The role of transvaginal ultrasound in screening for ovarian cancer

Ovarian masses suggested for MRI examination: assessment of deep learning models based on non-contrast-enhanced MRI sequences for predicting malignancy

PURPOSE

We aims to assessed and compare four deep learning(DL) models using non-contrast-enhanced magnetic resonance imaging(MRI) to differentiate benign from malignant ovarian tumors, considering diagnostic efficacy and associated development costs.

METHODS

526 patients (327 benign lesions vs 199 malignant lesions) who were recommended for MRI due to suspected ovarian masses, confirmed with histopathology, were included in this retrospective study. A training cohort (n=367) and a validation cohort (n=159) were constructed. Based on the images of T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), we evaluated the diagnostic performance of four DL models (ConvNeXt, FBNet, GhostNet, ResNet50) in distinguishing between benign and malignant ovarian tumors. Two radiologists with varying levels of experience independently reviewed all original non-contrast-enhanced MR images from the validation cohort to determine if each case was benign or malignant. The area under the receiver operating characteristic curve (AUC), confusion matrices, accuracy, sensitivity, specificity, positive predictive value(PPV) and negative predictive value(NPV) were used to compare performance.

RESULTS

The study of 526 ovarian mass patients (ages 1-92) evaluated four DL models for predicting malignant tumors, with AUCs ranging from 0.8091 to 0.8572 and accuracy between 81.1% and 85.5%. An experienced radiologist achieved 86.2% accuracy, slightly surpassing the DL models, while a less experienced radiologist had 69.2% accuracy. Resnet50 had the highest sensitivity (78.3%) and NPV (87.3%), while ConvNeXt excelled in specificity and PPV (100%). GhostNet and FBNet are more parameter-efficient than other models.

CONCLUSION

The four DL models effectively distinguished between benign and malignant ovarian tumors using non-contrast MRI. These models outperformed less experienced radiologists and were slightly less accurate than experienced ones. ResNet50 had the best predictive performance, while GhostNet was highly accurate with fewer parameters. Our study indicates that DL models based on non-contrast-enhanced MRI have the potential to assist in diagnosis.

Integrating Contrast-enhanced US to O-RADS US for Classification of Adnexal Lesions with Solid Components: Time-intensity Curve Analysis versus Visual Assessment

Purpose To compare the diagnostic performance of time-intensity curve (TIC) analysis and subjective visual assessment of contrast-enhanced US (CEUS) when integrated with the Ovarian-Adnexal Reporting and Data System (O-RADS) US risk stratification system for characterizing adnexal lesions with solid components. Materials and Methods In this prospective multicenter study conducted from September 2021 to December 2022, female individuals with suspected adnexal lesions containing solid components detected at routine US were enrolled. All participants underwent preoperative CEUS examinations. Histopathologic findings were used as the reference standard for diagnosis. Lesions were classified according to the O-RADS US system. Enhancement of solid tissue compared with the outer myometrium was evaluated using both TIC analysis and subjective visual assessment. The diagnostic performance of O-RADS alone and each CEUS assessment method when integrated with the O-RADS US system was assessed and compared using receiver operating characteristic curve analysis. Results A total of 180 lesions (80 malignant and 100 benign histopathologic outcomes) in 175 participants (median age, 47 years [IQR, 33-56]) were analyzed. Incorporating CEUS (assessed through both TIC analysis and subjective visual assessment) with O-RADS US showed significantly improved diagnostic performance over O-RADS US alone, with an area under the receiver operating characteristic curve (AUC) of 0.86 (95% CI: 0.80, 0.91) compared with 0.78 (95% CI: 0.71, 0.84). No evidence of a difference was observed between the AUCs of TIC analysis and subjective visual assessment in the enhancement evaluation of solid tissue with CEUS for adnexal malignancy categorization (P = .83). Conclusion Subjective visual assessment and TIC analysis of CEUS features when integrated with the O-RADS US scoring system showed comparable diagnostic performance in assigning adnexal malignancy risk. Keywords: Adnexal Lesions, Contrast-enhanced US, O-RADS, Time-intensity Curve Analysis Supplemental material is available for this article. © RSNA, 2024.

Sensitive and Cost-Effective Tools in the Detection of Ovarian Cancer Biomarkers

Women diagnosed with late-stage ovarian cancer suffer a very high rate of mortality. Accordingly, it is imperative to detect and diagnose the disease as early as possible in its development. Achievement of this aim implies relatively large-scale screening of women at an age of clinical significance through assay of biomarkers for disease present in blood or serum. Biosensor detection offers an attractive technology for the automated detection of such species. Among several biomarkers that have been identified that are present in patients with ovarian cancer, the only one that is commonly tested for in clinical use is cancer antigen 125, which is considered to be a poor biomarker for the disease. Here, we describe several biosensors that developed in the past decade for the detection of ovarian cancer biomarkers such as CA125, human epididymis protein 4 (HE4) and apolipoprotein A1. The challenges presented by the fabrication of biosensor devices for detecting ovarian cancer and the limited number of biosensors developed for this purpose are discussed.

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.

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.

Adherence to risk-reducing salpingo-oophorectomy guidelines among gynecologic oncologists compared to general gynecologists

BACKGROUND

Risk-reducing bilateral salpingo-oophorectomy reduces mortality from high-grade serous carcinoma in patients with hereditary breast and ovarian cancer associated gene mutations. Ideal surgical management includes 5 steps outlined in 2005 by the Society of Gynecologic Oncology and the American College of Obstetricians and Gynecologists. In addition, it is recommended that pathologic examination include serial sectioning of specimens. In practice, risk-reducing salpingo-oophorectomy is performed by both gynecologic oncologists and general gynecologists. To ensure optimal detection of occult malignancy, standardized adherence to outlined guidelines is necessary.

OBJECTIVE

This study aimed to evaluate the adherence to optimal surgical and pathologic examination guidelines and to compare the rate of occult malignancy at the time of surgery between 2 provider types.

STUDY DESIGN

Institutional review board exemption was obtained. A retrospective review of patients undergoing risk-reducing bilateral salpingo-oophorectomy without hysterectomy from October 1, 2015, to December 31, 2020, at 3 sites within a healthcare system was conducted. The inclusion criteria included age ≥18 years and a documented indication for surgery being a mutation in BRCA1 or BRCA2 or a strong family history of breast and/or ovarian cancer. Compliance with 5 surgical steps and pathologic specimen preparation was based on medical record documentation. Multivariable logistic regression was used to determine differences in adherence between provider groups and surgical and pathologic examination guidelines. A P value of <.025 was considered statistically significant for the 2 primary outcomes after Bonferroni correction was applied to adjust for multiple comparisons.

RESULTS

A total of 185 patients were included. Among the 96 cases performed by gynecologic oncologists, 69 (72%) performed all 5 steps of surgery, 22 (23%) performed 4 steps, 5 (5%) performed 3 steps, and none performed 1 or 2 steps. Among the 89 cases performed by general gynecologists, 4 (5%) performed all 5 steps, 33 (37%) performed 4 steps, 38 (43%) performed 3 steps, 13 (15%) performed 2 steps, and 1 (1%) performed 1 step. Gynecologic oncologists were more likely to document adherence to all 5 recommended surgical steps in their surgical dictation (odds ratio, 54.3; 95% confidence interval, 18.1-162.7; P<.0001). Among the 96 cases documented by gynecologic oncologists, 41 (43%) had serial sectioning of all specimens performed, compared with 23 of 89 cases (26%) performed by general gynecologists. No difference in adherence to pathologic guidelines was identified between the 2 provider groups (P=.0489; note: P value of >.025). Overall, 5 patients (2.70%) had occult malignancy diagnosed at the time of risk-reducing surgery, with all surgeries performed by general gynecologists.

CONCLUSION

Our results demonstrated greater compliance with surgical guidelines for risk-reducing bilateral salpingo-oophorectomy in gynecologic oncologists than in general gynecologists. No considerable difference was determined between the 2 provider types in adherence to pathologic guidelines. Our findings demonstrated a need for institution-wide protocol education and implementation of standardized nomenclature to ensure provider adherence to evidence-based guidelines.

Deep convolutional neural networks for multiple histologic types of ovarian tumors classification in ultrasound images

Objective

This study aimed to evaluate and validate the performance of deep convolutional neural networks when discriminating different histologic types of ovarian tumor in ultrasound (US) images.

Material and methods

Our retrospective study took 1142 US images from 328 patients from January 2019 to June 2021. Two tasks were proposed based on US images. Task 1 was to classify benign and high-grade serous carcinoma in original ovarian tumor US images, in which benign ovarian tumor was divided into six classes: mature cystic teratoma, endometriotic cyst, serous cystadenoma, granulosa-theca cell tumor, mucinous cystadenoma and simple cyst. The US images in task 2 were segmented. Deep convolutional neural networks (DCNN) were applied to classify different types of ovarian tumors in detail. We used transfer learning on six pre-trained DCNNs: VGG16, GoogleNet, ResNet34, ResNext50, DensNet121 and DensNet201. Several metrics were adopted to assess the model performance: accuracy, sensitivity, specificity, FI-score and the area under the receiver operating characteristic curve (AUC).

Results

The DCNN performed better in labeled US images than in original US images. The best predictive performance came from the ResNext50 model. The model had an overall accuracy of 0.952 for in directly classifying the seven histologic types of ovarian tumors. It achieved a sensitivity of 90% and a specificity of 99.2% for high-grade serous carcinoma, and a sensitivity of over 90% and a specificity of over 95% in most benign pathological categories.

Conclusion

DCNN is a promising technique for classifying different histologic types of ovarian tumors in US images, and provide valuable computer-aided information.

AI diagnostic performance based on multiple imaging modalities for ovarian tumor: A systematic review and meta-analysis

Background

In recent years, AI has been applied to disease diagnosis in many medical and engineering researches. We aimed to explore the diagnostic performance of the models based on different imaging modalities for ovarian cancer.

Methods

PubMed, EMBASE, Web of Science, and Wanfang Database were searched. The search scope was all published Chinese and English literatures about AI diagnosis of benign and malignant ovarian tumors. The literature was screened and data extracted according to inclusion and exclusion criteria. Quadas-2 was used to evaluate the quality of the included literature, STATA 17.0. was used for statistical analysis, and forest plots and funnel plots were drawn to visualize the study results.

Results

A total of 11 studies were included, 3 of them were modeled based on ultrasound, 6 based on MRI, and 2 based on CT. The pooled AUROCs of studies based on ultrasound, MRI and CT were 0.94 (95% CI 0.88-1.00), 0.82 (95% CI 0.71-0.93) and 0.82 (95% Cl 0.78-0.86), respectively. The values of I² were 99.92%, 99.91% and 92.64% based on ultrasound, MRI and CT. Funnel plot suggested no publication bias.

Conclusion

The models based on ultrasound have the best performance in diagnostic of ovarian cancer.

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.

Diagnostic Value of Two-Dimensional Transvaginal Ultrasound Combined with Contrast-Enhanced Ultrasound in Ovarian Cancer

Objective

Explore the value of two-dimensional transvaginal ultrasound combined with contrast-enhanced ultrasound in the differential diagnosis of ovarian cancer, so as to provide the basis for clinical diagnosis and treatment of ovarian cancer.

Methods

A total of 100 suspected ovarian cancer patients who were admitted to our hospital from January 2019 to December 2021 were selected as the research subjects, including 62 ovarian cancer patients (ovarian cancer group) and 38 ovarian benign tumor patients (benign group). Two-dimensional vaginal ultrasound and contrast-enhanced ultrasound were performed in both groups. The differences in PI, RI, EDV, PSV, and VM parameters of the two groups as well as those of patients with ovarian cancer of different grades were compared. Record the contrast-enhanced ultrasound parameters such as AT, TTP and IMAX, and determine the diagnostic value.

Results

The PI and RI of the ovarian cancer group were lower than those of the benign ovarian tumor group, and the EDV, PSV and VM of the ovarian cancer group were higher than those of the benign ovarian tumor group (p < 0.05). The PI and RI of the patients in stage I–II of the ovarian cancer group were higher than those in stage III–IV, and the EDV, PSV and VM were lower than those in the patients in stage III–IV, with statistical significance (p < 0.05). The results of contrast-enhanced ultrasound showed that the AT and TTP values in the ovarian cancer group were significantly shorter than those in the benign group, and the peak intensity was significantly higher than that in the benign group, and the differences were statistically significant (p < 0.05). The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of two-dimensional ultrasound combined with contrast-enhanced ultrasound in the diagnosis of ovarian cancer were high, 95.16%(59/62), 86.84%(33/38), 92.19%(59/64), 91.67%(33/36) and 92.00%(92/100), respectively.

Conclusion

Contrast-enhanced ultrasound to some extent makes up for the deficiencies of conventional ultrasound, is helpful to detect early ovarian cancer, and can be used for the differential diagnosis of small ovarian tumors with difficult two-dimensional ultrasound diagnosis. Two-dimensional ultrasound combined with contrast-enhanced ultrasound can effectively improve the detection rate and differential diagnosis value of ovarian cancer, which is of great significance in the early diagnosis and differentiation of ovarian cancer.

Deep Learning Prediction of Ovarian Malignancy at US Compared with O-RADS and Expert Assessment

Background Deep learning (DL) algorithms could improve the classification of ovarian tumors assessed with multimodal US. Purpose To develop DL algorithms for the automated classification of benign versus malignant ovarian tumors assessed with US and to compare algorithm performance to Ovarian-Adnexal Reporting and Data System (O-RADS) and subjective expert assessment for malignancy. Materials and Methods This retrospective study included consecutive women with ovarian tumors undergoing gray scale and color Doppler US from January 2019 to November 2019. Histopathologic analysis was the reference standard. The data set was divided into training (70%), validation (10%), and test (20%) sets. Algorithms modified from residual network (ResNet) with two fusion strategies (feature fusion [hereafter, DL_feature] or decision fusion [hereafter, DL_decision]) were developed. DL prediction of malignancy was compared with O-RADS risk categorization and expert assessment by area under the receiver operating characteristic curve (AUC) analysis in the test set. Results A total of 422 women (mean age, 46.4 years ± 14.8 [SD]) with 304 benign and 118 malignant tumors were included; there were 337 women in the training and validation data set and 85 women in the test data set. DL_feature had an AUC of 0.93 (95% CI: 0.85, 0.97) for classifying malignant from benign ovarian tumors, comparable with O-RADS (AUC, 0.92; 95% CI: 0.85, 0.97; P = .88) and expert assessment (AUC, 0.97; 95% CI: 0.91, 0.99; P = .07), and similar to DL_decision (AUC, 0.90; 95% CI: 0.82, 0.96; P = .29). DL_decision, DL_feature, O-RADS, and expert assessment achieved sensitivities of 92%, 92%, 92%, and 96%, respectively, and specificities of 80%, 85%, 89%, and 87%, respectively, for malignancy. Conclusion Deep learning algorithms developed by using multimodal US images may distinguish malignant from benign ovarian tumors with diagnostic performance comparable to expert subjective and Ovarian-Adnexal Reporting and Data System assessment. © RSNA, 2022 Online supplemental material is available for this article.

Ultrasound‑targeted microbubble destruction‑mediated overexpression of Sirtuin 3 inhibits the progression of ovarian cancer

Ultrasound-targeted microbubble destruction (UTMD) has recently been developed as a promising noninvasive tool for organ- and tissue-specific gene or drug delivery. The aim of the present study was to explore the role of UTMD-mediated Sirtuin 3 (SIRT3) overexpression in the malignant behaviors of human ovarian cancer (HOC) cells. Reverse transcription-quantitative PCR was performed to detect SIRT3 mRNA expression levels in normal human ovarian epithelial cells and HOC cell lines; low SIRT3 expression was found in HOC cell lines, and the SKOV3 cell line was used in the following experiments. The SIRT3-microbubble (MB) was prepared, and the effects of ultrasound-treated SIRT3-MB on biological processes of SKOV3 cells were determined. The proliferation, migration, invasion and apoptosis of SKOV3 cells were measured after SIRT3 upregulation by UTMD. Xenograft tumors in nude mice were induced to observe tumor growth in vivo. Upregulation of SIRT3 inhibited the malignant behaviors of SKOV3 cells, whereas UTMD-mediated SIRT3 upregulation further inhibited proliferation, epithelial-mesenchymal transition, invasion and migration, and induced apoptosis of SKOV3 cells, and it also inhibited tumor formation and growth in vivo. Moreover, the present study identified hypoxia inducible factor-1α (HIF-1α) as a target of SIRT3. The present study provided evidence that UTMD-mediated overexpression of SIRT3 may suppress HOC progression through the inhibition of HIF-1α.

Future Screening Prospects for Ovarian Cancer

Simple Summary

Ovarian cancer (OC) has the highest mortality rate of all gynecological cancers. It is usually diagnosed in late stages (FIGO III-IV), and therefore, overall survival is very poor. If diagnosed at the early stages, ovarian cancer has a 90% five-year survival rate. Liquid biopsy has a good potential to improve early ovarian cancer detection and is discussed in this review.

Abstract

Current diagnostic tools used in clinical practice such as transvaginal ultrasound, CA 125, and HE4 are not sensitive and specific enough to diagnose OC in the early stages. A lack of early symptoms and an effective asymptomatic population screening strategy leads to a poor prognosis in OC. New diagnostic and screening methods are urgently needed for early OC diagnosis. Liquid biopsies have been considered as a new noninvasive and promising method, using plasma/serum, uterine lavage, and urine samples for early cancer detection. We analyzed recent studies on molecular biomarkers with specific emphasis on liquid biopsy methods and diagnostic efficacy for OC through the detection of circulating tumor cells, circulating cell-free DNA, small noncoding RNAs, and tumor-educated platelets.

Anti-N-Methyl-D-Aspartate Receptor Encephalitis Associated with Ovarian Teratoma in South China-Clinical Features, Treatment, Immunopathology, and Surgical Outcomes of 21 Cases

Objective

To study the clinical characteristics and surgical outcomes of anti-NMDAR encephalitis and the immunopathology of associated teratomas.

Methods

Twenty-one patients were enrolled in this retrospective study, who were diagnosed with anti-NMDAR encephalitis with ovarian teratoma and admitted to two tertiary hospitals in South China from July 2014 to December 2019. The clinical data of patients were reviewed. Comparisons were made between the patients with different outcomes after surgery. Immunohistochemical analyses of associated ovarian teratomas were performed.

Results

The mean age of the patients was 24.33 ± 5.12 years. The peak seasons of disease onset were autumn and winter (30.61% and 32.65%). The symptoms could be divided into 8 categories, including psychiatric abnormalities, seizures, movement dysfunction, consciousness disorders, autonomic dysregulation, speech disturbance, central hypoventilation, and memory deficits. All patients developed four or more categories of symptoms within the first four weeks. Twelve patients (57.1%) had a maximum mRS of 5, and 11 patients (52.4%) were admitted to ICU. Twenty patients received surgery, and only 3 patients were diagnosed pathologically with immature ovarian teratomas, while the other 17 patients had mature ovarian teratomas. After surgery, 17 patients (85.0%) got clinical improvement. The central hypoventilation symptom and mature ovarian teratomas were associated with surgical outcome. Immunohistochemical analysis revealed that there were NMDAR-positive neural tissues in all 8 teratomas and in which 3 cases also contained large numbers of NMDAR-positive sebaceous glands and squamous epithelial tissues.

Conclusion

The disease is of high prevalence in autumn and winter. The central hypoventilation symptom and mature ovarian teratomas were associated with surgical outcome. NMDAR-positive neural tissue is not the only etiological factor of encephalitis. We speculate that encephalitis development in some patients may result from NMDAR expression in sebaceous glands and squamous epithelial tissues.

Can Circulating Cell-Free DNA or Circulating Tumor DNA Be a Promising Marker in Ovarian Cancer?

In recent years, the studies on ovarian cancer have made great progress, but the morbidity and mortality of patients with ovarian cancer are still very high. Due to the lack of effective early screening and detecting tools, 70% of ovarian cancer patients are diagnosed at an advanced stage. The overall survival rate of ovarian cancer patients treated with surgical combined with chemotherapy has not been significantly improved, and they usually relapse or resist chemotherapy. Therefore, a novel tumor marker is beneficial for the diagnosis and prognosis of patients with ovarian cancer. As the index of "liquid biopsy," circulating cell-free DNA/circulating tumor DNA (cfDNA/ctDNA) has attracted a lot of attention. It has more remarkable advantages than traditional methods and gives a wide range of clinical applications in kinds of solid tumors. This review attempts to illuminate the important value of cfDNA/ctDNA in ovarian cancer, including diagnosis, monitoring, and prognosis. Meanwhile, we will present future directions and challenges for detection of cfDNA/ctDNA.

Evaluation of Gonadal Alterations in a Population Environmentally Exposed to a Mixture of Endocrine Active Pesticides

Although there are studies that show that some pesticides produce gonadal dysfunction and gonadal cancer in different animals, there are not many studiesregardinghumans. This study determined the prevalence and risk in humans of developing ovarian or testicular dysfunction or cancer in areas with distinct exposure to pesticides, which have endocrine disrupting properties. A population-based case-control study was carried out on humans living in ten health districts of Andalusia (Southern Spain) classified as areas of high or low environmental exposure to pesticides according to agronomic criteria. The study population included 5332 cases and 13,606 controls. Data were collected from computerized hospital records between 2000 and 2018.The risk of gonadal dysfunction or cancer was significantly higher in areas with higher use of pesticides in relation to those with lower use.

The Abnormal Ovary: Evolving Concepts in Diagnosis and Management

Ovarian lesions are common and require a consistent approach to diagnosis and management for best patient outcomes. In the past 20 years, there has been an evolution in the approach to abnormal ovarian lesions, with increasing emphasis on reducing surgery for benign disease, standardizing terminology, assessing risk of malignancy through use of evidence-based scoring systems, and triaging suspicious abnormalities to dedicated oncology centers. This article provides an evidence-based review of how these changes in diagnosis and management of ultrasound-detected abnormal ovarian lesions have occurred. Current recommended practices are summarized. The current literature on transvaginal screening for ovarian cancer also is reviewed and summarized.