Imaging in gynecological disease (11): clinical and ultrasound features of mucinous ovarian tumors

Large uterine smooth muscle tumors of unknown malignant potential mimicking a mucinous ovarian tumor: A challenging preoperative diagnosis

We described a challenging case of a uterine smooth muscle tumors of unknown malignant potential (STUMP) presenting as an ovarian mucinous tumor. This differential diagnosis should be considered when preoperatively evaluating cystic multilocular lesions.

Comparison of sensitivity for Risk of Ovarian Malignancy Algorithm (ROMA) and Assessment of Different NEoplasias in the adneXa (ADNEX) model for predicting ovarian cancer in a woman with adnexal masses

OBJECTIVE

This study aimed to compare the diagnostic performance of the Risk of Ovarian Malignancy Algorithm (ROMA) and the Assessment of Different NEoplasias in the adneXa (ADNEX) model in predicting ovarian cancer in women presenting with adnexal masses METHODS: A prospective diagnostic study was conducted at the Faculty of Medicine, Vajira Hospital, Navamindradhiraj University, Thailand. A total of 110 women with adnexal masses confirmed by ultrasound were enrolled. Pre-operative transvaginal ultrasound findings, serum CA125, and HE4 levels were used to evaluate the diagnostic performance of the ROMA and ADNEX models, with histopathological examination as the reference standard. The ADNEX model applied a 10% malignancy risk cutoff.

RESULTS

Using a 10% cutoff, the ADNEX model achieved a sensitivity of 91.9% and a specificity of 65.7%. In comparison, ROMA demonstrated a sensitivity of 64.8% and a specificity of 86.3%. The combined use of ADNEX and ROMA did not significantly improve diagnostic specificity. The receiver operating characteristic analysis for the ADNEX model showed an area under the curve of 0.83, indicating good diagnostic accuracy. The optimal threshold for malignancy risk was identified at a 13.8% cutoff, balancing sensitivity and specificity.

CONCLUSIONS

The ADNEX model, with a 10% malignancy risk cutoff, provides superior sensitivity in diagnosing ovarian cancer in adnexal mass cases and could significantly contribute to early detection strategies. However, its lower specificity highlights the need for cautious interpretation. Further studies are warranted to refine these models and enhance their applicability across diverse clinical environments.

Diagnostic value of the ovarian adnexal reporting and data system ultrasound in ovarian masses: a 2-center study

OBJECTIVE

This study aimed to assess the diagnostic efficacy of the ovarian adnexal reporting and data system (O-RADS) and ultrasound (US) and its sub-classification system for distinguishing ovarian masses.

METHODS

O-RADS US was used for the retrospective analysis of 606 ovarian masses of Chinese from 2 medical centres by 2 gynaecologic sonographers with varying experience. The O-RADS 4 categories masses were further sub-classified into O-RADS 4a and O-RADS 4b through 3 different approaches (O-RADS A1/A2/A3).

RESULTS

The AUC of O-RADS US for differentiating benign from malignant ovarian masses was 0.927 (95% CI, 0.903-0.946, P < .001). The optimal cut-off value for predicting malignancy was >O-RADS 3, with sensitivity and specificity of 98.60% and 68.90%, respectively. The diagnostic efficacy of the 3 sub-classification systems surpassed that of O-RADS US (P < .05). Specifically, A2 approach (within O-RADS 4 lesions, unilocular and multilocular cysts with solid components were sub-classified as O-RADS 4b, whereas the remaining O-RADS 4 lesions were sub-classified as O-RADS 4a) resulted in an AUC of 0.942 (95% CI, 0.921-0.960, P < .001). The best cut-off value predicting malignancy was >O-RADS 4a, exhibiting relatively high specificity (82.51%) and maintaining a high sensitivity (93.01%).

CONCLUSION

The diagnostic efficacy of O-RADS US for identifying ovarian tumours is good, but specificity is slightly lower. This study enhanced diagnostic specificity after subclassifying O-RADS 4 lesions, especially A2 approach. It holds significant clinical value for Chinese women and merits further clinical promotion and application.

ADVANCES IN KNOWLEDGE

The sub-classification of O-RADS US allows better identifying ovarian tumours, facilitating informed preoperative clinical management and diagnosis.

Accuracy of ultrasound, magnetic resonance imaging and intraoperative frozen section in the diagnosis of ovarian tumours: data from a London tertiary centre

BACKGROUND

Ovarian cancer has the worst prognosis among all gynaecological cancers. The pre-operative and intraoperative diagnosis of ovarian tumours is imperative to ensure the right operation is performed and to improve patients' outcomes.

METHODOLOGY

A retrospective review of cases with a confirmed histological diagnosis of ovarian cases was undertaken from January 2017 to December 2021. Comparison was undertaken between this final diagnosis and the pre-operative ultrasound, MRI and frozen section (FS) to assess diagnostic accuracy of each. In the ultrasound cases, the level of the examiner was collected. Statistical analysis was performed using Stata MP v17.0 software (USA, 2023).

RESULTS

In total, 156 ovarian masses were examined by FS. In the histopathological examination, 123/156 of these tumours were epithelial tumours. Pre-operative US subjective impression was made in 63/156 cases and preoperative MRI subjective impression was made in 129/156 cases. For benign, borderline and malignant tumours, FS demonstrated a sensitivity of 90.8% (95%CI:81.9-96.2), 86.8% (95%CI:71.9-95.6) and 97.6% (95%CI:87.4-99.9) respectively. Ultrasound's sensitivities were 95.2% (95%CI:76.2-99.9), 20% (95%:4.33-48.1), 57.1% (95%CI:28.9-82.3) and MRI's sensitivities were 100% (95%CI:80.5-100), 31.5% (95%CI:19.5-45.6) and 63.2% (95%CI:46-78.2) respectively.

CONCLUSIONS

FS remains an accurate tool for diagnosing ovarian malignancy. However, across both imaging modalities and FS, the diagnosis of borderline ovarian tumours remains challenging.

AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging

Purpose

Segmentation of ovarian/adnexal masses from surrounding tissue on ultrasound images is a challenging task. The separation of masses into different components may also be important for radiomic feature extraction. Our study aimed to develop an artificial intelligence-based automatic segmentation method for transvaginal ultrasound images that (1) outlines the exterior boundary of adnexal masses and (2) separates internal components.

Approach

A retrospective ultrasound imaging database of adnexal masses was reviewed for exclusion criteria at the patient, mass, and image levels, with one image per mass. The resulting 54 adnexal masses (36 benign/18 malignant) from 53 patients were separated by patient into training (26 benign/12 malignant) and independent test (10 benign/6 malignant) sets. U-net segmentation performance on test images compared to expert detailed outlines was measured using the Dice similarity coefficient (DSC) and the ratio of the Hausdorff distance to the effective diameter of the outline (R HD - D ) for each mass. Subsequently, in discovery mode, a two-level fuzzy c-means (FCM) unsupervised clustering approach was used to separate the pixels within masses belonging to hypoechoic or hyperechoic components.

Results

The DSC (median [95% confidence interval]) was 0.91 [0.78, 0.96], andR HD - D was 0.04 [0.01, 0.12], indicating strong agreement with expert outlines. Clinical review of the internal separation of masses into echogenic components demonstrated a strong association with mass characteristics.

Conclusion

A combined U-net and FCM algorithm for automatic segmentation of adnexal masses and their internal components achieved excellent results compared with expert outlines and review, supporting future radiomic feature-based classification of the masses by components.

Discriminative diagnosis of ovarian endometriosis cysts and benign mucinous cystadenomas based on the ConvNeXt algorithm

PURPOSE

The objective of this study was to develop a deep learning model, using the ConvNeXt algorithm, that can effectively differentiate between ovarian endometriosis cysts (OEC) and benign mucinous cystadenomas (MC) by analyzing ultrasound images. The performance of the model in the diagnostic differentiation of these two conditions was also evaluated.

METHODS

A retrospective analysis was conducted on OEC and MC patients who had sought medical attention at the Fourth Affiliated Hospital of Harbin Medical University between August 2018 and May 2023. The diagnosis was established based on postoperative pathology or the characteristics of aspirated fluid guided by ultrasound, serving as the gold standard. Ultrasound images were collected and subjected to screening and preprocessing procedures. The data set was randomly divided into training, validation, and testing sets in a ratio of 5:3:2. Transfer learning was utilized to determine the initial weights of the ConvNeXt deep learning algorithm, which were further adjusted by retraining the algorithm using the training and validation ultrasound images to establish a new deep learning model. The weights that yielded the highest accuracy were selected to evaluate the diagnostic performance of the model using the validation set. Receiver operating characteristic (ROC) curves were generated, and the area under the curve (AUC) was calculated. Additionally, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, negative likelihood ratio, and odds ratio were calculated. Decision curve analysis (DCA) curves were plotted.

RESULTS

The study included 786 ultrasound images from 184 patients diagnosed with either OEC or MC. The deep learning model achieved an AUC of 0.90 (95 % CI: 0.85-0.95) in accurately distinguishing between the two conditions, with a sensitivity of 90 % (95 % CI: 84 %-95 %), specificity of 90 % (95 % CI: 77 %-97 %), a positive predictive value of 96 % (95 % CI: 91 %-99 %), a negative predictive value of 77 % (95 % CI: 63 %-88 %), a positive likelihood ratio of 9.27 (95 % CI: 3.65-23.56), and a negative likelihood ratio of 0.11 (95 % CI: 0.06-0.19). The DCA curve demonstrated the practical clinical utility of the model.

CONCLUSIONS

The deep learning model developed using the ConvNeXt algorithm exhibits high accuracy (90 %) in distinguishing between OEC and MC. This model demonstrates excellent diagnostic performance and clinical utility, providing a novel approach for the clinical differentiation of these two conditions.

Comparison of the diagnostic efficiency between the O-RADS US risk stratification system and doctors' subjective judgment

BACKGROUND

This study aimed to compare the diagnostic efficiency of Ovarian-Adnexal Reporting and Data System (O-RADS) and doctors' subjective judgment in diagnosing the malignancy risk of adnexal masses.

METHODS

This was an analysis of 616 adnexal masses between 2017 and 2020. The clinical findings, preoperative ultrasound images, and pathological diagnosis were recorded. Each adnexal mass was evaluated by doctors' subjective judgment and O-RADS by two senior doctors and two junior doctors. A mass with an O-RADS grade of 1 to 3 was a benign tumor, and a mass with an O-RADS grade of 4-5 was a malignant tumor. All outcomes were compared with the pathological diagnosis.

RESULTS

Of the 616 adnexal masses, 469 (76.1%) were benign, and 147 (23.9%) were malignant. There was no difference between the area under the curve of O-RADS and the subjective judgment for junior doctors (0.83 (95% CI: 0.79-0.87) vs. 0.79 (95% CI: 0.76-0.83), p = 0.0888). The areas under the curve of O-RADS and subjective judgment were equal for senior doctors (0.86 (95% CI: 0.83-0.89) vs. 0.86 (95% CI: 0.83-0.90), p = 0.8904). O-RADS had much higher sensitivity than the subjective judgment in detecting malignant tumors for junior doctors (84.4% vs. 70.1%) and senior doctors (91.2% vs. 81.0%). In the subgroup analysis for detecting the main benign lesions of the mature cystic teratoma and ovarian endometriosic cyst, the junior doctors' diagnostic accuracy was obviously worse than the senior doctors' on using O-RADS.

CONCLUSIONS

O-RADS had excellent performance in predicting malignant adnexal masses. It could compensate for the lack of experience of junior doctors to a certain extent. Better performance in discriminating various benign lesions should be expected with some complement.

Validation of the diagnostic efficacy of O-RADS in adnexal masses

The aim of this study was to validate the performance of the Ovarian-Adnexal Reporting and Data Systems (O-RADS) series models proposed by the American College of Radiology (ACR) in the preoperative diagnosis of adnexal masses (AMs). Two experienced sonologists examined 218 patients with AMs and gave the assessment results after the examination. Pathological findings were used as a reference standard. Of the 218 lesions, 166 were benign and 52 were malignant. Based on the receiver operating characteristic (ROC) curve, we defined a malignant lesion as O-RADS > 3 (i.e., lesions in O-RADS categories 4 and 5 were malignant). The area under the curve (AUC) of O-RADS (v2022) was 0.970 (95% CI 0.938-0.988), which wasn't statistically significantly different from the O-RADS (v1) combined Simple Rules Risk (SRR) assessment model with the largest AUC of 0.976 (95% CI 0.946-0.992) (p = 0.1534), but was significantly higher than the O-RADS (v1) (AUC = 0.959, p = 0.0133) and subjective assessment (AUC = 0.918, p = 0.0255). The O-RADS series models have good diagnostic performance for AMs. Where, O-RADS (v2022) has higher accuracy and specificity than O-RADS (v1). The accuracy and specificity of O-RADS (v1), however, can be further improved when combined with SRR assessment.

Application of Ultrasound Scores (Subjective Assessment, Simple Rules Risk Assessment, ADNEX Model, O-RADS) to Adnexal Masses of Difficult Classification

BACKGROUND

Ultrasound features help to differentiate benign from malignant masses, and some of them are included in the ultrasound (US) scores. The main aim of this work is to describe the ultrasound features of certain adnexal masses of difficult classification and to analyse them according to the most frequently used US scores.

METHODS

Retrospective studies of adnexal lesions are difficult to classify by US scores in women undergoing surgery. Ultrasound characteristics were analysed, and masses were classified according to the Subjective Assessment of the ultrasonographer (SA) and other US scores (IOTA Simple Rules Risk Assessment-SRRA, ADNEX model with and without CA125 and O-RADS).

RESULTS

A total of 133 adnexal masses were studied (benign: 66.2%, n:88; malignant: 33.8%, n:45) in a sample of women with mean age 56.5 ± 7.8 years. Malignant lesions were identified by SA in all cases. Borderline ovarian tumors (n:13) were not always detected by some US scores (SRRA: 76.9%, ADNEX model without and with CA125: 76.9% and 84.6%) nor were serous carcinoma (n:19) (SRRA: 89.5%), clear cell carcinoma (n:9) (SRRA: 66.7%) or endometrioid carcinoma (n:4) (ADNEX model without CA125: 75.0%). While most teratomas and serous cystadenomas have been correctly differentiated, other benign lesions were misclassified because of the presence of solid areas or papillae. Fibromas (n:13) were better identified by SA (23.1% malignancy), but worse with the other US scores (SRRA: 69.2%, ADNEX model without and with CA125: 84.6% and 69.2%, O-RADS: 53.8%). Cystoadenofibromas (n:10) were difficult to distinguish from malignant masses via all scores except SRRA (SA: 70.0%, SRRA: 20.0%, ADNEX model without and with CA125: 60.0% and 50.0%, O-RADS: 90.0%). Mucinous cystadenomas (n:12) were misdiagnosed as malignant in more than 15% of the cases in all US scores (SA: 33.3%, SRRA: 16.7%, ADNEX model without and with CA125: 16.7% and 16.7%, O-RADS:41.7%). Brenner tumors are also difficult to classify using all scores.

CONCLUSION

Some malignant masses (borderline ovarian tumors, serous carcinoma, clear cell carcinoma, endometrioid carcinomas) are not always detected by US scores. Fibromas, cystoadenofibromas, some mucinous cystadenomas and Brenner tumors may present solid components/papillae that may induce confusion with malignant lesions. Most teratomas and serous cystadenomas are usually correctly classified.

Ultrasound Features and Ultrasound Scores in the Differentiation between Benign and Malignant Adnexal Masses

BACKGROUND

Several ultrasound (US) features help ultrasound experts in the classification of benign vs. malignant adnexal masses. US scores serve in this differentiation, but they all have misdiagnoses. The main objective of this study is to evaluate what ultrasound characteristics are associated with malignancy influencing ultrasound scores.

METHODS

This is a retrospective analysis of ultrasound features of adnexal lesions of women managed surgically. Ultrasound characteristics were analyzed, and masses were classified by subjective assessment of the ultrasonographer (SA) and other ultrasound scores (IOTA Simple Rules Risk Assessment SRRA, ADNEX model, and O-RADS).

RESULTS

Of a total of 187 adnexal masses studied, 134 were benign (71.7%) and 53 were malignant (28.3%). SA, IOTA SRRA, ADNEX model with or without CA125 and O-RADS had high levels of sensitivity (93.9%, 81.1%, 94.3%, 88.7%, 98.1%) but lower specificity (80.2%, 82.1%, 82.8%, 77.6%, 73.1%) with similar AUC (0.87, 0.87, 0.92, 0.90, 0.86). Ultrasound features significantly related with malignancy were the presence of irregular contour, absence of acoustic shadowing, vascularized solid areas, ≥1 papillae, vascularized septum, and moderate-severe ascites.

CONCLUSION

IOTA SRRA, ADNEX model, and O-RADS can help in the classification of benign and malignant masses. Certain ultrasound characteristics studied in ultrasound scores are associated with malignancy.

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

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

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

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

Ultrasound evaluation of ovarian masses and assessment of the extension of ovarian malignancy

The current review sums up the literature on the diagnostic performance of models to predict malignancy in adnexal masses and the ability of ultrasound to make a specific diagnosis in adnexal masses. A summary of the role of ultrasound in assessing the extension of malignant ovarian disease is also provided.

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

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

Current update on malignant epithelial ovarian tumors

Epithelial ovarian cancer (EOC) represents the most frequently occurring gynecological malignancy, accounting for more than 70% of ovarian cancer deaths. Preoperative imaging plays an important role in assessing the extent of disease and guides the next step in surgical decision-making and operative planning. In this article, we will review the multimodality imaging features of various subtypes of EOC. We will also discuss the role of imaging in the staging, management, and surveillance of EOC.

Differential Diagnosis of Endometriosis by Ultrasound: A Rising Challenge

Ultrasound is an effective tool to detect and characterize endometriosis lesions. Variances in endometriosis lesions' appearance and distorted anatomy secondary to adhesions and fibrosis present as major difficulties during the complete sonographic evaluation of pelvic endometriosis. Currently, differential diagnosis of endometriosis to distinguish it from other diseases represents the hardest challenge and affects subsequent treatment. Several gynecological and non-gynecological conditions can mimic deep-infiltrating endometriosis. For example, abdominopelvic endometriosis may present as atypical lesions by ultrasound. Here, we present an overview of benign and malignant diseases that may resemble endometriosis of the internal genitalia, bowels, bladder, ureter, peritoneum, retroperitoneum, as well as less common locations. An accurate diagnosis of endometriosis has significant clinical impact and is important for appropriate treatment.

Validation of models to diagnose ovarian cancer in patients managed surgically or conservatively: multicentre cohort study

OBJECTIVE

To evaluate the performance of diagnostic prediction models for ovarian malignancy in all patients with an ovarian mass managed surgically or conservatively.

DESIGN

Multicentre cohort study.

SETTING

36 oncology referral centres (tertiary centres with a specific gynaecological oncology unit) or other types of centre.

PARTICIPANTS

Consecutive adult patients presenting with an adnexal mass between January 2012 and March 2015 and managed by surgery or follow-up.

MAIN OUTCOME MEASURES

Overall and centre specific discrimination, calibration, and clinical utility of six prediction models for ovarian malignancy (risk of malignancy index (RMI), logistic regression model 2 (LR2), simple rules, simple rules risk model (SRRisk), assessment of different neoplasias in the adnexa (ADNEX) with or without CA125). ADNEX allows the risk of malignancy to be subdivided into risks of a borderline, stage I primary, stage II-IV primary, or secondary metastatic malignancy. The outcome was based on histology if patients underwent surgery, or on results of clinical and ultrasound follow-up at 12 (±2) months. Multiple imputation was used when outcome based on follow-up was uncertain.

RESULTS

The primary analysis included 17 centres that met strict quality criteria for surgical and follow-up data (5717 of all 8519 patients). 812 patients (14%) had a mass that was already in follow-up at study recruitment, therefore 4905 patients were included in the statistical analysis. The outcome was benign in 3441 (70%) patients and malignant in 978 (20%). Uncertain outcomes (486, 10%) were most often explained by limited follow-up information. The overall area under the receiver operating characteristic curve was highest for ADNEX with CA125 (0.94, 95% confidence interval 0.92 to 0.96), ADNEX without CA125 (0.94, 0.91 to 0.95) and SRRisk (0.94, 0.91 to 0.95), and lowest for RMI (0.89, 0.85 to 0.92). Calibration varied among centres for all models, however the ADNEX models and SRRisk were the best calibrated. Calibration of the estimated risks for the tumour subtypes was good for ADNEX irrespective of whether or not CA125 was included as a predictor. Overall clinical utility (net benefit) was highest for the ADNEX models and SRRisk, and lowest for RMI. For patients who received at least one follow-up scan (n=1958), overall area under the receiver operating characteristic curve ranged from 0.76 (95% confidence interval 0.66 to 0.84) for RMI to 0.89 (0.81 to 0.94) for ADNEX with CA125.

CONCLUSIONS

Our study found the ADNEX models and SRRisk are the best models to distinguish between benign and malignant masses in all patients presenting with an adnexal mass, including those managed conservatively.

TRIAL REGISTRATION

ClinicalTrials.gov NCT01698632.

[Borderline Ovarian Tumours: CNGOF Guidelines for Clinical Practice - Imaging]

OBJECTIVE

To determine the place of imaging and the performance of different imaging techniques (transvaginal ultrasound with or without Doppler, scoring, CT, MRI) to differentiate benign tumour, borderline ovarian tumour (BOT) and malignant ovarian tumor. Differentiate the histological subtypes of BOT (serous, sero-mucinous, mucinous) and prediction in imaging of the possibility of conservative treatment.

METHODS

The research was carried out over the last 16 years using the terms "MeSH" based on the query of the Medline® database and supplemented by the review of references contained in the meta-analyzes, systematic reviews and original articles included.

RESULTS

Endo-vaginal and suprapubic ultrasonography is recommended for analysis of an ovarian mass (grade A). In the case of ultrasound by a referent, subjective analysis is the recommended technique (grade A). In case of echography by a non-referent, the use of "Simple Rules" is recommended (grade A) and should be best combined with subjective analysis to rejoin the performance of a sonographer refer (grade A). In cases of undetermined ovarian lesions in endovaginal ultrasound and suprapubic ultrasound, it is recommended to perform a pelvic MRI (grade A). The MRI protocol should include T2, T1, T1 sequences with fat saturation, diffusion, injected dynamics, and after gadolinium injection (grade B). To characterize an MRI-adnexal image, it is recommended to include a risk score for malignancy (ADNEX-MR/O-RADS) (grade C) in the report and to formulate an anatomopathological hypothesis (Grade C). The predictive signs of benignity in front of a cyst with endocystic vegetations are the low number, the small size, the presence of calcifications and the absence of Doppler flow in case of size greater than 10mm in echography (LP 4) and a curve of type 1 MRI (LP4). MRI is recommended for suspicious lesions of BOT in ultrasound (grade B) or indeterminate lesions in ultrasound (grade A). There is no data to support the usefulness of CT or PET-CT for BOT. Morphological criteria in ultrasound and MRI exist to differentiate BOT from invasive tumors regardless of grade (NP 2). Pelvic MRI is recommended to characterize a tumor suggestive of ultrasound BOT (grade C). No recommendations can be made about the use of combined ultrasound, biological, and menopausal status scores for the diagnosis of BOT. The diagnostic performance of imaging to detect peritoneal implants of BOT is not known. The assessment of the invasiveness of peritoneal implants of imaging BOT has not been evaluated. The association of macroscopic signs in MRI makes it possible to differentiate the different subtypes - serous, sero-mucinous and mucinous (intestinal type) - of BOT, despite the overlap of certain presentations (LP3). The analysis of macroscopic MRI signs must be performed to differentiate the different subtypes of TFO (grade C). No recommendation can be made on imaging prediction of the possibility of conservative BOT treatment.

Treatment strategy for pediatric giant mucinous cystadenoma: A case report

Because of their rarity, the treatment strategy for pediatric ovarian epithelial tumors is controversial, especially for a giant cystadenoma. We report the largest mucinous cystadenoma (MCA) case in the pediatric literature thus far. A 12-year-old girl had abdominal distention and visited our hospital. She had a multilocular cyst with some protuberance on the inside and high values of CA 19-9 and CA-125. We diagnosed her with a left MCA and performed a left oophorectomy. The tumor was the stage IA borderline malignant MCA and weighed 11.8 kg. Five years have passed, the patient has not experienced recurrence or metastasis. The resection of giant tumors can affect respiration and circulation. However, pre- or intra-operative drainage may lead to dissemination and adhesion. When we treat pediatric giant ovarian epithelial tumors, we must understand the findings that suggest the possibility of malignancy to decide appropriately as to whether drainage should be performed.

New sonographic marker of borderline ovarian tumor: microcystic pattern of papillae and solid components

OBJECTIVE

To describe and evaluate the utility of a new sonographic microcystic pattern, which is typical of borderline ovarian tumor (BOT) papillary projections, solid component(s) and/or septa, as a new ultrasound marker that is capable of distinguishing BOT from other adnexal masses, and to present/obtain histologic confirmation.

METHODS

In this retrospective study, we identified women with a histologic diagnosis of BOT following surgical resection who had undergone preoperative transvaginal ultrasound (TVS) examination. All images were reviewed for presence or absence of thin-walled, fluid-filled cluster(s) of 1-3-mm cystic formations, associated with solid component(s), papillary projections and/or septa. From the same cases, histopathologic slides of each BOT were examined for presence of any of these microcystic features which had been identified on TVS. To confirm that the microcystic TVS pattern is unique to BOTs, we also selected randomly from our ultrasound and surgical database 20 cases of epithelial ovarian cancer and 20 cases of benign cystadenoma, for review by the same pathologists. To confirm the novelty of our findings, we searched PubMed for literature published in the English language between 2010 and 2018 to determine whether the association between microcystic tissue pattern and BOT has been described previously.

RESULTS

Included in the final analysis were 62 patients (67 ovaries) with preoperative TVS and surgically confirmed BOT on pathologic examination. The mean patient age at surgery was 39.8 years. The mean BOT size at TVS was 60.7 mm. Of the 67 BOTs, 47 (70.1%) were serous, 15 (22.4%) were mucinous and five (7.5%) were seromucinous. We observed on TVS a microcystic pattern in the papillary projections, solid component(s) and/or septa in 60 (89.6%) of the 67 BOTs, including 46 (97.9%) of the 47 serous BOTs, 11 (73.3%) of the 15 mucinous BOTs and three (60.0%) of the five seromucinous BOTs. On microscopic evaluation, 60 (89.6%) of the 67 samples had characteristic 1-3-mm fluid-filled cysts similar to those seen on TVS. In seven cases there was a discrepancy between sonographic and histologic observation of a microcystic pattern. The 20 cystadenomas were mostly unilocular and/or multilocular and largely avascular. None of them or the 20 epithelial ovarian malignancies displayed microcystic characteristics, either on TVS or at histology. On review of 23 published articles in the English medical literature, containing 163 sonographic images of BOT, we found that, while all images contained it, there was no description of the microcystic tissue pattern.

CONCLUSION

We report herein a novel sonographic marker of BOT, a 'microcystic pattern' of BOT papillary projections, solid component(s) and/or septa. This was seen in the majority of both serous and mucinous BOT cases. Importantly, based on comparison of sonographic images and histopathology of benign entities and malignancies, the microcystic appearance seems to be unique to BOTs. No similar description has been published previously. Utilization of this new marker should help to identify BOT correctly, discriminating it from ovarian cancer and benign ovarian pathology, and should ensure appropriate clinical and surgical management. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

Role of CA125/CEA ratio and ultrasound parameters in identifying metastases to the ovaries in patients with multilocular and multilocular-solid ovarian masses

OBJECTIVES

To investigate ultrasound features and the best cut-off value of the cancer antigen 125/carcinoembryonic antigen (CA125/CEA) ratio to discriminate ovarian metastases from benign and primary malignant ovarian neoplasms in two selected groups of morphological ovarian masses, namely multilocular masses with five or more locules and multilocular-solid masses.

METHODS

Patients with multilocular (five or more locules) or multilocular-solid ovarian masses, operated on within 3 months of ultrasound examination, and with tumor markers (CEA and CA125) available at diagnosis, were identified retrospectively from three ultrasound centers. The masses were described using the International Ovarian Tumor Analysis (IOTA) terminology. Ultrasound and clinical characteristics were compared between those with an ovarian neoplasm (including benign and primary malignant neoplasms) and those with an ovarian metastasis. Receiver-operating characteristics curve (ROC) analysis was used to evaluate the ability of CA125, CEA and CA125/CEA to differentiate between ovarian neoplasms and ovarian metastases, and their predictive performance was assessed.

RESULTS

In total, 350 (88.4%) patients with an ovarian neoplasm (including 99 benign, 43 borderline and 197 primary epithelial ovarian carcinomas, seven malignant rare tumors and four other types of invasive ovarian tumor) and 46 (11.6%) patients with an ovarian metastasis were analyzed. On ultrasound examination, ovarian neoplasms were smaller than ovarian metastases (median largest diameter, 97 (range, 20-387) mm vs 146 (range, 43-259) mm, respectively; P < 0.0001) and presented with a lower number of cysts with > 10 locules (18.9% vs 54.3%; P < 0.0001). ROC curve analysis showed that the best cut-off value of CEA for distinguishing between ovarian neoplasms and ovarian metastases was 2.33 ng/mL. The predictive performance of this CEA cut-off value was: area under the curve (AUC), 0.791 (95% CI, 0.711-0.870); accuracy, 73.7%; sensitivity, 73.1%; specificity, 78.3%; positive predictive value (PPV), 96.2%; and negative predictive value (NPV), 27.7%. The best cut-off value of CA125/CEA for distinguishing between ovarian neoplasms and ovarian metastases was 11.92. The predictive performance of this CA125/CEA cut-off value was: AUC, 0.758 (95% CI, 0.683-0.833); accuracy, 79.8%; sensitivity, 82.3%; specificity, 60.9%; PPV, 94.1%; and NPV, 31.1%.

CONCLUSIONS

CA125/CEA ratio and CEA alone did not show any significant difference in their ability to distinguish between ovarian neoplasms (including benign and malignant) and ovarian metastases in masses with multilocular and those with multilocular-solid morphology. Therefore, in this morphological subgroup of ovarian masses, CEA alone is sufficient to differentiate between ovarian neoplasms and ovarian metastases. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

Imaging in gynecological disease (14): clinical and ultrasound characteristics of ovarian clear cell carcinoma

OBJECTIVE

To describe the clinical and ultrasound characteristics of ovarian pure clear cell carcinoma.

METHODS

This was a retrospective study involving data from 11 ultrasound centers. From the International Ovarian Tumor Analysis (IOTA) database, 105 patients who had undergone preoperative ultrasound examination by an experienced ultrasound examiner between 1999 and 2016 were identified with a histologically confirmed pure clear cell carcinoma of the ovary. An additional 47 patients diagnosed with pure clear cell carcinoma between 1999 and 2016 and with available complete preoperative ultrasound reports were identified retrospectively from the databases of the departments of gynecological oncology in the participating centers. The ultrasound images of all tumors were described using IOTA terminology. Clinical and ultrasound characteristics were analyzed for the whole group, and separately, for patients with and those without histologically confirmed endometriosis, and for patients with evidence of tumor developing from endometriosis.

RESULTS

Median age of the 152 patients was 53.5 (range, 28-92) years and 92/152 (60.5%) tumors were FIGO Stage I. Most tumors (128/152, 84.2%) were unilateral. On ultrasound examination, all tumors contained solid components and 36/152 (23.7%) were completely solid masses. The median largest diameter of the lesion was 117 (range, 25-310) mm. Papillary projections were present in 58/152 (38.2%) masses and, in most of these (51/56, 91.1%), vascularized papillary projections were seen. Information regarding the presence, site and type of pelvic endometriosis at histology was available for 130/152 patients. Endometriosis was noted in 54 (41.5%) of these. In 24/130 (18.6%) patients, the tumor was judged to have developed from endometriosis. Patients with, compared to those without, evidence of tumor developing from endometriosis were younger (median 47.5 vs 55.0 years, respectively), and ground-glass echogenicity of cyst fluid was more common in pure clear cell cancers developing from endometriosis (10/20 vs 13/79 (50.0% vs 16.5%), respectively).

CONCLUSIONS

Ovarian pure clear cell carcinoma is usually diagnosed at an early stage and typically appears as a large unilateral mass with solid components. Patients with clear cell carcinoma developing from endometriosis are younger than other patients with clear cell carcinoma, and clear cell cancers developing from endometriosis more often manifest ground-glass echogenicity of cyst fluid. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

Imaging in gynecological disease (13): clinical and ultrasound characteristics of endometrioid ovarian cancer

OBJECTIVE

To describe the clinical and ultrasound characteristics of ovarian pure endometrioid carcinomas.

METHODS

This was a retrospective multicenter study of patients with a histological diagnosis of pure endometrioid carcinoma. We identified 161 patients from the International Ovarian Tumor Analysis (IOTA) database who had undergone preoperative ultrasound examination by an experienced ultrasound examiner between 1999 and 2016, and another 78 patients from the databases of the departments of gynecological oncology in the participating centers. All tumors were described using IOTA terminology. In addition, one author reviewed all available ultrasound images and described them using pattern recognition.

RESULTS

Median age of the 239 patients was 55 years (range, 19-88 years). On ultrasound examination, two (0.8%) endometrioid carcinomas were described as unilocular cysts, three (1.3%) as multilocular cysts, 37 (15.5%) as unilocular-solid cysts, 115 (48.1%) as multilocular-solid cysts and 82 (34.3%) as solid masses. Median largest tumor diameter was 102.5 mm (range, 20-300 mm) and median largest diameter of the largest solid component was 63 mm (range, 9-300 mm). Papillary projections were present in 70 (29.3%) masses. Most cancers (188 (78.7%)) were unilateral. In 49 (20.5%) cases, the cancer was judged by the pathologist to develop from endometriosis. These cancers, compared with those without evidence of tumor developing from endometriosis, more often manifested papillary projections on ultrasound (46.9% (23/49) vs 24.7% (47/190)), were less often bilateral (8.2% (4/49) vs 24.7% (47/190)) and less often associated with ascites (6.1% (3/49) vs 28.4% (54/190)) and fluid in the pouch of Douglas (24.5% (12/49) vs 48.9% (93/190)). Retrospective analysis of available ultrasound images using pattern recognition revealed that many tumors without evidence of tumor developing from endometriosis (36.3% (41/113)) had a large central solid component entrapped within locules, giving the tumor a cockade-like appearance.

CONCLUSIONS

Endometrioid cancers are usually large, unilateral, multilocular-solid or solid tumors. The ultrasound characteristics of endometrioid carcinomas developing from endometriosis differ from those without evidence of tumor developing from endometriosis, the former being more often unilateral cysts with papillary projections and no ascites. Copyright © 2018 ISUOG. Published by John Wiley & Sons Ltd.

Role of Imaging Tools for the Diagnosis of Borderline Ovarian Tumors: A Systematic Review and Meta-Analysis

Borderline ovarian tumors (BOTs) have a low malignant potential and occur most often in young women. Its preoperative diagnosis can improve surgical strategy and reproductive approach. This study systematically reviews the literature for the accuracy of transvaginal ultrasound (TVUS), magnetic resonance (MRI), and computed tomography (CT) in the diagnostic of BOTs. We conducted a search in PubMed/Medline of articles in English from the last 5 years and included 14 studies for systematic review, 9 of them in the meta-analysis. The pooled sensibility and specificity was respectively 77.0% and 83.0% for TVUS (5 studies) and 85% and 74% for MRI (4 studies) in differentiating benign from malignant BOTs. CT and positron emission tomography (PET)-CT techniques were heterogeneous between studies, so a meta-analysis was not performed for the 4 studies on CT and PET-CT. However, these 4 studies have also shown a high accuracy in differentiating BOTs from malignant ovarian cancers.