Active Surveillance for Small Renal Masses in elderly patients do not increase overall mortality rates compared to primary intervention: A propensity score weighted analysis

Marchioni, M.; Pavan, N.; Capitanio, Umberto; Nicola, Marco Di; Antonelli, Alessandro; Derweesh, Ithaar; Linares, E; Takagi, Toshio; Rha, Koon Ho; Maurer, Tobias; Mottrie, Alexandre; Long, Jin; Nunzio, Cosimo De; Ferro, Michela; Micali, Salvatore; Júnior, E Lima; Crivellaro, Simone; Perdora, S.; Yang, Bishan; Porpiglia, F.; Autorino, Riccardo; Pierorazio, Phillip M.; Evarearts, W.; Kaouk, Jihad; Schips, Luigi; Mir, Mushtaq Ahmad

doi:10.1016/s1569-9056(19)33490-6

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Clinical Implication From the Use Of Biomarkers In Renal Cell Carcinoma1

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2021

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“…Finally, more accurate predictive tools could be fundamental for stratifying patients eligible to active surveillance for small renal masses. During last years, a growing interest emerged for such therapeutic strategy, and active surveillance showed survival outcomes that are comparable to active treatment [44,45]. Unfortunately, to the best of our knowledge, only a scoring system applies specifically to active surveillance patient selection [46].…”

Section: Clinical Implication From the Use Of Biomarkers In Renal Cell Carcinomamentioning

confidence: 99%

Biomarkers for Renal Cell Carcinoma Recurrence: State of the Art

et al. 2021

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Purpose of Review We aim to summarize the current state of art about the possible use of biomarkers for predicting renal cell carcinoma (RCC) recurrence after curative treatment. In addition, we aim to provide a snapshot about the clinical implication of biomarkers use for follow-up planification. Recent Findings A wide variety of biomarkers have been proposed. RCC biomarkers have been individuated in tumoral tissue, blood, and urine. A variety of molecules, including proteins, DNA, and RNA, warrant a good accuracy for RCC recurrence and progression prediction. Their use in prediction models might warrant a better patients’ risk stratification. Summary Future prognostic models will probably include a combination of classical features (tumor grade, stage, etc.) and novel biomarkers. Such models might allow a more accurate treatment and follow-up planification.

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Section: Clinical Implication From the Use Of Biomarkers In Renal Cell Carcinomamentioning

confidence: 99%