Clinical and Ultrasound Features of Type I and Type II Epithelial Ovarian Cancer:

A dual-center study: can ultrasound radiomics differentiate type I and type II epithelial ovarian cancer patients with normal CA125 levels?

OBJECTIVE

CA125 is recommended by many countries as the primary screening test for ovarian cancer. But there are patients with ovarian cancer having normal CA125. We hope to identify the types of EOC with normal CA125 levels better by building a refined model based on the ultrasound radiomics, thus providing precise medical treatment for patients.

METHODS

We included 58 patients with EOC with normal CA125 from 2 centres, who were confirmed by preoperative ultrasound and pathology. We extracted 1130 radiomics features based on the tumour's region of interest from the most typical ultrasound image of each patient. We selected radiomics and clinical features by LASSO and logistic regression to construct Rad-score and clinical models, respectively. Receiver operating characteristic curves judged their test efficacy. On the basis of the combined model, we developed a nomogram.

RESULTS

Area under the curves (AUCs) of 0.93 and 0.83 were achieved in both the training and test groups for the combined model. There were similar AUCs between the Rad-score and clinical models of 0.82 and 0.80, respectively. By analysing the calibration curves, it was determined that the nomogram matched actual observations in the training cohort.

CONCLUSION

Ultrasound radiomics can differentiate type I and type II EOC with normal CA125 levels.

ADVANCES IN KNOWLEDGE

This study is the first to focus on EOC cases with normal level of CA125. The subset of patients constituting 20% of the disease population may require more refined radiomics models.

Tumor Markers and Their Diagnostic Significance in Ovarian Cancer

Ovarian cancer (OC) is characterized by silent progression and late-stage diagnosis. It is critical to detect and accurately diagnose the disease early to improve survival rates. Tumor markers have emerged as valuable tools in the diagnosis and management of OC, offering non-invasive and cost-effective options for screening, monitoring, and prognosis.

PURPOSE

This paper explores the diagnostic importance of various tumor markers including CA-125, CA15-3, CA 19-9, HE4,hCG, inhibin, AFP, and LDH, and their impact on disease monitoring and treatment response assessment.

METHODS

Article searches were performed on PubMed, Scopus, and Google Scholar. Keywords used for the searching process were "Ovarian cancer", "Cancer biomarkers", "Early detection", "Cancer diagnosis", "CA-125","CA 15-3","CA 19-9", "HE4","hCG", "inhibin", "AFP", "LDH", and others.

RESULTS

HE4, when combined with CA-125, shows improved sensitivity and specificity, particularly in early-stage detection. Additionally, hCG holds promise as a prognostic marker, aiding treatment response prediction and outcome assessment. Novel markers like microRNAs, DNA methylation patterns, and circulating tumor cells offer potential for enhanced diagnostic accuracy and personalized management. Integrating these markers into a comprehensive panel may improve sensitivity and specificity in ovarian cancer diagnosis. However, careful interpretation of tumor marker results is necessary, considering factors such as age, menopausal status, and comorbidities. Further research is needed to validate and refine diagnostic algorithms, optimizing the clinical significance of tumor markers in ovarian cancer management. In conclusion, tumor markers such as CA-125, CA15-3, CA 19-9, HE4, and hCG provide valuable insights into ovarian cancer diagnosis, monitoring, and prognosis, with the potential to enhance early detection.

Diagnostic value of circN4BP2L2 in type I and type II epithelial ovarian cancer

BACKGROUND

CircN4BP2L2 was previously identified to be significantly decreased in epithelial ovarian cancer (EOC) and was associated with disease progression. The aim of this study was to evaluate the diagnostic value of plasma circN4BP2L2 using the unifying model of type I and type II EOC.

METHODS

A total of 540 plasma samples were obtained from 180 EOC patients, 180 benign ovarian cyst patients, and 180 healthy volunteers. CircN4BP2L2 was assessed using reverse transcription-quantitative polymerase chain reaction (RT-qPCR). Cancer antigen 125 (CA125) and human epididymis protein 4 (HE4) were assessed using enzyme-linked immunosorbent assay (ELISA). Receiver operating curve (ROC), the area under the curve (AUC), sensitivity and specificity were estimated.

RESULTS

Low level of circN4BP2L2 was associated with advanced tumor stage (p < 0.01) in type I EOC. Decreased circN4BP2L2 was associated with lymph node metastasis (LNM) (p = 0.04) in type II EOC. The expression level of circN4BP2L2 in type I was similar to that in type II. CircN4BP2L2 could significantly separate type I or type II from benign or normal cohort (p < 0.01). Early-stage type I or type II EOC vs. benign or normal cohort could also be distinguished by circN4BP2L2 (p < 0.01).

CONCLUSION

CircN4BP2L2 might serve as a promising diagnostic biomarker for both type I and type II EOC. The diagnostic safety for circN4BP2L2 in early-stage type I or type II EOC is also acceptable. Further large-scale well-designed studies are warranted to investigate whether circN4BP2L2 is specific for all histologic subgroups.

Nomogram based on ultrasound radiomics score and clinical variables for predicting histologic subtypes of epithelial ovarian cancer

OBJECTIVE

Ovarian cancer is one of the most common causes of death in gynecological tumors, and its most common type is epithelial ovarian cancer (EOC). This study aimed to establish a radiomics signature based on ultrasound images to predict the histopathological types of EOC.

METHODS

Overall, 265 patients with EOC who underwent preoperative ultrasonography and surgery were eligible. They were randomly sorted into two cohorts (training cohort: test cohort = 7:3). We outlined the region of interest of the tumor on the ultrasound images of the lesion. Then, the radiomics features were extracted. Clinical, Rad-score and combined models were constructed based on the least absolute shrinkage, selection operator, and logistic regression analysis. The performance of the models was evaluated using receiver operating characteristic curves and decision curve analysis (DCA). A nomogram was formulated based on the combined prediction model.

RESULTS

The combined model had good performance in predicting EOC histopathological types, with an AUC of 0.83 (95% CI: 0.77-0.90) and 0.82 (95% CI: 0.71-0.93) in the training and test cohorts, respectively. The calibration curves showed that the nomogram estimation was consistent with the actual observations. DCA also verified the clinical value of the combined model.

CONCLUSIONS

The combined model containing clinical and ultrasound radiomics features showed an excellent performance in predicting type I and type II EOC.

ADVANCES IN KNOWLEDGE

This study presents the first application of ultrasound radiomics features to distinguish EOC histopathological types. The proposed clinical-radiomics nomogram could help gynecologists non-invasively identify EOC types before surgery.

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.

MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes

OBJECTIVE

To compare the performance of clinical features, conventional MR image features, ADC value, T2WI, DWI, DCE-MRI radiomics, and a combined multiple features model in predicting the type of epithelial ovarian cancer (EOC).

METHODS

In this retrospective analysis, 61 EOC patients were confirmed by histology. Significant features (p < 0.05) by multivariate logistic regression were retained to establish a clinical model, conventional MRI morphological model, ADC model, and traditional model. The radiomics model included FS-T2WI, DWI, and DCE-MRI, and also, a multisequence model was established. A total of 1070 radiomics features of each sequence were extracted; then, univariate analysis and LASSO were used to select important features. Traditional models were combined with a combined radiomics model to establish a mixed model. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the traditional model in identifying early- and late-stage EOC.

RESULTS

Traditional models showed the highest performance (AUC = 0.96). The performance of the mixed model (AUC = 0.97) was not significantly different from that of the traditional model. The calibration curve showed that the traditional model had the highest reliability. Stratified analysis showed the potential of the combined radiomics model in the early distinction of the two tumor types.

CONCLUSION

The traditional model is an effective tool to distinguish EOC type I/II. Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease.

KEY POINTS

• The combined radiomics model resulted in a better predictive model than that from a single sequence model. • The traditional model showed higher classification accuracy than the combined radiomics model. • Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease.

Magnetic resonance imaging in categorization of ovarian epithelial cancer and survival analysis with focus on apparent diffusion coefficient value: correlation with Ki-67 expression and serum cancer antigen-125 level

Background

To determine whether magnetic resonance (MR) imaging features combined with apparent diffusion coefficient (ADC) values could be used as a tool for categorizing ovarian epithelial cancer (OEC) and predicting survival, as well as correlating with laboratory tests (serum cancer antigen 125, serum CA-125) and tumor proliferative index (Ki-67 expression).

Methods and materials

MRI examination was undertaken before invasive procedures. MRI features were interpreted and recorded on the picture archive communication system (PACS). ADC measurements were manually performed on post-process workstation. Clinical characteristics were individually retrieved and recorded through the hospital information system (HIS). Cox hazard model was used to estimate the effects of both clinical and MRI features on overall survival.

Results

Both clinical and MRI features differed significantly between Type I and Type II cancer groups (p < 0.05). The mean ADC value was inversely correlated with Ki-67 expression in Type I cancer (ρ = − 0.14, p < 0.05). A higher mean ADC value was more likely to suggest Type I ovarian cancer (Odds Ratio (OR) = 16.80, p < 0.01). Old age and an advanced International Federation of Gynecology and Obstetrics (FIGO) stage were significantly related to Type II ovarian cancer (OR = 0.22/0.02, p < 0.05). An advanced FIGO stage, solid components, and old age were significantly associated with poor survival (Hazard Ratio (HR) = 23.54/3.69/2.46, p < 0.05). Clear cell cancer type had a poorer survival than any other pathological subtypes of ovarian cancer (HR = 13.6, p < 0.01).

Conclusions

MR imaging features combined with ADC value are helpful in categorizing OEC. ADC values can reflect tumor proliferative ability. A solid mass may predict poor prognosis for OEC patients.

Electronic supplementary material

The online version of this article (10.1186/s13048-019-0534-0) contains supplementary material, which is available to authorized users.

Low-grade epithelial ovarian cancer: what a radiologist should know

Ovarian cancer accounts for the death of over 100,000 females every year and is the most lethal gynecological malignancy. Low-grade serous ovarian carcinoma (LGSOC) and high-grade serous ovarian carcinoma (HGSOC) have been found to represent two distinct entities based on their molecular differences, clinical course, and response to chemotherapy. Currently, all ovarian cancers are staged according to the revised staging system of the International Federation of Gynecology and Obstetrics (FIGO). Imaging plays an integral role in the diagnosis, staging, and follow-up of ovarian cancers. This review will be based on the two-tier grading system of epithelial ovarian cancers, with the main emphasis on serous ovarian cancer, and the role of imaging to characterize low-grade vs high-grade tumors and monitor disease recurrence during follow-up.

Clinical outcome and prognostic factors of patients with early-stage epithelial ovarian cancer

Some subsets of early stage ovarian cancer patients experience more recurrences than others. Studies on prognostics factors gave conflicting results. We investigated consecutive 221 patients with stage I/II ovarian cancer at our institution from 1999 to 2010. Univariate and multivariate analysis of progression free survival (PFS) and overall survival (OS) were performed. After a median follow-up of 79 months, the 5-year/10-year PFS and 5-year/10-year OS were 78% /76% and 90% /87% respectively. Multivariate analysis revealed that stage as the most prominent independent prognostic factor in terms of PFS (stage I vs stage IIA vs stage IIB, Hazard Ratio (HR): 1 vs 4 vs 6.1, P < 0.05) and OS (stage I vs stage II, HR: 1 vs 2.1, P < 0.05). Peritoneal biopsy reduced the risk of recurrence by 29% (95% CI: 0.15-0.58, P < 0.05). Ascites (HR = 2.8, 95% CI: 1.2-6.6, P < 0.05) and not the first-line chemotherapy (HR = 2.6, 95% CI: 1.1-6.5, P < 0.05) contributed to decreased OS. Overall, early-stage ovarian cancer had a favorable outcome, stage was the most powerful prognostic factor.

CT and MRI findings of type I and type II epithelial ovarian cancer

OBJECTIVE

To assess whether types I and II epithelial ovarian cancer (EOC) differ in CT and MRI imaging features.

METHODS

For this retrospective study, we enrolled 65 patients with 68 ovarian lesions that have been pathologically proven to be EOC. Of these patients, 38 cases underwent MR examinations only, 15 cases underwent CT examinations only, and 12 cases completed both examinations. The clinical information [age, CA-125, menopausal status, and Ki-67] and imaging findings were compared between two types of EOCs. The diagnostic performance of image findings were assessed by receiver-operating characteristic curve(ROC) analysis. The association between EOC type and imaging features was assessed by multivariate logistic regression analysis. The random forest approach was used to build a classifier in differential diagnosis between two types of EOCs.

RESULTS

Of the 68 EOC lesions, 24 lesions were categorized as types I and other 44 lesions as type II based on the immunohistochemical results, respectively. Patients in type I EOCs were more likely to involve menopausal women and showed lower CA-125 and Ki-67 values (Ki-67<30%) than patients in type II EOCs. The imaging characteristics of type II EOCs frequently demonstrated a solid or predominantly solid mass (38.6% vs. 12.5%, P<0.05), smaller lesions (diameter <6cm; 27.3% vs. 4.2%, P<0.05), absence of mural nodules (65.9% vs. 25.9%, P=0.001), and mild enhancement (84.1% vs. 54.2%, P<0.05) compared to type I EOCs. Combination of tumor size, morphology, mural nodule, enhancement degrees (AUC=0.808) has a higher specificity (87.50%) and positive predictive value (90.0%) than any single image finding alone in differential diagnosis between two types of EOCs. The multivariate logistic regression analysis showed that enhancement degrees(OR 0.200, P<0.05),mural nodule(OR 0.158, P<0.05) significantly influence EOC classification. Random forests model identified both as the most important discriminating variables. The diagnostic accuracy of the classifier was 73.53%.

CONCLUSIONS

Differences in imaging characteristics existed between two types of EOCs. Combination of several image findings improved the preoperative diagnostic performance, which is helpful for the clinical treatment and prognosis evaluation.

Accuracy of ultrasonography performed by examiners with varied training and experience in predicting specific pathology of adnexal masses

OBJECTIVES

To assess the diagnostic performance of subjective assessment by Level II ultrasound examiners in predicting the specific histology of adnexal masses.

METHODS

The women included in this prospective multicenter cross-sectional study were older than 16 years of age and had at least one adnexal mass. They underwent transvaginal sonography (TVS) performed by Level II examiners, all of whom were familiar with the International Ovarian Tumor Analysis (IOTA) group definitions of ultrasound features of ovarian masses. The final outcome was histology. Specific diagnoses were categorized into 16 groups. Agreement between subjective assessment and final histology was measured using unweighted kappa coefficients. Sensitivities and specificities were obtained for subjective assessment.

RESULTS

Of the 1279 women who underwent TVS, 313 were included in the final analysis. Overall agreement (16 × 16 table) between subjective assessment and histology was moderate, with a Cohen's kappa coefficient of 0.59 (95% CI, 0.53-0.65). The specificity of subjective assessment ranged between 91% and 100% for all histological subgroups. Highest sensitivities were achieved in the diagnosis of simple cysts (100% (95% CI, 61-100%)), hydrosalpinges (100% (95% CI, 34-100%)), mature teratomas (88% (95% CI, 74-96%)), endometriomas (75% (95% CI, 61-85%)), ovarian fibromas (88% (95% CI, 47-100%)), tubo-ovarian abscesses (88% (95% CI, 47-100%)) and serous cystadenocarcinomas (82% (95% CI, 66-93%)). Serous cystadenomas were misdiagnosed most commonly (40.5%). The sensitivity of subjective assessment in diagnosing adnexal torsion was 54% (95% CI, 25-81%); the 17 confirmed and/or suspected cases of adnexal torsion were not included in the 313 cases examined and analyzed for diagnostic performance.

CONCLUSION

Overall, subjective assessment by Level II examiners was good for the detection of simple cysts, endometriomas, mature teratomas, hydrosalpinges, fibroma, tubo-ovarian abscess and serous cystadenocarcinomas.

Discovery of prognostic factors for diagnosis and treatment of epithelial-derived ovarian cancer from laying hens

Ovarian cancer is a lethal gynecological cancer causing cancer-related deaths in women worldwide. It is difficult to diagnosis at an early stage when more than 90% patients can be cured because of lack of specific symptoms and early detection markers. Most of malignant ovarian tumors are originated from the germinal epithelium of the ovary. For investigation with animal models of epithelial-derived ovarian cancer (EOC), laying hens are the most relevant animal models because they spontaneously develop EOC as occurs in women through ovulating almost every day. As in women, EOC in the hen is age-related and grossly and histologically similar to that in women. However, domesticated animals are inappropriate for research human EOC due to multiple pregnancies and lactating or seasonally anestrous. In addition, the non-spontaneous nature of rodents EOC limits clinical relevance with human EOC. Recent studies have shown that ovarian cancer could arise from epithelium from the oviduct as oviduct-related genes are up-regulated in EOC of hens. Therefore, we showed in the review: 1) characterization and classification of EOC; 2) chicken models for EOC; 3) relationship estrogen with EOC; 4) candidate prognostic factors for EOC including serpin peptidase inhibior, clade B (ovalbumin), member 3 (SERPINB3), SERPINB11, gallicin 11 (GAL11), secreted phosphoprotein 1 (SPP1) and alpha 2 macroglobulin (A2M) in normal and cancerous ovaries of laying hens; 5) biological roles of microRNAs in development of EOC. Collectively, the present reviews indicate that expression of SERPINB3, SERPINB11, GAL11, SPP1 and A2M is clearly associated with the development of ovarian carcinogenesis. These results provide new insights into the prognostic biomarkers for EOC to diagnose and to evaluate responses to therapies for treating EOC of humans.