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Updated: Jun 20, 2026

Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
Predicting cancer-control outcomes in patients after nephron sparing surgery
Richard Zigeuner1, Karl Pummer
1Department of Urology, Medical University Graz, Austria. richard.zigeuner@medunigraz.at
Abstract:
This article focuses on prognostic factors after nephron-sparing surgery (NSS) for renal cell carcinoma (RCC). A variety of prognostic models has been developed to predict patients' outcome after surgery for RCC. The populations which these models have been based on, however, mainly consisted of patients undergoing radical nephrectomy, whereas NSS represented only a minority. On the other hand, NSS is increasingly gaining acceptance for unilateral small renal masses and a normal contralateral kidney (elective NSS). Meanwhile many large series reporting favourable outcomes after elective NSS have been published. Purely elective NSS series constantly fail to demonstrate prognostic factors due to the rarity of cancer relapse. In other series including larger proportions of imperative NSS cancer recurrence has been reported more frequently, thus prognostic capabilities of established prognostic factors for RCC like TNM-stage, tumour grade, tumour size, symptoms, histological subtypes, or necrosis could be shown. One additional prognostic parameter specific for NSS not applicable in a nephrectomy setting is imperative indication for NSS, which is associated with poorer prognosis than elective NSS. In contrast, positive surgical margins after NSS uniformly revealed no unfavourable impact. Laparoscopic NSS is increasingly performed at selected centres. Long-term outcome data are still very limited, longer follow-up is needed to conclude whether the laparoscopic approach by itself impacts on prognosis. At present, it is still unclear whether the published prognostic models are reliably applicable for the NSS population. Thus, validation of these models in large multi-institutional databases is encouraged.
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