Preoperative sonographic evaluation of ovarian masses is crucial for improving outcomes. The Risk of Malignancy Index (RMI) has been a standard for malignancy triage, while the International Ovarian Tumor Analysis Group (IOTA) has proposed a two-step strategy to estimate the risk of malignancy and suggest management steps by translating risks to Ovarian Adnexal Reporting Data System (O-RADS) categories. This study compares the accuracy of RMI and the IOTA two-step strategy in predicting malignancy.

We included patients with preoperative ultrasound and pathological reports. RMI and O-RADS scores based on the IOTA two-step strategy were assessed. Performance was evaluated using receiver operating characteristic (ROC) curves and calibration plots.

A total of 453 cases were included. Of these, 90 (19.9 %) were malignant, 21 (4.6 %) were borderline tumors (BOT), and 342 (75.5 %) were benign. The area under the ROC curve (AUC) for the IOTA two-step strategy was 0.958 (95 % CI, 0.938-0.978), compared to 0.904 (0.865-0.943) for RMI with a > 200 cut-off. The IOTA two-step strategy had a sensitivity of 96.4 %, specificity of 79.7 %, positive predictive value (PPV) 60.2 %, and negative predictive value (NPV) 98.6 %, while RMI showed sensitivity of 70.4 %, specificity 93.4 %, PPV 79.2 %, and NPV 89.8 %. For predicting BOTs, the IOTA two-step AUC was 0.902, compared to 0.719 for RMI.

The IOTA two-step strategy outperforms RMI in the preoperative assessment of adnexal masses, particularly in detecting BOTs. It should be implemented in routine clinical practice.

Ovarian cancer (OC) is the leading cause of gynecological cancer deaths. Current biomarkers of OC are not specific or sensitive enough. Extracellular vesicles (EVs), EV surface proteins and their cargo microRNA (miRNA) show potential as biomarkers. This study aimed to characterize the ability of EVs to identify early OC-biomarkers among blood donors six months before their diagnosis.

Study groups of OC patients, benign tumor patients (B), healthy blood donors (Control), and blood donors with incident OC diagnosis within six months of the last blood draw (Pre-diagnostic; PD) were established. Small EVs were enriched from plasma using ultracentrifugation. EVs were characterized by Dynamic Light Scattering (DLS), EV Array, NanoFlow Cytometry, Nanoparticle Tracking Analysis, and Western blots. RNA from EVs was isolated. A discovery study was performed on OC and B patients using the TaqMan Array Human MicroRNA A card. A validation study of 9 specific miRNAs was performed using RT-qPCR.

With DLS, it was identified that the OC patients' EVs were more heterogeneous in size compared to the other groups. Western blot identified CD63 and TSG101 in the EV enrichments. EV Array assessed 22 known protein biomarkers. TaqMan MicroRNA Array cards indicated a differential miRNA abundance between OC and B; however, technical replication and validation could not validate this pattern.

This study has analyzed EVs in OC, B, Control, and PD women. More extensive investigations of EV CD9, CD151, and CD81 in conjunction with other risk factors and well-known biomarkers like CA125 or HE4 should be the main objectives of future research.

Background/Objectives: We aimed to determine the reliability of simple ultrasound (US) markers and CA-125 measurements in diagnosing peri- and postmenopausal ovarian masses. Methods: The study was conducted in a retrospective setting. The preoperative imaging properties of peri- (PEM) and postmenopausal (POM) ovarian cysts were examined. Based on ultrasound findings, lesions were categorized as either (1) simple cysts, defined as unilocular, anechoic structures without solid components, or (2) complex cysts, characterized by any deviation from this morphology. Imaging characteristics, mass size, and demographic data were matched with histology and CA125 levels. Results: In total, 379 cystic structures (PEM: N = 195, average age: 45.6 years; range: 40-54 years, POM: N = 184, average age 61.2 years; range: 41-88 years) were analyzed. In the PEM group, there were 75 simple (Ø < 5 cm N = 32, Ø ≥ 5 cm N = 43) and 122 complex cysts (Ø < 5 cm N = 29, Ø ≥ 5 cm, N = 93), while in the POM group, 49 simple (Ø < 5 cm N = 9, Ø ≥ 5 cm N = 40) and 135 complex cysts (Ø < 5 cm N = 15, Ø ≥ 5 cm N = 120) were found. In the PEM group, malignancy was detected in complex cysts larger than 5 cm (N = 16, 17.58%). In the POM group, malignancy was present in 40 cases, and 3 of them proved to be smaller than 5 cm. The majority of cysts were functional (54.36%) in the PEM group. In the POM group, serous cysts were the most frequent (38.04%), followed by malignant (21.74%) and mucinous cysts (13.04%). CA125 was elevated in 66 of 217 cases (30.41%); only 23 were malignant (NPV: 0.95, PPV: 0.35). Conclusions: Functional cysts are frequently found among perimenopausal ovarian cysts, with malignancy occurring exclusively in complex cysts exceeding 5 cm in diameter. However, complex cysts of any size carry a significant risk of malignancy in menopause, thus, surgery is recommended. Simple cysts can be followed by serial scans in both groups. CA-125 does not give added value to the detection of malignancy in perimenopausal patients. However, in postmenopausal complex morphology cysts larger than 5 cm, it may give added value to the suspicion of malignancy.

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.

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.

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.

Background: Ovarian cancer encompasses a diverse range of neoplasms originating in the ovaries, fallopian tubes, and peritoneum. Despite being one of the commonest gynaecological malignancies, there are no validated screening strategies for early detection. A diagnosis typically relies on imaging, biomarkers, and multidisciplinary team discussions. The accurate interpretation of CTs and MRIs may be challenging, especially in borderline cases. This study proposes a methodological pipeline to develop and evaluate deep learning (DL) models that can assist in classifying ovarian masses from CT and MRI data, potentially improving diagnostic confidence and patient outcomes. Methods: A multi-institutional retrospective dataset was compiled, supplemented by external data from the Cancer Genome Atlas. Two classification workflows were examined: (1) whole-volume input and (2) lesion-focused region of interest. Multiple DL architectures, including ResNet, DenseNet, transformer-based UNeST, and Attention Multiple-Instance Learning (MIL), were implemented within the PyTorch-based MONAI framework. The class imbalance was mitigated using focal loss, oversampling, and dynamic class weighting. The hyperparameters were optimised with Optuna, and balanced accuracy was the primary metric. Results: For a preliminary dataset, the proposed framework demonstrated feasibility for the multi-class classification of ovarian masses. The initial experiments highlighted the potential of transformers and MIL for identifying the relevant imaging features. Conclusions: A reproducible methodological pipeline for DL-based ovarian mass classification using CT and MRI scans has been established. Future work will leverage a multi-institutional dataset to refine these models, aiming to enhance clinical workflows and improve patient outcomes.

Early detection is crucial for improving survival of patients with ovarian cancer (OC), yet current diagnostic tools lack adequate sensitivity and specificity, especially for early stage disease. The study aimed to validate the serum small extracellular vesicles (sEV) protein based Ovarian Cancer Score (OCS) in detecting OC.

This multicenter study included 1183 adult females with adnexal masses from four hospitals in China (October 2019-April 2023). Of these, 1024 samples were prospectively collected, and 159 were from biobanks. All serum samples were collected before surgery. The concentrations of sEV carbohydrate antigen 125 (CA125), human epididymis protein 4 (HE4), and complement component 5a protein (C5a) were quantified using chemiluminescence immunoassay and then used for calculating OCS. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated.

The OCS demonstrated high sensitivity (95.4 %) and specificity (90.4 %) in diagnosis of OC in the prospective cohort (n = 1024) and in total cases (n = 1183, 95.5 % and 90.2 %), with stable performance across menopausal status and FIGO stages. The OCS maintained a high specificity in premenopausal patients (89.6 %) and postmenopausal patients (92.1 %). The OCS showed high sensitivity in early stage epithelial OC (FIGO I: 89.7 %, I + II: 91.4 %), in patients aged ≤45 years (92.7 %), and in patients with normal CA125 levels (72.7 %), although these results were obtained from subgroups with small sample sizes.

This multicenter study demonstrated that the OCS is a promising non-invasive diagnostic tool for the detection of OC.

This study was registered at ClinicalTrials.gov: NCT06366997.

The accurate diagnosis of adnexal masses is a critical challenge in gynecological practice. Current ultrasound-based models, such as the ADNEX model, IOTA Simple Rules, and O-RADS, have demonstrated good diagnostic performance but are limited by the inclusion of demographic factors and solid confounding lesions. This study aimed to develop and validate a novel ultrasound score (ECO-SCORE) for cystic and solid-cystic lesions based solely on imaging characteristics to improve diagnostic accuracy and applicability in clinical practice. Methods: We conducted a retrospective study on 330 women diagnosed with adnexal masses, including 251 benign and 79 malignant cases. Ultrasound features were analyzed using logistic regression to identify key predictors of malignancy. A new scoring model was developed, excluding demographic or tumor-marker data. Diagnostic performance metrics, including sensitivity, specificity, AUC, and odds ratios, were calculated and compared to existing models using a testing set (20% of the data). Results: The ECO-SCORE achieved an AUC of 97.08%, outperforming ADNEX model (87.5%), IOTA Simple Rules (85.7%), and O-RADS (87.5%). Sensitivity and specificity were 92.98% and 88.88%, respectively, with an odds ratio of 106. Key predictors included irregular contour, absence of acoustic shadows, vascularization within solid areas, and vascularization of papillae. Conclusions: The ECO-SCORE demonstrated superior diagnostic accuracy compared to established models, highlighting its potential as a reliable tool for assessing adnexal masses using ultrasound features exclusively. Further multicenter validation is needed to confirm its robustness across different clinical settings.

There is an unmet need for effective ovarian cancer screening and diagnostic approaches that enable earlier-stage cancer detection and increased overall survival. We have developed a high-performing accessible approach that evaluates cfDNA fragmentomes and protein biomarkers to detect ovarian cancer.

Ovarian cancer has the highest mortality rate among gynaecological malignancies and is initially screened using ultrasound. Owing to the high complexity of ultrasound images of ovarian masses and the anatomical characteristics of the deep pelvic cavity, subjective assessment requires extensive experience and skill. Therefore, detecting the ovaries and ovarian masses and diagnose ovarian cancer are challenging. In the present study, we aimed to develop an automated deep learning framework, the Ovarian Multi-Task Attention Network (OvaMTA), for ovary and ovarian mass detection, segmentation, and classification, as well as further diagnosis of ovarian masses based on ultrasound screening.

Between June 2020 and May 2022, the OvaMTA model was trained, validated and tested on a training and validation cohort including 6938 images and an internal testing cohort including 1584 images which were recruited from 21 hospitals involving women who underwent ultrasound examinations for ovarian masses. Subsequently, we recruited two external test cohorts from another two hospitals. We obtained 1896 images between February 2024 and April 2024 as image-based external test dataset, and further obtained 159 videos for the video-based external test dataset between April 2024 and May 2024. We developed an artificial intelligence (AI) system (termed OvaMTA) to diagnose ovarian masses using ultrasound screening. It includes two models: an entire image-based segmentation model, OvaMTA-Seg, for ovary detection and a diagnosis model, OvaMTA-Diagnosis, for predicting the pathological type of ovarian mass using image patches cropped by OvaMTA-Seg. The performance of the system was evaluated in one internal and two external validation cohorts, and compared with doctors' assessments in real-world testing. We recruited eight physicians to assess the real-world data. The value of the system in assisting doctors with diagnosis was also evaluated.

In terms of segmentation, OvaMTA-Seg achieved an average Dice score of 0.887 on the internal test set and 0.819 on the image-based external test set. OvaMTA-Seg also performed well in ovarian mass detection from test images, including healthy ovaries and masses (internal test area under the curve [AUC]: 0.970; external test AUC: 0.877). In terms of classification diagnosis prediction, OvaMTA-Diagnosis demonstrated high performance on image-based internal (AUC: 0.941) and external test sets (AUC: 0.941). In video-based external testing, OvaMTA recognised 159 videos with ovarian masses with AUC of 0.911, and is comparable to the performance of senior radiologists (ACC: 86.2 vs. 88.1, p = 0.50; SEN: 81.8 vs. 88.6, p = 0.16; SPE: 89.2 vs. 87.6, p = 0.68). There was a significant improvement in junior and intermediate radiologists who were assisted by AI compared to those who were not assisted by AI (ACC: 80.8 vs. 75.3, p = 0.00015; SEN: 79.5 vs. 74.6, p = 0.029; SPE: 81.7 vs. 75.8, p = 0.0032). General practitioners assisted by AI achieved an average performance of radiologists (ACC: 82.7 vs. 81.8, p = 0.80; SEN: 84.8 vs. 82.6, p = 0.72; SPE: 81.2 vs. 81.2, p > 0.99).

The OvaMTA system based on ultrasound imaging is a simple and practical auxiliary tool for screening for ovarian cancer, with a diagnostic performance comparable to that of senior radiologists. This provides a potential tool for screening ovarian cancer.

This work was supported by the National Natural Science Foundation of China (Grant Nos. 12090020, 82071929, and 12090025) and the R&D project of the Pazhou Lab (Huangpu) (Grant No. 2023K0605).

This article aims to provide a multidisciplinary approach to ovarian/adnexal lesions in young patients. Functional cysts, torsions, benign tumors, and malignancies occur within the ovaries of children and adolescents at varying frequencies. Careful conservative management, based on the ultrasonographic sign of presence of normal ovarian tissue, in most circumstances can lead to appropriate ovarian-preserving treatments. The symptomatic ovarian cyst is often due to complications such as hemorrhage and ovarian torsion. Ovarian torsion represents surgical emergency, and a high index of clinical suspicion, based on ovarian volume and edema of the tissue, must be maintained to avoid inadvertent delay in therapy.

Objective: To evaluate the accuracy of the O-RADS system in differentiating between benign and malignant adnexal masses, as assessed by inexperienced gynecologists. Methods: Ten gynecologic residents attended a 20 h training course on the O-RADS system conducted by experienced examiners. Following the training, the residents performed ultrasound examinations on patients admitted with adnexal masses under supervision, recording the data in a database that included videos and still images. The senior author later accessed this ultrasound database and presented the cases offline to ten residents for O-RADS rating, with the raters being blinded to the final diagnosis. The efficacy of the O-RADS system by the residents and inter-observer variability were assessed. Results: A total of 201 adnexal masses meeting the inclusion criteria were evaluated, consisting of 136 (67.7%) benign masses and 65 (32.3%) malignant masses. The diagnostic performance of the O-RADS system showed a sensitivity of 90.8% (95% CI: 82.2-96.2%) and a specificity of 86.8% (95% CI: 80.4-91.8%). Inter-observer variability in scoring was analyzed using multi-rater Fleiss Kappa analysis, yielding Kappa indices of 0.642 (95% CI: 0.641-0.643). The false positive rate was primarily due to the misclassification of solid components in classic benign masses as O-RADS-4 or O-RADS-5. Conclusions: The O-RADS system demonstrates high diagnostic performance in distinguishing benign from malignant adnexal masses, even when used by inexperienced examiners. However, the false positive rate remains relatively high, mainly due to the over-interpretation of solid-appearing components in classic benign lesions. Despite this, inter-observer variability among non-expert raters was substantial. Incorporating O-RADS system training into residency programs is beneficial for inexperienced practitioners. This study could be an educational model for gynecologic residency training for other systems of sonographic features.

We aimed to compare the clinical utility and diagnostic accuracy of the ADNEX model, ROMA score, RMI I, and RMI IV, as well as two serum markers (CA125 and HE4) in preoperative discrimination between benign and malignant adnexal masses (AMs).

We conducted a retrospective study extracting all consecutive patients with AMs seen at our Institution between January 2015 and December 2020. Accuracy metrics included sensitivity (SE), specificity (SP), and area under the receiver operating characteristic curve (AUC), and their 95% confidence intervals (CI) were calculated for basic discrimination between AMs. Model performance was evaluated in terms of discrimination ability and clinical utility (net benefit, NB).

A total of 581 women were included; 481 (82.8%) had a benign ovarian tumor and 100 (17.2%) had a malignant tumor. The SE and SP of CA125, HE4, ROMA score, RMI I, RMI IV, and ADNEX model were 0.60 (0.54-0.66) and 0.80 (0.76-0.83); 0.39 (0.30-0.49) and 0.96 (0.94-0.98); 0.59 (0.50-0.68) and 0.92 (0.88-0.95); 0.56 (0.46-0.65) and 0.98 (0.96-0.99); 0.54 (0.44-0.63) and 0.96 (0.94-0.98); 0.82 (0.73-0.88) and 0.91 (0.89-0.94), respectively. The overall AUC was 0.76 (0.74-0.79) for CA125, 0.81 (0.78-0.83) for HE4, 0.82 (0.80-0.85) for ROMA, 0.86 (0.84-0.88) for RMI I, 0.83 (0.81-0.86) for RMI IV, and 0.92 (0.90-0.94) for ADNEX. The NB for ADNEX was higher than other biomarkers and models across all decision thresholds between 5% and 50%.

The ADNEX model showed a better discrimination ability and clinical utility when differentiating malignant from benign Ams, compared to CA125, HE4, ROMA score, RMI I, and RMI IV.

To determine whether a multimodal assay combining serum microRNA with protein biomarkers and metadata improves triage assessment of an adnexal mass.

Serum samples from 468 training subjects (191 cancer cases and 277 benign adnexal mass controls or healthy controls) were analyzed for seven protein biomarkers and 180 miRNA. Circulating analyte data were combined with age and menopausal status (metadata) into a neural network model to classify samples as cases or controls. Forward regression with ten-fold cross-validation minimized the dimensionality of the model while maximizing linear separation between cases and controls. Model validation proceeded using both internal (44 cases and 56 controls) and external validation sets (51 cases and 59 controls).

The total study population comprised 678 subjects, including 286 cases and 392 controls. Overall, 290 (43%) of the subjects were premenopausal. A panel of 10 miRNA delivered optimal performance when combined with protein and metadata features. The combined model improved the Receiver Operator Characteristic Area Under the Curve (ROC AUC) on the internal (AUC = 0.9; 95% CI 0.81-0.95) and external validation sets (AUC = 0.95; 95% CI 0.90-0.98) compared to miRNA alone or proteins plus metadata (without miRNA). On external validation, the combined model offered 92% sensitivity at 80% specificity overall, with 80% and 100% sensitivity for early and late-stage cancers, respectively, including 78% sensitivity for early-stage, serous ovarian cancers and 82% sensitivity for early-stage, non-serous cancers.

A multimodal assay combining miRNA with protein biomarkers, age, and menopausal status improves surgical triage of an adnexal mass.

High-grade serous ovarian cancer (HGSC) is the most common subtype of ovarian cancer. HGSC patients typically present with advanced disease, which is often resistant to chemotherapy and recurs despite initial responses to therapy, resulting in the poor prognosis associated with this disease. There is a need to utilise biomarkers to manage the various aspects of HGSC patient care. In this review we discuss the current state of biomarkers in HGSC, focusing on the various available immunohistochemical (IHC) and blood-based biomarkers, which have been examined for their diagnostic, prognostic and theranostic potential in HGSC. These include various routine clinical IHC biomarkers such as p53, WT1, keratins, PAX8, Ki67 and p16 and clinical blood-borne markers and algorithms such as CA125, HE4, ROMA, RMI, ROCA, and others. We also discuss various components of the liquid biopsy as well as a number of novel IHC biomarkers and non-routine blood-borne biomarkers, which have been examined in various ovarian cancer studies. We also discuss the future of ovarian cancer biomarker research and highlight some of the challenges currently facing the field.

To create a diagnostic framework for clinical behavior and pathological tissue prognosis in ovarian cancer by using machine-learning (ML) methods based on multiple biomarkers.

Overall, 713 patients with ovarian tumors at Sun Yat Sen Memorial Hospital were randomized into training and test cohorts. Four supervised ML classifiers, namely Support Vector Machine, Random Forest, k-nearest neighbor, and logistic regression were used to derive diagnostic and prognostic information from 10 parameters commonly available from pretreatment peripheral blood tests and age. The best prediction model was selected and validated by comparing the accuracy and the area under the ROC curve of each prediction model and by applying the external data of Guangdong Maternal and Child Health Center.

ML techniques were superior to conventional regression-based analyses in predicting multiple clinical parameters pertaining to ovarian tumor. Ensemble methods combining weak decision trees and RF showed the best reference in diagnosis, especially for malignant ovarian cancer. The values for the highest accuracy and area under the ROC curve for malignant ovarian cancer from benign or borderline ovarian tumors with RF were 99.82 % and 0.86 (micro-average ROC curve), respectively. The greatest accuracy and AUC for the diagnosis of pathological tissue with logistic regression curve were 78.0 % and 0.95 (micro-average ROC curve), respectively. In external validation, the random forest prediction model had an accuracy of 0.789 for applying data from external centers to verify tumor benignity and malignancy, and the logistic regression model had an accuracy of 0.719 for predicting the nature of the tumor.

An ovarian tumor can be diagnosed and characterized before initial treatment via ML systems to provide critical diagnostic and prognostic information. The use of predictive algorithms can facilitate customized treatment options with patient preprocessing stratification.

Multiple risk-prediction models are used in clinical practice to triage patients as being at low risk or high risk of ovarian cancer. In the ROCkeTS study, we aimed to identify the best diagnostic test for ovarian cancer in symptomatic patients, through head-to-head comparisons of risk-prediction models, in a real-world setting. Here, we report the results for the postmenopausal cohort.

In this multicentre, prospective diagnostic accuracy study, we recruited newly presenting female patients aged 16-90 years with non-specific symptoms and raised CA125 or abnormal ultrasound results (or both) who had been referred via rapid access, elective clinics, or emergency presentations from 23 hospitals in the UK. Patients with normal CA125 and simple ovarian cysts of smaller than 5 cm in diameter, active non-ovarian malignancy, or previous ovarian malignancy, or those who were pregnant or declined a transvaginal scan, were ineligible. In this analysis, only postmenopausal participants were included. Participants completed a symptom questionnaire, gave a blood sample, and had transabdominal and transvaginal ultrasounds performed by International Ovarian Tumour Analysis consortium (IOTA)-certified sonographers. Index tests were Risk of Malignancy 1 (RMI1) at a threshold of 200, Risk of Malignancy Algorithm (ROMA) at multiple thresholds, IOTA Assessment of Different Neoplasias in the Adnexa (ADNEX) at thresholds of 3% and 10%, IOTA SRRisk model at thresholds of 3% and 10%, IOTA Simple Rules (malignant vs benign, or inconclusive), and CA125 at 35 IU/mL. In a post-hoc analysis, the Ovarian Adnexal and Reporting Data System (ORADS) at 10% was derived from IOTA ultrasound variables using established methods since ORADS was described after completion of recruitment. Index tests were conducted by study staff masked to the results of the reference standard. The comparator was RMI1 at the 250 threshold (the current UK National Health Service standard of care). The reference standard was surgical or biopsy tissue histology or cytology within 3 months, or a self-reported diagnosis of ovarian cancer at 12 month follow-up. The primary outcome was diagnostic accuracy at predicting primary invasive ovarian cancer versus benign or normal histology, assessed by analysing the sensitivity, specificity, C-index, area under receiver operating characteristic curve, positive and negative predictive values, and calibration plots in participants with conclusive reference standard results and available index test data. This study is registered with the International Standard Randomised Controlled Trial Number registry (ISRCTN17160843).

Between July 13, 2015, and Nov 30, 2018, 1242 postmenopausal patients were recruited, of whom 215 (17%) had primary ovarian cancer. 166 participants had missing, inconclusive, or other reference standard results; therefore, data from a maximum of 1076 participants were used to assess the index tests for the primary outcome. Compared with RMI1 at 250 (sensitivity 82·9% [95% CI 76·7 to 88·0], specificity 87·4% [84·9 to 89·6]), IOTA ADNEX at 10% was more sensitive (difference of -13·9% [-20·2 to -7·6], p<0·0001) but less specific (difference of 28·5% [24·7 to 32·3], p<0·0001). ROMA at 29·9 had similar sensitivity (difference of -3·6% [-9·1 to 1·9], p=0·24) but lower specificity (difference of 5·2% [2·5 to 8·0], p=0·0001). RMI1 at 200 had similar sensitivity (difference of -2·1% [-4·7 to 0·5], p=0·13) but lower specificity (difference of 3·0% [1·7 to 4·3], p<0·0001). IOTA SRRisk model at 10% had similar sensitivity (difference of -4·3% [-11·0 to -2·3], p=0·23) but lower specificity (difference of 16·2% [12·6 to 19·8], p<0·0001). IOTA Simple Rules had similar sensitivity (difference of -1·6% [-9·3 to 6·2], p=0·82) and specificity (difference of -2·2% [-5·1 to 0·6], p=0·14). CA125 at 35 IU/mL had similar sensitivity (difference of -2·1% [-6·6 to 2·3], p=0·42) but higher specificity (difference of 6·7% [4·3 to 9·1], p<0·0001). In a post-hoc analysis, when compared with RMI1 at 250, ORADS achieved similar sensitivity (difference of -2·1%, 95% CI -8·6 to 4·3, p=0·60) and lower specificity (difference of 10·2%, 95% CI 6·8 to 13·6, p<0·0001).

In view of its higher sensitivity than RMI1 at 250, despite some loss in specificity, we recommend that IOTA ADNEX at 10% should be considered as the new standard-of-care diagnostic in ovarian cancer for postmenopausal patients.

UK National Institute of Heath Research.

To externally and prospectively validate the International Ovarian Tumor Analysis (IOTA) Simple Rules (SRs), Logistic Regression model 2 (LR2) and Assessment of Different NEoplasias in the adneXa (ADNEX) model in a Portuguese population, comparing these approaches with subjective assessment and the risk-of-malignancy index (RMI), as well as with each other. This study also aimed to retrospectively validate the IOTA two-step strategy, using modified benign simple descriptors (MBDs) followed by the ADNEX model in cases in which MBDs were not applicable.

This was a prospective multicenter diagnostic accuracy study conducted between January 2016 and December 2021 of consecutive patients with an ultrasound diagnosis of at least one adnexal tumor, who underwent surgery at one of three tertiary referral centers in Lisbon, Portugal. All ultrasound assessments were performed by Level-II or -III sonologists with IOTA certification. Patient clinical data and serum CA 125 levels were collected from hospital databases. Each adnexal mass was classified as benign or malignant using subjective assessment, RMI, IOTA SRs, LR2 and the ADNEX model (with and without CA 125). The reference standard was histopathological diagnosis. In the second phase, all adnexal tumors were classified retrospectively using the two-step strategy (MBDs + ADNEX). Sensitivity, specificity, positive and negative predictive values, positive and negative likelihood ratios and overall accuracy were determined for all methods. Receiver-operating-characteristics curves were constructed and corresponding areas under the curve (AUC) were determined for RMI, LR2, the ADNEX model and the two-step strategy. The ADNEX model calibration plots were constructed using locally estimated scatterplot smoothing (LOESS).

Of the 571 patients included in the study, 428 had benign disease and 143 had malignant disease (prevalence of malignancy, 25.0%), of which 42 had borderline ovarian tumor, 93 had primary invasive adnexal cancer and eight had metastatic tumors in the adnexa. Subjective assessment had an overall sensitivity of 97.9% and a specificity of 83.6% for distinguishing between benign and malignant lesions. RMI showed high specificity (95.6%) but very low sensitivity (58.7%), with an AUC of 0.913. The IOTA SRs were applicable in 80.0% of patients, with a sensitivity of 94.8% and specificity of 98.6%. The IOTA LR2 had a sensitivity of 84.6%, specificity of 86.9% and an AUC of 0.939, at a malignancy risk cut-off of 10%. At the same cut-off, the sensitivity, specificity and AUC for the ADNEX model with vs without CA 125 were 95.8% vs 98.6%, 82.5% vs 79.7% and 0.962 vs 0.960, respectively. The ADNEX model gave heterogeneous results for distinguishing between benign masses and different subtypes of malignancy, with the highest AUC (0.991) for discriminating benign masses from primary invasive adnexal cancer Stages II-IV, and the lowest AUC (0.696) for discriminating primary invasive adnexal cancer Stage I from metastatic lesion in the adnexa. The calibration plot suggested underestimation of the risk by the ADNEX model compared with the observed proportion of malignancy. The MBDs were applicable in 26.3% (150/571) of cases, of which none was malignant. The two-step strategy using the ADNEX model in the second step only, with and without CA 125, had AUCs of 0.964 and 0.961, respectively, which was similar to applying the ADNEX model in all patients.

The IOTA methods showed good-to-excellent performance in the Portuguese population, outperforming RMI. The ADNEX model was superior to other methods in terms of accuracy, but interpretation of its ability to distinguish between malignant subtypes was limited by sample size and large differences in the prevalence of tumor subtypes. The IOTA MBDs are reliable in identifying benign disease. The two-step strategy comprising application of MBDs followed by the ADNEX model if MBDs are not applicable, is suitable for daily clinical practice, circumventing the need to calculate the risk of malignancy in all patients. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

Few studies have evaluated the risk of cancer among older patients with stable adnexal masses in community-based settings to determine the duration of observation time needed.

This study aimed to assess the ovarian cancer risk among older patients with stable adnexal masses on ultrasound.

This was a retrospective cohort study of patients in a large community-based health system aged ≥50 years with an adnexal mass <10 cm on ultrasound between 2016 and 2020 who had at least 1 follow-up ultrasound performed ≥6 weeks after initial ultrasound. Masses were considered stable on follow-up examination if they did not exhibit an increase of >1 cm in the greatest dimension or a change in standardized reported ultrasound characteristics. Ovarian cancer risk was determined at increasing time intervals of stability after initial ultrasound.

Among 4061 patients with stable masses, the average age was 61 years (range, 50-99), with an initial mass size of 3.8 cm (range, 0.2-9.9). With a median follow-up of 3.7 years, 11 cancers were detected, with an absolute risk of 0.27%. Ovarian cancer risk declined with longer duration of stability, from 0.73 (95% confidence interval, 0.30-1.17) per 1000 person-years at 6 to 12 weeks, 0.63 (95% confidence interval, 0.19-1.07) at 13 to 24 weeks, 0.44 (95% confidence interval, 0.01-0.87) at 25 to 52 weeks, and 0.00 (95% confidence interval, 0.00-0.00) at >52 weeks. Expressed as number needed to reimage, ongoing ultrasound imaging would be needed for 369 patients whose masses show stability at 6 to 12 weeks, 410 patients at 13 to 24 weeks, 583 patients at 25 to 52 weeks, and >1142 patients with stable masses at 53 to 104 weeks to detect 1 case of ovarian cancer.

In a diverse community-based setting, among patients aged ≥50 years with an adnexal mass that was stable for at least 6 weeks after initial ultrasound, the risk of ovarian cancer was very low at 0.27%. Longer demonstrated duration of stability was associated with progressively lower risk, with no cancer cases observed after 52 weeks of stability. These findings suggest that the benefit of ultrasound monitoring of stable masses beyond 12 months is minimal and may be outweighed by potential risks of repeated imaging.

To investigate psychological correlates in women referred with suspected ovarian cancer via the fast-track pathway, explore how anxiety and distress levels change at 12 months post-testing, and report cancer conversion rates by age and referral pathway.

Single-arm prospective cohort study.

Multicentre. Secondary care including outpatient clinics and emergency admissions.

A cohort of 2596 newly presenting symptomatic women with a raised CA125 level, abnormal imaging or both.

Women completed anxiety and distress questionnaires at recruitment and at 12 months for those who had not undergone surgery or a biopsy within 3 months of recruitment.

Anxiety and distress levels measured using a six-item short form of the State-Trait Anxiety Inventory (STAI-6) and the Impact of Event Scale - Revised (IES-r) questionnaire. Ovarian cancer (OC) conversion rates by age, menopausal status and referral pathway.

Overall, 1355/2596 (52.1%) and 1781/2596 (68.6%) experienced moderate-to-severe distress and anxiety, respectively, at recruitment. Younger age and emergency presentations had higher distress levels. The clinical category for anxiety and distress remained unchanged/worsened in 76% of respondents at 12 months, despite a non-cancer diagnosis. The OC rates by age were 1.6% (95% CI 0.5%-5.9%) for age <40 years and 10.9% (95% CI 8.7%-13.6%) for age ≥40 years. In women referred through fast-track pathways, 3.3% (95% CI 1.9%-5.7%) of pre- and 18.5% (95% CI 16.1%-21.0%) of postmenopausal women were diagnosed with OC.

Women undergoing diagnostic testing display severe anxiety and distress. Younger women are especially vulnerable and should be targeted for support. Women under the age of 40 years have low conversion rates and we advocate reducing testing in this group to reduce the harms of testing.

The Ovarian-Adnexal Reporting and Data System (O-RADS) is an evidence-based clinical support system for ovarian and adnexal lesion assessment in women of average risk. The system has both US and MRI components with separate but complementary lexicons and assessment categories to assign the risk of malignancy. US is an appropriate initial imaging modality, and O-RADS US can accurately help to characterize most adnexal lesions. MRI is a valuable adjunct imaging tool to US, and O-RADS MRI can help to both confirm a benign diagnosis and accurately stratify lesions that are at risk for malignancy. This article will review the O-RADS US and MRI systems, highlight their similarities and differences, and provide an overview of the interplay between the systems. When used together, the O-RADS US and MRI systems can help to accurately diagnose benign lesions, assess the risk of malignancy in lesions suspicious for malignancy, and triage patients for optimal management.

Background Managing ovarian lesions requires differentiating between benign and malignant cases. The development of a multiparametric MRI approach combining anatomical and functional criteria has led to the creation of the Ovarian-Adnexal Reporting and Data System (O-RADS) MRI scoring system, which enhances diagnostic accuracy. Objectives To study ovarian lesions and their characteristics, along with their risk stratification based on MRI O-RADS. Methods  A prospective study used the O-RADS MRI criteria to categorize ovarian lesions. Clinical findings and MRI results were compared with histopathological outcomes to assess diagnostic accuracy. Results We identified abdominal pain as the most prevalent clinical finding among our cases (64, 91.43%), followed by a lump in the abdomen (33, 47.5%), dysmenorrhea (33, 47.5%), bleeding per vaginal (15, 21.43%), and weight loss (11, 15.71%). A total of 80 ovarian lesions were examined and characterized on the basis of the O-RADS MRI risk stratification system. Among the 80 ovarian lesions, 54 were histopathologically confirmed ovarian lesions (39 (72.22%) were benign, and 15 (27.77%) were malignant). The most common benign lesions were ovarian serous cystadenoma (28.20%) and ovarian mucinous cystadenoma (20.51%), while the most common malignant lesions were serous carcinoma (33.33%) and mucinous carcinoma (20%). Using the O-RADS MRI scoring system, we categorized six lesions (7.5%) as O-RADS 1 (all benign), 34 lesions (42.50%) as O-RADS 2 (32 benign and 2 malignant), 24 lesions (30%) as O-RADS 3 (23 benign and 1 malignant), seven lesions (8.75%) as O-RADS 4 (four benign and three malignant), and nine lesions (11.25%) as O-RADS 5 (all malignant). Our findings revealed significant differences in the size of lesions, the presence of thick septa, high T2-weighted signal intensity within solid tissue, and patterns of solid component enhancement and wall irregularity between malignant and benign lesions. The MRI cut-off score of ≥4 for malignancy demonstrated a sensitivity of 94.59%, a specificity of 97.5%, an accuracy of 97.62%, a positive predictive value of 94.5%, and a negative predictive value of 97.5%. The positive likelihood ratio was 32.7, while the negative likelihood ratio was 0.025. These results affirm the high diagnostic accuracy of the O-RADS MRI scoring system in distinguishing benign from malignant ovarian lesions. Conclusion The O-RADS MRI score is a highly accurate tool for differentiating between benign and malignant ovarian lesions. Its application can significantly enhance the management and treatment outcomes for patients with adnexal masses. The study confirms the scoring system's high sensitivity, specificity, and overall diagnostic accuracy.

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.

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).

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.

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

Endometriosis affects 10% of women of reproductive age. There is evidence for a left lateral predisposition of endometriotic lesions and a 1.9-fold greater risk of ovarian cancer in endometriosis. The aim of this study is to determine whether a left lateral predisposition of ovarian clear-cell carcinoma (CCC) and endometrioid carcinoma (EC) exists.

A retrospective cohort study of all EC and CCC patients in Northern Ireland between March-2011 and June-2018. ANOVA was used to analyse preoperative prediction of stage, chi-squared (χ2) was used to compare left- and right-sided masses. Survival was estimated using Kaplan-Meier and log-rank test. A p-value <0.05 was considered significant.

158 patients were identified (95 EC, 55 CCC, 8 mixed). Mean age was 57.65 years with 69% presenting at stage 1. The mean CA125 was 559 U/mL (p = 0.850) and mean abdominal mass size was 14.12 cm (p = 0.732). The most common presenting symptom was an abdominal mass (37%). Despite 67% of patients having endometriosis on final pathology, only 8.9% had a known history pre-operatively. 51% of tumours were located on the left (p = 0.036). For unilateral tumours this was significant for EC (P = 0.002) but not for CCC (P = 0.555). The 1-, 3- and 5-year overall survival for all types/stages was 85%, 78% and 71% respectively.

While CCC and EC are associated with endometriosis, only EC exhibits a left lateral predisposition. There is no association between preoperative CA125 or abdominal mass size and stage of disease.

Pre-surgical clinical assessment of an adnexal mass typically relies on transvaginal ultrasound for comprehensive morphological assessment, with further support provided by biomarker measurements and clinical evaluation. Whilst effective for masses that are obviously benign or malignant, a large proportion of masses remain sonographically indeterminate at surgical referral. As a consequence, post-surgical diagnoses of benign disease can outnumber malignancies up to 9-fold, while less than 50% of cancer cases receive a primary referral to a gynecological oncology specialist. We recently described a blood biomarker signature (multi-marker panel-MMP) that differentiated patients with benign from malignant ovarian disease with high accuracy. In this study, we have examined the use of the MMP, both individually and in combination with transvaginal ultrasound, as an alternative tool to CA-125 for enhanced decision making in the pre-surgical referral process.

Distant metastases of ovarian cancer are rarely detected alone. The effectiveness of surgical intervention for pulmonary metastases from ovarian cancer remains uncertain. This study aimed to investigate the clinicopathologic characteristics and outcomes of patients undergoing resection for pulmonary metastasis from ovarian cancer.

The clinicopathologic characteristics and outcomes of radical surgery for pulmonary metastasis from ovarian cancer were investigated. Out of 537 patients who underwent pulmonary metastasis resection at two affiliated hospitals between 2010 and 2021, four (0.74%) patients who underwent radical surgery for pulmonary metastasis from ovarian cancer were included. The patients were aged 67, 47, 21, and 59 years; the intervals from primary surgery to detection of pulmonary metastasis from ovarian cancer were 94, 21, 36, and 50 months; and the overall survival times after pulmonary metastasectomy were 53, 50, 94, and 34 months, respectively. Three of the four patients experienced recurrence after pulmonary metastasectomy. Further, preoperative carbohydrate antigen (CA) 125 levels were normal in two surviving patients and elevated in the two deceased patients.

In this study, three of the four patients experienced recurrence after pulmonary metastasectomy, but all patients survived for > 30 months after surgery. Patients with ovarian cancer and elevated CA125 levels may not be optimal candidates for pulmonary metastasectomy. To establish appropriate criteria for pulmonary metastasectomy in patients with ovarian cancer, further research on a larger patient cohort is warranted.

To assess the feasibility of scalable, objective, and minimally invasive liquid biopsy-derived biomarkers such as cell-free DNA copy number profiles, human epididymis protein 4 (HE4), and cancer antigen 125 (CA125) for pre-operative risk assessment of early-stage ovarian cancer in a clinically representative and diagnostically challenging population and to compare the performance of these biomarkers with the Risk of Malignancy Index (RMI).

In this case-control study, we included 100 patients with an ovarian mass clinically suspected to be early-stage ovarian cancer. Of these 100 patients, 50 were confirmed to have a malignant mass (cases) and 50 had a benign mass (controls). Using WisecondorX, an algorithm used extensively in non-invasive prenatal testing, we calculated the benign-calibrated copy number profile abnormality score. This score represents how different a sample is from benign controls based on copy number profiles. We combined this score with HE4 serum concentration to separate cases and controls.

Combining the benign-calibrated copy number profile abnormality score with HE4, we obtained a model with a significantly higher sensitivity (42% vs 0%; p<0.002) at 99% specificity as compared with the RMI that is currently employed in clinical practice. Investigating performance in subgroups, we observed especially large differences in the advanced stage and non-high-grade serous ovarian cancer groups.

This study demonstrates that cell-free DNA can be successfully employed to perform pre-operative risk of malignancy assessment for ovarian masses; however, results warrant validation in a more extensive clinical study.

In recent years, rapid advancement in gene/protein analysis technology has resulted in target molecule identification that may be useful in cancer treatment. Therefore, "Clinical Practice Guidelines for Molecular Tumor Marker, Second Edition" was published in Japan in September 2021. These guidelines were established to align the clinical usefulness of external diagnostic products with the evaluation criteria of the Pharmaceuticals and Medical Devices Agency. The guidelines were scoped for each tumor, and a clinical questionnaire was developed based on a serious clinical problem. This guideline was based on a careful review of the evidence obtained through a literature search, and recommendations were identified following the recommended grades of the Medical Information Network Distribution Services (Minds). Therefore, this guideline can be a tool for cancer treatment in clinical practice. We have already reported the review portion of "Clinical Practice Guidelines for Molecular Tumor Marker, Second Edition" as Part 1. Here, we present the English version of each part of the Clinical Practice Guidelines for Molecular Tumor Marker, Second Edition.

One of the greatest challenges in modern gynecological oncology is ovarian cancer. Despite the numerous studies currently being conducted, it is still sometimes detected at late clinical stages, where the prognosis is unfavorable. One significant contributing factor is the absence of sensitive and specific parameters that could aid in early diagnosis. An ideal screening test, in view of the low incidence of ovarian cancer, should have a sensitivity of greater than 75% and a specificity of at least 99.6%. To enhance sensitivity and specificity, diagnostic panels are being created by combining individual markers. The drive to develop better screening tests for ovarian cancer focuses on modern diagnostic methods based on molecular testing, which in turn aims to find increasingly effective biomarkers. Currently, researchers' efforts are focused on the search for a complementary parameter to those most commonly used that would satisfactorily enhance the sensitivity and specificity of assays. Several biomarkers, including microRNA molecules, autoantibodies, cDNA, adipocytokines, and galectins, are currently being investigated by researchers. This article reviews recent studies comparing the sensitivity and specificity of selected parameters used alone and in combination to increase detection of ovarian cancer at an early stage.

Ovarian cancer is one of the most aggressive and significant malignant tumor forms in the female reproductive system. It is the leading cause of death among gynecological cancers owing to its metastasis. Since its preliminary disease symptoms are lacking, it is imperative to develop early diagnostic biomarkers to aid in treatment optimization and personalization. In this vein, microRNAs, which are short sequence non-coding molecules, displayed great potential as highly specific and sensitive biomarker. miRNAs have been extensively advocated and proven to serve an instrumental part in the clinical management of cancer, especially ovarian cancer, by promoting the cancer cell progression, invasion, delayed apoptosis, epithelial-mesenchymal transition, metastasis of cancer cells, chemosensitivity and resistance and disease therapy. Here, we cover our present comprehension of the most up-to-date microRNA-based approaches to detect ovarian cancer, as well as current diagnostic and treatment strategies, the role of microRNAs as oncogenes or tumor suppressor genes, and their significance in ovarian cancer progression, prognosis, and therapy.

Accurate prediction of ovarian masses preoperatively is crucial for optimal management of ovarian cancers.

The objective of this study was to identify the risk of malignancy index (RMI) incorporating menopausal status, serum carbohydrate antigen 125 levels, and imaging findings for presurgical differentiation of benign from malignant ovarian masses and to evaluate the diagnostic ability of four different RMIs.

Women presenting with ovarian masses from August 2018 to January 2020 were evaluated preoperatively with detailed history, examination, imaging, and tumor markers. RMI 1-4 was calculated for all patients. Evaluation of the diagnostic utility of four different RMIs for preoperative identification of malignancy was based on the increment of the area under the receiver operating characteristic curve. Histopathological diagnosis was used as the gold standard test.

One hundred and twenty-one patients fulfilling the eligibility criteria were enrolled in this study. Benign tumors constituted 61 (50.4%) out of 121 cases, followed by malignant tumors and borderline tumors constituting 49 (40.49%) cases and 11 (9.09%) cases, respectively. The sensitivity of RMIs 1, 2, 3, and 4 was 77.0%, 63%, 77.0%, and 77.0%, respectively, and the specificity was 84%, 86%, 77%, and 71%, respectively. The RMI 2 had higher specificity at predicting malignancy than other RMIs while diagnostic accuracy was highest in RMI 1.

The RMI method is a simple and cost-effective technique in preoperative differentiation of ovarian masses.

Several diagnostic prediction models to help clinicians discriminate between benign and malignant adnexal masses are available. This study is a head-to-head comparison of the performance of the Assessment of Different NEoplasias in the adneXa (ADNEX) model with that of the Risk of Ovarian Malignancy Algorithm (ROMA).

This is a retrospective study based on prospectively included consecutive women with an adnexal tumour scheduled for surgery at five oncology centres and one non-oncology centre in four countries between 2015 and 2019. The reference standard was histology. Model performance for ADNEX and ROMA was evaluated regarding discrimination, calibration, and clinical utility.

The primary analysis included 894 patients, of whom 434 (49%) had a malignant tumour. The area under the receiver operating characteristic curve (AUC) was 0.92 (95% CI 0.88-0.95) for ADNEX with CA125, 0.90 (0.84-0.94) for ADNEX without CA125, and 0.85 (0.80-0.89) for ROMA. ROMA, and to a lesser extent ADNEX, underestimated the risk of malignancy. Clinical utility was highest for ADNEX. ROMA had no clinical utility at decision thresholds <27%.

ADNEX had better ability to discriminate between benign and malignant adnexal tumours and higher clinical utility than ROMA.

clinicaltrials.gov NCT01698632 and NCT02847832.

IOTA proposed Simple Ultrasound Rules in 2009 for preoperative diagnosis of ovarian masses based on ultrasound only. It is an accurate, simple and inexpensive method. RMI, however, requires CA125 level. While RMI-4 is the latest, RMI-1 is still the most widely used method. The present study was done to compare IOTA Rules with RMI-1 and RMI-4.

To differentiate benign and malignant adnexal masses preoperatively using IOTA simple rules and compare its accuracy with RMI-1 and RMI-4.

A prospective observational study was performed from 1st November 2019 to 31st March 2021 in the Department of Obstetrics and Gynaecology, ABVIMS and Dr. RML Hospital, New Delhi. This study was conducted on 70 patients with adnexal masses who underwent pre-operative evaluation using IOTA Simple Rules, RMI-1 and RMI-4. Histopathology was used to compare the results.

Out of 70 patients, 59 (84.3%) cases were benign and 11 (15.7%) were malignant. The IOTA Rules were applicable to 60 cases (85.7%), and the results were inconclusive in 10 cases (14.3%). Where applicable, the sensitivity and specificity of the IOTA Rules (88.9% and 94.1%, respectively) were significantly higher than RMI-1 (45.5% and 93.2%, respectively) and RMI-4 (45.5% and 89.8%, respectively). When inconclusive results were included as malignant, the sensitivity of the IOTA Rules increased (88.9% vs 90.9%); however, the specificity decreased (94.1% vs 81.4%).

IOTA Simple Rules were more accurate at diagnosing benign from malignant adnexal masses than RMI-1 and RMI-4. However, the rules were not applicable to 14% of the cases.

The 28-days-to-diagnosis pathway is the current expected standard of care for women with symptoms of ovarian cancer in the UK. However, the anticipated conversion rate of symptoms to cancer is only 3%, and use of the pathway is increasing. A rapid triage at the moment of receipt of the referral might allow resources to be allocated more appropriately. In secondary care, multidisciplinary teams (MDTs) use the risk of malignancy index (RMI) score, (multiply menopausal status pre = 1 or post = 3 × ultrasound score = 0 - 3 × the CA 125 level), using a score of >200, to triage urgency and management in possible ovarian cancer cases. The most powerful determinant of the RMI score variables is CA 125 level, an objective number. Could a simple modification of the RMI score retain a high sensitivity for cancer whilst improving specificity and, consequently, decrease the morbidity of false-positive classification? To test this hypothesis, a retrospective evaluation of an ovarian two-week-wait telephone clinic of one consultant gynaecological oncologist was undertaken. Enquiry re menopause status was scored as one for pre- and three for postmenopausal or uncertain. CA 125 levels of >67 u/mL for premenopausal and >23 u/mL for postmenopausal women were used to precipitate urgent cross-sectional imaging requests and MDT opinions. These CA 125 cut thresholds were calculated using an assumption that the RMI imaging score, regardless of whether the result was available, could be three. We contemplate that women who did not exceed a provisional RMI score of >200 might be informed they are extremely unlikely to have cancer, removed from the malignancy tracker and appropriate follow-up arranged. One hundred and forty consecutive cases were analysed; 43% were deemed premenopausal and 57% postmenopausal. Twenty of the women had cancer, eighteen (90%) of whom had an RMI > 200. One hundred and twenty were benign, and only twenty-three (19%) classified as urgent cases in need of accelerated referral to imaging. In contrast, CA 125 > 35 u/mL, whilst retaining the sensitivity of 90%, misclassified 36 (30%) of the benign cases. It is possible that a telephone triage via a questionnaire determining menopausal status and the CA 125 result could offer a sensitivity for cancer of 90% and urgent expert review of under 20% of benign cases. This rapid initial telephone assessment could be presented by a trained pathway navigator, physician associate or nurse specialist. Substantial savings in NHS cancer services resources, anxieties all around and reduced patient morbidity may occur as a result.