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The Optimal Cut-Off Point of the ADNEX Model for the Prediction of the Ovarian Cancer Risk
Lam Huong, Le; Thi Phuong Dung, Nguyen; Hoang Lam, Vo; Tran Thao Nguyen, Nguyen; Minh Tam, Le; Vu Quoc Huy, Nguyen.
Afiliação
  • Lam Huong L; Department of Obstetrics and Gynecology, University of Medicine & Pharmacy, Hue University, Vietnam.
  • Thi Phuong Dung N; Department of Obstetrics and Gynecology, University of Medicine & Pharmacy, Hue University, Vietnam.
  • Hoang Lam V; Department of Obstetrics and Gynecology, University of Medicine & Pharmacy, Hue University, Vietnam.
  • Tran Thao Nguyen N; Department of Obstetrics and Gynecology, University of Medicine & Pharmacy, Hue University, Vietnam.
  • Minh Tam L; Department of Obstetrics and Gynecology, University of Medicine & Pharmacy, Hue University, Vietnam.
  • Vu Quoc Huy N; Department of Obstetrics and Gynecology, University of Medicine & Pharmacy, Hue University, Vietnam.
Asian Pac J Cancer Prev ; 23(8): 2713-2718, 2022 08 01.
Article em En | MEDLINE | ID: mdl-36037125
Objective: This study aimed to assess the effectiveness and determine the optimal cut-off point of the ADNEX model in women presenting with a pelvic or adnexal tumor. Method: All women presented with adnexal mass and were scheduled for operation at Hue University of Medicine and Pharmacy Hospital and Hue Central Hospital, Vietnam during June 2019 ­ May 2021 were included and categorized according to their histopathologic reports into ovarian cancer groups and benign ovarian tumor groups. Multivariable logistic regression was used to explore for potential predictors. The ADNEX model with and without CA125 was used to assess the risk of ovarian cancer preoperative. The goldden standard to evaluate the accuracy of ultrasonography using the ADNEX model was the pathological report. In addition, the accuracy as well as optimum cut-off point of the ADNEX model was estimated with and without CA125. Results: A total of 461 participants were included in analysis and predictive model development, 65 patients in ovarian cancer group and 361 in benign tumor group. The ADNEX model combined with CA125 proved to be a useful predictor with an area under ROC of 0.961 (0.940 ­ 0.977) with Youden's index of 0.8395, p < 0.001. The ADNEX model without CA125 also had high predictive value between benign and malignant tumors, with an area under ROC of 0.956 (0.933 ­ 0.973) with Youden's index of 0.8551, p < 0.001. Cut-off of the ADNEX with CA125 was 13.5 and without CA125 was 13.1 for sensitivities were 90.8 (81.0 ­ 96.5) and 93.9 (85.0 ­ 97.5), specificities 93.2 (90.2 ­ 95.5) and 91.67 (88.5 ­ 94.2). The difference in the predictive value of malignancy-risk between the ADNEX model with CA125, without CA125 was not statistically significant, p=0.4883. Conclusion: The ADNEX model, with or without the combining marker CA 125, provides a valuable predictive value for ovarian tumor malignancy preoperative.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Ovarianas / Doenças dos Anexos Tipo de estudo: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Ovarianas / Doenças dos Anexos Tipo de estudo: Diagnostic_studies / Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Female / Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article