Ultrasound, macroscopic and histological features of malignant ovarian tumors

Moro, Francesca; Verdecchia, Valeria; Romeo, Paola; Ciccarone, Francesca; Zannoni, Gian Franco; Valentin, Lil; Timmerman, Dirk; Bourne, Tom; Froyman, Wouter; Scambia, Giovanni; Testa, Antonia Carla

doi:10.1136/ijgc-2020-001435

Int J Gynecol Cancer

2020

DOI: 10.1136/ijgc-2020-001435

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Ultrasound, macroscopic and histological features of malignant ovarian tumors

Francesca Moro

Valeria Verdecchia

Paola Romeo

et al.

Abstract: Contributors We present a video describing how to apply the simple ultrasound rules, the IOTA ADNEX model and the O-RADS model, and explanatory examples for each model.

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Cited by 2 publications

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Neural network-derived multivariate index assay demonstrates effective clinical performance in longitudinal monitoring of ovarian cancer risk

Pappas,

Roy Choudhury,

Chacko

et al. 2024

Gynecologic Oncology

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Neural network-derived multivariate index assay demonstrates effective clinical performance in longitudinal monitoring of ovarian cancer risk

Pappas,

Roy Choudhury,

Chacko

et al. 2024

Gynecologic Oncology

View full text Add to dashboard Cite

Nomogram for predicting lymph node metastasis in patients with ovarian cancer using ultrasonography: a multicenter retrospective study

Yang,

Ye,

Zhou

et al. 2023

BMC Cancer

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Background Ovarian cancer is a common cancer among women globally, and the assessment of lymph node metastasis plays a crucial role in the treatment of this malignancy. The primary objective of our study was to identify the risk factors associated with lymph node metastasis in patients with ovarian cancer and develop a predictive model to aid in the selection of the appropriate surgical procedure and treatment strategy. Methods We conducted a retrospective analysis of data from patients with ovarian cancer across three different medical centers between April 2014 and August 2022. Logistic regression analysis was employed to establish a prediction model for lymph node metastasis in patients with ovarian cancer. We evaluated the performance of the model using receiver operating characteristic (ROC) curves, calibration plots, and decision analysis curves. Results Our analysis revealed that among the 368 patients in the training set, 101 patients (27.4%) had undergone lymph node metastasis. Maximum tumor diameter, multifocal tumor, and Ki67 level were identified as independent risk factors for lymph node metastasis. The area under the curve (AUC) of the ROC curve in the training set was 0.837 (95% confidence interval [CI]: 0.792–0.881); in the validation set this value was 0.814 (95% CI: 0.744–0.884). Calibration plots and decision analysis curves revealed good calibration and clinical application value. Conclusions We successfully developed a model for predicting lymph node metastasis in patients with ovarian cancer, based on ultrasound examination results and clinical data. Our model accurately identified patients at high risk of lymph node metastasis and may guide the selection of appropriate treatment strategies. This model has the potential to significantly enhance the precision and efficacy of clinical management in patients with ovarian cancer.

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Ultrasound, macroscopic and histological features of malignant ovarian tumors

Abstract: Contributors We present a video describing how to apply the simple ultrasound rules, the IOTA ADNEX model and the O-RADS model, and explanatory examples for each model.

Cited by 2 publications

References 10 publications

Neural network-derived multivariate index assay demonstrates effective clinical performance in longitudinal monitoring of ovarian cancer risk

Neural network-derived multivariate index assay demonstrates effective clinical performance in longitudinal monitoring of ovarian cancer risk

Nomogram for predicting lymph node metastasis in patients with ovarian cancer using ultrasonography: a multicenter retrospective study

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