Early detection of ovarian cancer

Recent advances in surface plasmon resonance for the detection of ovarian cancer biomarkers: a thorough review

Early detection of ovarian cancer (OC) is crucial for effective management and treatment, as well as reducing mortality rates. However, the current diagnostic methods for OC are time-consuming and have low accuracy. Surface plasmon resonance (SPR) biosensors offer a promising alternative to conventional techniques, as they enable rapid and less invasive screening of various circulating indicators. These biosensors are widely used for biomolecular interaction analysis and detecting tumor markers, and they are currently being investigated as a rapid diagnostic tool for early-stage cancer detection. Our main focus is on the fundamental concepts and performance characteristics of SPR biosensors. We also discuss the latest advancements in SPR biosensors that enhance their sensitivity and enable high-throughput quantification of OC biomarkers, including CA125, HE4, CEA, and CA19-9. Finally, we address the future challenges that need to be overcome to advance SPR biosensors from research to clinical applications. The ultimate goal is to facilitate the translation of SPR biosensors into routine clinical practice for the early detection and management of OC.

The application value of two-dimensional ultrasound combined with contrast-enhanced ultrasound in the differential diagnosis of benign, borderline, and malignant ovarian epithelial tumors

OBJECTIVE

The aim of this study was to explore the clinical application value of two-dimensional ultrasound (2D-US) combined with contrast-enhanced ultrasonography (CEUS) in the differential diagnosis of benign, borderline ovarian tumors (BOTs), and malignant ovarian epithelial tumors (OETs).

METHODS

The clinical data of 108 patients who underwent surgery for pathologically confirmed of OETs at Peking University People's Hospital between December 2018 and November 2023 were retrospectively studied. The diagnostic value of 2D-US combined with CEUS for diagnosing OETs was analyzed using chi-square tests, receiver operating characteristic (ROC) curves, and random forest models.

RESULTS

Among the 108 cases of OETs, 23 were benign, 34 were BOTs, and 51 were malignant. Chi-square tests confirmed that the perfusion pattern of the contrast agent plays an important role in the differential diagnosis of OETs. Compared with those in the benign group, the BOTs were not significantly different in terms of perfusion phase and enhancement intensity, but the regression time of the BOTs was earlier (P < 0.05). Compared with the BOTs, the malignant tumors group showed earlier perfusion and higher enhancement intensity, with no significant difference in regression time. The ROC curve results indicated that the combined diagnostic efficiency of 2D-US and CEUS in distinguishing OETs was significantly higher than that of a single diagnostic technique in terms of sensitivity, specificity, accuracy, and AUC. The random forest model results revealed that among the various parameters used in the differential diagnosis of OETs, the perfusion pattern was the most significant factor.

CONCLUSION

2D-US combined with CEUS helps improve the differential diagnostic efficiency for benign, BOTs, and malignant OETs.

Enhancing Ovarian Tumor Diagnosis: Performance of Convolutional Neural Networks in Classifying Ovarian Masses Using Ultrasound Images

Background/Objectives: This study aims to create a strong binary classifier and evaluate the performance of pre-trained convolutional neural networks (CNNs) to effectively distinguish between benign and malignant ovarian tumors from still ultrasound images. Methods: The dataset consisted of 3510 ultrasound images from 585 women with ovarian tumors, 390 benign and 195 malignant, that were classified by experts and verified by histopathology. A 20% to80% split for training and validation was applied within a k-fold cross-validation framework, ensuring comprehensive utilization of the dataset. The final classifier was an aggregate of three pre-trained CNNs (VGG16, ResNet50, and InceptionNet), with experimentation focusing on the aggregation weights and decision threshold probability for the classification of each mass. Results: The aggregate model outperformed all individual models, achieving an average sensitivity of 96.5% and specificity of 88.1% compared to the subjective assessment's (SA) 95.9% sensitivity and 93.9% specificity. All the above results were calculated at a decision threshold probability of 0.2. Notably, misclassifications made by the model were similar to those made by SA. Conclusions: CNNs and AI-assisted image analysis can enhance the diagnosis and aid ultrasonographers with less experience by minimizing errors. Further research is needed to fine-tune CNNs and validate their performance in diverse clinical settings, potentially leading to even higher sensitivity and overall accuracy.

The value of combined MRI, enhanced CT and 18F-FDG PET/CT in the diagnosis of recurrence and metastasis after surgery for ovarian cancer

OBJECTIVE

The purpose of this article was to investigate the value of combined MRI, enhanced CT and 18F-FDG PET/CT in the diagnosis of recurrence and metastasis after surgery for ovarian cancer.

METHODS

Ninety-five ovarian cancer patients were selected as the study subjects, all of them underwent surgical treatment, and MRI, enhanced CT and 18F-FDG PET/CT were performed on all of them in the postoperative follow-up, and the pathological results after the second operation were used as the diagnostic "gold standard". The diagnostic value (sensitivity, specificity, accuracy, negative predictive value and positive predictive value) of the three examination methods alone or in combination for the diagnosis of postoperative recurrence and metastasis of ovarian cancer was compared, and the detection rate was calculated when the lesion was the unit of study, so as to compare the efficacy of the three methods in the diagnosis of postoperative recurrent metastatic lesions of ovarian cancer.

RESULTS

The sensitivity, specificity, accuracy, positive predictive value and negative predictive value of the combined group were higher than those of MRI and enhanced CT for recurrence and metastasis of ovarian cancer after surgery, and the specificity, accuracy and positive predictive value of the combined group were higher than those of the 18F-FDG PET/CT group, and those of the 18F-FDG PET/CT group were higher than those of the enhanced CT group (all P < 0.05). When the postoperative recurrent metastatic lesions of ovarian cancer were used as the study unit, the detection rate of lesions in the combined group was higher than that of the three examinations detected individually, and the detection rate of lesions in 18F-FDG PET/CT was higher than that of enhanced CT and MRI (P < 0.05).

CONCLUSION

The combination of MRI, enhanced CT and 18F-FDG PET/CT can accurately diagnose recurrence and metastasis of ovarian cancer after surgery, detect recurrent metastatic lesions as early as possible, and improve patients' prognosis.

Intra-sample reversed pairs based on differentially ranked genes reveal biosignature for ovarian cancer

Ovarian cancer, a major gynecological malignancy, often remains undetected until advanced stages, necessitating more effective early screening methods. Existing biomarker based on differential genes often suffers from variations in clinical practice. To overcome the limitations of absolute gene expression values including batch effects and biological heterogeneity, we introduced a pairwise biosignature leveraging intra-sample differentially ranked genes (DRGs) and machine learning for ovarian cancer detection across diverse cohorts. We analyzed ten cohorts comprising 872 samples with 796 ovarian cancer and 76 normal. Our method, DRGpair, involves three stages: intra-sample ranking differential analysis, reversed gene pair analysis, and iterative LASSO regression. We identified four DRG pairs, demonstrating superior diagnostic performance compared to current state-of-the-art biomarkers and differentially expressed genes in seven independent cohorts. This rank-based approach not only reduced computational complexity but also enhanced the specificity and effectiveness of biomarkers, revealing DRGs as promising candidates for ovarian cancer detection and offering a scalable model adaptable to varying cohort characteristics.

Revolutionizing Women's Health: A Comprehensive Review of Artificial Intelligence Advancements in Gynecology

Artificial intelligence has yielded remarkably promising results in several medical fields, namely those with a strong imaging component. Gynecology relies heavily on imaging since it offers useful visual data on the female reproductive system, leading to a deeper understanding of pathophysiological concepts. The applicability of artificial intelligence technologies has not been as noticeable in gynecologic imaging as in other medical fields so far. However, due to growing interest in this area, some studies have been performed with exciting results. From urogynecology to oncology, artificial intelligence algorithms, particularly machine learning and deep learning, have shown huge potential to revolutionize the overall healthcare experience for women's reproductive health. In this review, we aim to establish the current status of AI in gynecology, the upcoming developments in this area, and discuss the challenges facing its clinical implementation, namely the technological and ethical concerns for technology development, implementation, and accountability.

An assessment of survival outcomes among ovarian cancer patients at the National and Referral Hospital in Kenya

BACKGROUND

Ovarian cancer has been shown to have poor survival outcomes attributed to late presentation. In Kenya, information on the survival outcomes of ovarian cancer patients is scarce. Therefore, the objective of this study was to examine the survival outcomes among patients with ovarian cancer treated at Kenyatta National Hospital (KNH).

AIMS

A hospital-based retrospective cohort study was performed at KNH to examine the survival outcomes of 112 ovarian cancer patients. The study employed a structured data abstraction tool to acquire patients' relevant socio-demographic and clinical characteristics from the patient's medical records. The data obtained were analyzed using SPSS version 29.0 statistical software. Kaplan-Meier and Cox regression analyses were used to determine the survival outcome and predictors of mortality among ovarian cancer patients, respectively.

METHODS AND RESULTS

The mean age of the patients in this study was 51.28 ± 14.24 years. Most patients (59.8%) had evidence of distant metastasis during the follow-up period. One-third (33%) of patients were deceased. The mean-cancer-specific survival time among the study participants was 40.0 ± 3.0 months. The 5-year survival rate was 44%, with most patients experiencing disease progression during the last follow-up. Combination therapy (p < .001) was the only statistically significant predictor of mortality in ovarian cancer patients.

CONCLUSION

The study found that the 5-year survival rate among ovarian cancer patients was poor, with most patients experiencing disease progression during the last follow-up period.

Unveiling the Promise: A Comprehensive Review of Salpingectomy as a Vanguard for Ovarian Cancer Prevention

This comprehensive review explores the potential of salpingectomy as a groundbreaking strategy for the prevention of ovarian cancer. The discussion encompasses the biological rationale behind salpingectomy, emphasizing its foundation in the tubal hypothesis, which posits the fallopian tubes as a possible origin site for certain ovarian cancers. Ongoing clinical trials and observational studies provide evolving evidence supporting the safety and efficacy of salpingectomy, particularly in high-risk populations. The procedure's ethical considerations, including its impact on fertility and equitable access, are thoroughly examined. Implications for clinical practice underscore the importance of informed decision-making, risk-benefit assessments, and the integration of emerging evidence into reproductive health discussions. Looking ahead, the future landscape of ovarian cancer prevention involves continued research, technological innovations, and collaborative efforts to ensure a holistic and evidence-based approach. The goal is to forge a future where ovarian cancer is not only treatable but also preventable, with salpingectomy potentially playing a pivotal role in this transformative journey.

Associating Peritoneal Metastasis With T2-Weighted MRI Images in Epithelial Ovarian Cancer Using Deep Learning and Radiomics: A Multicenter Study

BACKGROUND

The preoperative diagnosis of peritoneal metastasis (PM) in epithelial ovarian cancer (EOC) is challenging and can impact clinical decision-making.

PURPOSE

To investigate the performance of T2 -weighted (T2W) MRI-based deep learning (DL) and radiomics methods for PM evaluation in EOC patients.

STUDY TYPE

Retrospective.

POPULATION

Four hundred seventy-nine patients from five centers, including one training set (N = 297 [mean, 54.87 years]), one internal validation set (N = 75 [mean, 56.67 years]), and two external validation sets (N = 53 [mean, 55.58 years] and N = 54 [mean, 58.22 years]).

FIELD STRENGTH/SEQUENCE

1.5 or 3 T/fat-suppression T2W fast or turbo spin-echo sequence.

ASSESSMENT

ResNet-50 was used as the architecture of DL. The largest orthogonal slices of the tumor area, radiomics features, and clinical characteristics were used to construct the DL, radiomics, and clinical models, respectively. The three models were combined using decision-level fusion to create an ensemble model. Diagnostic performances of radiologists and radiology residents with and without model assistance were evaluated.

STATISTICAL TESTS

Receiver operating characteristic analysis was used to assess the performances of models. The McNemar test was used to compare sensitivity and specificity. A two-tailed P < 0.05 was considered significant.

RESULTS

The ensemble model had the best AUCs, outperforming the DL model (0.844 vs. 0.743, internal validation set; 0.859 vs. 0.737, external validation set I) and clinical model (0.872 vs. 0.730, external validation set II). After model assistance, all readers had significantly improved sensitivity, especially for those with less experience (junior radiologist1, from 0.639 to 0.820; junior radiologist2, from 0.689 to 0.803; resident1, from 0.623 to 0.803; resident2, from 0.541 to 0.738). One resident also had significantly improved specificity (from 0.633 to 0.789).

DATA CONCLUSIONS

T2W MRI-based DL and radiomics approaches have the potential to preoperatively predict PM in EOC patients and assist in clinical decision-making.

EVIDENCE LEVEL

4 TECHNICAL EFFICACY: Stage 2.

Prediction model of adnexal masses with complex ultrasound morphology

Background

Based on the ovarian-adnexal reporting and data system (O-RADS), we constructed a nomogram model to predict the malignancy potential of adnexal masses with sophisticated ultrasound morphology.

Methods

In a multicenter retrospective study, a total of 430 subjects with masses were collected in the adnexal region through an electronic medical record system at the Fourth Hospital of Harbin Medical University during the period of January 2019-April 2023. A total of 157 subjects were included in the exception validation cohort from Harbin Medical University Tumor Hospital. The pathological tumor findings were invoked as the gold standard to classify the subjects into benign and malignant groups. All patients were randomly allocated to the validation set and training set in a ratio of 7:3. A stepwise regression analysis was utilized for filtering variables. Logistic regression was conducted to construct a nomogram prediction model, which was further validated in the training set. The forest plot, C-index, calibration curve, and clinical decision curve were utilized to verify the model and assess its accuracy and validity, which were further compared with existing adnexal lesion models (O-RADS US) and assessments of different types of neoplasia in the adnexa (ADNEX).

Results

Four predictors as independent risk factors for malignancy were followed in the preparation of the diagnostic model: O-RADS classification, HE4 level, acoustic shadow, and protrusion blood flow score (all p < 0.05). The model showed moderate predictive power in the training set with a C-index of 0.959 (95%CI: 0.940-0.977), 0.929 (95%CI: 0.884-0.974) in the validation set, and 0.892 (95%CI: 0.843-0.940) in the external validation set. It showed that the predicted consequences of the nomogram agreed well with the actual results of the calibration curve, and the novel nomogram was clinically beneficial in decision curve analysis.

Conclusion

The risk of the nomogram of adnexal masses with complex ultrasound morphology contained four characteristics that showed a suitable predictive ability and provided better risk stratification. Its diagnostic performance significantly exceeded that of the ADNEX model and O-RADS US, and its screening performance was essentially equivalent to that of the ADNEX model and O-RADS US classification.

Optimizing disease progression assessment using blinded central independent review and comparing it with investigator assessment in the PRIMA/ENGOT-ov26/GOG-3012 trial: challenges and solutions

OBJECTIVE

Progression-free survival is an established clinically meaningful endpoint in ovarian cancer trials, but it may be susceptible to bias; therefore, blinded independent centralized radiological review is often included in trial designs. We compared blinded independent centralized review and investigator-assessed progressive disease performance in the PRIMA/ENGOT-ov26/GOG-3012 trial examining niraparib monotherapy.

METHODS

PRIMA/ENGOT-ov26/GOG-3012 was a randomized, double-blind phase 3 trial; patients with newly diagnosed stage III/IV ovarian cancer received niraparib or placebo. The primary endpoint was progression-free survival (per Response Evaluation Criteria in Solid Tumors [RECIST] v1.1), determined by two independent radiologists, an arbiter if required, and by blinded central clinician review. Discordance rates between blinded independent centralized review and investigator assessment of progressive disease and non-progressive disease were routinely assessed. To optimize disease assessment, a training intervention was developed for blinded independent centralized radiological reviewers, and RECIST refresher training was provided for investigators. Discordance rates were determined post-intervention.

RESULTS

There was a 39% discordance rate between blinded independent centralized review and investigator-assessed progressive disease/non-progressive disease in an initial patient subset (n=80); peritoneal carcinomatosis was the most common source of discordance. All reviewers underwent training, and as a result, changes were implemented, including removal of two original reviewers and identification of 10 best practices for reading imaging data. Post-hoc analysis indicated final discordance rates between blinded independent centralized review and investigator improved to 12% in the overall population. Median progression-free survival and hazard ratios were similar between blinded independent centralized review and investigators in the overall population and across subgroups.

CONCLUSION

PRIMA/ENGOT-ov26/GOG-3012 highlights the need to optimize blinded independent centralized review and investigator concordance using early, specialized, ovarian-cancer-specific radiology training to maximize validity of outcome data.

Diagnostic performance of noninvasive imaging using computed tomography, magnetic resonance imaging, and positron emission tomography for the detection of ovarian cancer: a meta-analysis

OBJECTIVE

The aim of this meta-analysis was to compare the diagnostic value of noninvasive imaging methods computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) in the detection of ovarian cancer (OC).

METHODS

PubMed, Embase, and Ovid were comprehensively searched from the date of inception to 31st, March, 2022. Pooled sensitivity, specificity, positive likelihood ratio (+ LR), negative likelihood ratio (- LR), diagnostic odds ratio (DOR), and area under the curve (AUC) of summary receiver operating characteristic (SROC) with their respective 95% confidence intervals (CIs) were calculated.

RESULTS

Sixty-one articles including 4284 patients met the inclusion criteria of this study. Pooled estimates of sensitivity, specificity, and AUC of SROC with respective 95% CIs of CT on patient level were 0.83 (0.73, 0.90), 0.69 (0.54, 0.81), and 0.84 (0.80, 0.87). The overall sensitivity, specificity, SROC value with respective 95% CIs of MRI were 0.95 (0.91, 0.97),0.81 (0.76, 0.85), and 0.90 (0.87, 0.92) on patient level. Pooled estimates of sensitivity, specificity, SROC value of PET/CT on patient level were 0.92 (0.88, 0.94), 0.88 (0.83, 0.92), and 0.96 (0.94, 0.97).

CONCLUSION

Noninvasive imaging modalities including CT, MRI, PET (PET/CT, PET/MRI) yielded favorable diagnostic performance in the detection of OC. Hybrid implement of different tools (PET/MRI) is more accurate for identifying metastatic OC.

Fabrication of a Novel Au Star@AgAu Yolk-Shell Nanostructure for Ovarian Cancer Early Diagnosis and Targeted Therapy

Purpose

A novel CYPA-targeted, SiO₂ encapsulated Au star@AgAu yolk-shell nanostructure (YSNS) was synthesized and used for ovarian cancer early diagnosis and therapy.

Methods

Diverse spectroscopic and microscopic methods were utilized to investigate the pattern of the yolk-shell nanostructure. In addition, in vitro and in vivo experiments were carried out.

Results

It can be found that the ratio of HAuCl₄ and AgNO₃ played a critical role in the constitution of the yolk-shell nanostructure. The as-prepared yolk-shell nanostructure showed excellent SERS performance, which could be utilized as SERS substrate for specific sensitivity analysis of ovarian cancer markers cyclophilin A (CYPA) with detectable limit of 7.76*10^-10 μg/mL. In addition, the as-prepared yolk-shell nanostructure possessed outstanding photothermal performance, which could be used as photothermal agent for ovarian cancer therapy. Experiments in vitro and in vivo proved that the as-prepared yolk-shell nanostructures are ideal candidate for early diagnosis and therapy for ovarian cancer in one platform.

Conclusion

This work holds promise to offer a new method for the detection and therapy of ovarian cancer in the early stage.

Epidemiology and risk factors for ovarian cancer

Ovarian cancer is a complex disease, mostly observed in postmenopausal women, and is associated with poor survival rates. It is the sixth most common cancer and the fifth most common cause of death due to cancer among women in developed countries. Thus, despite representing less than one third of all gynaecologic cancers, deaths due to ovarian cancer account for more than two thirds of deaths due to gynaecologic cancers. Its prevalence is higher in Western Europe and Northern America than Asia and Africa. In sub-Saharan Africa, there is a considerably lower prevalence of ovarian cancer than other parts of Africa. Ovarian cancer is multifaceted, involving many factors, complex biological processes and unpredictable consequences. Unlike other female cancers that have early warning symptoms, ovarian cancer's symptoms are non-specific. As a result, ovarian cancers are normally undetected until advanced stages (III or IV). The major risk factors for ovarian cancer include older age, genetics, family history, hormone replacement therapy, nulliparity, and dietary fat. Controversial factors include obesity, infertility, talc powder, radiation exposure, fertility medications and in vitro fertilization. The current review discusses the aetiology, epidemiology and risk factors for ovarian cancer. Nevertheless, identification of the main risk factors for ovarian cancer may increase the awareness among women of the general population. This should help to decrease the incidence rate of ovarian cancer and increase the five-year survival rate.

The Utility of Transvaginal Ultrasound After Intrauterine Pregnancy Identification on Transabdominal Ultrasound in Emergency Department Patients

Introduction

Ultrasonography has an important role in the evaluation of Emergency Department (ED) patients presenting with early pregnancy complaints. Both transabdominal (TAUS) and transvaginal ultrasound (TVUS) can be utilized. While TVUS generally allows for greater detail, it is unclear how much added benefit exists in performing TVUS once an intrauterine pregnancy (IUP) has been identified on TAUS.

Methods

This was a retrospective study utilizing Radiology Department ultrasound examinations obtained in first trimester pregnancy ED patients during a consecutive four month period in 2019. Studies wherein both TAUS and TVUS were both performed were included. Two ED physicians with specialized training in point of care ultrasound reviewed only the TAUS images from these studies. Their findings were compared to the Radiologist interpretation, which was inclusive of both TAUS and TVUS components of the study.

Results

108 studies met inclusion criteria. Amongst these, 82 had IUP's identified on the radiologist report. 69 studies had an IUP identified by ED physician review of the TAUS images, with 1 false positive. Each case of intrauterine fetal demise (IUFD) was identified on ED physician review of TAUS. Two ectopic pregnancies were present, neither of which was mistaken for IUP on ED physician TAUS review. There were 15 studies with subchorionic hemorrhage and 3 studies with an ovarian cyst noted on the radiologist report.

Conclusion

Following the identification of an IUP on TAUS, the added diagnostic value of TVUS amongst this cohort of ED patients was low. Given the added time and cost of TVUS, selective instead of routine usage should be encouraged.

Neoadjuvant chemotherapy induces an elevation of tumour apparent diffusion coefficient values in patients with ovarian cancer

OBJECTIVES

Multiparametric magnetic resonance imaging (mMRI) is the modality of choice in the imaging of ovarian cancer (OC). We aimed to investigate the feasibility of different types of regions of interest (ROIs) in the measurement of apparent diffusion coefficient (ADC) values of diffusion-weighted imaging in OC patients treated with neoadjuvant chemotherapy (NACT).

METHODS

We retrospectively enrolled 23 consecutive patients with advanced OC who had undergone NACT and mMRI. Seventeen of them had been imaged before and after NACT. Two observers independently measured the ADC values in both ovaries and in the metastatic mass by drawing on a single slice of (1) freehand large ROIs (L-ROIs) covering the solid parts of the whole tumour and (2) three small round ROIs (S-ROIs). The side of the primary ovarian tumour was defined. We evaluated the interobserver reproducibility and statistical significance of the change in tumoural pre- and post-NACT ADC values. Each patient's disease was defined as platinum-sensitive, semi-sensitive, or resistant. The patients were deemed either responders or non-responders.

RESULTS

The interobserver reproducibility of the L-ROI and S-ROI measurements ranged from good to excellent (ICC range: 0.71-0.99). The mean ADC values were significantly higher after NACT in the primary tumour (L-ROI p  < 0.001, S-ROIs p < 0.01), and the increase after NACT was associated with sensitivity to platinum-based chemotherapy. The changes in the ADC values of the omental mass were associated with a response to NACT.

CONCLUSION

The mean ADC values of the primary tumour increased significantly after NACT in the OC patients, and the amount of increase in omental mass was associated with the response to platinum-based NACT. Our study indicates that quantitative analysis of ADC values with a single slice and a whole tumour ROI placement is a reproducible method that has a potential role in the evaluation of NACT response in patients with OC.

TRIAL REGISTRATION

Retrospectively registered (institutional permission code: 5302501; date of the permission: 31.7.2020).

Performance of IOTA Simple Rules Risks, ADNEX Model, Subjective Assessment Compared to CA125 and HE4 with ROMA Algorithm in Discriminating between Benign, Borderline and Stage I Malignant Adnexal Lesions

BACKGROUND

Borderline ovarian tumors (BOTs) and early clinical stage malignant adnexal masses can make sonographic diagnosis challenging, while the clinical utility of tumor markers, e.g., CA125 and HE4, or the ROMA algorithm, remains controversial in such cases.

OBJECTIVE

To compare the IOTA group Simple Rules Risk (SRR), the ADNEX model and the subjective assessment (SA) with serum CA125, HE4 and the ROMA algorithm in the preoperative discrimination between benign tumors, BOTs and stage I malignant ovarian lesions (MOLs).

METHODS

A multicenter retrospective study was conducted with lesions classified prospectively using subjective assessment and tumor markers with the ROMA. The SRR assessment and ADNEX risk estimation were applied retrospectively. The sensitivity, specificity, positive and negative likelihood ratios (LR+ and LR-) were calculated for all tests.

RESULTS

In total, 108 patients (the median age: 48 yrs, 44 postmenopausal) with 62 (79.6%) benign masses, 26 (24.1%) BOTs and 20 (18.5%) stage I MOLs were included. When comparing benign masses with combined BOTs and stage I MOLs, SA correctly identified 76% of benign masses, 69% of BOTs and 80% of stage I MOLs. Significant differences were found for the presence and size of the largest solid component (p = 0.0006), the number of papillary projections (p = 0.01), papillation contour (p = 0.008) and IOTA color score (p = 0.0009). The SRR and ADNEX models were characterized by the highest sensitivity (80% and 70%, respectively), whereas the highest specificity was found for SA (94%). The corresponding likelihood ratios were as follows: LR+ = 3.59 and LR- = 0.43 for the ADNEX; LR+ = 6.40 and LR- = 0.63 for SA and LR+ = 1.85 with LR- = 0.35 for the SRR. The sensitivity and specificity of the ROMA test were 50% and 85%, respectively, with LR+ = 3.44 and LR- = 0.58. Of all the tests, the ADNEX model had the highest diagnostic accuracy of 76%.

CONCLUSIONS

This study demonstrates the limited value of diagnostics based on CA125 and HE4 serum tumor markers and the ROMA algorithm as independent modalities for the detection of BOTs and early stage adnexal malignant tumors in women. SA and IOTA methods based on ultrasound examination may present superior value over tumor marker assessment.

Molecular mechanism of Wilms' tumor (Wt1) (+/-KTS) variants promoting proliferation and migration of ovarian epithelial cells by bioinformatics analysis

Epithelial ovarian cancer (EOC) is a gynecological disease with the highest mortality. With the lack of understanding of its pathogenesis, no accurate early diagnosis and screening method has been established for EOC. Studies revealed the multi-faceted function of Wilms' tumor (Wt1) genes in cancer, which may be related to the existence of multiple alternative splices. Our results show that Wt1 (+KTS) or Wt1 (-KTS) overexpression can significantly promote the proliferation and migration of human ovarian epithelial cells HOSEpiC, and Wt1 (+KTS) effects were more evident. To explore the Wt1 (+/-KTS) variant mechanism in HOSEpiC proliferation and migration and ovarian cancer (OC) occurrence and development, this study explored the differential regulation of Wt1 (+/-KTS) in HOSEpiC proliferation and migration by transcriptome sequencing. OC-related hub genes were screened by bioinformatics analysis to further explore the differential molecular mechanism of Wt1 (+/-KTS) in the occurrence of OC. Finally, we found that the regulation of Wt1 (+/-KTS) variants on the proliferation and migration of HOSEpiC may act through different genes and signaling pathways and screened out key genes and differentially regulated genes that regulate the malignant transformation of ovarian epithelial cells. The implementation of this study will provide new clues for the early diagnosis and precise treatment of OC.

Analysis of computer-aided diagnostics in the preoperative diagnosis of ovarian cancer: a systematic review

OBJECTIVES

Different noninvasive imaging methods to predict the chance of malignancy of ovarian tumors are available. However, their predictive value is limited due to subjectivity of the reviewer. Therefore, more objective prediction models are needed. Computer-aided diagnostics (CAD) could be such a model, since it lacks bias that comes with currently used models. In this study, we evaluated the available data on CAD in predicting the chance of malignancy of ovarian tumors.

METHODS

We searched for all published studies investigating diagnostic accuracy of CAD based on ultrasound, CT and MRI in pre-surgical patients with an ovarian tumor compared to reference standards.

RESULTS

In thirty-one included studies, extracted features from three different imaging techniques were used in different mathematical models. All studies assessed CAD based on machine learning on ultrasound, CT scan and MRI scan images. Per imaging method, subsequently ultrasound, CT and MRI, sensitivities ranged from 40.3 to 100%; 84.6-100% and 66.7-100% and specificities ranged from 76.3-100%; 69-100% and 77.8-100%. Results could not be pooled, due to broad heterogeneity. Although the majority of studies report high performances, they are at considerable risk of overfitting due to the absence of an independent test set.

CONCLUSION

Based on this literature review, different CAD for ultrasound, CT scans and MRI scans seem promising to aid physicians in assessing ovarian tumors through their objective and potentially cost-effective character. However, performance should be evaluated per imaging technique. Prospective and larger datasets with external validation are desired to make their results generalizable.

Evaluation of IOTA-ADNEX Model and Simple Rules for Identifying Adnexal Masses by Operators with Varying Levels of Expertise: A Single-Center Diagnostic Accuracy Study

Objectives The discrimination of ovarian lesions presents a significant problem in everyday clinical practice with ultrasonography appearing to be the most effective diagnostic technique. The aim of our study was to externally evaluate the performance of different diagnostic models when applied by examiners with various levels of experience. Methods This was a diagnostic accuracy study including women who were admitted for adnexal masses, between July 2018 and April 2021, to a Greek tertiary oncology center. Preoperatively sonographic data were evaluated by an expert gynecologist, a 6 th and a 1 st year gynecology resident, who applied the International Ovarian Tumor Analysis (IOTA) Simple Rules (SR) and Assessment of Different NEoplasias in the adneXa (ADNEX) model to discriminate between benign and malignant ovarian tumors. The explant pathology report was used as the reference diagnosis. Kappa statistics were used for the investigation of the level of agreement between the examined systems and the raters. Results We included 66 women, 39 with benign and 27 with malignant ovarian tumors. ADNEX (with and without "CA-125") had high sensitivity (96-100%) when applied by all raters but a rather low specificity (36%) when applied by the 1st year resident. SR could not be applied in 6% to 17% of the cases. It had slightly lower sensitivity, higher specificity, and higher overall accuracy, especially when applied by the 1st year resident (61% vs. 92%), compared to ADNEX. Conclusion Both ADNEX and SR can be utilized for screening in non-oncology centers since they offer high sensitivity even when used by less experienced examiners. In the hands of inexperienced examiners, SR appears to be the best model for assessing ovarian lesions.

Identification and prioritization of tumour-associated antigens for immunotherapeutic and diagnostic capacity in epithelial ovarian cancer: a systematic literature review

Epithelial ovarian cancer (EOC) is a prevalent carcinoma in the female population associated with poor prognostic outcomes, in part due to the late stage of the disease at diagnosis. Aiming to identify tumour-associated antigens (TAAs) with the potential to facilitate earlier detection and targeted therapy of EOC, five scientific literature repositories were systemically searched for primary literature sources reporting the expression of a TAA in the tissue or serum of adult females diagnosed with EOC and healthy women. We identified 7120 articles of which 32 met our inclusion criteria and passed the bias-quality assessment. Subsequently, data were collated on 29 TAAs whose expression had been analysed in 2181 patients and 589 healthy individuals. Reports of CA125 and EpCAM expression were numerous while tissue expression data were available for 28 TAAs. Data were segregated into three meta-cohorts for statistical scrutiny and their capacity for diagnostic and treatment targeting was assessed. We showed that CA-125 was expressed homogenously in EOC patients while EpCAM was expressed heterogeneously. CA-125 was the most promising TAA target for both diagnosis and treatment, gaining a priority score of 12 (/12) while EpCAM gained a priority score of seven. Tissue expression of EOC TAAs was homogenous; 90% of the EOC population express any identified TAA while just 20% of healthy individuals will be positive for the same TAA. We suggest TAA profiling should be a fundamental aspect of EOC diagnosis, sitting alongside the FIGO framework, promoting reduced mortality and directing the development of TAA-targeted therapeutics.

A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients

In a growing number of social and clinical scenarios, machine learning (ML) is emerging as a promising tool for implementing complex multi-parametric decision-making algorithms. Regarding ovarian cancer (OC), despite the standardization of features that can support the discrimination of ovarian masses into benign and malignant, there is a lack of accurate predictive modeling based on ultrasound (US) examination for progression-free survival (PFS). This retrospective observational study analyzed patients with epithelial ovarian cancer (EOC) who were followed in a tertiary center from 2018 to 2019. Demographic features, clinical characteristics, information about the surgery and post-surgery histopathology were collected. Additionally, we recorded data about US examinations according to the International Ovarian Tumor Analysis (IOTA) classification. Our study aimed to realize a tool to predict 12 month PFS in patients with OC based on a ML algorithm applied to gynecological ultrasound assessment. Proper feature selection was used to determine an attribute core set. Three different machine learning algorithms, namely Logistic Regression (LR), Random Forest (RFF), and K-nearest neighbors (KNN), were then trained and validated with five-fold cross-validation to predict 12 month PFS. Our analysis included n. 64 patients and 12 month PFS was achieved by 46/64 patients (71.9%). The attribute core set used to train machine learning algorithms included age, menopause, CA-125 value, histotype, FIGO stage and US characteristics, such as major lesion diameter, side, echogenicity, color score, major solid component diameter, presence of carcinosis. RFF showed the best performance (accuracy 93.7%, precision 90%, recall 90%, area under receiver operating characteristic curve (AUROC) 0.92). We developed an accurate ML model to predict 12 month PFS.

Computed Tomographic Radiomics in Differentiating Histologic Subtypes of Epithelial Ovarian Carcinoma

IMPORTANCE

Epithelial ovarian carcinoma is heterogeneous and classified according to the World Health Organization Tumour Classification, which is based on histologic features and molecular alterations. Preoperative prediction of the histologic subtypes could aid in clinical management and disease prognostication.

OBJECTIVE

To assess the value of radiomics based on contrast-enhanced computed tomography (CT) in differentiating histologic subtypes of epithelial ovarian carcinoma in multicenter data sets.

DESIGN, SETTING, AND PARTICIPANTS

In this diagnostic study, 665 patients with histologically confirmed epithelial ovarian carcinoma were retrospectively recruited from 4 centers (Hong Kong, Guangdong Province of China, and Seoul, South Korea) between January 1, 2012, and February 28, 2022. The patients were randomly divided into a training cohort (n = 532) and a testing cohort (n = 133) with a ratio of 8:2. This process was repeated 100 times. Tumor segmentation was manually delineated on each section of contrast-enhanced CT images to encompass the entire tumor. The Mann-Whitney U test and voted least absolute shrinkage and selection operator were performed for feature reduction and selection. Selected features were used to build the logistic regression model for differentiating high-grade serous carcinoma and non-high-grade serous carcinoma.

EXPOSURES

Contrast-enhanced CT-based radiomics.

MAIN OUTCOMES AND MEASURES

Intraobserver and interobserver reproducibility of tumor segmentation were measured by Dice similarity coefficients. The diagnostic efficiency of the model was assessed by receiver operating characteristic curve and area under the curve.

RESULTS

In this study, 665 female patients (mean [SD] age, 53.6 [10.9] years) with epithelial ovarian carcinoma were enrolled and analyzed. The Dice similarity coefficients of intraobserver and interobserver were all greater than 0.80. Twenty radiomic features were selected for modeling. The areas under the curve of the logistic regression model in differentiating high-grade serous carcinoma and non-high-grade serous carcinoma were 0.837 (95% CI, 0.835-0.838) for the training cohort and 0.836 (95% CI, 0.833-0.840) for the testing cohort.

CONCLUSIONS AND RELEVANCE

In this diagnostic study, radiomic features extracted from contrast-enhanced CT were useful in the classification of histologic subtypes in epithelial ovarian carcinoma. Intraobserver and interobserver reproducibility of tumor segmentation was excellent. The proposed logistic regression model offered excellent discriminative ability among histologic subtypes.

Nomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma

Objective

To compare the performance of clinical factors, FS-T2WI, DWI, T1WI+C based radiomics and a combined clinic-radiomics model in predicting the type of serous ovarian carcinomas (SOCs).

Methods

In this retrospective analysis, 138 SOC patients were confirmed by histology. Significant clinical factors (P < 0.05, and with the area under the curve (AUC) > 0.7) was retained to establish a clinical model. The radiomics model included FS-T2WI, DWI, and T1WI+C, and also, a multisequence model was established. A total of 1,316 radiomics features of each sequence were extracted; the univariate and multivariate logistic regressions, cross-validations were performed to reduce valueless features and then radiomics signatures were developed. Nomogram models using clinical factors, combined with radiomics features, were developed in the training cohort. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the clinical model in identifying low- and high-grade SOC.

Results

The AUC of the clinical model and multisequence radiomics model in the training and validation cohorts was 0.90 and 0.89, 0.91 and 0.86, respectively. By incorporating clinical factors and multi-radiomics signature, the AUC of the radiomic-clinical nomogram in the training and validation cohorts was 0.98 and 0.95. The model comparison results show that the AUC of the combined model is higher than that of the uncombined models (P= 0.05, 0.002).

Conclusion

The nomogram models of clinical factors combined with MRI multisequence radiomics signatures can help identifying low- and high-grade SOCs and a provide a more comprehensive, effective method to evaluate preoperative risk stratification for SOCs.

Efficacy of IOTA simple rules, O-RADS, and CA125 to distinguish benign and malignant adnexal masses

Objective

Ovarian cancer is the most deadly deadliest gynecological tumor in the female reproductive system. Therefore, the present study sought to determine the diagnostic performance of International Ovarian Tumor Analysis Simple Rules (IOTA SR), the Ovarian-Adnexal Reporting and Data System (O-RADS), and Cancer Antigen 125 (CA125) in discriminating benign and malignant ovarian tumors. The study also assessed whether a combination of the two ultrasound categories systems and CA125 can improve the diagnostic performance.

Methods

A total of 453 patients diagnosed with ovarian tumors were retrospectively enrolled from Fujian Cancer Hospital between January 2017 and September 2020. The data collected from patients included age, maximum lesion diameter, location, histopathology, levels of CA125, and detailed ultrasound reports. Additionally, all ultrasound images were independently assessed by two ultrasound physicians with more than 5 years of experience in the field, according to the IOTA simple rules and O-RADS guidelines. Furthermore, the area under the curve (AUC), sensitivity, and specificity of the above mentioned predictors were calculated using the receiver operating characteristic curve.

Results

Out of the 453 patients, 184 had benign lesions, while 269 had malignant ovarian tumors. In addition, the AUCs of IOTA SR, O-RADS, and CA125 in the overall population were 0.831, 0.804, and 0.812, respectively, and the sensitivities of IOTA SR, O-RADS, and CA125 were 94.42, 94.42, and 80.30%, respectively. On the other hand, the AUCs of IOTA SR combined with CA125, O-RADS combined with CA125, and IOTA SR plus O-RADS combined with CA125 were 0.900, 0.891, and 0.909, respectively. The findings also showed that the AUCs of a combination of the three approaches were significantly higher than those of individual strategies (p<0.05) but not significantly higher than the AUC of a combination of two methods (p>0.05).

Conclusion

The findings showed that a combination of IOTA SR or O-RADS in combination with CA125 may improve the ability to distinguish benign from malignant ovarian tumors.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13048-022-00947-9.

Plasma circN4BP2L2 is a promising novel diagnostic biomarker for epithelial ovarian cancer

BACKGROUND

Circular RNAs (circRNAs) are more stable than linear RNA molecules, which makes them promising diagnostic biomarkers for diseases. By circRNA-sequencing analysis, we previously found that circN4BP2L2 was significantly decreased in epithelial ovarian cancer (EOC) tissues, and was predictive of disease progression. The aim of this study was to evaluate the diagnostic value of plasma circN4BP2L2 in EOC.

METHODS

Three hundred seventy-eight plasma samples were acquired prior to surgery. Samples were obtained from 126 EOC patients, 126 benign ovarian cyst patients, and 126 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). EOC cells were transfected with small interference RNAs (siRNAs) and cell proliferation, migration, invasion, cell cycle and cell apoptosis were performed to assess the effect of circN4BP2L2 in EOC. Receiver operating curve (ROC), the area under the curve (AUC), sensitivity and specificity were estimated.

RESULTS

Plasma circN4BP2L2 was significantly downregulated in EOC patients. Decreased circN4BP2L2 was significantly associated with advanced tumor stage, worse histological grade, lymph node metastasis and distant metastasis in EOC. CircN4BP2L2 inhibited tumor cell migration and invasion in vitro. CircN4BP2L2 could significantly separate EOC from benign (AUC = 0.82, P <  0.01) or normal (AUC = 0.90, P <  0.01) cohort. Early stage EOC vs benign (AUC = 0.81, P <  0.01) or normal (AUC = 0.90, P <  0.01) cohort could also be distinguished by circN4BP2L2. In discrimination between EOC cohort and benign or normal cohort, circN4BP2L2 performed equally well in both pre- and post-menopausal women. The combination of circN4BP2L2, CA125 and HE4 showed high sensitivity and specificity in detecting EOC cases.

CONCLUSIONS

Plasma circN4BP2L2 is significantly downregulated in EOC and might serve as a promising novel diagnostic biomarker for EOC patients, especially in early stage EOC cases. CircN4BP2L2 might act as an adjunct to CA125 and HE4 in detecting EOC. Further large-scale studies are warranted to verify our results.

Screening of CXC chemokines in the microenvironment of ovarian cancer and the biological function of CXCL10

BACKGROUND

This study aims to screen and identify the biological functions and prognostic value of CXC chemokines in ovarian cancer (OC) through bioinformatics and molecular biology methods, and to provide data support for the selection of biomarkers and prognostic analysis of OC.

METHODS

In this study, GEO, ONCOMINE, GEPIA, cBioPortal, GeneMANIA, Metascape, STRING, TRRUST, and TIMER databases were used to study CXC chemokines. Angiogenesis and T cell killing assay were used to detect the effect of CXCL10 on tumor cell immunity and angiogenesis. Real-time quantitative PCR (qRT-PCR), immunoblotting, and ectopic tumor formation experiments were used to verify the effect of CXCL10 on ovarian cancer tumors.

RESULTS

We found that CXCL1, CXCL10, CXCL11, CXCL13, and CXCL14 were significantly upregulated in OC samples compared with normal tissues. Our data showed that there was a relationship between the expression of CXC chemokines and the infiltration of six types of immune cells significant correlation. In vitro assay confirmed that overexpression of CXCL10 could enhance the killing effect of T cells and inhibit angiogenesis. Further in vivo assay had shown that CXCL10 could affect the progression of ovarian cancer by increasing the expression of cytotoxic T cells and inhibiting angiogenesis.

CONCLUSION

In conclusion, we hope that our data will provide new insights into the development of immunotherapy and the selection of prognostic markers for patients with OC.

Investigation on factors associated with ovarian cancer: an umbrella review of systematic review and meta-analyses

Following cervical and uterine cancer, ovarian cancer (OC) has the third rank in gynecologic cancers. It often remains non-diagnosed until it spreads throughout the pelvis and abdomen. Identification of the most effective risk factors can help take prevention measures concerning OC. Therefore, the presented review aims to summarize the available studies on OC risk factors. A comprehensive systematic literature search was performed to identify all published systematic reviews and meta-analysis on associated factors with ovarian cancer. Web of Science, Cochrane Library databases, and Google Scholar were searched up to 17th January 2020. This study was performed according to Smith et al. methodology for conducting a systematic review of systematic reviews. Twenty-eight thousand sixty-two papers were initially retrieved from the electronic databases, among which 20,104 studies were screened. Two hundred seventy-seven articles met our inclusion criteria, 226 of which included in the meta-analysis. Most commonly reported genetic factors were MTHFR C677T (OR=1.077; 95 % CI (1.032, 1.124); P-value<0.001), BSML rs1544410 (OR=1.078; 95 %CI (1.024, 1.153); P-value=0.004), and Fokl rs2228570 (OR=1.123; 95 % CI (1.089, 1.157); P-value<0.001), which were significantly associated with increasing risk of ovarian cancer. Among the other factors, coffee intake (OR=1.106; 95 % CI (1.009, 1.211); P-value=0.030), hormone therapy (RR=1.057; 95 % CI (1.030, 1.400); P-value<0.001), hysterectomy (OR=0.863; 95 % CI (0.745, 0.999); P-value=0.049), and breast feeding (OR=0.719, 95 % CI (0.679, 0.762) and P-value<0.001) were mostly reported in studies. Among nutritional factors, coffee, egg, and fat intake significantly increase the risk of ovarian cancer. Estrogen, estrogen-progesterone, and overall hormone therapies also are related to the higher incidence of ovarian cancer. Some diseases, such as diabetes, endometriosis, and polycystic ovarian syndrome, as well as several genetic polymorphisms, cause a significant increase in ovarian cancer occurrence. Moreover, other factors, for instance, obesity, overweight, smoking, and perineal talc use, significantly increase the risk of ovarian cancer.

Serum Anti-PDLIM1 Autoantibody as Diagnostic Marker in Ovarian Cancer

Background

Serum autoantibodies (AAbs) against tumor-associated antigens (TAAs) could be useful biomarkers for cancer detection. This study aims to evaluate the diagnostic value of autoantibody against PDLIM1 for improving the detection of ovarian cancer (OC).

Methods

Immunohistochemistry (IHC) test in tissue array containing 280 OC tissues, 20 adjacent tissues, and 8 normal ovarian tissues was performed to analyze the expression of PDLIM1 in tissues. Enzyme-linked immunosorbent assay (ELISA) was employed to measure the autoantibody to PDLIM1 in 545 sera samples from 182 patients with OC, 181 patients with ovarian benign diseases, and 182 healthy controls.

Results

The results of IHC indicated that 84.3% (236/280) OC tissues were positively stained with PDLIM1, while no positive staining was found in adjacent or normal ovarian tissues. The frequency of anti-PDLIM1 autoantibody was significantly higher in OC patients than that in healthy and ovarian benign controls in both training (n=122) and validation (n=423) sets. The area under the curves (AUCs) of anti-PDLIM1 autoantibody for discriminating OC from healthy controls were 0.765 in training set and 0.740 in validation set, and the AUC of anti-PDLIM1 autoantibody for discriminating OC from ovarian benign controls was 0.757 in validation set. Overall, it was able to distinguish 35.7% of OC, 40.6% of patients with early-stage, and 39.5% of patients with late-stage. When combined with CA125, the AUC increased to 0.846, and 79.2% of OC were detected, which is statistically higher than CA125 (61.7%) or anti-PDLIM1(35.7%) alone (p<0.001). Also, anti-PDLIM1 autoantibody could identify 15% (18/120) of patients that were negative with CA125 (CA125 <35 U/ml).

Conclusions

The anti-PDLIM1 autoantibody response in OC patients was positively correlated with PDLIM1 high expression in OC tissues, suggesting that the autoantibody against PDLIM1 might have the potential to be a novel serological biomarker of OC, serving as a complementary measure of CA125, which could improve the power of OC detection.

Multi-analytical test based on serum miRNAs and proteins quantification for ovarian cancer early detection

Advanced ovarian cancer is one of the most lethal gynecological tumor, mainly due to late diagnoses and acquired drug resistance. MicroRNAs (miRNAs) are small-non coding RNA acting as tumor suppressor/oncogenes differentially expressed in normal and epithelial ovarian cancer and has been recognized as a new class of tumor early detection biomarkers as they are released in blood fluids since tumor initiation process. Here, we evaluated by droplet digital PCR (ddPCR) circulating miRNAs in serum samples from healthy (N = 105) and untreated ovarian cancer patients (stages I to IV) (N = 72), grouped into a discovery/training and clinical validation set with the goal to identify the best classifier allowing the discrimination between earlier ovarian tumors from health controls women. The selection of 45 candidate miRNAs to be evaluated in the discovery set was based on miRNAs represented in ovarian cancer explorative commercial panels. We found six miRNAs showing increased levels in the blood of early or late-stage ovarian cancer groups compared to healthy controls. The serum levels of miR-320b and miR-141-3p were considered independent markers of malignancy in a multivariate logistic regression analysis. These markers were used to train diagnostic classifiers comprising miRNAs (miR-320b and miR-141-3p) and miRNAs combined with well-established ovarian cancer protein markers (miR-320b, miR-141-3p, CA-125 and HE4). The miRNA-based classifier was able to accurately discriminate early-stage ovarian cancer patients from health-controls in an independent sample set (Sensitivity = 80.0%, Specificity = 70.3%, AUC = 0.789). In addition, the integration of the serum proteins in the model markedly improved the performance (Sensitivity = 88.9%, Specificity = 100%, AUC = 1.000). A cross-study validation was carried out using four data series obtained from Gene Expression Omnibus (GEO), corroborating the performance of the miRNA-based classifier (AUCs ranging from 0.637 to 0.979). The clinical utility of the miRNA model should be validated in a prospective cohort in order to investigate their feasibility as an ovarian cancer early detection tool.

Low-grade serous epithelial ovarian cancer: a comprehensive review and update for radiologists

Low-grade serous carcinoma (LGSC) is an infrequent subtype of ovarian cancer, corresponding to 5% of epithelial neoplasms. This subtype of ovarian carcinoma characteristically has molecular features, pathogenesis, clinical behaviour, sensitivity to chemotherapy, and prognosis distinct to high-grade serous carcinoma (HGSC). Knowing the difference between LGSC and other ovarian serous tumours is vital to guide clinical management, which currently is only possible histologically. However, imaging can provide several clues that allow differentiating LGSC from other tumours and enable precise staging and follow-up of ovarian cancer treatment. Characteristically, LGSC appears as mixed lesions with variable papillary projections and solid components, usually in different proportions from those detected in serous borderline tumour and HGSC. Calcified extracellular bodies, known as psammoma bodies, are also a common feature of LGSC, frequently detectable within lymphadenopathies and metastases associated with this type of tumour. In addition, the characterisation of magnetic resonance imaging enhancement also plays an essential role in calculating the probability of malignancy of these lesions. As such, in this review, we discuss and update the distinct radiological modalities features and the clinicopathologic characteristics of LGSC to allow radiologists to be familiarised with them and to narrow the differential diagnosis when facing this type of tumour.

Radiomics based on multisequence magnetic resonance imaging for the preoperative prediction of peritoneal metastasis in ovarian cancer

OBJECTIVES

To develop a radiomics signature based on multisequence magnetic resonance imaging (MRI) to preoperatively predict peritoneal metastasis (PM) in ovarian cancer (OC).

METHODS

Eighty-nine patients with OC were divided into a training cohort including patients (n = 54) with a single lesion and a validation cohort including patients (n = 35) with bilateral lesions. Radiomics features were extracted from the T2-weighted images (T2WIs), fat-suppressed T2WIs, multi-b-value diffusion-weighted images (DWIs), and corresponding parametric maps. A radiomics signature and nomogram incorporating the radiomics signature and clinical predictors were developed and validated on the training and validation cohorts, respectively.

RESULTS

The radiomics signature generated by 6 selected features showed a favorable discriminatory ability to predict PM in OC with an area under the curve (AUC) of 0.963 in the training cohort and an AUC of 0.928 in the validation cohort. The nomogram, comprising the radiomics signature, pelvic fluid, and CA-125 level, showed more favorable discrimination with an AUC of 0.969 in the training cohort and 0.944 in the validation cohort. Net reclassification index with values of 0.548 in the training cohort and 0.500 in the validation cohort.

CONCLUSION

Radiomics signature based on multisequence MRI serves as an effective quantitative approach to predict PM in OC patients. A nomogram of radiomics signature and clinical predictors could further improve the prediction ability of PM in patients with OC.

KEY POINTS

• Multisequence MRI-based radiomics showed a favorable discriminatory ability to predict PM in OC. • The nomogram incorporating the radiomics signature and clinical predictors was clinically useful to preoperatively predict PM in patients with OC.

Dihydroartemisinin regulates apoptosis, migration, and invasion of ovarian cancer cells via mediating RECK

BACKGROUND

Dihydroartemisinin (DHA) possesses an inhibitory effect on ovarian cancer and promotes reversion-inducing cysteine-rich protein with Kazal motifs (RECK) expression in glioma cells. This study explored the role of DHA and RECK on ovarian cancer.

METHODS

The RECK level in ovarian cancer was analyzed under GEPIA 2 database and proved by RT-qPCR. After being treated with DHA or infected with siRECK lentivirus, the viability, apoptosis, migration, and invasion of ovarian cancer cells were evaluated by CCK-8, flow cytometry, wound healing, and transwell assays. Also, the expressions of factors related to apoptosis and epithelial-mesenchymal transition were measured by Western blot or RT-qPCR.

RESULTS

DHA-treatment weakened the viability, migration, invasion, and enhanced apoptosis of ovarian cancer cells. DHA also down-regulated the levels of Bcl-2, N-cadherin, and Vimentin, and up-regulated the levels of Bax, C-caspase-3 and E-cadherin in ovarian cancer cells. RECK was lowly expressed in both ovarian cancer tissues and cells. siRECK not only had an effect opposite to DHA on the viability, apoptosis, migration, invasion, and related-factors of ovarian cancer cells but also offset the effect of DHA on ovarian cancer cells.

CONCLUSION

DHA regulated apoptosis, migration, and invasion of ovarian cancer cells via mediating RECK.