Transvaginal ultrasound versus magnetic resonance imaging for preoperative assessment of myometrial infiltration in patients with low-grade endometrioid endometrial cancer: A systematic review and head-to-head meta-analysis.

PURPOSE: We aimed to compare the diagnostic performance of magnetic resonance imaging (MRI) and transvaginal ultrasound (TVS) for detecting myometrial invasion (MI) in patients with low-grade endometrioid endometrial carcinoma. METHODS: A comprehensive search of MEDLINE (Pubmed), Web of Science, Embase and Scopus (from January 1990 to December 2022) was performed for articles comparing TVS and MRI in the evaluation of myometrial infiltration in low-grade (grade 1 or 2) endometrioid endometrial carcinoma in the same group of patients. We used QUADAS-2 tool for assessing the risk of bias of studies. RESULTS: We found 104 citations in our extensive research. Four articles were ultimately included in the meta-analysis, after excluding 100 reports. All articles were considered low risk of bias in most of the domains assessed in QUADAS-2. We observed that pooled sensitivity and specificity for detecting deep MI were 65% (95% confidence interval [CI] = 54%-75%) and 85% (95% CI = 79%-89%) for MRI, and 71% (95% CI = 63%-78%) and 76% (95% CI = 67%-83%) for TVS, respectively. No statistical differences were found between both imaging techniques (p > 0.05). We observed low heterogeneity for sensitivity and high for specificity regarding TVS; and moderate for both sensitivity and specificity in case of MRI. CONCLUSIONS: The diagnostic performance of TVS and MRI for the evaluation of deep MI in women with low-grade endometrioid endometrial cancer is similar. However, further research is needed as the number of studies is scanty.

Gynecologic Imaging and Reporting Data System for classifying adnexal masses.

INTRODUCTION: To perform a systematic review and meta-analysis of the diagnostic performance of the so-called Gynecologic Imaging and Report Data System (GI-RADS) for classifying adnexal masses. EVIDENCE ACQUISITION: A search for studies reporting about the use of GI-RADS system for classifying adnexal masses from January 2009 to December 2021 was performed in Medline (Pubmed), Google Scholar, Scopus, Cochrane, and Web of Science databases. Pooled sensitivity, specificity, positive and negative likelihood ratios and diagnostic odd ratio (DOR) were calculated. Studies' quality was evaluated using QUADAS-2. EVIDENCE SYNTHESIS: We identified 510 citations. Ultimately, 26 studies comprising 7350 masses were included. Mean prevalence of ovarian malignancy was 26%. The risk of bias was high in eight studies for domain "patient selection" and low for "index test," "reference test" domains for all studies. Overall, pooled estimated sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio and DOR of GI-RADS system for classifying adnexal masses were 94% (95% confidence interval [CI]=91-96%), 90% (95% CI=87-92%), 9.1 (95% CI=7.0-11.9), and 0.07 (95% CI=0.05-0.11), and 132 (95% CI=78-221), respectively. Heterogeneity was high for both sensitivity and specificity. Meta-regression showed that multiple observers and study's design explained this heterogeneity among studies. CONCLUSIONS: GI-RADS system has a good diagnostic performance for classifying adnexal masses.

Transvaginal ultrasound versus magnetic resonance imaging for diagnosing adenomyosis: A systematic review and head-to-head meta-analysis.

BACKGROUND: Transvaginal ultrasound (TVS) and magnetic resonance imaging (MRI) are used for the clinical diagnosis of adenomyosis. OBJECTIVES: To compare the diagnostic accuracy of TVS and MRI for the diagnosis of adenomyosis. SEARCH STRATEGY: A search of studies was performed in five databases comparing TVS and MRI for the diagnosis of adenomyosis from January 1990 to May 2022. SELECTION CRITERIA: Studies were eligible if they reported on the use of TVS and MRI in the same set of patients. The reference standard must be pathology (hysterectomy). DATA COLLECTION AND ANALYSIS: The quality of studies was assessed using the QUADAS-2 tool. Pooled sensitivity and specificity of both techniques were estimated and compared. MAIN RESULTS: Six studies comprising 595 women were included. The risk of bias of patient selection was high in three studies. The risk of bias for index tests and reference test was low. Pooled estimated sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio for TVS were 75%, 81%, 3.9, and 0.31, respectively. These figures for MRI were 69%, 80%, 3.5, and 0.39, respectively. No statistically significant differences were found (p = 0.7509). Heterogeneity was high. CONCLUSIONS: MRI and TVS have similar performances for the diagnosis of adenomyosis.

O-RADS Classification for Ultrasound Assessment of Adnexal Masses: Agreement between IOTA Lexicon and ADNEX Model for Assigning Risk Group.

BACKGROUND: The O-RADS system is a new proposal for establishing the risk of malignancy of adnexal masses using ultrasound. The objective of this study is to assess the agreement and diagnostic performance of O-RADS when using the IOTA lexicon or ADNEX model for assigning the O-RADS risk group. METHODS: Retrospective analysis of prospectively collected data. All women diagnosed as having an adnexal mass underwent transvaginal/transabdominal ultrasound. Adnexal masses were classified according to the O-RADS classification, using the criterion of the IOTA lexicon and according to the risk of malignancy determined by the ADNEX model. The agreement between both methods for assigning the O-RADS group was estimated using weighted Kappa and the percentage of agreement. The sensitivity and specificity of both approaches were calculated. RESULTS: 454 adnexal masses in 412 women were evaluated during the study period. There were 64 malignant masses. The agreement between the two approaches was moderate (Kappa: 0.47), and the percentage of agreement was 46%. Most disagreements occurred for the groups O-RADS 2 and 3 and for groups O-RADS 3 and 4. The sensitivity and specificity for O-RADS using the IOTA lexicon and O-RADS using the ADNEX model were 92.2% and 86.1%, and 85.9% and 87.4%, respectively. CONCLUSION: The diagnostic performance of O-RADS classification using the IOTA lexicon as opposed to the IOTA ADNEX model is similar. However, O-RADS group assignment varies significantly, depending on the use of the IOTA lexicon or the risk estimation using the ADNEX model. This fact might be clinically relevant and deserves further research.

Endometrial Cancer Individualized Scoring System (ECISS): A machine learning-based prediction model of endometrial cancer prognosis.

OBJECTIVE: To establish a prognostic model for endometrial cancer (EC) that individualizes a risk and management plan per patient and disease characteristics. METHODS: A multicenter retrospective study conducted in nine European gynecologic cancer centers. Women with confirmed EC between January 2008 to December 2015 were included. Demographics, disease characteristics, management, and follow-up information were collected. Cancer-specific survival (CSS) and disease-free survival (DFS) at 3 and 5 years comprise the primary outcomes of the study. Machine learning algorithms were applied to patient and disease characteristics. Model I: pretreatment model. Calculated probability was added to management variables (model II: treatment model), and the second calculated probability was added to perioperative and postoperative variables (model III). RESULTS: Of 1150 women, 1144 were eligible for 3-year survival analysis and 860 for 5-year survival analysis. Model I, II, and III accuracies of prediction of 5-year CSS were 84.88%/85.47% (in train and test sets), 85.47%/84.88%, and 87.35%/86.05%, respectively. Model I predicted 3-year CSS at an accuracy of 91.34%/87.02%. Accuracies of models I, II, and III in predicting 5-year DFS were 74.63%/76.72%, 77.03%/76.72%, and 80.61%/77.78%, respectively. CONCLUSION: The Endometrial Cancer Individualized Scoring System (ECISS) is a novel machine learning tool assessing patient-specific survival probability with high accuracy.

Ovarian Adnexal Reporting Data System (O-RADS) for Classifying Adnexal Masses: A Systematic Review and Meta-Analysis.

In this systematic review and meta-analysis, we aimed to assess the pooled diagnostic performance of the so-called Ovarian Adnexal Report Data System (O-RADS) for classifying adnexal masses using transvaginal ultrasound, a classification system that was introduced in 2020. We performed a search for studies reporting the use of the O-RADS system for classifying adnexal masses from January 2020 to April 2022 in several databases (Medline (PubMed), Google Scholar, Scopus, Cochrane, and Web of Science). We selected prospective and retrospective cohort studies using the O-RADS system for classifying adnexal masses with histologic diagnosis or conservative management demonstrating spontaneous resolution or persistence in cases of benign appearing masses after follow-up scan as the reference standard. We excluded studies not related to the topic under review, studies not addressing O-RADS classification, studies addressing MRI O-RADS classification, letters to the editor, commentaries, narrative reviews, consensus documents, and studies where data were not available for constructing a 2 × 2 table. The pooled sensitivity, specificity, positive and negative likelihood ratios, and diagnostic odds ratio (DOR) were calculated. The quality of the studies was evaluated using QUADAS-2. A total of 502 citations were identified. Ultimately, 11 studies comprising 4634 masses were included. The mean prevalence of ovarian malignancy was 32%. The risk of bias was high in eight studies for the "patient selection" domain. The risk of bias was low for the "index test" and "reference test" domains for all studies. Overall, the pooled estimated sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and DOR of the O-RADS system for classifying adnexal masses were 97% (95% confidence interval (CI) = 94%-98%), 77% (95% CI = 68%-84%), 4.2 (95% CI = 2.9-6.0), 0.04 (95% CI = 0.03-0.07), and 96 (95% CI = 50-185), respectively. Heterogeneity was moderate for sensitivity and high for specificity. In conclusion, the O-RADS system has good sensitivity and moderate specificity for classifying adnexal masses.