ADNEX Model-Based Diagnosis of Ovarian Cancer Using MRI Images

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.

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.

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.