한글목차

신세포암(RCC)(ICD-10 코드 C64)은 신장 상피로부터 발생하는 암으로 신장암의 90%를 차지하며, 암 관련 사망의 가장 흔한 원인이며, 조직학적으로 10가지 이상의 아형으로 나뉘는데, 가장 흔한 것은 세뇨관 신세포암(ccRCC)입니다(Hsieh 등). 환자는 수술로 신생을 절제하고 소작하기 전에 개별적인 특징, 위험 요인, 질병의 정도를 평가합니다. 또는 전신 치료 계획을 결정하기 위해 샘플을 생검하고 면역조직화학적으로 짜낼 수도 있다(Padala et al.). 초기에 진단된 국소 RCC는 수술로 효과적으로 치료할 수 있지만, 전이가 있는 환자의 예후는 좋지 않습니다. 전이성 RCC 환자의 5년 생존율은 약 12%이다(NIH, 2024, Padala 등). 말기에는 암이 림프절과 체내 원격 장기로 전이되었을 가능성이 있습니다(Cancer Research UK, 2024 c).

주요 8 시장(미국, 프랑스, 독일, 이탈리아, 스페인, 영국, 일본, 중국)의 신세포암(RCC)에 대해 조사분석했으며, RCC의 위험 요인, 합병증, 세계의 역학 동향 등의 정보를 제공하고 있습니다.

목차

표 리스트

도표 리스트

제1장 신세포암(RCC) : 개요

• 촉매
• 관련 리포트
• 향후 리포트

제2장 역학

• 질환 배경
• 위험 요인과 합병증
• 세계의 과거 동향
• 주요 8 시장의 예측 방법
• RCC의 역학적 예측(2023-2033년)
  • RCC로 진단된 발증례
  • RCC로 진단된 발증례 : 연령별
  • RCC로 진단된 발증례 : 성별
  • RCC로 진단된 발증례 : 진단시 스테이지별
  • RCC로 진단된 발증례 : 리스크 그룹별
  • 스테이지 IV nccRCC로 진단된 발증례
  • RCC로 진단된 발증례 : 서브타입별 - 유두상, 혐색소성 RCC
  • VHL 유전자 변이를 수반하는 RCC로 진단된 발증례 : RCC 서브타입별
  • BAP1, SETD2, ARID1A 유전자 변이를 수반하는 RCC로 진단된 발증례
  • 과거 5년간 RCC로 진단된 발증례
• 논의
  • 역학 예측 인사이트
  • COVID-19의 영향
  • 분석 한계
  • 분석 강점

제3장 부록

영문목차

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

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• Develop business strategies by understanding the trends shaping and driving the global Renal Cell Carcinoma market.
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• 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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