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A new prognostic model for survival in second line for metastatic renal cell carcinoma: development and external validation. | LitMetric

A new prognostic model for survival in second line for metastatic renal cell carcinoma: development and external validation.

Angiogenesis

Departments of Medical Oncology, Gustave Roussy, 114 Rue Edward Vaillant, 94800, Villejuif, France.

Published: August 2019

AI Article Synopsis

  • A study aimed to develop and validate a new model to predict overall survival (OS) in patients with metastatic renal cell carcinoma (mRCC) who receive second-line treatment, especially focusing on those with high tumor volume or multiple risk factors.
  • The research involved analyzing a set of patients from clinical trials at Gustave Roussy Cancer Campus and validating the model using data from two phase III trials, incorporating new prognostic factors through a multivariable Cox model.
  • Results showed that the model successfully identified two significant prognostic factors—time from first to second-line treatment and tumor burden—and categorized patients into four risk groups, predicting varying median OS times based on these classifications.

Article Abstract

Background: In patients with metastatic renal cell carcinoma (mRCC), the oncologic benefit of second-line treatment for high volume tumors or presence of more than five risk factors remain to be defined. Our aim was to develop and externally validate a new model most likely to correctly predict overall survival (OS) categories in second line.

Method: mRCC patients treated within clinical trials at Gustave Roussy Cancer Campus (GRCC) formed the discovery set. Patients from two phase III trials from Pfizer database (PFIZERDB), AXIS (NCT00678392), and INTORSECT (NCT00474786), formed the external validation set. New prognostic factors were analyzed using a multivariable Cox model with a backward selection procedure. Performance of the GRCC model and the prognostic classification scheme derived from it, measuring by R, c-index, and calibration, was evaluated on the validation set and compared to MSKCC and IMDC models.

Results: Two hundred and twenty-one patients were included in the GRCC cohort and 855 patients in the PFIZERDB. Median OS was similar in the discovery and validation cohorts (16.8 [95% CI 12.9-21.7] and 15.3 [13.6-17.2] months, respectively). Backward selection procedure identified time from first to second-line treatment and tumor burden as new independent prognostic factors significantly associated to OS after adjusting for IMDC prognostic factors (HR 1.68 [1.23-2.31] and 1.43 [1.03-1.99], respectively). Dividing patients into four risk groups, based on the number of factors selected in GRCC model, median OS from the start of second line in the validation cohort was not reached (NE) [95% CI 24.9-NE] in the favorable risk group (n = 20), 21.8 months [18.6-28.2] in the intermediate-risk group (n = 367), 12.7 months [11.0-15.8] in the low poor-risk group (n = 347), and 5.5 months [4.7-6.4] in the high poor-risk group (n = 121). Finally, this model and its prognostic classification scheme provided the better fit, with higher R and higher c-index compared to other possible classification schemes.

Conclusion: A new prognostic model was developed and validated to estimate overall survival of patients with previously treated mRCC. This model is an easy-to-use tool that allows accurate estimation of patient survival to inform decision making and follow-up after first line for mRCC.

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Source
http://dx.doi.org/10.1007/s10456-019-09664-2DOI Listing

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