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Hamel C, Amir B, Avard B, Fung-Kee-Fung K, Furey B, Garel J, Ghandehari H. Canadian Association of Radiologists Obstetrics and Gynecology Diagnostic Imaging Referral Guideline. Can Assoc Radiol J 2024; 75:261-268. [PMID: 37624360 DOI: 10.1177/08465371231185292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/26/2023] Open
Abstract
The Canadian Association of Radiologists (CAR) Obstetrics and Gynecology Expert Panel consists of radiologists specializing in obstetrics and gynecology, obstetrics and gynecology physicians, a patient advisor, and an epidemiologist/guideline methodologist. After developing a list of 12 clinical/diagnostic scenarios, a systematic rapid scoping review was undertaken to identify systematically produced referral guidelines that provide recommendations for one or more of these clinical/diagnostic scenarios. Recommendations from 46 guidelines and contextualization criteria in the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) for guidelines framework were used to develop 68 recommendation statements across the 12 scenarios related to the evaluation of obstetrics and gynecology clinical and diagnostic scenarios. This guideline presents the methods of development and the imaging recommendations for a variety of obstetrical and gynecological conditions including pregnancy assessment, recurrent first trimester pregnancy loss, post-partum indications, disorders of menstruation, localization of intra-uterine contraceptive device, infertility assessment, assessment of adnexal mass, pelvic pain of presumed gynecological origin, and pelvic floor evaluation.
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Affiliation(s)
- Candyce Hamel
- Canadian Association of Radiologists, Ottawa, ON, Canada
| | | | - Barb Avard
- North York General Hospital, Toronto, ON, Canada
| | | | - Beth Furey
- Dalhousie University, Halifax, NS, Canada
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Yoeli-Bik R, Longman RE, Wroblewski K, Weigert M, Abramowicz JS, Lengyel E. Diagnostic Performance of Ultrasonography-Based Risk Models in Differentiating Between Benign and Malignant Ovarian Tumors in a US Cohort. JAMA Netw Open 2023; 6:e2323289. [PMID: 37440228 PMCID: PMC10346125 DOI: 10.1001/jamanetworkopen.2023.23289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2023] [Accepted: 05/30/2023] [Indexed: 07/14/2023] Open
Abstract
Importance Ultrasonography-based risk models can help nonexpert clinicians evaluate adnexal lesions and reduce surgical interventions for benign tumors. Yet, these models have limited uptake in the US, and studies comparing their diagnostic accuracy are lacking. Objective To evaluate, in a US cohort, the diagnostic performance of 3 ultrasonography-based risk models for differentiating between benign and malignant adnexal lesions: International Ovarian Tumor Analysis (IOTA) Simple Rules with inconclusive cases reclassified as malignant or reevaluated by an expert, IOTA Assessment of Different Neoplasias in the Adnexa (ADNEX), and Ovarian-Adnexal Reporting and Data System (O-RADS). Design, Setting, and Participants This retrospective diagnostic study was conducted at a single US academic medical center and included consecutive patients aged 18 to 89 years with adnexal masses that were managed surgically or conservatively between January 2017 and October 2022. Exposure Evaluation of adnexal lesions using the Simple Rules, ADNEX, and O-RADS. Main Outcomes and Measures The main outcome was diagnostic performance, including area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios. Surgery or follow-up were reference standards. Secondary analyses evaluated the models' performances stratified by menopause status and race. Results The cohort included 511 female patients with a 15.9% malignant tumor prevalence (81 patients). Mean (SD) ages of patients with benign and malignant adnexal lesions were 44.1 (14.4) and 52.5 (15.2) years, respectively, and 200 (39.1%) were postmenopausal. In the ROC analysis, the AUCs for discriminative performance of the ADNEX and O-RADS models were 0.96 (95% CI, 0.93-0.98) and 0.92 (95% CI, 0.90-0.95), respectively. After converting the ADNEX continuous individualized risk into the discrete ordinal categories of O-RADS, the ADNEX performance was reduced to an AUC of 0.93 (95% CI, 0.90-0.96), which was similar to that for O-RADS. The Simple Rules combined with expert reevaluation had 93.8% sensitivity (95% CI, 86.2%-98.0%) and 91.9% specificity (95% CI, 88.9%-94.3%), and the Simple Rules combined with malignant classification had 93.8% sensitivity (95% CI, 86.2%-98.0%) and 88.1% specificity (95% CI, 84.7%-91.0%). At a 10% risk threshold, ADNEX had 91.4% sensitivity (95% CI, 83.0%-96.5%) and 86.3% specificity (95% CI, 82.7%-89.4%) and O-RADS had 98.8% sensitivity (95% CI, 93.3%-100%) and 74.4% specificity (95% CI, 70.0%-78.5%). The specificities of all models were significantly lower in the postmenopausal group. Subgroup analysis revealed high performances independent of race. Conclusions and Relevance In this diagnostic study of a US cohort, the Simple Rules, ADNEX, and O-RADS models performed well in differentiating between benign and malignant adnexal lesions; this outcome has been previously reported primarily in European populations. Risk stratification models can lead to more accurate and consistent evaluations of adnexal masses, especially when used by nonexpert clinicians, and may reduce unnecessary surgeries.
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Affiliation(s)
- Roni Yoeli-Bik
- Department of Obstetrics and Gynecology, University of Chicago, Chicago, Illinois
| | - Ryan E. Longman
- Department of Obstetrics and Gynecology, University of Chicago, Chicago, Illinois
| | - Kristen Wroblewski
- Department of Public Health Sciences, University of Chicago, Chicago, Illinois
| | - Melanie Weigert
- Department of Obstetrics and Gynecology, University of Chicago, Chicago, Illinois
| | | | - Ernst Lengyel
- Department of Obstetrics and Gynecology, University of Chicago, Chicago, Illinois
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Li J, Wang F, Ma J, Zhang Z, Zhang N, Cui S, Ye Z. A CT-based radiomics nomogram for differentiating ovarian cystadenomas and endometriotic cysts. Clin Radiol 2023:S0009-9260(23)00215-5. [PMID: 37336676 DOI: 10.1016/j.crad.2023.05.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 04/25/2023] [Accepted: 05/05/2023] [Indexed: 06/21/2023]
Abstract
AIM To construct and validate a computed tomography (CT)-based radiomics nomogram integrating radiomics signature and clinical factors to distinguish ovarian cystadenomas and endometriotic cysts. MATERIALS AND METHODS A total of 287 patients with ovarian cystadenomas (n=196) or endometriotic cysts (n=91) were divided randomly into a training cohort (n=200) and a validation cohort (n=87). Radiomics features based on the portal venous phase of CT images were extracted by PyRadiomics. The least absolute shrinkage and selection operation regression was applied to select the significant features and develop the radiomics signature. A radiomics score (rad-score) was calculated. The clinical model was built by the significant clinical factors. Multivariate logistic regression analysis was employed to construct the radiomics nomogram based on significant clinical factors and rad-score. The diagnostic performances of the radiomics nomogram, radiomics signature, and clinical model were evaluated and compared in the training and validation cohorts. Diagnostic confusion matrices of these models were calculated for the validation cohort and compared with those of the radiologists. RESULTS Seventeen radiomics features from CT images were used to build the radiomics signature. The radiomics nomogram incorporating cancer antigen 125 (CA-125) level and rad-score showed the best performance in both the training and validation cohorts with AUCs of 0.925 (95% confidence interval [CI]: 0.885-0.965), and 0.942 (95% CI: 0.891-0.993), respectively. The accuracy of radiomics nomogram in the confusion matrix outperformed the radiologists. CONCLUSIONS The radiomics nomogram performed well for differentiating ovarian cystadenomas and endometriotic cysts, and may help in clinical decision-making process.
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Affiliation(s)
- J Li
- Department of Radiology, First Affiliated Hospital of Hebei North University, Zhangjiakou 075000, China; Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - F Wang
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - J Ma
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China
| | - Z Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - N Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - S Cui
- Department of Radiology, First Affiliated Hospital of Hebei North University, Zhangjiakou 075000, China.
| | - Z Ye
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin 300060, China.
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Pelayo M, Pelayo-Delgado I, Sancho-Sauco J, Sanchez-Zurdo J, Abarca-Martinez L, Corraliza-Galán V, Martin-Gromaz C, Pablos-Antona MJ, Zurita-Calvo J, Alcázar JL. Comparison of Ultrasound Scores in Differentiating between Benign and Malignant Adnexal Masses. Diagnostics (Basel) 2023; 13:diagnostics13071307. [PMID: 37046525 PMCID: PMC10093240 DOI: 10.3390/diagnostics13071307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 03/26/2023] [Accepted: 03/28/2023] [Indexed: 04/03/2023] Open
Abstract
Subjective ultrasound assessment by an expert examiner is meant to be the best option for the differentiation between benign and malignant adnexal masses. Different ultrasound scores can help in the classification, but whether one of them is significantly better than others is still a matter of debate. The main aim of this work is to compare the diagnostic performance of some of these scores in the evaluation of adnexal masses in the same set of patients. This is a retrospective study of a consecutive series of women diagnosed as having a persistent adnexal mass and managed surgically. Ultrasound characteristics were analyzed according to IOTA criteria. Masses were classified according to the subjective impression of the sonographer and other ultrasound scores (IOTA simple rules -SR-, IOTA simple rules risk assessment -SRRA-, O-RADS classification, and ADNEX model -with and without CA125 value-). A total of 122 women were included. Sixty-two women were postmenopausal (50.8%). Eighty-one women had a benign mass (66.4%), and 41 (33.6%) had a malignant tumor. The sensitivity of subjective assessment, IOTA SR, IOTA SRRA, and ADNEX model with or without CA125 and O-RADS was 87.8%, 66.7%, 78.1%, 95.1%, 87.8%, and 90.2%, respectively. The specificity for these approaches was 69.1%, 89.2%, 72.8%, 74.1%, 67.9%, and 60.5%, respectively. All methods with similar AUC (0.81, 0.78, 0.80, 0.88, 0.84, and 0.75, respectively). We concluded that IOTA SR, IOTA SRRA, and ADNEX models with or without CA125 and O-RADS can help in the differentiation of benign and malignant masses, and their performance is similar to the subjective assessment of an experienced sonographer.
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Affiliation(s)
- Mar Pelayo
- Department of Radiology, Hospital HM Puerta del Sur, 28938 Móstoles, Spain;
- Department of Radiology, Hospital HM Rivas, 28521 Madrid, Spain
| | - Irene Pelayo-Delgado
- Department of Obstetrics and Gynecology, Hospital Ramon y Cajal, 28034 Madrid, Spain; (J.S.-S.); (L.A.-M.); (V.C.-G.); (C.M.-G.); (M.J.P.-A.); (J.Z.-C.)
- Correspondence: (I.P.-D.); (J.L.A.)
| | - Javier Sancho-Sauco
- Department of Obstetrics and Gynecology, Hospital Ramon y Cajal, 28034 Madrid, Spain; (J.S.-S.); (L.A.-M.); (V.C.-G.); (C.M.-G.); (M.J.P.-A.); (J.Z.-C.)
| | | | - Leopoldo Abarca-Martinez
- Department of Obstetrics and Gynecology, Hospital Ramon y Cajal, 28034 Madrid, Spain; (J.S.-S.); (L.A.-M.); (V.C.-G.); (C.M.-G.); (M.J.P.-A.); (J.Z.-C.)
| | - Virginia Corraliza-Galán
- Department of Obstetrics and Gynecology, Hospital Ramon y Cajal, 28034 Madrid, Spain; (J.S.-S.); (L.A.-M.); (V.C.-G.); (C.M.-G.); (M.J.P.-A.); (J.Z.-C.)
| | - Carmen Martin-Gromaz
- Department of Obstetrics and Gynecology, Hospital Ramon y Cajal, 28034 Madrid, Spain; (J.S.-S.); (L.A.-M.); (V.C.-G.); (C.M.-G.); (M.J.P.-A.); (J.Z.-C.)
| | - María Jesús Pablos-Antona
- Department of Obstetrics and Gynecology, Hospital Ramon y Cajal, 28034 Madrid, Spain; (J.S.-S.); (L.A.-M.); (V.C.-G.); (C.M.-G.); (M.J.P.-A.); (J.Z.-C.)
| | - Julia Zurita-Calvo
- Department of Obstetrics and Gynecology, Hospital Ramon y Cajal, 28034 Madrid, Spain; (J.S.-S.); (L.A.-M.); (V.C.-G.); (C.M.-G.); (M.J.P.-A.); (J.Z.-C.)
| | - Juan Luis Alcázar
- Department of Obstetrics and Gynecology, Clinica Universidad de Navarra, 31008 Pamplona, Spain
- Correspondence: (I.P.-D.); (J.L.A.)
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Li J, Zhang T, Ma J, Zhang N, Zhang Z, Ye Z. Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors. Front Oncol 2022; 12:934735. [PMID: 36016613 PMCID: PMC9395674 DOI: 10.3389/fonc.2022.934735] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2022] [Accepted: 06/16/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesThis study aims to evaluate the diagnostic performance of machine-learning-based contrast-enhanced CT radiomic analysis for categorizing benign and malignant ovarian tumors.MethodsA 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.ResultsThe 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<0.001).ConclusionsMachine-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.
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Affiliation(s)
- Jiaojiao Li
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- Department of Radiology, First Affiliated Hospital of Hebei North University, Zhangjiakou, China
| | - Tianzhu Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Juanwei Ma
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Ningnannan Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
| | - Zhang Zhang
- Department of Radiology, Tianjin Medical University General Hospital, Tianjin, China
- *Correspondence: Zhaoxiang Ye, ; Zhang Zhang,
| | - Zhaoxiang Ye
- Department of Radiology, National Clinical Research Center for Cancer, Tianjin Key Laboratory of Cancer Prevention and Therapy, Tianjin’s Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
- *Correspondence: Zhaoxiang Ye, ; Zhang Zhang,
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