We hypothesized that pT3a stage at nephrectomy can be accurately predicted in cT1N0M0 clear cell-renal cell carcinoma (cc-RCC) patients. Of 236 patients, treated with either partial or radical nephrectomy (2005–2019), 25 (10.6%) harbored pT3a stage. Multivariable logistic regression models predicting pT3a were fitted using age, tumor size, tumor location and exophytic rate. The new model was 81% accurate. In calibration plots, minimal departures from ideal prediction were recorded. In decision curve analyses, a net-benefit throughout all threshold probabilities was recorded relative to the treat-all or treat-none strategies. Using a probability cut-off of 21% for presence of pT3a stage, 38 patients (16.1%) were identified, in whom pT3a rate was 36.8%. Conversely, in 198 patients (83.9%) below that cut-off, the rate of pT3a was 5.6%. Alternative user-defined cut-offs may be selected. The new model more accurately identifies a subgroup of cT1N0M0 cc-RCC patients with substantially higher risk of pT3a stage than average.
Predicting the risk of pT3a stage in cT1 clear cell renal cell carcinoma / Nocera, L.; Stolzenbach, L. F.; Colla' Ruvolo, C.; Wenzel, M.; Tian, Z.; Rosiello, G.; Bravi, C. A.; Candela, L.; Basile, G.; Larcher, A.; Shariat, S. F.; Bertini, R.; Capitanio, U.; Salonia, A.; Montorsi, F.; Briganti, A.; Karakiewicz, P. I.. - In: EUROPEAN JOURNAL OF SURGICAL ONCOLOGY. - ISSN 0748-7983. - 47:5(2021), pp. 1187-1190. [10.1016/j.ejso.2020.10.040]
Predicting the risk of pT3a stage in cT1 clear cell renal cell carcinoma
Colla' Ruvolo C.;Candela L.;Briganti A.;
2021
Abstract
We hypothesized that pT3a stage at nephrectomy can be accurately predicted in cT1N0M0 clear cell-renal cell carcinoma (cc-RCC) patients. Of 236 patients, treated with either partial or radical nephrectomy (2005–2019), 25 (10.6%) harbored pT3a stage. Multivariable logistic regression models predicting pT3a were fitted using age, tumor size, tumor location and exophytic rate. The new model was 81% accurate. In calibration plots, minimal departures from ideal prediction were recorded. In decision curve analyses, a net-benefit throughout all threshold probabilities was recorded relative to the treat-all or treat-none strategies. Using a probability cut-off of 21% for presence of pT3a stage, 38 patients (16.1%) were identified, in whom pT3a rate was 36.8%. Conversely, in 198 patients (83.9%) below that cut-off, the rate of pT3a was 5.6%. Alternative user-defined cut-offs may be selected. The new model more accurately identifies a subgroup of cT1N0M0 cc-RCC patients with substantially higher risk of pT3a stage than average.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
