title: Novel risk models for early detection and screening of ovarian cancer.
abstract: OBJECTIVE
  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.
  
  
  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 pre-clinical 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.
authors.affiliation: Stoller Biomarker Discovery Centre and Pathology Node, Division of Molecular and Clinical Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
authors.forename: Matthew R
authors.initials: MR
authors.lastname: Russell
authors.affiliation: Stoller Biomarker Discovery Centre and Pathology Node, Division of Molecular and Clinical Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
authors.forename: Alfonsina
authors.initials: A
authors.lastname: D'Amato
authors.affiliation: School of Healthcare Science, Manchester Metropolitan University, UK.
authors.forename: Ciaren
authors.initials: C
authors.lastname: Graham
authors.affiliation: Gynaecological Oncology Research Group, Division of Molecular and Clinical Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
authors.forename: Emma J
authors.initials: EJ
authors.lastname: Crosbie
authors.affiliation: Gynaecological Cancer Research Centre, Women's Cancer, Institute for Women's Health, University College London, London, UK.
authors.forename: Aleksandra
authors.initials: A
authors.lastname: Gentry-Maharaj
authors.affiliation: Gynaecological Cancer Research Centre, Women's Cancer, Institute for Women's Health, University College London, London, UK.
authors.forename: Andy
authors.initials: A
authors.lastname: Ryan
authors.affiliation: Gynaecological Cancer Research Centre, Women's Cancer, Institute for Women's Health, University College London, London, UK.
authors.forename: Jatinderpal K
authors.initials: JK
authors.lastname: Kalsi
authors.affiliation: Gynaecological Cancer Research Centre, Women's Cancer, Institute for Women's Health, University College London, London, UK.
authors.forename: Evangelia-Ourania
authors.initials: EO
authors.lastname: Fourkala
authors.affiliation: Clinical and Experimental Pharmacology Group, Cancer Research UK Manchester Institute, University of Manchester, Manchester, UK.
authors.forename: Caroline
authors.initials: C
authors.lastname: Dive
authors.affiliation: Stoller Biomarker Discovery Centre and Pathology Node, Division of Molecular and Clinical Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
authors.forename: Michael
authors.initials: M
authors.lastname: Walker
authors.affiliation: Stoller Biomarker Discovery Centre and Pathology Node, Division of Molecular and Clinical Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
authors.forename: Anthony D
authors.initials: AD
authors.lastname: Whetton
authors.affiliation: Gynaecological Cancer Research Centre, Women's Cancer, Institute for Women's Health, University College London, London, UK.
authors.forename: Usha
authors.initials: U
authors.lastname: Menon
authors.affiliation: Stoller Biomarker Discovery Centre and Pathology Node, Division of Molecular and Clinical Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.|Gynaecological Cancer Research Centre, Women's Cancer, Institute for Women's Health, University College London, London, UK.|University of New South Wales, Australia.
authors.forename: Ian
authors.initials: I
authors.lastname: Jacobs
authors.affiliation: Stoller Biomarker Discovery Centre and Pathology Node, Division of Molecular and Clinical Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
authors.forename: Robert L J
authors.initials: RL
authors.lastname: Graham
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor; D055088:Early Detection of Cancer / Q000379:methods; D015981:Epidemiologic Factors; D005260:Female; D006801:Humans; D008403:Mass Screening; D015233:Models, Statistical; D009367:Neoplasm Staging; D010051:Ovarian Neoplasms / Q000175:diagnosis / Q000453:epidemiology*; D012372:ROC Curve; D015203:Reproducibility of Results; D012306:Risk
chemical_list: D014408:Biomarkers, Tumor
keywords: UKCTOCS; early detection; logit; ovarian cancer; risk estimation