External validation of 2 models to predict necrosis/fibrosis in postchemotherapy residual retroperitoneal masses of patients with advanced testicular cancer.

Nonseminomatous testicular germ cell tumors with residual retroperitoneal lesions >1 cm are treated with postchemotherapy retroperitoneal lymph node dissection (pcRPLND). However, up to 50% of patients are overtreated since the histology shows only residual necrosis/fibrosis. We aim to validate the 2 currently best performing prediction models (Vergouwe and Leao) for postchemotherapy residual mass histology.

We performed a retrospective analysis including 402 patients who underwent a pcRPLND from 2008 to 2015. The study cohort was used to validate the 2 prediction models by Vergouwe and Leao using the published formulas and thresholds.

Using our validation cohort, the Vergouwe model reached a significantly better area under the curve compared to the Leao model (0.760 (confidence interval 0.713-0.807) vs. 0.692 (0.640-0.744), P = 0.002) in the prediction of benign histology. At a threshold of >70% for the predicted probability of benign disease, the Leao model revealed that pcRPLND would be avoided in 10.2% of patients with benign disease with an error rate of 3.8% for viable tumor, while the Vergouwe model would avoid pcRPLND in 27.4% of all patients with benign disease with an error rate of 10.1% for viable tumor and 2.9% for teratoma. Adjusting the models to our data had no significant improvement. Limitations include the retrospective design.

The discriminatory accuracy of both models is not sufficient to safely select patients for surveillance strategy instead of pcRPLND. Therefore, further studies including new biomarkers are needed to optimize the accuracy of potential prediction models and to minimize pcRPLND overtreatment.

Urologic oncology. 2019 Sep 17 [Epub ahead of print]

Pia Paffenholz, Tim Nestler, Simon Hoier, David Pfister, Martin Hellmich, Axel Heidenreich

Department of Urology, University Hospital Cologne, Cologne, Germany., Institute of Medical Statistics and Computational Biology, University of Cologne, Cologne, Germany., Department of Urology, University Hospital Cologne, Cologne, Germany; Department of Urology, Medical University Vienna, Austria. Electronic address: .