Sub-classification of patients with intermediate-risk metastatic renal cell carcinoma treated with targeted therapy

Go Kaneko, Suguru Shirotake, Koshiro Nishimoto, Yasumasa Miyazaki, Keiichi Ito, Yujiro Ito, Masayuki Hagiwara, Kent Kanao, Ken Nakagawa, Tetsuo Momma, Tomohiko Asano, Nobuyuki Tanaka, Ryuichi Mizuno, Mototsugu Oya, Masafumi Oyama

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

Background: International Metastatic Renal Cell Carcinoma Database Consortium model predicts the outcomes of metastatic renal cell carcinoma stratified into favorable, intermediate, and poor risk groups (FG, IG, and PG, respectively), with approximately 50% of patients being classified as IG. We aimed to generate better risk model based on the sub-classification of IG. Methods: We analyzed records of 213 consecutive patients receiving molecular targeted therapy. Age, gender, histology, type of initial molecular targeted therapy, serum laboratory data, previous nephrectomy and immunotherapy, and metastatic sites were used for IG sub-stratification. Modified and original models were compared using a concordance correlation coefficient analysis. Results: Median follow-up was 17.8 months. Serum albumin, serum C-reactive protein, and bone metastases were independent predictors of overall survival (OS) in IG. IG was sub-classified into low-, middle-, and high-risk IG according to the number of predictors. The following modified model was developed: modified FG (FG & low-risk IG), modified IG (middle-risk IG), and modified PG (PG & high-risk IG). Concordance indices for original and modified models were 0.68 and 0.73, respectively (P < 0.001). OS was significantly longer in modified PG treated with mammalian target of rapamycin inhibitors as second-line therapy than with tyrosine kinase inhibitors, whereas this was not observed in the original model. Conclusions: We successfully developed modified IMDC model using a two-step process: the original IMDC plus an IG sub-stratification, and demonstrated that it predicts outcomes more accurately than original model.

Original languageEnglish
Pages (from-to)780-785
Number of pages6
JournalJapanese journal of clinical oncology
Volume49
Issue number8
DOIs
Publication statusPublished - 01-08-2019
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Cancer Research

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