OBJECTIVES: Determining the recurrence risk in patients treated for renal cell carcinoma (RCC) is important for providing prognostic information and planning potential surveillance strategies. The pathologic stage has been the most widely used single prognostic variable. However, with minimally invasive treatment modalities, the pathologic stage may not be readily available. We developed a biostatistical prognostic model for postoperative RCC that is independent of the pathologic stage. METHODS: The records of 296 patients who underwent open nephrectomy for RCC at Johns Hopkins Hospital between 1990 and 1999 were reviewed. Cox proportional hazards regression analysis was used to generate a prognostic model. RESULTS: The recurrence risk (R(rec)) was determined from this model: R(rec)=1.55 x presentation (0-1)+0.19 x clinical size (in centimeters). Using this equation, 79% of patients were identified as low risk compared with 45% of patients considered low risk by pathologic stage (pT1). Moreover, the separation between the high and low-risk survival curves increased. CONCLUSIONS: This model is the first to our knowledge that uses purely clinical variables to assess the postoperative prognosis in patients with RCC. These results, although not validated, provide substantial evidence that preoperative clinical variables may be used instead of the pathologic stage to determine the risk of recurrence. Uncoupling the reliance on pathologic stage for prognostic information removes a potential barrier to novel minimally invasive treatments for renal malignancy and provides a standard to which observation protocols can be compared. In the future, this model may facilitate selection of appropriate patients for less toxic adjuvant or neoadjuvant therapies.
OBJECTIVES: Determining the recurrence risk in patients treated for renal cell carcinoma (RCC) is important for providing prognostic information and planning potential surveillance strategies. The pathologic stage has been the most widely used single prognostic variable. However, with minimally invasive treatment modalities, the pathologic stage may not be readily available. We developed a biostatistical prognostic model for postoperative RCC that is independent of the pathologic stage. METHODS: The records of 296 patients who underwent open nephrectomy for RCC at Johns Hopkins Hospital between 1990 and 1999 were reviewed. Cox proportional hazards regression analysis was used to generate a prognostic model. RESULTS: The recurrence risk (R(rec)) was determined from this model: R(rec)=1.55 x presentation (0-1)+0.19 x clinical size (in centimeters). Using this equation, 79% of patients were identified as low risk compared with 45% of patients considered low risk by pathologic stage (pT1). Moreover, the separation between the high and low-risk survival curves increased. CONCLUSIONS: This model is the first to our knowledge that uses purely clinical variables to assess the postoperative prognosis in patients with RCC. These results, although not validated, provide substantial evidence that preoperative clinical variables may be used instead of the pathologic stage to determine the risk of recurrence. Uncoupling the reliance on pathologic stage for prognostic information removes a potential barrier to novel minimally invasive treatments for renal malignancy and provides a standard to which observation protocols can be compared. In the future, this model may facilitate selection of appropriate patients for less toxic adjuvant or neoadjuvant therapies.
Authors: Paul L Crispen; Stephen A Boorjian; Christine M Lohse; Bradley C Leibovich; Eugene D Kwon Journal: Cancer Date: 2008-08-01 Impact factor: 6.860