Ovarian cancer prediction in adnexal masses using ultrasound‐based logistic regression models: a temporal and external validation study by the IOTA group

Timmerman, Dirk; Calster, Ben Van; Testa, Antonia Carla; Guerriero, S.; Fischerová, D.; Lissoni, Andrea Alberto; Holsbeke, C. Van; Fruscio, Robert; Czekierdowski, Artur; Jurkovic, D.; Savelli, L.; Vergote, Ignace; Bourne, Tom; Huffel, Sabine Van; Valentin, Lil

doi:10.1002/uog.7636

Cited by 173 publications

(185 citation statements)

References 32 publications

(12 reference statements)

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“…HE4 expression has been proposed to be a biomarker for preoperative assessment of BOTs [38,65]. An increased immunohistochemical staining in serous BOT, with a similar staining intensity to that in serous carcinoma, in comparison to the low intensity of benign serous cystadenomas, was reported [27,66].…”

Section: Nonmalignant Cyst Of Ovarymentioning

confidence: 99%

Physiopathological factors affecting the diagnostic value of serum HE4-test for gynecologic malignancies

Duan

et al. 2016

Expert Review of Molecular Diagnostics

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Section: Nonmalignant Cyst Of Ovarymentioning

confidence: 99%

Physiopathological factors affecting the diagnostic value of serum HE4-test for gynecologic malignancies

Duan

et al. 2016

Expert Review of Molecular Diagnostics

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“…Four different types of mathematical models were developed to evaluate which type of model did best (scoring system, logistic regression models, artificial neural networks (ANN), and vector machine models). On internal and temporal validation in the centers that previously developed the models, they had excellent diagnostic performance (16,(22)(23)(24)(25). After temporal validation, all models had similar performance (AUCs between 0.945 and 0.950).…”

Section: Introductionmentioning

confidence: 95%

External Validation of Diagnostic Models to Estimate the Risk of Malignancy in Adnexal Masses

Holsbeke

Calster

Bourne

et al. 2012

Clinical Cancer Research

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Purpose: To externally validate and compare the performance of previously published diagnostic models developed to predict malignancy in adnexal masses.Experimental Design: We externally validated the diagnostic performance of 11 models developed by the International Ovarian Tumor Analysis (IOTA) group and 12 other (non-IOTA) models on 997 prospectively collected patients. The non-IOTA models included the original risk of malignancy index (RMI), three modified versions of the RMI, six logistic regression models, and two artificial neural networks. The ability of the models to discriminate between benign and malignant adnexal masses was expressed as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and likelihood ratios (LR þ , LR À ). Results: Seven hundred and forty-two (74%) benign and 255 (26%) malignant masses were included. The IOTA models did better than the non-IOTA models (AUCs between 0.941 and 0.956 vs. 0.839 and 0.928). The difference in AUC between the best IOTA and the best non-IOTA model was 0.028 [95% confidence interval (CI), 0.011-0.044]. The AUC of the RMI was 0.911 (difference with the best IOTA model, 0.044; 95% CI, 0.024-0.064). The superior performance of the IOTA models was most pronounced in premenopausal patients but was also observed in postmenopausal patients. IOTA models were better able to detect stage I ovarian cancer.Conclusion: External validation shows that the IOTA models outperform other models, including the current reference test RMI, for discriminating between benign and malignant adnexal masses. Clin Cancer Res; 18(3); 815-25. Ó2011 AACR.

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“…Logistic regression has a wide range of applications such as making predictions in healthcare settings [34][35][36]. Logistic regression has seen modern applications in medical diagnoses [37] and in data science and analysis [38].…”

Section: Logistic Regressionmentioning

confidence: 99%

A Comparison of the Effects of K-Anonymity on Machine Learning Algorithms

Wimmer¹,

Powell²

2014

ijacsa

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Abstract-While research has been conducted in machine learning algorithms and in privacy preserving in data mining (PPDM), a gap in the literature exists which combines the aforementioned areas to determine how PPDM affects common machine learning algorithms. The aim of this research is to narrow this literature gap by investigating how a common PPDM algorithm, K-Anonymity, affects common machine learning and data mining algorithms, namely neural networks, logistic regression, decision trees, and Bayesian classifiers. This applied research reveals practical implications for applying PPDM to data mining and machine learning and serves as a critical first step learning how to apply PPDM to machine learning algorithms and the effects of PPDM on machine learning. Results indicate that certain machine learning algorithms are more suited for use with PPDM techniques.

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Ovarian cancer prediction in adnexal masses using ultrasound‐based logistic regression models: a temporal and external validation study by the IOTA group

Abstract: Objectives

Cited by 173 publications

References 32 publications

Physiopathological factors affecting the diagnostic value of serum HE4-test for gynecologic malignancies

Physiopathological factors affecting the diagnostic value of serum HE4-test for gynecologic malignancies

External Validation of Diagnostic Models to Estimate the Risk of Malignancy in Adnexal Masses

A Comparison of the Effects of K-Anonymity on Machine Learning Algorithms

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