mass. The ROMA-based risk group was compared against biopsy (N=309) or clinical follow-up with imaging (N=134) results. The ROMA sensitivity and specificity for predicting epithelial OC (EOC) and borderline ovarian tumor (BOT) were calculated for the suggested and adjusted cutoff values.

Results

When targeting BOT and EOC, the prevalence was 7.4% and sensitivity and specificity at the suggested cutoff were 63.6% and 90.7%, respectively. Sensitivity was 81.8% at the 4.65%/13.71% cutoff set to a specificity of 75.0%. When targeting only EOC, the prevalence was 4.1% and sensitivity and specificity at the suggested cutoff were 77.8% and 89.4%, respectively. Sensitivity was 88.9% at the 4.78%/14.35% cutoff set to a specificity of 75.0%.

Conclusions

The sensitivity of ROMA was lower than expected when using the suggested cutoff. When using the adjusted cutoff, its sensitivity reached 80.0%.

The contribution and perspectives of proteomics to uncover ovarian cancer tumor markers

Despite all the advances in understanding the mechanisms involved in ovarian cancer (OC) development, many aspects still need to be unraveled and understood. Tumor markers (TMs) are of special interest in this disease. Some aspects of clinical management of OC might be improved by the use of validated TMs, such as differentiating subtypes, defining the most appropriate treatment, monitoring the course of the disease, or predicting clinical outcome. The Food and Drug Administration (FDA) has approved a few TMs for OC: CA125 (cancer antigen 125; monitoring), HE4 (Human epididymis protein; monitoring), ROMA (Risk Of Malignancy Algorithm; HE4+CA125; prediction of malignancy) and OVA1 (Vermillion's first-generation Multivariate Index Assay [MIA]; prediction of malignancy). Proteomics can help advance the research in the field of TMs for OC. A variety of biological materials are being used in proteomic analysis, among them tumor tissues, interstitial fluids, tumor fluids, ascites, plasma, and ovarian cancer cell lines. However, the discovery and validation of new TMs for OC is still very challenging. The enormous heterogeneity of histological types of samples and the individual variability of patients (lifestyle, comorbidities, drug use, and family history) are difficult to overcome in research protocols. In this work, we sought to gather relevant information regarding TMs, OC, biological samples for proteomic analysis, as well as markers and algorithms approved by the FDA for use in clinical routine.

Biomarkers and algorithms for diagnosis of ovarian cancer: CA125, HE4, RMI and ROMA, a review

Ovarian cancer is the 5th leading cause of death for women with cancer worldwide. In more than 70% of cases, it is only diagnosed at an advanced stage. Our study aims to give an update on the biological markers for diagnosing ovarian cancer, specifically HE4, CA 125, RMI and ROMA algorithms.

Serum CA125 assay has low sensitivity in the early stages and can be increased in certain conditions such as menstruation or endometriosis. The level of HE4 is overexpressed in ovarian tumors. Its specificity is 94% and its level is not affected by endometriosis cysts. The combined measures of CA125 and HE4 have proved to be highly efficient with an area under the curve (AUC) of up to 0.96. Furthermore, this combined measure of CA125 can correct the variations in HE4 which are due to smoking or contraception combining estrogen plus progestin. While the specificity of RMI sometimes reaches 92%, the rather low AUC of 0.86 does not make it the best diagnostic tool. The specificity of ROMA is lower than HE4 (84% compared to 94%).

To date, the most efficient biological diagnostic tool to diagnose ovarian cancer is the combination of CA125 and HE4.

[Diagnostic and prognostic value of tumor markers, scores (clinical and biological) algorithms, in front of an ovarian mass suspected of an epithelial ovarian cancer: Article drafted from the French Guidelines in oncology entitled "Initial management of patients with epithelial ovarian cancer" developed by FRANCOGYN, CNGOF, SFOG, GINECO-ARCAGY under the aegis of CNGOF and endorsed by INCa]

OBJECTIVES

To evaluate the diagnostic value of serum/urinary biomarkers and the operability diagnosis strategy to make management recommendations.

METHODS

Bibliographical search in French and English languages by consultation of Pubmed, Cochrane and Embase databases.

RESULTS

For the diagnosis of a suspicious adnexal mass on imaging: Serum CA125 antigen is recommended (grade A). Serum CAE is not recommended (grade C). The low evidence in literature concerning diagnostic value of CA19.9 does not allow any recommendation concerning its use. Serum Human epididymis protein 4 (HE4) is recommended (grade A). Comparison of data concerning diagnosis value of CA125 and HE4 show similar results for the prediction of malignancy in case of a suspicious adnexal mass on imaging (NP1). Urinary HE4 is not recommended (grade A). The use of circulating tumor DNA is not recommended (grade A). Tumor associated antigen-antibodies (AAbs) is not recommended (grade B). The use of ROMA score (Risk of Ovarian Malignancy Algorithm) is recommended (grade A). The use of Copenhagen index (CPH-I), R-OPS score, OVA500 is not recommended (grade C). For the prediction of resectability of an ovarian cancer with peritoneal carcinomatosis in the context of a primary debulking surgery: It is not recommendend to use serum CA125 (grade A). The low evidence in literature concerning diagnostic value of HE4 does not allow any recommendation concerning its use in this context. No recommendation can be given concerning CA19.9 and CAE. For the prediction of resectability of an ovarian cancer with peritoneal carcinomatosis in the context of surgery after neoadjuvant chemotherapy: the low evidence in literature concerning diagnostic value of serum markers in this context does not allow any recommendation concerning their use in this context. Place of laparoscopy for the prediction of resectability in case of upfront surgery of an ovarian cancer with peritoneal carcinomatosis robust data shows that the use of laparoscopy significantly reduce futile laparotomies (LE1). Laparoscopy is recommended in this context (grade A). Fagotti score is a reproducible tool (LE1) permitting the evaluation of feasibility of an optimal upfront debulking (NP4), its use is recommended (grade C). A Fagotti score≥8 is correlated to a low probability of complete or optimal debulking surgery (LE4) (grade C). There is no sufficient evidence to recommend the use of the modified Fagotti score or any other laparoscopic score (LE4). In case of laparotomy for an ovarian cancer with peritoneal carcinomatosis, the use of Peritoneal Cancer Index (PCI) is recommended (grade C). For the prediction of overall survival, disease free survival and the prediction of postoperative complications, the clinical and statistical of actually available tools do not allow any recommendation.

Diagnostic value of HE4 in ovarian cancer: A meta-analysis

OBJECTIVE

To analyze and evaluate the overall diagnostic value of Serum Human Epididymis Protein 4 (HE4) in ovarian cancer.

METHODS

We searched PubMed, EMBASE, Cochrane Library and Web of Science databases to collect articles in English that evaluated the diagnostic value of HE4 in patients with gynecological or pelvic masses. Two reviewers independently assessed the methodological quality of each study using the QUADAS-2 tool. A chart of literature quality was made using Revman 5.3 software. Finally, we built Summary Receiver Operating Characteristic (SROC) curves, Hierarchical Summary Receiver Operating Characteristic (HSROC) models, a Deek's funnel figure as well as a meta-analysis of included studies using STATA12.0 and Meta-Disc1.4 software.

RESULTS

Eighteen studies from the published literature met all inclusion criteria for this analysis. Remarkably, no publication bias was found among the included studies. HE4 had a pooled sensitivity of 81% (95% confidence interval (CI): 77-85) and a pooled specificity of 91% (95% CI: 86-93,). Overall, the positive likelihood ratio (PLR) was 8.2, (95% CI: 5.60-12.00,) the negative likelihood ratio (NLR) was 0.21 (95% CI: 0.17-0.26), the diagnostic odds ratio (DOR) was 39 (95% CI: 25-62), the AUC of SROC was 0.91, and Cochrane-Q value was 86.02.

CONCLUSIONS

HE4 is a valuable marker in the clinical diagnosis of ovarian cancer with both high AUC and Cochrane-Q. More studies are needed to determine if HE4 in the range of 100 mmol/L ≤ cutoff≤150 mmol/L than 60 mmol/L＜cutoff＜100 mmol/L holds more diagnostic value.

A quantitative performance study of two automatic methods for the diagnosis of ovarian cancer

We present a quantitative study of the performance of two automatic methods for the early detection of ovarian cancer that can exploit longitudinal measurements of multiple biomarkers. The study is carried out for a subset of the data collected in the UK Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). We use statistical analysis techniques, such as the area under the Receiver Operating Characteristic (ROC) curve, for evaluating the performance of two techniques that aim at the classification of subjects as either healthy or suffering from the disease using time-series of multiple biomarkers as inputs. The first method relies on a Bayesian hierarchical model that establishes connections within a set of clinically interpretable parameters. The second technique is a purely discriminative method that employs a recurrent neural network (RNN) for the binary classification of the inputs. For the available dataset, the performance of the two detection schemes is similar (the area under ROC curve is 0.98 for the combination of three biomarkers) and the Bayesian approach has the advantage that its outputs (parameters estimates and their uncertainty) can be further analysed by a clinical expert.

Standard and optimal cut-off values of serum ca-125, HE4 and ROMA in preoperative prediction of ovarian cancer in Vietnam

Objectives

To evaluate the validity of serum CA-125, Human Epididymis protein 4 (HE4) and Risk of Malignancy Algorithm (ROMA) at standard and optimal cut-offs, in preoperative prediction of epithelial ovarian carcinoma (EOC) in Vietnam.

Subjects and methods

Cross-sectional, descriptive study on 277 patients with ovarian masses hospitalized at the OBGYN Departments, Hue University Hospital and Hue Central Hospital, Vietnam, from 01/2016 to 11/2017. All patients had measurements of serum CA-125 by Elecsys 2010 system and HE4 by immunoassay ARCHITECT® HE4 kits; ROMA calculated, and preoperative malignancy risk estimated. Matching these values to postoperative histopathology resulted in the preoperative prediction values.

Results

There were 30 (10.8%) cases of EOC. Median values of CA 125, HE4, and ROMA of EOC and benign tumors were 214.20 U/ml, 18.91 U/ml; 90.00 pmol/l, 39.80 pmol/l; and 55.20%, 4.80%, respectively. The sensitivities and specificity of CA125, HE4, and ROMA to distinguish between malignant and benign tumors at standard cut-offs were 83.3% and 78.5%; 50% and 98.38%; 80.0% and 84,6%, and those at optimal cut-offs were 83.3% and 86.6%; 80.0% and 91.5%, 86.7% and 88.7%, respectively. AUCs of CA-125, HE4, and ROMA were 0.872, 0.894, 0.912; and those for the post-menopausal group were 0.900, 0.894 and 0.924, respectively.

Conclusion

Serum CA 125 and HE4 levels and ROMA have good validity in the diagnosis of EOC, of which ROMA gives the best result. The ROMA index should be applied in clinical practice to help in the assessment and management of patients with suspected ovarian cancer.

•

HE4 and ROMA yielded good validity in the preoperative diagnosis of epithelial ovarian cancer.

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In pre-menopausal group, the modified ROMA cut-off value (9.89%) yielded a better specificity.

•

ROMA can help with the assessment and management of patients with suspected ovarian cancer.

HE4 and eIF3a Expression Correlates with Surgical Outcome and Overall Survival in Ovarian Cancer Patients with Secondary Cytoreduction

For recurrent ovarian cancer (ROC), secondary cytoreductive surgery (SCS) is recommended as one optional treatment. However, little is known about the expression and clinical significance of biomarkers during SCS. Human epididymis protein 4 (HE4) is a clinical biomarker for ovarian cancer. Eukaryotic translation initiation factor 3a (eIF3a) is investigated extensively as a potential biomarker for malignancy. The purpose of this study was to investigate the expressions of HE4 and eIF3a at SCS, as well as their associations with surgical outcome and survival in ROC patients. Immunohistochemistry was performed to determine the expressions of HE4 and eIF3a in ovarian tumors taken from both initial and secondary cytoreductive surgery of 35 ROC patients. eIF3a levels were significantly increased at SCS, compared to those at initial cytoreductive surgery (ICS), while HE4 levels were similar. Both HE4 and eIF3a expressions were associated with surgical outcome, in terms of residual tumor. For ICS, patients with high HE4 expression achieved a higher incidence of optimal cytoreduction than those with low HE4 expression (81.0% vs. 33.3%, P = 0.015). A similar result happened in SCS, indicated by higher incidence of no residual tumor in patients with high HE4 expression (76.4% vs. 44.4%, P = 0.046). And high HE4 expression at SCS was more likely to enhance surgical outcome of SCS (77.8% vs. 29.4%, P = 0.038). Therefore, high HE4 expression at either surgery is a predictor of better overall survival (OS) (P = 0.011 and 0.002). Furthermore, patients with an elevated total score (TS) of HE4 between the two surgeries tended to have prolonged OS, compared to those with a non-elevated TS of HE4 (P = 0.076). For eIF3a, initial eIF3a expression was associated with secondary residual tumor (P = 0.035), and the difference in eIF3a expression between the two surgeries correlated with OS (P = 0.052). The expressions of HE4 and eIF3a in tumor specimens correlated with surgical outcome and predicted OS in ROC patients with SCS, thus meriting further investigation.

Clean Colorectum at Diagnostic Colonoscopy: Subsequent Detection of Extracolonic Malignancies by Plasma Protein Biomarkers?

Introduction

Most of the subjects undergoing diagnostic colonoscopy do not have neoplastic bowel lesions. Potentially, some of the symptoms may therefore be caused by extracolonic malignancy, and subjects with persisting symptoms may need subsequent examinations. Blood-based, cancer-associated biomarkers may aid in directing the examinations for other specific malignant diseases.

Methods

EDTA plasma samples available from a previous prospective study of subjects undergoing diagnostic colonoscopy were used for analysis of 18 protein biomarkers. The study population of 3732 subjects included 400 patients with colorectal cancer (CRC) and 177 patients with extracolonic malignancies. Univariable analysis of the association of specific biomarkers and extracolonic cancers included those with 10 or more cases. Subsequently, reduced models of 4 or 6 biomarkers, respectively, were established by choosing those with the highest likelihood; age and sex were included as well.

Results

Univariable analyses showed that CyFra21-1 had an area under curve (AUC) of 0.87 for lung cancers (n = 33), CA19-9 had an AUC of 0.85 for pancreatic cancer (n = 22), CA125 had an AUC of 0.95 for ovary cancer (n = 16), B2M had an AUC of 0.81 for non-Hodgkin lymphoma (n = 12), and total prostate-specific antigen had an AUC of 0.99 for prostate cancer (n = 10). The multivariable analysis of 4 or 6 biomarkers plus age and sex as explanatory variables showed AUCs of 0.82 to 0.85 both for extracolonic cancers and CRC. The 4 biomarkers included in the model for detection of extracolonic cancers were CA125, hsCRP, CA19-9, and CyFra21-1; the 2 additional for the 6 biomarkers model were CEA and Galectin-3. Similarly, the 4 biomarkers included in the model for detection of CRC were CEA, CyFra21-1, Ferritin, and HE4; the two additional for the 6 biomarkers model were hsCRP and Pepsinogen 2.

Conclusions

Results of this study indicate that it may be possible to detect subjects that have an increased risk of extracolonic cancer following a colonoscopy without findings of neoplastic lesions. Combinations of various protein biomarkers may direct subsequent examination after colonoscopy with clean colorectum. The results, although preliminary, may form the basis for additional research directed both for primary examinations of subjects with symptoms of malignancy and subsequent examinations after colonoscopy.