Improving strategies for diagnosing ovarian cancer: a summary of the International Ovarian Tumor Analysis (IOTA) studies.

@article{Kaijser2013ImprovingSF,
  title={Improving strategies for diagnosing ovarian cancer: a summary of the International Ovarian Tumor Analysis (IOTA) studies.},
  author={Jeroen Kaijser and Tom Bourne and Lil Valentin and Ahmad Sayasneh and Caroline Van Holsbeke and Ignace Vergote and Antonia Carla Testa and Dorella Franchi and Ben van Calster and Dirk Timmerman},
  journal={Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology},
  year={2013},
  volume={41 1},
  pages={
          9-20
        }
}
In order to ensure that ovarian cancer patients access appropriate treatment to improve the outcome of this disease, accurate characterization before any surgery on ovarian pathology is essential. [...] Key ResultsThe main IOTA prediction models (logistic regression model 1 (LR1) and logistic regression model 2 (LR2)) have both shown excellent diagnostic performance (area under the curve (AUC) values of 0.96 and 0.95, respectively) and outperform previous diagnostic algorithms. Expand Abstract

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