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Uterine Cancer Forecast in 25 Major Markets 2016-2026


Uterine Cancer, or Corpus Uteri, is defined as any invasive neoplasm of the uterine corpus. It most commonly occurs in the endometrium (~75% of all uterine cancers), the mucous membrane that lines the inner surface of the uterus. It is not currently known what causes uterine cancer, although it is considered to be dependent on endogenous hormonal levels for growth and in particular with exposure to excess oestrogen.

This report provides the current incident population for Uterine Cancer across 25 Major Markets (USA, France, Germany, Italy, Spain, United Kingdom, Poland, Netherlands, Austria, Sweden, Hungary, Romania, Japan, China, India, Russia, Australia, Canada, Turkey, South Korea, South Africa, Saudi Arabia, Argentina, Brazil and Mexico) split by 5-year age cohort. Along with the current incidence, the report also contains a disease overview of the risk factors, disease diagnosis and prognosis along with specific variations by geography and ethnicity.

Providing a value-added level of insight from the analysis team at Black Swan, several of the main symptoms and co-morbidities of Uterine Cancer have been quantified and presented alongside the overall incidence figures. These sub-populations within the main disease are also included at a country level across the 10-year forecast snapshot.

Main symptoms and co-morbidities for Uterine Cancer include:

  • Hypertension
  • Diabetes
  • Lynch II syndrome
  • Cowden’s syndrome
  • Nulliparity / infertile
  • Polycystic ovary syndrome

This report is built using data and information sourced from the proprietary Epiomic patient segmentation database. To generate accurate patient population estimates, the Epiomic database utilises a combination of several world class sources that deliver the most up to date information from patient registries, clinical trials and epidemiology studies. All of the sources used to generate the data and analysis have been identified in the report.

Reason to buy
  • Able to quantify patient populations in global Uterine Cancer’s market to target the development of future products, pricing strategies and launch plans.
  • Gain further insight into the incidence of the subdivided types of Uterine Cancer and identify patient segments with high potential.
  • Delivery of more accurate information for clinical trials in study sizing and realistic patient recruitment for various countries.
  • Provide a level of understanding on the impact from specific co-morbid conditions on Uterine Cancer’s incident population.
  • Identify sub-populations within Uterine Cancer which require treatment.
  • Gain an understanding of the specific markets that have the largest number of Uterine Cancer patients.
Table of Contents
  • List of Tables and Figures
  • Introduction
  • Cause of the Disease
  • Risk Factors & Prevention
  • Diagnosis of the Disease
  • Variation by Geography/Ethnicity
  • Disease Prognosis & Clinical Course
  • Key Co-morbid Conditions Associated with the Disease
  • Methodology for Quantification of Patient Numbers
  • Top-Line Uterine Cancer Incidence
  • Stages and Histopathology of Uterine Cancer
    • FIGO Staging of Uterine Cancer
    • Anatomical Location of Uterine Cancer
    • Major Histopathology in Endometrial Cancer
    • Mutations and Frequency in Uterine Cancer
  • Abbreviations and Acronyms used in the Report
  • Other Black Swan Analysis Publications
  • Black Swan Analysis Online Patient-Based Databases
  • Patient-Based Offering
  • Online Pricing Data and Platforms
  • References
  • Appendix
List of Figures
  • Percentage total incident cases by age group
List of Tables
  • FIGO Stage Descriptors of Uterine Cancer
  • Incidence of Uterine Cancer, females (000s)
  • FIGO staging in Uterine Cancer patients, females (000s)
  • FIGO Stage I breakdown, females (000s)
  • FIGO Stage III breakdown, females (000s)
  • Anatomical Location of Uterine Cancer, females (000s)
  • Major histopathological type of Endometrial Cancer, females (000s)
  • Endometrial Type, females (000s)
  • VEGF Mutation in Uterine Cancer, females (000s)
  • HIF-1-alpha Mutation in Uterine Cancer, females (000s)
  • Abbreviations and Acronyms used in the Report
  • USA Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • France Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Germany Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Italy Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Spain Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • United Kingdom Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Poland Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Netherlands Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Austria Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Sweden Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Romania Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Hungary Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Japan Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • India Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • China Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Russia Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Turkey Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Australia Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Canada Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Argentina Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Brazil Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Mexico Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • South Korea Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • South Africa Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
  • Saudi Arabia Incidence of Uterine Cancer by 5-yr age cohort, females (000s)
Argentina, Australia, Austria, Brazil, Canada, China, France, Germany, Hungary, India, Italy, Japan, Mexico, Netherlands, Poland, Republic of Korea, Romania, Russian Federation, Saudi Arabia, South Africa, Spain, Sweden, Turkey, United Kingdom, United States of America