W. Xie , Q. Zhang , Y. Wang , Z. Xiang , P. Zeng , R. Huo , Z. Du , L. Tang
{"title":"基于超声的ADNEX模型鉴别良性、交界性和恶性卵巢上皮肿瘤。","authors":"W. Xie , Q. Zhang , Y. Wang , Z. Xiang , P. Zeng , R. Huo , Z. Du , L. Tang","doi":"10.1016/j.crad.2024.106761","DOIUrl":null,"url":null,"abstract":"<div><h3>Background</h3><div>The purpose of this study was to evaluate the ability of the International Ovarian Tumor Analysis-Assessment of Different NEoplasias in the adneXa (IOTA-ADNEX) model to distinguish among benign, borderline, and malignant epithelial ovarian tumours (BeEOTs, BEOTs, and MEOTs, respectively).</div></div><div><h3>Methods</h3><div>The study included 813 patients with BeEOTs, BEOTs, and MEOTs who underwent ultrasound examinations and pelvic operations. Comparisons were made between the clinical information and ultrasonographic features of the three patient groups, and the histopathological diagnosis was the gold standard. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the ADNEX model were calculated.</div></div><div><h3>Results</h3><div>This was a single-centre retrospective study. Of the 813 patients, 257 (31.6%) had BeEOTs, 114 (14.0%) had BEOTs, and 442 (54.4%) had MEOTs. For a cut-off value of 10% to identify the overall risk for ovarian cancer (OC), the sensitivity and specificity were 99.1% and 73.2%, respectively. According to the receiver operating characteristicscurves, the AUC was 0.987 (95% CI: 0.981-0.993) for BeEOTs compared with MEOTs, 0.820 (95% CI: 0.768-0.872) for BeEOTs compared with BEOTs, 0.912 (95% CI: 0.876-0.948) for BeEOTs compared with stage I OC, and 0.995 (95% CI: 0.992-0.998) for BeEOTs compared with stages II-IV OC. The AUC was 0.614 (95% CI: 0.519-0.709) for BEOTs compared with stage I OC, 0.903 (95% CI: 0.869-0.937) for BEOTs compared with stages II-IV OC, and 0.851 (95% CI: 0.800-0.902) for stage I OC compared with stages II-IV OC.</div></div><div><h3>Conclusions</h3><div>The IOTA-ADNEX model demonstrated good diagnostic performance for the three categories of EOTs and may have the potential to be popularised in assisting radiologists in the assessment of adnexal masses in the future.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"81 ","pages":"Article 106761"},"PeriodicalIF":2.1000,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Ultrasound-based ADNEX model for differentiating between benign, borderline, and malignant epithelial ovarian tumours\",\"authors\":\"W. Xie , Q. Zhang , Y. Wang , Z. Xiang , P. Zeng , R. Huo , Z. Du , L. Tang\",\"doi\":\"10.1016/j.crad.2024.106761\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>The purpose of this study was to evaluate the ability of the International Ovarian Tumor Analysis-Assessment of Different NEoplasias in the adneXa (IOTA-ADNEX) model to distinguish among benign, borderline, and malignant epithelial ovarian tumours (BeEOTs, BEOTs, and MEOTs, respectively).</div></div><div><h3>Methods</h3><div>The study included 813 patients with BeEOTs, BEOTs, and MEOTs who underwent ultrasound examinations and pelvic operations. Comparisons were made between the clinical information and ultrasonographic features of the three patient groups, and the histopathological diagnosis was the gold standard. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the ADNEX model were calculated.</div></div><div><h3>Results</h3><div>This was a single-centre retrospective study. Of the 813 patients, 257 (31.6%) had BeEOTs, 114 (14.0%) had BEOTs, and 442 (54.4%) had MEOTs. For a cut-off value of 10% to identify the overall risk for ovarian cancer (OC), the sensitivity and specificity were 99.1% and 73.2%, respectively. According to the receiver operating characteristicscurves, the AUC was 0.987 (95% CI: 0.981-0.993) for BeEOTs compared with MEOTs, 0.820 (95% CI: 0.768-0.872) for BeEOTs compared with BEOTs, 0.912 (95% CI: 0.876-0.948) for BeEOTs compared with stage I OC, and 0.995 (95% CI: 0.992-0.998) for BeEOTs compared with stages II-IV OC. The AUC was 0.614 (95% CI: 0.519-0.709) for BEOTs compared with stage I OC, 0.903 (95% CI: 0.869-0.937) for BEOTs compared with stages II-IV OC, and 0.851 (95% CI: 0.800-0.902) for stage I OC compared with stages II-IV OC.</div></div><div><h3>Conclusions</h3><div>The IOTA-ADNEX model demonstrated good diagnostic performance for the three categories of EOTs and may have the potential to be popularised in assisting radiologists in the assessment of adnexal masses in the future.</div></div>\",\"PeriodicalId\":10695,\"journal\":{\"name\":\"Clinical radiology\",\"volume\":\"81 \",\"pages\":\"Article 106761\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2025-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0009926024006470\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical radiology","FirstCategoryId":"3","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0009926024006470","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Ultrasound-based ADNEX model for differentiating between benign, borderline, and malignant epithelial ovarian tumours
Background
The purpose of this study was to evaluate the ability of the International Ovarian Tumor Analysis-Assessment of Different NEoplasias in the adneXa (IOTA-ADNEX) model to distinguish among benign, borderline, and malignant epithelial ovarian tumours (BeEOTs, BEOTs, and MEOTs, respectively).
Methods
The study included 813 patients with BeEOTs, BEOTs, and MEOTs who underwent ultrasound examinations and pelvic operations. Comparisons were made between the clinical information and ultrasonographic features of the three patient groups, and the histopathological diagnosis was the gold standard. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the ADNEX model were calculated.
Results
This was a single-centre retrospective study. Of the 813 patients, 257 (31.6%) had BeEOTs, 114 (14.0%) had BEOTs, and 442 (54.4%) had MEOTs. For a cut-off value of 10% to identify the overall risk for ovarian cancer (OC), the sensitivity and specificity were 99.1% and 73.2%, respectively. According to the receiver operating characteristicscurves, the AUC was 0.987 (95% CI: 0.981-0.993) for BeEOTs compared with MEOTs, 0.820 (95% CI: 0.768-0.872) for BeEOTs compared with BEOTs, 0.912 (95% CI: 0.876-0.948) for BeEOTs compared with stage I OC, and 0.995 (95% CI: 0.992-0.998) for BeEOTs compared with stages II-IV OC. The AUC was 0.614 (95% CI: 0.519-0.709) for BEOTs compared with stage I OC, 0.903 (95% CI: 0.869-0.937) for BEOTs compared with stages II-IV OC, and 0.851 (95% CI: 0.800-0.902) for stage I OC compared with stages II-IV OC.
Conclusions
The IOTA-ADNEX model demonstrated good diagnostic performance for the three categories of EOTs and may have the potential to be popularised in assisting radiologists in the assessment of adnexal masses in the future.
期刊介绍:
Clinical Radiology is published by Elsevier on behalf of The Royal College of Radiologists. Clinical Radiology is an International Journal bringing you original research, editorials and review articles on all aspects of diagnostic imaging, including:
• Computed tomography
• Magnetic resonance imaging
• Ultrasonography
• Digital radiology
• Interventional radiology
• Radiography
• Nuclear medicine
Papers on radiological protection, quality assurance, audit in radiology and matters relating to radiological training and education are also included. In addition, each issue contains correspondence, book reviews and notices of forthcoming events.