Novel risk models for early detection and screening of ovarian cancer

Matthew R. Russell, Alfonsina D'Amato, Ciaren Graham, Emma J. Crosbie, Aleksandra Gentry-Maharaj, Andy Ryan, Jatinderpal K. Kalsi, Evangelia Ourania Fourkala, Caroline Dive, Michael Walker, Anthony D. Whetton, Usha Menon, Ian Jacobs*, Robert L.J. Graham

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
132 Downloads (Pure)


Purpose: Ovarian cancer (OC) is the most lethal gynaecological cancer. Early detection is required to improve patient survival. Risk estimation models were constructed for Type I (Model I) and Type II (Model II) OC from analysis of Protein Z, Fibronectin, C-reactive protein and CA125 levels in prospectively collected samples from the United Kingdom Collaborative Trial of Ovarian Cancer Screening (UKCTOCS). Results: Model I identifies cancers earlier than CA125 alone, with a potential lead time of 3-4 years. Model II detects a number of high grade serous cancers at an earlier stage (Stage I/II) than CA125 alone, with a potential lead time of 2-3 years and assigns high risk to patients that the ROCA Algorithm classified as normal. Materials and Methods: This nested case control study included 418 individual serum samples serially collected from 49 OC cases and 31 controls up to six years pre-diagnosis. Discriminatory logit models were built combining the ELISA results for candidate proteins with CA125 levels. Conclusions: These models have encouraging sensitivities for detecting preclinical ovarian cancer, demonstrating improved sensitivity compared to CA125 alone. In addition we demonstrate how the models improve on ROCA for some cases and outline their potential future use as clinical tools.

Original languageEnglish
Pages (from-to)785-797
Issue number1
Publication statusPublished - 26 Nov 2016


  • Early detection
  • Logit
  • Ovarian cancer
  • Risk estimation

ASJC Scopus subject areas

  • Oncology

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