신세포암(RCC) 역학 분석과 예측(-2033년)

Renal cell carcinoma (RCC) (International Classification of Diseases, 10th Revision [ICD-10] code C64) is a cancer that originates from the renal epithelium and accounts for 90% of kidney cancer cases. RCC accounts for the most cancer-related deaths; it is divided into more than 10 histologically distinct subtypes, but the most common is clear cell renal cell carcinoma (ccRCC) (Hsieh et al., 2017). Patients are evaluated on their individual characteristics, risk factors, and the extent of disease before the neoplasm is surgically resected and ablated; alternatively, samples can be biopsied and immunohistochemically strained to determine a systemic therapy plan (Padala et al., 2020). Localized RCC that is diagnosed in its early stages of disease can be effectively treated with surgery, but patients with metastatic disease have poorer outcomes. The five-year survival rate of patients with metastatic RCC is approximately 12% (NIH, 2024; Padala et al., 2020). In the late stages of the disease, the cancer may have spread to and beyond the lymph nodes and other distant organs of the body (Cancer Research UK, 2024c).

Scope

• This report provides an overview of the risk factors and comorbidities, and the global and historical epidemiological trends for RCC in the eight major markets (8MM: US, France, Germany, Italy, Spain, UK, Japan, and China). The report includes a 10-year epidemiology forecast for the diagnosed incident cases of RCC and the five-year diagnosed prevalent cases of RCC. The diagnosed incident cases of RCC are segmented by age (18 years and older) and sex (men and women).
• The diagnosed incident cases of RCC among men and women are segmented by stage at diagnosis (stage I, stage II, stage III, and stage IV), by International mRCC Database Consortium Prognostic Model (IMDC) risk group (favorable, intermediate, and poor risk), by stage IV non-clear cell RCC (nccRCC) patients, by RCC subtype (papillary RCC [pRCC] and chromophobe RCC [chRCC]), and by gene mutations (VHL, BAP1, SETD2, and ARID1A). The five-year diagnosed prevalent cases of RCC are also included in the report. This epidemiology forecast for RCC is supported by data obtained from country-specific oncology databases, peer-reviewed articles, and population-based studies. The forecast methodology was kept consistent across the 8MM to allow for a meaningful comparison of the forecast diagnosed incident and diagnosed prevalent cases of RCC across these markets.

Reasons to Buy

The Renal Cell Carcinoma epidemiology series will allow you to -

• Develop business strategies by understanding the trends shaping and driving the global Renal Cell Carcinoma market.
• Quantify patient populations in the global Renal Cell Carcinoma market to improve product design, pricing, and launch plans.
• Organize sales and marketing efforts by identifying the age groups that present the best opportunities for Renal Cell Carcinoma therapeutics in each of the markets covered.
• Understand magnitude of the Renal Cell Carcinoma population by age, sex, stage at diagnosis, subtypes, and genetic mutations.

Table of Contents

Table of Contents

• About GlobalData
• List of Contents

List of Tables

List of Figures

1 Renal Cell Carcinoma (RCC): Executive Summary

• 1.1 Catalyst
• 1.2 Related reports
• 1.3 Upcoming reports

2 Epidemiology

• 2.1 Disease background
• 2.2 Risk factors and comorbidities
• 2.3 Global and historical trends
• 2.4 8MM forecast methodology.
  • 2.4.1 Sources
  • 2.4.2 Forecast assumptions and methods.
  • 2.4.3 Forecast assumption and methods: diagnosed incident cases of RCC
  • 2.4.4 Forecast assumptions and methods: diagnosed incident cases of RCC by stage at diagnosis.
  • 2.4.5 Forecast assumptions and methods: diagnosed incident cases of stage IV ccRCC by risk group.
  • 2.4.6 Forecast assumptions and methods: diagnosed incident cases of stage IV nccRCC.
  • 2.4.7 Forecast assumptions and methods: diagnosed incident cases of RCC by subtypes, papillary and chromophobe RCC.
  • 2.4.8 Forecast assumptions and methods: diagnosed incident cases of RCC with VHL mutation by RCC subtype.
  • 2.4.9 Forecast assumptions and methods: diagnosed incident cases RCC by BAP1 gene mutation.
  • 2.4.10 Forecast assumptions and methods: diagnosed incident cases RCC by SETD2 gene mutation.
  • 2.4.11 Forecast assumptions and methods: diagnosed incident cases RCC by ARID1A gene mutation.
• 2.5 Epidemiological forecast for RCC (2023-33)
  • 2.5.1 Diagnosed incident cases of RCC.
  • 2.5.2 Age-specific diagnosed incident cases of RCC
  • 2.5.3 Sex-specific diagnosed incident cases of RCC
  • 2.5.4 Diagnosed incident cases of RCC by stage at diagnosis.
  • 2.5.5 Diagnosed incident cases of RCC by risk group.
  • 2.5.6 Diagnosed incident cases of stage IV nccRCC.
  • 2.5.7 Diagnosed incident cases of RCC by subtype - papillary and chromophobe RCC.
  • 2.5.8 Diagnosed incident cases of RCC with VHL gene mutation by RCC subtype.
  • 2.5.9 Diagnosed incident cases of RCC with genetic mutations BAP1, SETD2, and ARID1A
  • 2.5.10 Five-year diagnosed prevalent cases of RCC
• 2.6 Discussion
  • 2.6.1 Epidemiological forecast insight
  • 2.6.2 COVID-19 impact.
  • 2.6.3 Limitations of the analysis
  • 2.6.4 Strengths of the analysis

3 Appendix

• 3.1 Bibliography
• 3.2 About the Authors
  • 3.2.1 Epidemiologist
  • 3.2.2 Reviewers
  • 3.2.3 Vice President of Disease Intelligence and Epidemiology
  • 3.2.4 Global Head of Pharma Research, Analysis and Competitive Intelligence
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