External validation of a novel diagnostic nomogram for prediction of malignancy in adnexal masses in comparison with IOTA-ADNEX model

Background: Ovarian cancer is one of the most common and deadliest gynecological malignant tumors. Most individuals receive a diagnosis at an advanced stage since they do not exhibit typical symptoms in the early stages. Therefore, to increase overall survival, early detection measures are needed. To predict malignancy preoperatively in patients with ovarian masses, a unique diagnostic nomogram was developed. It was important to assess this nomogram's ability to predict malignancy in ovarian masses and to compare its results with those of the commonly used IOTA-ADNEX model. Methods: This prospective cohort study was conducted at the Department of Obstetrics and Gynecology, Zagazig University on patients with ovarian masses. Each woman was subjected to clinical examination, and US examination to detect the presence of M features &features of the ADNEX model and laboratory investigations including the needed serum markers. The risk of malignancy was calculated by applying both the nomogram & IOTA ADNEX model. Results were compared to results of histopathologic examination or the follow-up US for non-operated patients. .Results: There was a good agreement between the nomogram score and the ADNEX risk of malignancy in diagnosing malignant ovarian tumors, with an AUC of 0.933 of at a cut-off value of 128 for the nomogram and an AUC of 0.921 at a cut-off value of 12% for the ADNEX model. Conclusion: We conclude that this cost effective and easy-to-use nomogram can effectively predict the risk of malignancy in cases with ovarian masses with results comparable to the most currently used IOTA-ADNEX model.

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Publication Details

Journal
Zagazig University Medical Journal
Published
2024-10-27
DOI
https://doi.org/10.21608/zumj.2024.329897.3655
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

External validation of a novel diagnostic nomogram for prediction of malignancy in adnexal masses in comparison with IOTA-ADNEX model

Mostafa Shafiek, Somayya M. Sadek, Sepeedeh Saleh, Mohamed Helmy et al.
Zagazig University Medical Journal
Radiomics and Machine Learning in Medical Imaging
article

External validation of a novel diagnostic nomogram for prediction of malignancy in adnexal masses in comparison with IOTA-ADNEX model

Mostafa Shafiek, Somayya M. Sadek, Sepeedeh Saleh, Mohamed Helmy, Reda A. Ahmad, Reem Ismail Awad-Allah Esawy
article en

Abstract

Background: Ovarian cancer is one of the most common and deadliest gynecological malignant tumors. Most individuals receive a diagnosis at an advanced stage since they do not exhibit typical symptoms in the early stages. Therefore, to increase overall survival, early detection measures are needed. To predict malignancy preoperatively in patients with ovarian masses, a unique diagnostic nomogram was developed. It was important to assess this nomogram's ability to predict malignancy in ovarian masses and to compare its results with those of the commonly used IOTA-ADNEX model. Methods: This prospective cohort study was conducted at the Department of Obstetrics and Gynecology, Zagazig University on patients with ovarian masses. Each woman was subjected to clinical examination, and US examination to detect the presence of M features &features of the ADNEX model and laboratory investigations including the needed serum markers. The risk of malignancy was calculated by applying both the nomogram & IOTA ADNEX model. Results were compared to results of histopathologic examination or the follow-up US for non-operated patients. .Results: There was a good agreement between the nomogram score and the ADNEX risk of malignancy in diagnosing malignant ovarian tumors, with an AUC of 0.933 of at a cut-off value of 128 for the nomogram and an AUC of 0.921 at a cut-off value of 12% for the ADNEX model. Conclusion: We conclude that this cost effective and easy-to-use nomogram can effectively predict the risk of malignancy in cases with ovarian masses with results comparable to the most currently used IOTA-ADNEX model.

Zagazig University Medical JournalVol. 0(0)
Zagazig University (EG)
Openalex Percentile: Top 68%
Radiomics and Machine Learning in Medical Imaging
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