ESUR recommendations for MR imaging of the sonographically indeterminate adnexal mass: an update

CT-based radiomic analysis for categorization of ovarian sex cord-stromal tumors and epithelial ovarian cancers

PURPOSE

To evaluate the diagnostic potential of radiomic analyses based on machine learning that rely on contrast-enhanced computerized tomography (CT) for categorizing ovarian sex cord-stromal tumors (SCSTs) and epithelial ovarian cancers (EOCs).

METHODS

We included a total of 225 patients with 230 tumors, who were randomly divided into training and test cohorts with a ratio of 8:2. Radiomic features were extracted from each tumor and dimensionally reduced using LASSO. We used univariate and multivariate analyses to identify independent predictors from clinical features and conventional CT parameters. Clinic-radiological model, radiomics model and mixed model were constructed respectively. We evaluated model performance via analysis of the receiver operating characteristic (ROC) curve and area under ROC curves (AUCs), and compared it across models using the Delong test.

RESULTS

We selected a support vector machine as the best classifier. Both radiomic and mixed model achieved good classification accuracy with AUC values of 0.923/0.930 in the training cohort, and 0.879/0.909 in the test cohort. The mixed model performed significantly better than the model based on clinical radiological information, with AUC values of 0.930 versus 0.826 (p = 0.000) in the training cohort and 0.905 versus 0.788 (p = 0.042) in the test cohort.

CONCLUSION

Radiomic analysis based on CT images is a reliable and noninvasive tool for identifying SCSTs and EOCs, outperforming experience radiologists.

ESR Essentials: characterisation and staging of adnexal masses with MRI and CT-practice recommendations by ESUR

Ovarian masses encompass various conditions, from benign to highly malignant, and imaging plays a vital role in their diagnosis and management. Ultrasound, particularly transvaginal ultrasound, is the foremost diagnostic method for adnexal masses. Magnetic Resonance Imaging (MRI) is advised for more precise characterisation if ultrasound results are inconclusive. The ovarian-adnexal reporting and data system (O-RADS) MRI lexicon and scoring system provides a standardised method for describing, assessing, and categorising the risk of each ovarian mass. Determining a histological differential diagnosis of the mass may influence treatment decision-making and treatment planning. When ultrasound or MRI suggests the possibility of cancer, computed tomography (CT) is the preferred imaging technique for staging. It is essential to outline the extent of the malignancy, guide treatment decisions, and evaluate the feasibility of cytoreductive surgery. This article provides a comprehensive overview of the key imaging processes in evaluating and managing ovarian masses, from initial diagnosis to initial treatment. It also includes pertinent recommendations for properly performing and interpreting various imaging modalities. KEY POINTS: MRI is the modality of choice for indeterminate ovarian masses at ultrasound, and the O-RADS MRI lexicon and score enable unequivocal communication with clinicians. CT is the recommended modality for suspected ovarian masses to tailor treatment and surgery. Multidisciplinary meetings integrate information and help decide the most appropriate treatment for each patient.

ACR Appropriateness Criteria® Clinically Suspected Adnexal Mass, No Acute Symptoms: 2023 Update

Asymptomatic adnexal masses are commonly encountered in daily radiology practice. Although the vast majority of these masses are benign, a small subset have a risk of malignancy, which require gynecologic oncology referral for best treatment outcomes. Ultrasound, using a combination of both transabdominal, transvaginal, and duplex Doppler technique can accurately characterize the majority of these lesions. MRI with and without contrast is a useful complementary modality that can help characterize indeterminate lesions and assess the risk of malignancy is those that are suspicious. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation.

Comparison of conventional diffusion-weighted imaging, diffusion kurtosis imaging and intravoxel incoherent motion in characterization of sonographically indeterminate adnexal masses

PURPOSE

To evaluate the role of conventional diffusion weighted imaging, diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) in distinguishing benign from malignant adnexal masses.

METHODS

38 patients with 45 adnexal masses were enrolled in this prospective study and assessed with multiparametric MRI, including the IVIM-DKI sequence, on a 3 T MRI system. The mean apparent diffusion coefficient (ADC) from conventional DWI, the apparent diffusion coefficient derived from DKI (Dapp), the apparent kurtosis coefficient (Kapp), true diffusion coefficient (Dt), perfusion fraction (f) and pseudo-diffusion coefficient (Dp) were measured.

RESULTS

The mean ADC, Dapp, and Dt were significantly higher in benign adnexal masses than in malignant adnexal masses (p < 0.001). f and Dp were also significantly higher in benign adnexal masses, with p values of 0.026 and 0.002, respectively. Kapp was higher in malignant masses (p < 0.001). Among mean ADC, Dapp, and Dt, mean ADC had the highest area under the curve (AUC) of 0.885. However, no statistically significant differences were observed between the ROCs of various diffusion parameters.

CONCLUSION

The mean ADC, Dapp, and Kapp are useful parameters in discriminating between benign and malignant adnexal masses. Dt derived from IVIM also helps in distinguishing benign and malignant adnexal masses; however, no incremental role of IVIM and DKI over ADC could be identified in our study.

Deep learning-accelerated T2-weighted imaging versus conventional T2-weighted imaging in the female pelvic cavity: image quality and diagnostic performance

BACKGROUND

The deep learning (DL)-based reconstruction algorithm reduces noise in magnetic resonance imaging (MRI), thereby enabling faster MRI acquisition.

PURPOSE

To compare the image quality and diagnostic performance of conventional turbo spin-echo (TSE) T2-weighted (T2W) imaging with DL-accelerated sagittal T2W imaging in the female pelvic cavity.

METHODS

This study evaluated 149 consecutive female pelvic MRI examinations, including conventional T2W imaging with TSE (acquisition time = 2:59) and DL-accelerated T2W imaging with breath hold (DL-BH) (1:05 [0:14 × 3 breath-holds]) in the sagittal plane. In 294 randomly ordered sagittal T2W images, two radiologists independently assessed image quality (sharpness, subjective noise, artifacts, and overall image quality), made a diagnosis for uterine leiomyomas, and scored diagnostic confidence. For the uterus and piriformis muscle, quantitative imaging analysis was also performed. Wilcoxon signed rank tests were used to compare the two sets of T2W images.

RESULTS

In the qualitative analysis, DL-BH showed similar or significantly higher scores for all features than conventional T2W imaging (P <0.05). In the quantitative analysis, the noise in the uterus was lower in DL-BH, but the noise in the muscle was lower in conventional T2W imaging. In the uterus and muscle, the signal-to-noise ratio was significantly lower in DL-BH than in conventional T2W imaging (P <0.001). The diagnostic performance of the two sets of T2W images was not different for uterine leiomyoma.

CONCLUSIONS

DL-accelerated sagittal T2W imaging obtained with three breath-holds demonstrated superior or comparable image quality to conventional T2W imaging with no significant difference in diagnostic performance for uterine leiomyomas.

Malignancy in Abdominal Wall Endometriosis: Is There a Way to Avoid It? A Systematic Review

Background: Malignant-associated abdominal wall endometriosis (AWE) is a rare pathology, likely to occur in 1% of scar endometriosis. The objectives of this study were to update the evidence on tumor degeneration arising from AWE to notify about the clinical characteristics, the different treatments offered to patients and their outcomes. Methods: A comprehensive systematic review of the literature was conducted. PubMed, Embase and Cochrane Library databases were used. Prospero (ID number: CRD42024505274). Results: Out of the 152 studies identified, 63 were included, which involved 73 patients. The main signs and symptoms were a palpable abdominal mass (85.2%) and cyclic pelvic pain (60.6%). The size of the mass varied between 3 and 25 cm. Mean time interval from the first operation to onset of malignant transformation was 20 years. Most common cancerous histological types were clear cell and endometrioid subtypes. Most widely accepted treatment is the surgical resection of local lesions with wide margins combined with adjuvant chemotherapy. The prognosis for endometriosis-associated malignancy in abdominal wall scars is poor, with a five-year survival rate of around 40%. High rates of relapse have been reported. Conclusions: Endometrial implants in the abdominal wall should be considered as preventable complications of gynecological surgeries. Special attention should be paid to women with a history of cesarean section or uterine surgery.

Pattern Recognition or Adnexal MR Scoring System: Which Is More Accurate in Evaluating Adnexal Lesions?

PURPOSE

This study aims to compare the accuracy of the ADNEX MR scoring system and pattern recognition system to evaluate adnexal lesions indeterminate on the US exam.

METHODS

In this cross-sectional retrospective study, pelvic DCE-MRI of 245 patients with 340 adnexal masses was studied based on the ADNEX MR scoring system and pattern recognition system.

RESULTS

ADNEX MR scoring system with a sensitivity of 96.6% and specificity of 91% has an accuracy of 92.9%. The pattern recognition system's sensitivity, specificity, and accuracy are 95.8%, 93.3%, and 94.7%, respectively. PPV and NPV for the ADNEX MR scoring system were 85.1 and 98.1, respectively. PPV and NPV for the pattern recognition system were 89.7% and 97.7%, respectively. The area under the ROC curve for the ADNEX MR scoring system and pattern recognition system is 0.938 (95% CI, 0.909-0.967) and 0.950 (95% CI, 0.922-0.977). Pairwise comparison of these AUCs showed no significant difference (p = 0.052).

CONCLUSION

The pattern recognition system is less sensitive than the ADNEX MR scoring system, yet more specific.

Diagnostic Value of MRI Compared to Histopathological Results in Differentiating Benign from Malignant Ovarian Masses

Introduction and aim: Ovarian cancer is a prevalent neoplastic condition among females. Early diagnosis is essential in improving patient outcomes. This study aimed to determine the diagnostic value of magnetic resonance imaging (MRI) compared to histopathological diagnosis to distinguish between benign and malignant ovarian masses. Methods:The present cross-sectional study, which was conducted between 2021 and 2022, included a cohort of women with ovarian mass. Gyneco-oncologists referred all patients to the MRI center. The imaging protocol encompassed T1 and T2 weighted images, T1 fat-suppressed sequence, post-contrast and diffusion-weighted images (DWI). After surgery, the histopathological results were compared to the MRI diagnosis. Statistical analysis was done by using SPSS v.25 software. Results:A total of 67 women aged 15-82 years old were included in this study. Compared to histopathological diagnosis, MRI had a sensitivity of 96%, a specificity of 69%, a positive predictive value of 64.9% and a negative predictive value of 96.7%. Among patients under 40 years old, MRI showed a sensitivity of 100%, a specificity of 76.2%, a positive predictive value of 72.2% and a negative predictive value of 100%. Solid component and contrast enhancement within the solid component was significantly more frequent in patients with malignant diagnoses than those with benign masses (p<0.05). Conclusion:According to the results of the study, MRI is valuable for discriminating between benign and malignant ovarian masses, especially in patients under 40.

CT-Based Radiomics and Machine Learning for Differentiating Benign, Borderline, and Early-Stage Malignant Ovarian Tumors

To explore the value of CT-based radiomics model in the differential diagnosis of benign ovarian tumors (BeOTs), borderline ovarian tumors (BOTs), and early malignant ovarian tumors (eMOTs). The retrospective research was conducted with pathologically confirmed 258 ovarian tumor patients from January 2014 to February 2021. The patients were randomly allocated to a training cohort (n = 198) and a test cohort (n = 60). By providing a three-dimensional (3D) characterization of the volume of interest (VOI) at the maximum level of images, 4238 radiomic features were extracted from the VOI per patient. The Wilcoxon-Mann-Whitney (WMW) test, least absolute shrinkage and selection operator (LASSO), and support vector machine (SVM) were employed to select the radiomic features. Five machine learning (ML) algorithms were applied to construct three-class diagnostic models. Leave-one-out cross-validation (LOOCV) was implemented to evaluate the performance of the radiomics models. The test cohort was used to verify the generalization ability of the radiomics models. The receiver-operating characteristic (ROC) was used to evaluate diagnostic performance of radiomics model. Global and discrimination performance of five models was evaluated by average area under the ROC curve (AUC). The average ROC indicated that random forest (RF) diagnostic model in training cohort demonstrated the best diagnostic performance (micro/macro average AUC, 0.98/0.99), which was then confirmed with by LOOCV (micro/macro average AUC, 0.89/0.88) and external validation (test cohort) (micro/macro average AUC, 0.81/0.79). Our proposed CT-based radiomics diagnostic models may effectively assist in preoperatively differentiating BeOTs, BOTs, and eMOTs.

O-RADS MRI to classify adnexal tumors: from clinical problem to daily use

Eighteen to 35% of adnexal masses remain non-classified following ultrasonography, leading to unnecessary surgeries and inappropriate management. This finding led to the conclusion that ultrasonography was insufficient to accurately assess adnexal masses and that a standardized MRI criteria could improve these patients' management. The aim of this work is to present the different steps from the identification of the clinical issue to the daily use of a score and its inclusion in the latest international guidelines. The different steps were the following: (1) preliminary work to formalize the issue, (2) physiopathological analysis and finding dynamic parameters relevant to increase MRI performances, (3) construction and internal validation of a score to predict the nature of the lesion, (4) external multicentric validation (the EURAD study) of the score named O-RADS MRI, and (5) communication and education work to spread its use and inclusion in guidelines. Future steps will include studies at patients' levels and a cost-efficiency analysis. Critical relevance statement We present translating radiological research into a clinical application based on a step-by-step structured and systematic approach methodology to validate MR imaging for the characterization of adnexal mass with the ultimate step of incorporation in the latest worldwide guidelines of the O-RADS MRI reporting system that allows to distinguish benign from malignant ovarian masses with a sensitivity and specificity higher than 90%. Key points • The initial diagnostic test accuracy studies show the limitation of a preoperative assessment of adnexal masses using solely ultrasonography.• The technical developments (DCE/DWI) were investigated with the value of dynamic MRI to accurately predict the nature of benign or malignant lesions to improve management.• The first developing score named ADNEX MR Score was constructed using multiple easily assessed criteria on MRI to classify indeterminate adnexal lesions following ultrasonography.• The multicentric adnexal study externally validated the score creating the O-RADS MR score and leading to its inclusion for daily use in international guidelines.

Artificial Intelligence in Ultrasound Diagnoses of Ovarian Cancer: A Systematic Review and Meta-Analysis

Ovarian cancer is the sixth most common malignancy, with a 35% survival rate across all stages at 10 years. Ultrasound is widely used for ovarian tumour diagnosis, and accurate pre-operative diagnosis is essential for appropriate patient management. Artificial intelligence is an emerging field within gynaecology and has been shown to aid in the ultrasound diagnosis of ovarian cancers. For this study, Embase and MEDLINE databases were searched, and all original clinical studies that used artificial intelligence in ultrasound examinations for the diagnosis of ovarian malignancies were screened. Studies using histopathological findings as the standard were included. The diagnostic performance of each study was analysed, and all the diagnostic performances were pooled and assessed. The initial search identified 3726 papers, of which 63 were suitable for abstract screening. Fourteen studies that used artificial intelligence in ultrasound diagnoses of ovarian malignancies and had histopathological findings as a standard were included in the final analysis, each of which had different sample sizes and used different methods; these studies examined a combined total of 15,358 ultrasound images. The overall sensitivity was 81% (95% CI, 0.80-0.82), and specificity was 92% (95% CI, 0.92-0.93), indicating that artificial intelligence demonstrates good performance in ultrasound diagnoses of ovarian cancer. Further prospective work is required to further validate AI for its use in clinical practice.

Clinical Utility of Diffusion-Weighted Imaging in Gynecological Imaging: Revisited

ABSTRACT

Diffusion-weighted imaging (DWI) is an increasingly valuable sequence in daily clinical practice, providing both functional and morphological information. The use of DWI can help quantify diffusion using the apparent diffusion coefficient, which reflects the physiological features of the tissue and tumor microcirculation. This knowledge is crucial for understanding and interpreting gynecological imaging. This article reviews the clinical utility of DWI for gynecological imaging, highlighting its ability to aid in the detection of endometrial and cervical cancers, as well as tumor extension and metastasis. In addition, DWI can easily detect the solid components of ovarian cancer (including dissemination), assist in the diagnosis of adnexal torsion, and potentially show bone marrow status. Apparent diffusion coefficient measurement is useful for differentiating between endometrial lesions, uterine leiomyomas, and sarcomas, and may provide important information for predicting the prognosis of gynecological cancers.

Imaging of Acute Abdominopelvic Pain in Pregnancy and Puerperium-Part II: Non-Obstetric Complications

Emergency imaging in pregnancy and puerperium poses unique challenges both for clinicians and radiologists, requiring timely and accurate diagnosis. Delay in treatment may result in poor outcomes for both the patient and the foetus. Pregnant and puerperal patients may present in the emergency setting with acute abdominopelvic pain for various complications that can be broadly classified into obstetric and non-obstetric related diseases. Ultrasonography (US) is the primary diagnostic imaging test; however, it may be limited due to the patient's body habitus and the overlapping of bowel loops. Computed tomography (CT) carries exposure to ionising radiation to the foetus, but may be necessary in selected cases. Magnetic resonance imaging (MRI) is a valuable complement to US in the determination of the etiology of acute abdominal pain and can be used in most settings, allowing for the identification of a broad spectrum of pathologies with a limited protocol of sequences. In this second section, we review the common non-obstetric causes for acute abdominopelvic pain in pregnancy and post partum, offering a practical approach for diagnosis and pointing out the role of imaging methods (US, MRI, CT) with the respective imaging findings.

Pictorial essay: MRI evaluation of endometriosis-associated neoplasms

Endometriosis is a frequent pathology mostly affecting women of young age. When typical aspects are present, the diagnosis can easily be made at imaging, especially at MRI. Transformation of benign endometriosis to endometriosis-associated neoplasms is rare. The physiopathology is complex and remains controversial. Endometrioid carcinoma and clear cell carcinoma are the main histological subtypes. Our goal was to review the main imaging characteristics that should point to an ovarian or extra-ovarian endometriosis-related tumor, especially at MRI, as it may be relevant prior to surgical management.Key points• Transformation of benign endometriosis to endometriosis-associated neoplasms is rare.• MRI is useful when displaying endometriosis lesions associated to an ovarian tumor.• Subtraction imaging should be used in the evaluation of complex endometriomas.

Deep Learning Radiomics Nomogram Based on Magnetic Resonance Imaging for Differentiating Type I/II Epithelial Ovarian Cancer

RATIONALE AND OBJECTIVES

To develop and validate a T2-weighted magnetic resonance imaging (MRI)-based deep learning radiomics nomogram (DLRN) to differentiate between type I and type II epithelial ovarian cancer (EOC).

MATERIALS AND METHODS

This multicenter study incorporated 437 patients from five centers, divided into training (n = 271), internal validation (n = 68), and external validation (n = 98) sets. The deep learning (DL) model was constructed using the largest orthogonal slices of the tumor area. The extracted radiomics features were employed in building the radiomics model. The clinical model was developed based on clinical characteristics. A DLRN was built by integrating the DL signature, radiomics signature, and independent clinical predictors. Model performances were evaluated through receiver operating characteristic (ROC) analysis, Brier score, calibration curve, and decision curve analysis (DCA). The areas under the ROC curve (AUCs) were compared using the DeLong test. A two-tailed P < 0.05 was considered significantly different.

RESULTS

The DLRN exhibited satisfactory discrimination between type I and type II EOC with the AUC of 0.888 (95% confidence interval [CI] 0.810, 0.966) and 0.866 (95% CI 0.786, 0.946) in the internal and external validation sets, respectively. These AUCs significantly exceeded those of the clinical model (P = 0.013 and 0.043, in the internal and external validation sets, respectively). The DLRN demonstrated optimal classification accuracy and clinical application value, according to Brier scores, calibration curves, and DCA.

CONCLUSION

A T2-weighted MRI-based DLRN showed promising potential in differentiating between type I and type II EOC, which could offer assistance in clinical decision-making.

Adnexal Mass Imaging: Contemporary Guidelines for Clinical Practice

Several recent guidelines have been published to improve accuracy and consistency of adnexal mass imaging interpretation and to guide management. Guidance from the American College of Radiology (ACR) Appropriateness Criteria establishes preferred adnexal imaging modalities and follow-up. Moreover, the ACR Ovarian-Adnexal Reporting Data System establishes a comprehensive, unified set of evidence-based guidelines for classification of adnexal masses by both ultrasound and MR imaging, communicating risk of malignancy to further guide management.

Magnetic Resonance Imaging in the Diagnosis of Female Adnexal Masses: Comparison With Histopathological Examination

Introduction Adnexal masses present a special diagnostic challenge because it is difficult to differentiate between benign and malignant lesions clinically. The diagnosis of malignancy is vital, and imaging is the most important part of the evaluation of adnexal masses. This study was conducted with the goal of comparing the accuracy of magnetic resonance imaging (MRI) in diagnosing female adnexal masses in comparison with histopathology examination (HPE). A total of 70 female patients with suspected adnexal lesions were selected for the study. After obtaining informed consent from the patients, an MRI was performed with a subsequent histopathological examination of the lesion. Results The study revealed that MRI demonstrated 27% non-neoplastic, 47% benign, and 26% malignant lesions. HPE, the gold standard for identifying and classifying pathological masses, positively identified the lesions and classified them as non-neoplastic, surface epithelial-stromal, germ cell, and sex cord-stromal tumors. The present study of 70 cases with adnexal masses showed a strong positive correlation between MRI and HPE findings. Conclusion MRI provides the added advantage of visualization of the tumor matrix with differential identification of the fatty and cystic tissue through heterogeneous signals and enhancement indicating aggressiveness and local spread. MRI has greater diagnostic accuracy when compared to ultrasonography (USG), with HPE as the gold standard for discriminating between benign and malignant adnexal masses.

MR Imaging of Gynecologic Tumors: Pearls, Pitfalls, and Tumor Mimics

MR imaging is the modality of choice for the pre-treatment evaluation of patients with gynecologic malignancies, given its excellent soft tissue contrast and multi-planar capability. However, it is not without pitfalls. Challenges can be encountered in the assessment of the infiltration of myometrium, vagina, cervical stroma, and parametria, which are crucial prognostic factors for endometrial and cervical cancers. Other challenges can be encountered in the distinction between solid and non-solid tissue and in the identification of peritoneal carcinomatosis for the sonographically indeterminate adnexal mass.

Comparative Study of the Efficacy of the Ovarian-Adnexa Reporting and Data System Ultrasound Combined With Contrast-Enhanced Ultrasound and the ADNEX MR Scoring System in the Diagnosis of Adnexal Masses

OBJECTIVE

The aims of this study were to develop the Ovarian-Adnexa Reporting and Data System (O-RADS) and O-RADS + contrast-enhanced ultrasound (O-RADS CEUS) scoring system to distinguish adnexal masses (AMs) and to compare the diagnostic efficacy of these systems with that of a magnetic resonance imaging scoring system (ADNEX MR).

METHODS

We retrospectively evaluated 278 ovarian masses from 240 patients between May 2017 and July 2022. Pathology and adequate follow-up were used as reference standards for comparing the validity of O-RADS, O-RADS CEUS and ADNEX MR scoring to diagnose AMs. Area under the curve (AUC), sensitivity and specificity were calculated. The inter-class correlation coefficient (ICC) was calculated to evaluate inter-reader agreement (IRA) between the two sonographers and two radiologists who analyzed the findings with the three modalities.

RESULTS

The AUCs of O-RADS, O-RADS CEUS and ADNEX MR scores were 0.928 (95% confidence interval [CI]: 0.895-0.956), 0.951(95% CI: 0.919-0.973) and 0.964 (95% CI: 0.935-0.983), respectively. Their sensitivities were 95.7%, 94.3 and 91.4%, and their specificities were 81.3%, 92.3% and 97.1%, respectively. The three modalities had accuracies of 84.9%, 92.8% and 95.7%, respectively. O-RADS had the highest sensitivity but significantly lower specificity (p < 0.001), whereas the ADNEX MR scoring had the highest specificity (p < 0.001) but lower sensitivity (p < 0.001). O-RADS CEUS had intermediate sensitivity and specificity (p < 0.001).

CONCLUSION

The addition of CEUS significantly improves the efficacy of O-RADS in diagnosing AMs. The diagnostic efficacy of the combination is comparable to that of the ADNEX MR scoring system.

Adnexal Lesion Imaging: Past, Present, and Future

Currently, imaging is part of the standard of care for patients with adnexal lesions prior to definitive management. Imaging can identify a physiologic finding or classic benign lesion that can be followed up conservatively. When one of these entities is not present, imaging is used to determine the probability of ovarian cancer prior to surgical consultation. Since the inclusion of imaging in the evaluation of adnexal lesions in the 1970s, the rate of surgery for benign lesions has decreased. More recently, data-driven Ovarian-Adnexal Reporting and Data System (O-RADS) scoring systems for US and MRI with standardized lexicons have been developed to allow for assignment of a cancer risk score, with the goal of further decreasing unnecessary interventions while expediting the care of patients with ovarian cancer. US is used as the initial modality for the assessment of adnexal lesions, while MRI is used when there is a clinical need for increased specificity and positive predictive value for the diagnosis of cancer. This article will review how the treatment of adnexal lesions has changed due to imaging over the decades; the current data supporting the use of US, CT, and MRI to determine the likelihood of cancer; and future directions of adnexal imaging for the early detection of ovarian cancer.

Ovarian-Adnexal Reporting and Data System for Magnetic Resonance Imaging (O-RADS MRI): Genesis and Future Directions

Imaging plays an important role in characterizing and risk-stratifying commonly encountered adnexal lesions. Recently, the American College of Radiology (ACR) released the Ovarian-Adnexal Reporting and Data System (O-RADS) for ultrasound and subsequently for magnetic resonance imaging (MRI). The goal of the recently developed ACR O-RADS MRI risk stratification system is to improve the quality of imaging reports as well as the reproducibility of evaluating adnexal lesions on MRI. This review focuses on exploring this new system and its future refinements.

Ovarian cancer during pregnancy

Adnexal masses during pregnancy are a relatively uncommon entity. Their clinical management is challenging given the overlapping features of certain entities on imaging and histopathology, which can mimic malignancy, and the potential side effects to the mother and fetus, whether expectant management versus surgery is pursued. Ultrasonography with Doppler evaluation is the modality of choice for evaluating adnexal masses during pregnancy. Magnetic resonance imaging is the second-line modality useful when US findings are inconclusive/indeterminate. Most adnexal masses in pregnant patients are benign in origin (e.g., functional cysts, mature cystic teratoma, decidualization of endometrioma), but a few are malignant in origin (e.g., dysgerminoma, granulosa cell tumor). Most cases of adnexal masses are asymptomatic, but complications such as ovarian torsion can occur. This review aims to familiarize the radiologist with the imaging of adnexal lesions during pregnancy so that the radiologist can identify ovarian cancer. Specifically, the review will detail the most common benign and malignant adnexal masses in pregnancy, mimickers, and their corresponding imaging findings on US and MRI.

Ultrasound examination, MRI, or ROMA for discriminating between inconclusive adnexal masses as determined by IOTA Simple Rules: a prospective study

OBJECTIVE

To determine the best second-step approach for discriminating benign from malignant adnexal masses classified as inconclusive by International Ovarian Tumour Analysis Simple Rules (IOTA-SR).

METHODS

Single-center prospective study comprising a consecutive series of patients diagnosed as having an adnexal mass classified as inconclusive according to IOTA-SR. All women underwent Risk of Ovarian Malignancy Algorithm (ROMA) analysis, MRI interpreted by a radiologist, and ultrasound examination by a gynecological sonologist. Cases were clinically managed according to the result of the ultrasound expert examination by either serial follow-up for at least 1 year or surgery. Reference standard was histology (patient was submitted to surgery if any of the tests was suspicious) or follow-up (masses with no signs of malignancy after 12 months were considered benign). Diagnostic performance of all three approaches was calculated and compared. Direct cost analysis of the test used was also performed.

RESULTS

Eighty-two adnexal masses in 80 women (median age 47.6 years, range 16 to 73 years) were included. Seventeen patients (17 masses) were managed expectantly (none had diagnosis of ovarian cancer after at least 12 months of follow-up) and 63 patients (65 masses) underwent surgery and tumor removal (40 benign and 25 malignant tumors). Sensitivity and specificity for ultrasound, MRI, and ROMA were 96% and 93%, 100% and 81%, and 24% and 93%, respectively. The specificity of ultrasound was better than that for MRI (p=0.021), and the sensitivity of ultrasound was better than that for ROMA (p<0.001), sensitivity was better for MRI than for ROMA (p<0.001) and the specificity of ROMA was better than that for MRI (p<0.001). Ultrasound evaluation was the most effective and least costly method as compared with MRI and ROMA.

CONCLUSION

In this study, ultrasound examination was the best second-step approach in inconclusive adnexal masses as determined by IOTA-SR, but the findings require confirmation in multicenter prospective trials.

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSION

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

TRIAL REGISTRATION

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

Manifestations of Ovarian Cancer in Relation to Other Pelvic Diseases by MRI

Imaging plays a pivotal role in the diagnostic approach of women with suspected ovarian cancer. MRI is widely used for preoperative characterization and risk stratification of adnexal masses. While epithelial ovarian cancer (EOC) has typical findings on MRI; there are several benign and malignant pelvic conditions that may mimic its appearance on imaging. Knowledge of the origin and imaging characteristics of a pelvic mass will help radiologists diagnose ovarian cancer promptly and accurately. Finally, in special subgroups, including adolescents and gravid population, the prevalence of various ovarian tumors differs from that of the general population and there are conditions which uniquely manifest during these periods of life.

Accuracy and reproducibility of the O-RADS MRI risk stratification system based on enhanced non-DCE MRI in the assessment of adnexal masses

OBJECTIVE

Evaluation of the diagnostic performance and reproducibility of the Ovarian-Adnexal Reporting and Data System (O-RADS) Magnetic Resonance Imaging (MRI) risk stratification system based on enhanced non-dynamic contrast-enhanced (non-DCE) MRI in the diagnosis of adnexal masses.

METHODS

Patients who underwent conventional pelvic enhanced non-DCE MRI examination within one month prior to surgery formed the study population. Two experienced radiologists independently evaluated the images and assigned a score according to the O-RADS MRI risk stratification system. One of the radiologists reviewed the images and reassigned the scores after three months. Intra- and inter-observer agreement was evaluated with the k coefficient value. The adnexal masses that attained scores between 1 and 3 were considered benign, while those with scores of 4 or 5 were considered malignant. Analyses were conducted to determine the sensitivity, specificity, positive and negative predictive values, and receiver operating characteristic (ROC) curve, which were then used for evaluating the diagnostic efficacy of the developed system based on enhanced non-DCE MRI scan. The reference standard was histology.

RESULTS

A total of 308 patients (mean age: 42.09 ± 12.42 years, age range: 20-84 years) were enrolled in the study. Among the 362 adnexal masses from the included patients, there were 320 benign masses and 42 malignant masses. In the case of three readers, there were no malignant tumors scored 1-2. The O-RADS MRI score ≥ 4 was associated with malignancy resulted in a good diagnostic efficacy with the areas under the curve (AUC) values of 0.918 (95 % CI, 0.864-0.972), 0.905 (95 % CI, 0.842-0.968), and 0.882 (95 % CI, 0.815-0.950), the sensitivity values of 90.5 % (95 % CI, 87.5-93.5 %), 85.7 % (95 % CI, 82.1-89.3 %), and 83.3 % (95 % CI, 79.5-87.2 %), and the specificity values of 93.1 % (95 % CI, 90.5-95.7 %), 95.3 % (95 % CI, 93.1-97.5 %), and 93.1 % (95 % CI, 90.5-95.7 %) obtained for the three readers, respectively. Excellent intra-observer agreement and inter-observer agreement were observed with the k values of 0.883 (95 % CI, 0.814-0.952) and 0.848 (95 % CI, 0.770-0.926), respectively.

CONCLUSIONS

The O-RADS MRI risk stratification system based on enhanced non-DCE MRI scans exhibited high accuracy and reproducibility in the prediction of adnexal masses malignancy. Enhanced non-DCE MRI scan may offer an alternative diagnostic tool when DCE is not possible.

Current Update on PET/MRI in Gynecological Malignancies-A Review of the Literature

Early detection of gynecological malignancies is vital for patient management and prolonging the patient's survival. Molecular imaging, such as positron emission tomography (PET)/computed tomography, has been increasingly utilized in gynecological malignancies. PET/magnetic resonance imaging (MRI) enables the assessment of gynecological malignancies by combining the metabolic information of PET with the anatomical and functional information from MRI. This article will review the updated applications of PET/MRI in gynecological malignancies.

Diffusion-weighted magnetic resonance sequence and CA125/CEA ratio can be used as add-on tools to ultrasound for the differentiation of ovarian from non-ovarian pelvic masses

OBJECTIVE

To provide a straightforward approach to the sequential use of ultrasound (US), magnetic resonance (MR) and serum biomarkers in order to differentiate the origin of pelvic masses, making the most efficient use of these diagnostic resources.

STUDY DESIGN

This is a cross-sectional study with 159 patients (133 with ovarian and 26 with non-ovarian tumors) who underwent surgery/biopsy for an adnexal mass. Preoperative CA125 and CEA serum measurements were obtained and a pelvic/abdominal ultrasound was performed. Preoperative pelvic MR studies were performed for all patients. Morphological and advanced MR sequences were obtained. Using a recursive partitioning algorithm to predict tumor origin, we devised a roadmap to determine the probability of non-ovarian origin using only statistically significant US, laboratory and MR parameters.

RESULTS

Upfront US classification as ovarian versus non-ovarian and CA125/CEA ratio were significantly associated with non-ovarian tumors. Signal diffusion (absent/low versus high) was the only MR parameter significantly associated with non-ovarian tumors. When upfront US designated a tumor as being of ovarian origin, further MR signal diffusion and CA125/CEA ratio were corrected nearly all US errors: patients with MR signal diffusion low/absent and those with signal high but CA125/CEA ratio ≥25 had an extremely low chance (<1%) of being of non-ovarian origin. However, for women whose ovarian tumors were incorrectly rendered as non-ovarian by upfront US, neither MR nor CA125/CEA ratio were able to determine tumor origin precisely.

CONCLUSION

MR signal diffusion is an extremely useful MR parameter to help determine adnexal mass origin when US and laboratory findings are inconclusive.

O-RADS MRI After Initial Ultrasound for Adnexal Lesions: AJR Expert Panel Narrative Review

The Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound (US) and MRI risk stratification systems were developed by an international group of experts in adnexal imaging to aid radiologists in assessing adnexal lesions. The goal of imaging is to appropriately triage patients with adnexal lesions. US is the first-line imaging modality for assessment, whereas MRI can be used as a problem-solving tool. Both US and MRI can accurately characterize benign lesions such as simple cysts, endometriomas, hemorrhagic cysts, and dermoid cysts, avoiding unnecessary or inappropriate surgery. In patients with a lesion that does not meet criteria for one of these benign diagnoses, MRI can further characterize the lesion with an improved specificity for cancer and the ability to provide a probable histologic subtype in the presence of certain MRI features. This allows personalized treatment, including avoiding overly extensive surgery or allowing fertility-sparing procedures for suspected benign, borderline, or low-grade tumors. When MRI findings indicate a risk of an invasive cancer, patients can be expeditiously referred to a gynecologic oncologic surgeon. This narrative review provides expert opinion on the utility of multiparametric MRI when using the O-RADS US and MRI management systems.

Comparison of ultrasound-based ADNEX model with magnetic resonance imaging for discriminating adnexal masses: a multi-center study

Objectives

The ADNEX model offered a good diagnostic performance for discriminating adnexal tumors, but research comparing the abilities of the ADNEX model and MRI for characterizing adnexal tumors has not been reported to our knowledge. The aim of this study was to evaluate the diagnostic accuracy of the ultrasound-based ADNEX (Assessment of Different NEoplasias in the adneXa) model in comparison with that of magnetic resonance imaging (MRI) for differentiating benign, borderline and malignant adnexal masses.

Methods

This prospective study included 529 women with adnexal masses who underwent assessment via the ADNEX model and subjective MRI analysis before surgical treatment between October 2019 and April 2022 at two hospitals. Postoperative histological diagnosis was considered the gold standard.

Results

Among the 529 women, 92 (17.4%) masses were diagnosed histologically as malignant tumors, 67 (12.7%) as borderline tumors, and 370 (69.9%) as benign tumors. For the diagnosis of malignancy, including borderline tumors, overall agreement between the ADNEX model and MRI pre-operation was 84.9%. The sensitivity of the ADNEX model of 0.91 (95% confidence interval [CI]: 0.85-0.95) was similar to that of MRI (0.89, 95% CI: 0.84-0.94; P=0.717). However, the ADNEX model had a higher specificity (0.90, 95% CI: 0.87-0.93) than MRI (0.81, 95% CI: 0.77-0.85; P=0.001). The greatest sensitivity (0.96, 95% CI: 0.92-0.99) and specificity (0.94, 95% CI: 0.91-0.96) were achieved by combining the ADNEX model and subjective MRI assessment. While the total diagnostic accuracy did not differ significantly between the two methods (P=0.059), the ADNEX model showed greater diagnostic accuracy for borderline tumors (P<0.001).

Conclusion

The ultrasound-based ADNEX model demonstrated excellent diagnostic performance for adnexal tumors, especially borderline tumors, compared with MRI. Accordingly, we recommend that the ADNEX model, alone or with subjective MRI assessment, should be used for pre-operative assessment of adnexal masses.

Combination of clinical and MRI features in diagnosing ovarian granulosa cell tumor: A comparison with other ovarian sex cord-gonadal stromal tumors

PURPOSE

To evaluate the combination of magnetic resonance imaging (MRI) findings and clinical features in diagnosing ovarian granulosa cell tumor (OGCT) and comparing OGCTs with other ovarian sex cord-gonadal stromal tumors (OSGTs).

METHODS

Women who underwent MRI and were surgically confirmed with OSGTs between January 2015 and January 2022 were included in the study. Histology was used as a primary method of diagnosis. T1WI, T2WI, and DWI MR scans were performed for all patients. All MR images were reviewed by two radiologists. The clinic baseline characteristics of all patients were recorded.

RESULTS

A total of 58 patients were enrolled, with 21 OGCTs found in 20 patients and 39 other OSGTs found in 38 patients. In terms of clinical, the proportion of vaginal discharge/bleeding and menstrual abnormalities were significantly higher in OGCTs than in the control group. A multivariate analysis of the combined clinical MRI revealed that symptomatic, T2 signals of the solid component, Honeycomb-sign, Swiss cheese-sign, and ADC values were independent features for discriminating between OGCTs and other OSGTs. Clinical features, MRI features, and a combined model were established; the areas under the curve of the three models in predicting OGCTs and other OSGTs were 0.694, 0.852, and 0.927, respectively. The DeLong test showed that the combined model had the highest efficiency in predicting OGCTs (p < 0.05), which was significantly different from the AUC of the other two models (p < 0.05).

CONCLUSIONS

Combining clinic and MRI findings helps differentiate OGCTs from other OSGTs. These results help optimize clinical management and indicate that radiologists should focus on clinical information to help improve diagnostic accuracy.

O-RADS Ultrasound Version 1: A Scenario-Based Review of Implementation Challenges

The Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound (US) risk stratification and management system was first published by the American College of Radiology in 2020. It provides standardized terminology for evaluation of ovarian and adnexal masses, aids risk stratification, and provides management guidelines for different categories of lesions. This system has been validated by subsequent research and found to be a useful diagnostic and management tool. However, as noted in the system's governing concepts, in some clinical scenarios, such as patients with acute symptoms or with a history of ovarian malignancy, O-RADS US does not apply, or the system's standard management may be adjusted. Additional scenarios, such as an adnexal mass in pregnancy, present challenges in the application of O-RADS US to assist diagnosis and management. The purpose of this article is to highlight 10 clinical scenarios in which O-RADS US version 1 may not apply, may be difficult to apply, or may require modified management. Additional scenarios in which O-RADS US can be appropriately applied are also described.

Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis

BACKGROUND

Accurate identification of ovarian cancer (OC) is of paramount importance in clinical treatment success. Artificial intelligence (AI) is a potentially reliable assistant for the medical imaging recognition. We systematically review articles on the diagnostic performance of AI in OC from medical imaging for the first time.

METHODS

The Medline, Embase, IEEE, PubMed, Web of Science, and the Cochrane library databases were searched for related studies published until August 1, 2022. Inclusion criteria were studies that developed or used AI algorithms in the diagnosis of OC from medical images. The binary diagnostic accuracy data were extracted to derive the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022324611.

FINDINGS

Thirty-four eligible studies were identified, of which twenty-eight studies were included in the meta-analysis with a pooled SE of 88% (95%CI: 85-90%), SP of 85% (82-88%), and AUC of 0.93 (0.91-0.95). Analysis for different algorithms revealed a pooled SE of 89% (85-92%) and SP of 88% (82-92%) for machine learning; and a pooled SE of 88% (84-91%) and SP of 84% (80-87%) for deep learning. Acceptable diagnostic performance was demonstrated in subgroup analyses stratified by imaging modalities (Ultrasound, Magnetic Resonance Imaging, or Computed Tomography), sample size (≤300 or >300), AI algorithms versus clinicians, year of publication (before or after 2020), geographical distribution (Asia or non Asia), and the different risk of bias levels (≥3 domain low risk or < 3 domain low risk).

INTERPRETATION

AI algorithms exhibited favorable performance for the diagnosis of OC through medical imaging. More rigorous reporting standards that address specific challenges of AI research could improve future studies.

FUNDING

This work was supported by the Natural Science Foundation of China (No. 82073647 to Q-JW and No. 82103914 to T-TG), LiaoNing Revitalization Talents Program (No. XLYC1907102 to Q-JW), and 345 Talent Project of Shengjing Hospital of China Medical University (No. M0268 to Q-JW and No. M0952 to T-TG).

How to improve O-RADS MRI score for rating adnexal masses with cystic component?

OBJECTIVES

To test the performance of the Ovarian-Adnexal Reporting Data System (O-RADS) MRI in characterizing adnexal masses with cystic components and to test new specific MRI features related to cystic components to improve the ability of the O-RADS MRI score to stratify lesions according to their risk of malignancy.

METHODS

The EURopean ADnexal study (EURAD) database was retrospectively queried to identify adnexal masses with a cystic component. One junior and 13 radiologists independently reviewed cases blinded to the pathological diagnosis. For each lesion, the size of the whole lesion, morphological appearance, number of loculi, presence of a thickened wall, thickened septae, signal intensity of the cystic components on T1-weighted/T2-weighted/diffusion weighted, mean value of the apparent diffusion coefficient, and O-RADS MRI score were reported. Univariate and multivariate logistic regression analysis was performed to determine significant features to predict malignancy.

RESULTS

The final cohort consisted of 585 patients with 779 pelvic masses who underwent pelvic MRI to characterize an adnexal mass(es). Histology served as the standard of reference. The diagnostic performance of the O-RADS MRI score was 0.944, 95%CI [0.922-0.961]. Significant criteria associated with malignancy included an O-RADS MRI score ≥ 4, ADCmean of cystic component > 1.69, number of loculi > 3, lesion size > 75 mm, the presence of a thick wall, and a low T1-weighted, a high T2-weighted, and a low diffusion-weighted signal intensity of the cystic component. Multivariate analysis demonstrated that an O-RADS MRI score ≥ combined with an ADC mean of the cystic component > 1.69, size > 75 mm, and low diffusion-weighted signal of the cystic component significantly improved the diagnostic performance up to 0.958, 95%CI [0.938-0.973].

CONCLUSION

Cystic component analysis may improve the diagnosis performance of the O-RADS MRI score in adnexal cystic masses.

KEY POINTS

• O-RADS MRI score combined with specific cystic features (area under the receiving operating curve, AUROC = 0.958) improves the diagnostic performance of the O-RADS MRI score (AUROC = 0.944) for predicting malignancy in this cohort. • Cystic features that improve the prediction of malignancy are ADC mean > 1.69 (OR = 7); number of loculi ≥ 3 (OR = 5.16); lesion size > 75 mm (OR = 4.40); the presence of a thick wall (OR = 3.59); a high T2-weighted signal intensity score 4 or 5 (OR = 3.30); a low T1-weighted signal intensity score 1, 2, or 3 (OR = 3.45); and a low diffusion-weighted signal intensity (OR = 2.12). • An adnexal lesion with a cystic component rated O-RADS MRI score 4 and an ADC value of the cystic component < 1.69 associated with a low diffusion-weighted signal, has virtually a 0% risk of malignancy.

Interreader Agreement and Intermodality Concordance of O-RADS US and MRI for Assessing Large, Complex Ovarian-Adnexal Cysts

Purpose To assess interreader agreement of the Ovarian-Adnexal Reporting and Data System (O-RADS) and intermodality concordance between US and MRI for characterizing complex adnexal cysts measuring 5 cm or larger. Materials and Methods This retrospective study included 58 "complex cysts" measuring at least 5 cm in size observed at both US and MRI in 54 women (median age, 37 years ± 12 [SD]; seven postmenopausal women) between July 2017 and June 2020, identified from an electronic US database. A separate set of two blinded radiologists independently reviewed the US or MR images to assign the O-RADS category, and an adjudicator resolved discrepancies (a total of six readers). Lesion outcome (49 benign, eight malignant, one lost to follow-up) was recorded. Interreader agreement of O-RADS US and O-RADS MRI and concordance between US and MRI were analyzed. Results Interreader agreement was fair for US (κ = 0.31), moderate for MRI (κ = 0.43), and moderate between US and MRI (κ = 0.58). A significant positive correlation was found between O-RADS US and MRI (τ = 0.72, P < .001). The O-RADS 4 threshold yielded the highest accuracy for both US and MRI (area under the receiver operating characteristic curve = 0.92 and 0.995, respectively). Considering O-RADS US 4 or 5 as potentially malignant and 1-3 as benign, eight lesions that were assessed as potentially malignant at US were correctly downgraded to benign by using findings at MRI. Using findings at MRI, one malignant lesion that was assessed as benign at US was upgraded to potentially malignant. Conclusion O-RADS US and MRI had excellent performance and positive correlation, but significant interobserver variability remains. Keywords: Ovary, MR Imaging, Ultrasonography © RSNA, 2022 See also the commentary by Baumgarten in this issue.

Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors

Objectives

This study aims to evaluate the diagnostic performance of machine-learning-based contrast-enhanced CT radiomic analysis for categorizing benign and malignant ovarian tumors.

Methods

A total of 1,329 patients with ovarian tumors were randomly divided into a training cohort (N=930) and a validation cohort (N=399). All tumors were resected, and pathological findings were confirmed. Radiomic features were extracted from the portal venous phase images of contrast-enhanced CT. The clinical predictors included age, CA-125, HE-4, ascites, and margin of tumor. Both radiomics model (including selected radiomic features) and mixed model (incorporating selected radiomic features and clinical predictors) were constructed respectively. Six classifiers [k-nearest neighbor (KNN), support vector machines (SVM), random forest (RF), logistic regression (LR), multi-layer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost)] were used for each model. The mean relative standard deviation (RSD) and area under the receiver operating characteristic curve (AUC) were applied to evaluate and select the best classifiers. Then, the performances of the two models with selected classifiers were assessed in the validation cohort.

Results

The MLP classifier with the least RSD (1.21 and 0.53, respectively) was selected as the best classifier in both radiomics and mixed models. The two models with MLP classifier performed well in the validation cohort, with the AUCs of 0.91 and 0.96 and with accuracies (ACCs) of 0.83 and 0.87, respectively. The Delong test showed that the AUC of mixed model was statistically different from that of radiomics model (p&lt;0.001).

Conclusions

Machine-learning-based CT radiomic analysis could categorize ovarian tumors with good performance preoperatively. The mixed model with MLP classifier may be a potential tool in clinical applications.

MR imaging of acute abdominal pain in pregnancy

Abdominal pain in pregnancy is a diagnostic challenge with many potential aetiologies. Diagnostic imaging is a valuable tool in the assessment of these patients, with ultrasound commonly employed first line. MRI is an excellent problem-solving adjunct to ultrasound and has many advantages in terms of improved spatial resolution and soft tissue characterisation. This pictorial review aims to outline the role of MRI in the work up of acute abdominal pain in pregnancy and provide imaging examples of pathologies which may be encountered.

Imaging Cancer in Pregnancy

Pregnancy-associated cancer (PAC) is defined as cancer that is detected during pregnancy and up to 1 year postpartum. Although rare (~1:1000 pregnancies), PAC is increasing owing to postponed childbearing and advanced maternal age at conception. Cancer-related symptoms masked by physiologic gestational changes may delay diagnosis. Imaging, clinical management, and treatment require a carefully choreographed multidisciplinary team approach. The risk-benefit of every imaging modality, the strategies to balance the safety of mother and fetus, and the support of the patient and family at every step are crucial. US and MRI are preferred imaging modalities that lack ionizing radiation. Radiation dose concerns should be addressed, noting that most imaging examinations (including mammography, radiography, CT, and technetium 99m-labeled sulfur colloid sentinel lymph node staging) are performed at radiation levels below thresholds at which deterministic side effects are seen. Dose estimates should be provided after each examination. The use of iodinated intravenous contrast material is safe during pregnancy, but gadolinium-based contrast material should be avoided. Accurate diagnosis and staging combined with gestational age affect decisions about surgery and chemotherapy. Whole-body MRI with diffusion-weighted sequences is ideal to screen for primary and metastatic sites, determine disease stage, identify biopsy targets, and guide further cancer site-specific imaging. The authors provide an update of the imaging triage, safety considerations, cancer-specific imaging, and treatment options for cancer in pregnancy. An invited commentary by Silverstein and Van Loon is available online. Online supplemental material is available for this article. ^©RSNA, 2022.

Management of the Adnexal Mass: Considerations for the Family Medicine Physician

Ovarian cancer is the most deadly gynecological cancer, so proper assessment of a pelvic mass is necessary in order to determine which are at high risk for malignancy and should be referred to a gynecologic oncologist. However, in a family medicine setting, evaluation and treatment of these masses can be challenging due to a lack of resources. A number of risk assessment tools are available to family medicine physicians, including imaging techniques, imaging systems, and blood-based biomarker assays each with their respective pros and cons, and varying ability to detect malignancy in pelvic masses. Effective utilization of these assessment tools can inform the care pathway for patients which present with an adnexal mass, such as expectant management for those with a low risk of malignancy, or referral to a gynecologic oncologist for surgery and staging, for those at high risk of malignancy. Triaging patients to the appropriate care pathway improves patient outcomes and satisfaction, and family medicine physicians can play a key role in this decision-making process.

Assessment of the performance of the O-RADS MRI score for the evaluation of adnexal masses, with technical notes

Objective

To assess the performance of the Ovarian-Adnexal Reporting and Data System Magnetic Resonance Imaging (O-RADS MRI) score in the evaluation of adnexal masses and to provide technical notes about its current MRI parameters and concepts.

Materials and Methods

This was a prospective study of 226 patients with 287 adnexal masses (190 submitted to surgery or biopsy and 97 followed for at least one year). We calculated the sensitivity, specificity, positive predictive value, and negative predictive value for the O-RADS MRI score, using ≥ 4 as the cutoff for malignancy. We performed a technical analysis of the main updates to the score, announced in September 2020 by the American College of Radiology, in comparison with the original (2013) version.

Results

We found that an O-RADS MRI score of 4 or 5 was associated with malignancy of an adnexal mass, with a sensitivity of 91.11% (95% CI: 83.23-96.08), specificity of 94.92% (95% CI: 90.86-97.54), positive predictive value of 89.13% (95% CI: 81.71-93.77), negative predictive value of 95.90% (95% CI: 92.34-97.84), and overall accuracy of 93.73% (95% CI: 90.27-96.24).

Conclusion

Our findings support the use of the O-RADS MRI score for evaluating adnexal masses, especially those considered indeterminate on ultrasound. The updates made recently to the O-RADS MRI score facilitate its interpretation and will allow its more widespread use, with no loss of diagnostic accuracy.

Diffusion-Weighted MRI in the Genitourinary System

Diffusion weighted imaging (DWI) constitutes a major functional parameter performed in Magnetic Resonance Imaging (MRI). The DW sequence is performed by acquiring a set of native images described by their b-values, each b-value representing the strength of the diffusion MR gradients specific to that sequence. By fitting the data with models describing the motion of water in tissue, an apparent diffusion coefficient (ADC) map is built and allows the assessment of water mobility inside the tissue. The high cellularity of tumors restricts the water diffusion and decreases the value of ADC within tumors, which makes them appear hypointense on ADC maps. The role of this sequence now largely exceeds its first clinical apparitions in neuroimaging, whereby the method helped diagnose the early phases of cerebral ischemic stroke. The applications extend to whole-body imaging for both neoplastic and non-neoplastic diseases. This review emphasizes the integration of DWI in the genitourinary system imaging by outlining the sequence's usage in female pelvis, prostate, bladder, penis, testis and kidney MRI. In gynecologic imaging, DWI is an essential sequence for the characterization of cervix tumors and endometrial carcinomas, as well as to differentiate between leiomyosarcoma and benign leiomyoma of the uterus. In ovarian epithelial neoplasms, DWI provides key information for the characterization of solid components in heterogeneous complex ovarian masses. In prostate imaging, DWI became an essential part of multi-parametric Magnetic Resonance Imaging (mpMRI) to detect prostate cancer. The Prostate Imaging-Reporting and Data System (PI-RADS) scoring the probability of significant prostate tumors has significantly contributed to this success. Its contribution has established mpMRI as a mandatory examination for the planning of prostate biopsies and radical prostatectomy. Following a similar approach, DWI was included in multiparametric protocols for the bladder and the testis. In renal imaging, DWI is not able to robustly differentiate between malignant and benign renal tumors but may be helpful to characterize tumor subtypes, including clear-cell and non-clear-cell renal carcinomas or low-fat angiomyolipomas. One of the most promising developments of renal DWI is the estimation of renal fibrosis in chronic kidney disease (CKD) patients. In conclusion, DWI constitutes a major advancement in genitourinary imaging with a central role in decision algorithms in the female pelvis and prostate cancer, now allowing promising applications in renal imaging or in the bladder and testicular mpMRI.

MRI in female pelvis: an ESUR/ESR survey

Objectives

While magnetic resonance imaging (MRI) is considered the gold standard for the imaging of female pelvis, there is an ongoing debate about the most appropriate indications and optimal imaging protocols. The European Society of Urogenital Radiology (ESUR) launched a survey to evaluate the current utilization of female pelvic MRI in clinical practice.

Methods

The ESUR female imaging subgroup developed an online survey that was then approved by the ESR board and circulated among the ESR members. The questions in the survey encompassed training and experience, indications for imaging and MR imaging protocols, reporting styles and preferences. The results of the survey were tabulated, and subgroups were compared using χ2 test.

Results

A total of 5900 ESR members with an interest in both MRI and female pelvic imaging were invited to participate; 840 (14.23%) members completed the survey. Approximately 50% of respondents were academic radiologists (50.6%) and nearly 60% women (59.69%). One third of the respondents were subspecialized in Gynecological imaging. Nearly half of the survey participants were aware of the presence of ESUR guidelines for imaging of the female pelvis (47.1%). The adoption of the ESUR recommendations was higher among subspecialized and/or academic and/or senior and/or European radiologists compared to all others. The current ESUR recommendations about female pelvic MRI protocols were generally followed. However wide variations in practice were identified with respect to the use of contrast media.

Conclusion

Female pelvic MRI protocol was generally following the ESUR recommendations, especially among subspecialized and academic radiologists. However, the fact that they are followed by only half of the participants highlights the need for wider awareness of these recommendations.

Uterine extension determined by MRI: a useful parameter for differentiating subserosal leiomyomas from ovarian tumors

PURPOSE

This study aimed to evaluate the utility of uterine extension determined via MRI for the differentiation of large subserosal leiomyomas from ovarian tumors.

METHODS

In total, 55 patients with subserosal leiomyomas and 127 patients with ovarian tumors were included in this study. These patients were selected from a cohort of female patients whose pelvic masses were larger than 10 cm and who underwent preoperative MRI. We retrospectively reviewed the MRI and compared the diagnostic ability of uterine extension measurements and bridging vascular signs for differentiating subserosal leiomyomas from ovarian tumors.

RESULTS

The vertical height of the uterus (107.2 ± 36.4 mm vs. 59.9 ± 24.9 mm, p < 0.01), the uterine length (114.4 ± 34.9 mm vs. 80.4 ± 23.8 mm, p < 0.01), and the frequency of the bridging vascular sign (78% vs. 6%, p < 0.01) were significantly higher in subserosal leiomyomas than in ovarian tumors. For diagnosing subserosal leiomyoma, the area under the curve, sensitivity, and specificity of vertical height of the uterus, using cutoff threshold > 81 mm, were 0.89, 89%, and 80% and those of the uterine length, using cutoff threshold > 84 mm, were 0.85, 69%, and 93%, respectively. Alternatively, the sensitivity and specificity of bridging vascular sign were 78% and 94%, respectively.

CONCLUSION

Uterine extension determined via MRI is a useful parameter for differentiating large subserosal leiomyomas from ovarian tumors.

O-RADS MRI Classification of Indeterminate Adnexal Lesions: Time-Intensity Curve Analysis Is Better Than Visual Assessment

Background The MRI Ovarian-Adnexal Reporting and Data System (O-RADS) enables risk stratification of sonographically indeterminate adnexal lesions, partly based on time-intensity curve (TIC) analysis, which may not be universally available. Purpose To compare the diagnostic accuracy of visual assessment with that of TIC assessment of dynamic contrast-enhanced MRI scans to categorize adnexal lesions as benign or malignant and to evaluate the influence on the O-RADS MRI score. Materials and Methods The European Adnex MR Study Group, or EURAD, database, a prospective multicenter study of women undergoing MRI for indeterminate adnexal lesions between March 2013 and March 2018, was queried retrospectively. Women undergoing surgery for an adnexal lesion with solid tissue were included. Solid tissue enhancement relative to outer myometrium was assessed visually and with TIC. Contrast material washout was recorded. Lesions were categorized according to the O-RADS MRI score with visual and TIC assessment. Per-lesion diagnostic accuracy was calculated. Results A total of 320 lesions (207 malignant, 113 benign) in 244 women (mean age, 55.3 years ± 15.8 [standard deviation]) were analyzed. Sensitivity for malignancy was 96% (198 of 207) and 76% (157 of 207) for TIC and visual assessment, respectively. TIC was more accurate than visual assessment (86% [95% CI: 81, 90] vs 78% [95% CI: 73, 82]; P < .001) for benign lesions, predominantly because of higher specificity (95% [95% CI: 92, 98] vs 76% [95% CI: 68, 81]). A total of 21% (38 of 177) of invasive lesions were rated as low risk visually. Contrast material washout and high-risk enhancement (defined as earlier enhancement than in the myometrium) were highly specific for malignancy for both TIC (97% [95% CI: 91, 99] and 94% [95% CI: 90, 97], respectively) and visual assessment (97% [95% CI: 92, 99] and 93% [95% CI: 88, 97], respectively). O-RADS MRI score was more accurate with TIC than with visual assessment (area under the receiver operating characteristic curve, 0.87 [95% CI: 0.83, 0.90] vs 0.73 [95% CI: 0.68, 0.78]; P < .001). Conclusion Time-intensity curve analysis was more accurate than visual assessment for achieving optimal diagnostic accuracy with the Ovarian-Adnexal Reporting and Data System MRI score. Clinical trial registration no. NCT01738789 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Vargas and Woo in this issue.

Application of Multimodal Imaging Biomarker in the Differential Diagnosis of Ovarian Mass: Integration of Conventional and Molecular Imaging

PURPOSE

The aim is to investigate the diagnostic performance of multimodal imaging with 18F-FDG PET/CT, MRI, and contrast-enhanced CT (CECT) in cases with unilateral or bilateral ovarian mass without ancillary findings of malignancy.

METHODS

Retrospectively, 307 patients who had unilateral or bilateral ovarian masses and underwent preoperative FDG PET/CT and/or MRI/CECT were included. The criterion standard for the ovarian mass was the final pathology. The peak standardized uptake value (SULpeak) among benign tumors (BTs), borderline ovarian tumors (BoTs), and malignant ovarian tumors (MTs) were compared. The cutoff value of SULpeak to discriminate between BT/BoT and MT was determined from the training (n = 200) and validation (n = 131) cohorts. Diagnostic performances of SULpeak, Ovarian-Adnexal Reporting Data System (O-RADS) MRI score, CECT findings, and combination of multimodal imagings were analyzed.

RESULTS

SULpeak of MT was significantly higher than that of BT or BoT (P < 0.05). There was no significant difference in SULpeak between BT and BoT (P = 0.147). The cutoff value of SULpeak for discriminating between BT/BoT and MT was 1.76 (sensitivity, 87.0%; specificity, 83.0%). Diagnostic performance for BT/BoT versus MT of O-RADS MRI, CECT, FDG PET/CT plus O-RADS MRI score, and FDG PET/CT plus CECT yielded the respective sensitivities of 100%, 94%, 95%, and 82%, and specificities of 43%, 46%, 88%, and 91%, respectively.

CONCLUSIONS

Multimodal imaging biomarkers including FDG PET/CT and MR/CECT could provide additional information to differentiate ovarian masses.

O-RADS MRI Risk Stratification System: Guide for Assessing Adnexal Lesions from the ACR O-RADS Committee

MRI plays an important role as a secondary test or problem-solving modality in the evaluation of adnexal lesions depicted at US. MRI has increased specificity compared with US, decreasing the number of false-positive diagnoses for malignancy and thereby avoiding unnecessary or over-extensive surgery in patients with benign lesions or borderline tumors, while women with possible malignancies can be expeditiously referred for oncologic surgical evaluation. The Ovarian-Adnexal Reporting and Data System (O-RADS) MRI Committee is an international collaborative effort formed under the direction of the American College of Radiology and includes a diverse group of experts on adnexal imaging and management who developed the O-RADS MRI risk stratification system. This scoring system assigns a probability of malignancy based on the MRI features of an adnexal lesion and provides information to facilitate optimal patient management. The widespread implementation of a codified reporting system will lead to improved interpretation agreement and standardized communication between radiologists and referring physicians. In addition, it will allow for high-quality multi-institutional collaborations-an important unmet need that has hampered the performance of high-quality research in this area in the past. This article provides guidelines on using the O-RADS MRI risk stratification system in clinical practice, as well as in the educational and research settings.