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Predicting Oncologic Outcomes in Renal Cell Carcinoma After Surgery.

Bradley C Leibovich1, Christine M Lohse2, John C Cheville3

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|February 6, 2018
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Summary

New prognostic models predict progression-free survival (PFS) and cancer-specific survival (CSS) for clear cell, papillary, and chromophobe renal cell carcinoma patients. These models utilize readily available clinical and pathologic features to improve patient prognosis and guide clinical trial design.

Keywords:
Prediction modelsPrognosisRenal cell carcinomaSurgerySurvival

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Area of Science:

  • Urology
  • Oncology
  • Nephrology

Background:

  • Accurate prediction of oncologic outcomes is crucial for patient management, clinical trial development, and biomarker research.
  • Renal cell carcinoma (RCC) encompasses several subtypes, each with distinct prognostic characteristics.

Purpose of the Study:

  • To develop and validate prognostic models for progression-free survival (PFS) and cancer-specific survival (CSS).
  • Models were specifically created for clear cell (ccRCC), papillary (papRCC), and chromophobe (chrRCC) subtypes of renal cell carcinoma.

Main Methods:

  • Retrospective analysis of the Mayo Clinic Nephrectomy registry (1980-2010) including 3633 patients with nonmetastatic RCC.
  • Multivariable Cox proportional hazards regression was employed to build parsimonious models using clinicopathologic features.
  • Models were evaluated using c-indexes and converted into risk scores for predicting PFS and CSS, accounting for competing risks.

Main Results:

  • Prognostic models were generated for each RCC histologic subtype, with c-indexes for PFS ranging from 0.77 to 0.83 and for CSS from 0.83 to 0.86.
  • Risk scores were developed for each subtype and outcome, enabling prediction of survival rates.
  • A multivariable model for chrRCC CSS was not assessed due to a limited number of events (22 deaths).

Conclusions:

  • The study successfully generated specific prognostic models for ccRCC, papRCC, and chrRCC using established clinicopathologic features.
  • These updated models are expected to enhance patient prognosis assessment, inform biomarker study design, and guide clinical trial enrollment.
  • The identified features accurately predict progression and death from RCC post-surgery, aiding in patient counseling and treatment planning.