Multi-institutional re-evaluation of prognostic factors in chromophobe renal cell carcinoma: proposal of a novel two-tiered grading scheme

Virchows Archiv(2019)

引用 44|浏览32
暂无评分
摘要
A histological grading system of chromophobe renal cell carcinoma (chRCC) is highly desirable to identify approximately 5–10% of tumors at risk for progression. Validation studies failed to demonstrate a correlation between the four-tiered WHO/ISUP grade and outcome. Previous proposals with three-tiered chromophobe grading systems could not be validated. In this study, the presence of sarcomatoid differentiation, necrosis, and mitosis was analyzed in a Swiss cohort ( n = 42), an Italian cohort ( n = 103), a German cohort ( n = 54), a Japanese cohort ( n = 119), and The Cancer Genome Atlas cohort ( n = 64). All 3 histological parameters were significantly associated with shorter time to tumor progression and overall survival in univariate analysis. Interobserver variability for identification of these parameters was measured by Krippendorff’s alpha coefficient and showed high concordance for the identification of sarcomatoid differentiation and tumor necrosis, but only low to medium concordance for the identification of mitosis. Therefore, we tested a two-tiered tumor grading system (low versus high grade) based only on the presence of sarcomatoid differentiation and/or necrosis finding in the combined cohorts ( n = 382). pT stage, patient’s age (> 65 vs ≤ 65), lymph node and/or distant metastasis, and the two-tiered grading system (low versus high grade) were significantly associated with overall survival and were independent prognostic parameters in multivariate analysis (Cox proportional hazard). This multi-institutional evaluation of prognostic parameters suggests tumor necrosis and sarcomatoid differentiation as reproducible components of a two-tiered chromophobe tumor grading system.
更多
查看译文
关键词
Chromophobe renal cell carcinoma,Kidney,Mitosis,Necrosis,Prognosis,Sarcomatoid differentiation,The Cancer Genome Atlas (TCGA),Tumor grade
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要