Benign descriptors and ADNEX in two-step strategy to estimate risk of malignancy in ovarian tumors: retrospective validation in IOTA5 multicenter cohort

MUC16/CA125 in cancer: new advances

MUC16/CA125 is a common diagnostic marker for many types of cancer. However, due to the widespread expression of MUC16 in cancer, its specificity and sensitivity as a target are poor, which severely limits its clinical application. In recent years, various studies have shown that the clinical application potential of MUC16/CA125 has been greatly improved. The update of detection technology improves the accuracy and range of detection, and improves the early diagnosis rate of cancer. Targeting MUC16/CA125 is an important strategy for tumor therapy. Targeting residual amino acids, n-glycoylation structures or other targets on the surface of MUC16 cells can greatly improve the accuracy of detection and therapy. The new drug delivery method broke through the original technical shackles, targeted MUC16 positive cells more specifically and improved the drug efficacy. In this paper, the technological advances in detecting and identifying MUC16 targets and the great progress in cancer screening and treatment based on MUC16 as a target are described in detail, revealing the great potential of MUC16 as a target in cancer screening and treatment, and illustrating the potential clinical application value of MUC16.

Evaluating use of two-step International Ovarian Tumor Analysis strategy to classify adnexal masses identified in pregnancy: pilot study

OBJECTIVES

The primary aim was to validate the International Ovarian Tumor Analysis (IOTA) benign simple descriptors (BDs) followed by the Assessment of Different NEoplasias in the adneXa (ADNEX) model, if BDs cannot be applied, in a two-step strategy to classify adnexal masses identified during pregnancy. The secondary aim was to describe the natural history of adnexal masses during pregnancy.

METHODS

This was a retrospective analysis of prospectively collected data from women with an adnexal mass identified on ultrasonography during pregnancy between 2017 and 2022 at Queen Charlotte's and Chelsea Hospital, London, UK. Clinical and ultrasound data were extracted from medical records and ultrasound software. Adnexal masses were classified and managed according to expert subjective assessment (SA). Borderline ovarian tumors (BOTs) were classified as malignant. BDs were applied retrospectively to classify adnexal masses, and if BDs were not applicable, the ADNEX model (using a risk- of-malignancy threshold ≥ 10%) was used, in a two-step strategy. The reference standard was histology (where available) or expert SA at the postnatal ultrasound scan.

RESULTS

A total of 291 women with a median age of 33 (interquartile range (IQR), 29-36) years presented with an adnexal mass during pregnancy, at a median gestational age of 12 (IQR, 8-17) weeks. Of those, 267 (91.8%) were followed up to the postnatal period. Based on the reference standard, 4.1% (11/267) of adnexal masses were classified as malignant (all BOTs) and 95.9% (256/267) as benign. BDs were applicable in 68.9% (184/267) of adnexal masses; of these, only one (0.5%) BOT was misclassified as benign. The ADNEX model was used to classify the 83 residual masses and misclassified 3/10 (30.0%) BOTs as benign and 25/73 (34.2%) benign masses as malignant, of which 13/25 (52.0%) were classified as decidualized endometrioma on expert SA. The two-step strategy had a specificity of 90.2%, sensitivity of 63.6%, negative predictive value of 98.3% and positive predictive value of 21.9%. A total of 56 (21.0%) women underwent surgical intervention: four (1.5%) as an emergency during pregnancy, four (1.5%) electively during Cesarean section and 48 (18.0%) postnatally. During follow-up, 64 (24.0%) adnexal masses resolved spontaneously. Cyst-related complications occurred in four (1.5%) women during pregnancy (ovarian torsion, n = 2; cyst rupture, n = 2) and six (2.2%) women in the postnatal period (all ovarian torsion). Overall, 196/267 (73.4%) women had a persistent adnexal mass at postnatal ultrasound. Presumed decidualization occurred in 31.1% (19/61) of endometriomas and had resolved in 89.5% (17/19) by the first postnatal ultrasound scan.

CONCLUSIONS

BDs apply to most adnexal masses during pregnancy. However, the small number of malignant tumors in this cohort (4.1%) restricted the evaluation of the ADNEX model, so expert SA should be used to classify adnexal masses during pregnancy when BDs do not apply. A larger multicenter prospective study is required to evaluate the use of the ADNEX model to classify adnexal masses during pregnancy. Our data suggest that most adnexal masses can be managed expectantly during pregnancy, given the high rate of spontaneous resolution and low risk of complications. © 2024 The Author(s). Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Exploratory study on the enhancement of O-RADS application effectiveness for novice ultrasonographers via deep learning

PURPOSE

The study aimed to create a deep convolutional neural network (DCNN) model based on ConvNeXt-Tiny to identify classic benign lesions (CBL) from other lesions (OL) within the Ovarian-Adnexal Reporting and Data System (O-RADS), enhancing the system's utility for novice ultrasonographers.

METHODS

Two sets of sonographic images of pathologically confirmed adnexal lesions were retrospectively collected [development dataset (DD) and independent test dataset (ITD)]. The ConvNeXt-Tiny model, optimized through transfer learning, was trained on the DD using the original images directly and after automatic lesion segmentation by a U-Net model. Models derived from both training paradigms were validated on the ITD for sensitivity, specificity, accuracy, and area under the curve (AUC). Two novice ultrasonographers were assessed in O-RADS with and without assistance from the model for Application Effectiveness.

RESULTS

The ConvNeXt-Tiny model trained on original images scored AUCs of 0.978 for DD and 0.955 for ITD, while the U-Net segmented image model achieved 0.967 for DD and 0.923 for ITD; neither showed significant differences. When assessing the malignancy of lesions using O-RADS 4 and 5, the diagnostic performances of two novice ultrasonographers and senior ultrasonographer, as well as model-assisted classifications, showed no significant differences, except for one novice's low accuracy. This approach reduced classification time by 62 and 64 min. The kappa values with senior doctors' classifications rose from 0.776 and 0.761 to 0.914 and 0.903, respectively.

CONCLUSION

The ConvNeXt-Tiny model demonstrated excellent and stable performance in distinguishing CBL from OL within O-RADS. The diagnostic performance of novice ultrasonographers using O-RADS is essentially equivalent to that of senior ultrasonographer, and the assistance of the model can enhance their classification efficiency and consistency with the results of senior ultrasonographer.

Role of artificial intelligence applied to ultrasound in gynecology oncology: A systematic review

The aim of this paper was to explore the role of artificial intelligence (AI) applied to ultrasound imaging in gynecology oncology. Web of Science, PubMed, and Scopus databases were searched. All studies were imported to RAYYAN QCRI software. The overall quality of the included studies was assessed using QUADAS-AI tool. Fifty studies were included, of these 37/50 (74.0%) on ovarian masses or ovarian cancer, 5/50 (10.0%) on endometrial cancer, 5/50 (10.0%) on cervical cancer, and 3/50 (6.0%) on other malignancies. Most studies were at high risk of bias for subject selection (i.e., sample size, source, or scanner model were not specified; data were not derived from open-source datasets; imaging preprocessing was not performed) and index test (AI models was not externally validated) and at low risk of bias for reference standard (i.e., the reference standard correctly classified the target condition) and workflow (i.e., the time between index test and reference standard was reasonable). Most studies presented machine learning models (33/50, 66.0%) for the diagnosis and histopathological correlation of ovarian masses, while others focused on automatic segmentation, reproducibility of radiomics features, improvement of image quality, prediction of therapy resistance, progression-free survival, and genetic mutation. The current evidence supports the role of AI as a complementary clinical and research tool in diagnosis, patient stratification, and prediction of histopathological correlation in gynecological malignancies. For example, the high performance of AI models to discriminate between benign and malignant ovarian masses or to predict their specific histology can improve the diagnostic accuracy of imaging methods.

Clinical Utility and Diagnostic Accuracy of ROMA, RMI, ADNEX, HE4, and CA125 in the Prediction of Malignancy in Adnexal Masses

OBJECTIVE

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).

METHODS

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).

RESULTS

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%.

CONCLUSIONS

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.

Prospective external validation of IOTA methods for classifying adnexal masses and retrospective assessment of two-step strategy using benign descriptors and ADNEX model: Portuguese multicenter study

OBJECTIVES

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.

METHODS

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).

RESULTS

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.

CONCLUSIONS

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.

Validation of ADNEX and IOTA two-step strategy and estimation of risk of complications during follow-up of adnexal masses in low-risk population

OBJECTIVES

To evaluate the ability of the Assessment of Different NEoplasias in the adneXa (ADNEX) model and the International Ovarian Tumour Analysis (IOTA) two-step strategy to predict malignancy in adnexal masses detected in an outpatient low-risk setting, and to estimate the risk of complications in masses with benign ultrasound morphology managed using clinical and ultrasound follow-up.

METHODS

This single-center study was performed at Hospital Universitari Dexeus, Barcelona, Spain, using interim data from the ongoing prospective observational IOTA Phase-5 (IOTA5) study. The primary aim of the IOTA5 study is to describe the cumulative incidence of complications during follow-up of adnexal masses classified as benign on ultrasound examination. Consecutive patients with an adnexal mass detected between June 2012 and September 2016 in a private center offering screening for gynecological cancer were included and followed up until February 2020. Tumors were classified as benign or malignant based on histology (if patients underwent surgery) or the outcome of clinical and ultrasound follow-up at 12 (range, 10-14) months. Multiple imputation was used when outcomes were uncertain. The ability of the ADNEX model without CA125 and of the IOTA two-step strategy to distinguish benign from malignant masses was evaluated retrospectively using the prospectively collected data. We assessed performance with regard to discrimination (area under the receiver-operating-characteristics curve (AUC)), calibration, classification (sensitivity and specificity) and clinical utility (Net Benefit). In the group of patients with a mass judged to be benign who were selected for conservative management, we evaluated the occurrence of spontaneous resolution or any mass complication during the first 5 years of follow-up by assessing the cumulative incidence of malignancy, torsion, cyst rupture and minor mass complications (inflammation, infection or adhesions) and the time to occurrence of an event.

RESULTS

A total of 2654 patients were recruited to the study. After application of exclusion criteria, 2039 patients with a newly detected mass were included for the model validation. Of those, 1684 (83%) masses were benign, 49 (2%) masses were malignant and, for 306 (15%) masses, the outcome was uncertain and therefore imputed. The AUC was 0.95 (95% CI, 0.89-0.98) for ADNEX without CA125 and 0.94 (95% CI, 0.88-0.97) for the two-step strategy. Calibration performance could not be meaningfully interpreted because the small number of malignancies resulted in very wide confidence intervals. The two-step strategy had better clinical utility than did the ADNEX model at malignancy risk thresholds < 3%. There were 1472 (72%) patients whose mass was judged to be benign based on pattern recognition by an experienced ultrasound examiner and were managed with clinical and ultrasound follow-up. In this group, the 5-year cumulative incidence was 66% (95% CI, 63-69%) for spontaneous resolution of the mass, 0% (95% CI, 0-0.2%) for torsion, 0.1% (95% CI, < 0.1-0.4%) for cyst rupture, 0.2% (95% CI, 0.1-0.6%) for a borderline tumor and 0.2% (95% CI, 0.1-0.6%) for invasive malignancy.

CONCLUSIONS

The ADNEX model and IOTA two-step strategy performed well to distinguish benign from malignant adnexal masses detected in a low-risk population. Conservative management is safe for masses with a benign ultrasound appearance in this population. © 2024 International Society of Ultrasound in Obstetrics and Gynecology.

The Roles of Ovarian-Adnexal Reporting and Data System US and Ovarian-Adnexal Reporting and Data System MRI in the Evaluation of Adnexal Lesions

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.

Staging by imaging in gynecologic cancer and the role of ultrasound: an update of European joint consensus statements

In recent years the role of diagnostic imaging by pelvic ultrasound in the diagnosis and staging of gynecological cancers has been growing exponentially. Evidence from recent prospective multicenter studies has demonstrated high accuracy for pre-operative locoregional ultrasound staging in gynecological cancers. Therefore, in many leading gynecologic oncology units, ultrasound is implemented next to pelvic MRI as the first-line imaging modality for gynecological cancer. The work herein is a consensus statement on the role of pre-operative imaging by ultrasound and other imaging modalities in gynecological cancer, following European Society guidelines.

Imaging in gynecological disease (27): clinical and ultrasound characteristics of recurrent ovarian stromal cell tumors

OBJECTIVE

To describe the clinical and ultrasound characteristics of recurrent granulosa cell and Sertoli-Leydig cell tumors.

METHODS

This was a retrospective observational study performed at Fondazione Policlinico Universitario A. Gemelli, Istituto di Ricovero e Cura a Carattere Scientifico, IRCCS, Rome (Gemelli center), Italy. Patients with a histological diagnosis of recurrent granulosa cell tumor or Sertoli-Leydig cell tumor were identified from the database of the Department of Gynecological Oncology. Those who had undergone a preoperative ultrasound examination at the Gemelli center between 2012 and 2020 were included, and the data retrieved from the original ultrasound reports. In all of these reports, the recurrent tumors were described using International Ovarian Tumor Analysis (IOTA) terminology. If a patient had more than one episode of relapse, information from all episodes was collected. If there was more than one recurrent tumor at the same ultrasound examination, all tumors were included. One expert sonographer also reviewed all available ultrasound images to identify typical ultrasound patterns using pattern recognition.

RESULTS

We identified 30 patients with a histological diagnosis of recurrent granulosa cell tumor (25 patients, 55 tumors) or Sertoli-Leydig cell tumor (five patients, seven tumors). All 30 had undergone at least one preoperative ultrasound examination at the Gemelli center and were included. These women had a total of 66 episodes of relapse, of which a preoperative ultrasound examination had been performed at the Gemelli center in 34, revealing 62 recurrent lesions: one in 22/34 (64.7%) episodes of relapse, two in 4/34 (11.8%) episodes and three or more in 8/34 (23.5%) episodes. Most recurrent granulosa cell tumors (38/55, 69.1%) and recurrent Sertoli-Leydig tumors (6/7, 85.7%) were classified as solid or multilocular-solid tumors, while 8/55 (14.5%) recurrent granulosa cell tumors and 1/7 (14.3%) recurrent Sertoli-Leydig cell tumors were unilocular cysts and 9/55 (16.4%) recurrent granulosa cell tumors were multilocular cysts. The nine unilocular cysts had contents that were anechoic (n = 2) or had low-level echogenicity (n = 7), had either smooth (n = 4) or irregular (n = 5) internal cyst walls, and ranged in largest diameter from 8 to 38 mm, with three being < 20 mm and five being 20-30 mm. On retrospective review of the images, two typical ultrasound patterns were described: small solid tumor measuring < 2 cm (15/62, 24.2%) and tumor with vascularized echogenic ground-glass-like content (12/62, 19.4%).

CONCLUSIONS

Some granulosa cell and Sertoli-Leydig cell recurrences manifest one of two typical ultrasound patterns, while some appear as unilocular cysts. These are usually classified as benign, but in patients being followed up for a granulosa cell tumor or Sertoli-Leydig cell tumor, a unilocular cyst should be considered suspicious of recurrence. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

ADNEX risk prediction model for diagnosis of ovarian cancer: systematic review and meta-analysis of external validation studies

Objectives

To conduct a systematic review of studies externally validating the ADNEX (Assessment of Different Neoplasias in the adnexa) model for diagnosis of ovarian cancer and to present a meta-analysis of its performance.

Design

Systematic review and meta-analysis of external validation studies.

Data sources

Medline, Embase, Web of Science, Scopus, and Europe PMC, from 15 October 2014 to 15 May 2023.

Eligibility criteria for selecting studies

All external validation studies of the performance of ADNEX, with any study design and any study population of patients with an adnexal mass. Two independent reviewers extracted the data. Disagreements were resolved by discussion. Reporting quality of the studies was scored with the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) reporting guideline, and methodological conduct and risk of bias with PROBAST (Prediction model Risk Of Bias Assessment Tool). Random effects meta-analysis of the area under the receiver operating characteristic curve (AUC), sensitivity and specificity at the 10% risk of malignancy threshold, and net benefit and relative utility at the 10% risk of malignancy threshold were performed.

Results

47 studies (17 007 tumours) were included, with a median study sample size of 261 (range 24-4905). On average, 61% of TRIPOD items were reported. Handling of missing data, justification of sample size, and model calibration were rarely described. 91% of validations were at high risk of bias, mainly because of the unexplained exclusion of incomplete cases, small sample size, or no assessment of calibration. The summary AUC to distinguish benign from malignant tumours in patients who underwent surgery was 0.93 (95% confidence interval 0.92 to 0.94, 95% prediction interval 0.85 to 0.98) for ADNEX with the serum biomarker, cancer antigen 125 (CA125), as a predictor (9202 tumours, 43 centres, 18 countries, and 21 studies) and 0.93 (95% confidence interval 0.91 to 0.94, 95% prediction interval 0.85 to 0.98) for ADNEX without CA125 (6309 tumours, 31 centres, 13 countries, and 12 studies). The estimated probability that the model has use clinically in a new centre was 95% (with CA125) and 91% (without CA125). When restricting analysis to studies with a low risk of bias, summary AUC values were 0.93 (with CA125) and 0.91 (without CA125), and estimated probabilities that the model has use clinically were 89% (with CA125) and 87% (without CA125).

Conclusions

The results of the meta-analysis indicated that ADNEX performed well in distinguishing between benign and malignant tumours in populations from different countries and settings, regardless of whether the serum biomarker, CA125, was used as a predictor. A key limitation was that calibration was rarely assessed.

Systematic review registration

PROSPERO CRD42022373182.

Current and Emerging Strategies for Tubo-Ovarian Cancer Diagnostics

Tubo-ovarian cancer is the most lethal gynaecological cancer. More than 75% of patients are diagnosed at an advanced stage, which is associated with poorer overall survival. Symptoms at presentation are vague and non-specific, contributing to late diagnosis. Multimodal risk models have improved the diagnostic accuracy of adnexal mass assessment based on patient risk factors, coupled with findings on imaging and serum-based biomarker tests. Newly developed ultrasonographic assessment algorithms have standardised documentation and enable stratification of care between local hospitals and cancer centres. So far, no screening test has proven to reduce ovarian cancer mortality in the general population. This review is an update on the evidence behind ovarian cancer diagnostic strategies.

Simple rules, O-RADS, ADNEX and SRR model: Single oncologic center validation of diagnostic predictive models alone and combined (two-step strategy) to estimate the risk of malignancy in adnexal masses and ovarian tumors

OBJECTIVE

To compare performance of Assessment of Different NEoplasias in the adneXa (ADNEX model), Ovarian-Adnexal Reporting and Data System (O-RADS), Simple Rules Risk (SRR) assessment and the two-step strategy based on the application of Simple Rules (SR) followed by SRR and SR followed by ADNEX in the pre-operative discrimination between benign and malignant adnexal masses (AMs).

METHODS

We conducted a retrospective study from January-2018 to December-2021 in which consecutive patients with at AMs were recruited. Accuracy metrics included sensitivity (SE) and specificity (SP) with their 95% confidence intervals (CI) were calculated for ADNEX, O-RADS and SRR. When SR was inconclusive a "two-step strategy" was adopted applying SR + ADNEX model and SR + SRR assessment.

RESULTS

A total of 514 women were included, 400 (77.8%) had a benign ovarian tumor and 114 (22.2%) had a malignant tumor. At a threshold malignancy risk of >10%, the SE and SP of ADNEX model, O-RADS and SRR were: 0.92 (95% CI, 0.86-0.96) and 0.88 (95% CI, 0.85-0.91); 0.93 (95% CI, 0.87-0.97) and 0.89 (95% CI, 0.96-0.92); 0.88 (95% CI, 0.80-0.93) and 0.84 (95% CI, 0.80-0.87), respectively. When we applied SR, 109 (21.2%) cases resulted inconclusive. The SE and SP of two-step strategy SR + SRR assessment and SR + ADNEX model were 0.88 (95% CI, 0.80-0.93) and 0.92 (95% CI, 0.89-0.94), SR + ADNEX model 0.90 (95% CI, 0.83-0.95) and 0.93 (95% CI, 0.90-0.96), respectively.

CONCLUSIONS

O-RADS presented the highest SE, similar to ADNEX model and SR + ADNEX model. However, the SR + ADNEX model presented the higher performance accuracy with the higher SP and PPV. This two-step strategy, SR and ADNEX model applicated to inconclusive SR, is convenient for clinical evaluation.

O-RADS US v2022: An Update from the American College of Radiology's Ovarian-Adnexal Reporting and Data System US Committee

First published in 2019, the Ovarian-Adnexal Reporting and Data System (O-RADS) US provides a standardized lexicon for ovarian and adnexal lesions, enables stratification of these lesions with use of a numeric score based on morphologic features to indicate the risk of malignancy, and offers management guidance. This risk stratification system has subsequently been validated in retrospective studies and has yielded good interreader concordance, even with users of different levels of expertise. As use of the system increased, it was recognized that an update was needed to address certain clinical challenges, clarify recommendations, and incorporate emerging data from validation studies. Additional morphologic features that favor benignity, such as the bilocular feature for cysts without solid components and shadowing for solid lesions with smooth contours, were added to O-RADS US for optimal risk-appropriate scoring. As O-RADS US 4 has been shown to be an appropriate cutoff for malignancy, it is now recommended that lower-risk O-RADS US 3 lesions be followed with US if not excised. For solid lesions and cystic lesions with solid components, further characterization with MRI is now emphasized as a supplemental evaluation method, as MRI may provide higher specificity. This statement summarizes the updates to the governing concepts, lexicon terminology and assessment categories, and management recommendations found in the 2022 version of O-RADS US.

External Validation of the Ovarian-Adnexal Reporting and Data System (O-RADS) Lexicon and the International Ovarian Tumor Analysis 2-Step Strategy to Stratify Ovarian Tumors Into O-RADS Risk Groups

Importance

Correct diagnosis of ovarian cancer results in better prognosis. Adnexal lesions can be stratified into the Ovarian-Adnexal Reporting and Data System (O-RADS) risk of malignancy categories with either the O-RADS lexicon, proposed by the American College of Radiology, or the International Ovarian Tumor Analysis (IOTA) 2-step strategy.

Objective

To investigate the diagnostic performance of the O-RADS lexicon and the IOTA 2-step strategy.

Design, Setting, and Participants

Retrospective external diagnostic validation study based on interim data of IOTA5, a prospective international multicenter cohort study, in 36 oncology referral centers or other types of centers. A total of 8519 consecutive adult patients presenting with an adnexal mass between January 1, 2012, and March 1, 2015, and treated either with surgery or conservatively were included in this diagnostic study. Twenty-five patients were excluded for withdrawal of consent, 2777 were excluded from 19 centers that did not meet predefined data quality criteria, and 812 were excluded because they were already in follow-up at recruitment. The analysis included 4905 patients with a newly detected adnexal mass in 17 centers that met predefined data quality criteria. Data were analyzed from January 31 to March 1, 2022.

Exposures

Stratification into O-RADS categories (malignancy risk <1%, 1% to <10%, 10% to <50%, and ≥50%). For the IOTA 2-step strategy, the stratification is based on the individual risk of malignancy calculated with the IOTA 2-step strategy.

Main Outcomes and Measures

Observed prevalence of malignancy in each O-RADS risk category, as well as sensitivity and specificity. The reference standard was the status of the tumor at inclusion, determined by histology or clinical and ultrasonographic follow-up for 1 year. Multiple imputation was used for uncertain outcomes owing to inconclusive follow-up information.

Results

Median age of the 4905 patients was 48 years (IQR, 36-62 years). Data on race and ethnicity were not collected. A total of 3441 tumors (70%) were benign, 978 (20%) were malignant, and 486 (10%) had uncertain classification. Using the O-RADS lexicon resulted in 1.1% (24 of 2196) observed prevalence of malignancy in O-RADS 2, 4% (34 of 857) in O-RADS 3, 27% (246 of 904) in O-RADS 4, and 78% (732 of 939) in O-RADS 5; the corresponding results for the IOTA 2-step strategy were 0.9% (18 of 1984), 4% (58 of 1304), 30% (206 of 690), and 82% (756 of 927). At the 10% risk threshold (O-RADS 4-5), the O-RADS lexicon had 92% sensitivity (95% CI, 87%-96%) and 80% specificity (95% CI, 74%-85%), and the IOTA 2-step strategy had 91% sensitivity (95% CI, 84%-95%) and 85% specificity (95% CI, 80%-88%).

Conclusions and Relevance

The findings of this external diagnostic validation study suggest that both the O-RADS lexicon and the IOTA 2-step strategy can be used to stratify patients into risk groups. However, the observed malignancy rate in O-RADS 2 was not clearly below 1%.