Predicting Renal Cancer Recurrence: Defining Limitations of Existing Prognostic Models With Prospective Trial-Based Validation

被引:96
作者
Correa, Andres F. [1 ]
Jegede, Opeyemi [2 ]
Haas, Naomi B. [3 ]
Flaherty, Keith T. [4 ]
Pins, Michael R. [5 ]
Messing, Edward M. [6 ]
Manola, Judith [2 ]
Wood, Christopher G. [7 ]
Kane, Christopher J. [8 ]
Jewett, Michael A. S. [9 ]
Dutcher, Janice P. [10 ]
DiPaola, Robert S. [11 ]
Carducci, Michael A. [12 ]
Uzzo, Robert G. [1 ]
机构
[1] Fox Chase Canc Ctr, 7701 Burholme Ave, Philadelphia, PA 19111 USA
[2] Dana Farber Canc Inst, Boston, MA 02115 USA
[3] Univ Penn, Philadelphia, PA 19104 USA
[4] Massachusetts Gen Hosp, Boston, MA 02114 USA
[5] Advocate Lutheran Gen Hosp, Park Ridge, IL USA
[6] Univ Rochester, Rochester, NY USA
[7] Univ Texas MD Anderson Canc Ctr, Houston, TX 77030 USA
[8] Univ Calif San Diego, La Jolla, CA 92093 USA
[9] Univ Toronto, Toronto, ON, Canada
[10] Canc Res Fdn, New York, NY USA
[11] Univ Kentucky, Coll Med, Lexington, KY USA
[12] Johns Hopkins Univ Hosp, Baltimore, MD 21287 USA
基金
美国国家卫生研究院;
关键词
CELL CARCINOMA; RADICAL NEPHRECTOMY; ADJUVANT SUNITINIB; OUTCOME PREDICTION; HIGH-RISK; SURVIVAL; NOMOGRAM; PLACEBO;
D O I
10.1200/JCO.19.00107
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
PURPOSETo validate currently used recurrence prediction models for renal cell carcinoma (RCC) by using prospective data from the ASSURE (ECOG-ACRIN E2805; Adjuvant Sorafenib or Sunitinib for Unfavorable Renal Carcinoma) adjuvant trial.PATIENTS AND METHODSEight RCC recurrence models (University of California at Los Angeles Integrated Staging System [UISS]; Stage, Size, Grade, and Necrosis [SSIGN]; Leibovich; Kattan; Memorial Sloan Kettering Cancer Center [MSKCC]; Yaycioglu; Karakiewicz; and Cindolo) were selected on the basis of their use in clinical practice and clinical trial designs. These models along with the TNM staging system were validated using 1,647 patients with resected localized high-grade or locally advanced disease (>= pT1b grade 3 and 4/pTanyN1Mo) from the ASSURE cohort. The predictive performance of the model was quantified by assessing its discriminatory and calibration abilities.RESULTSProspective validation of predictive and prognostic models for localized RCC showed a substantial decrease in each of the predictive abilities of the model compared with their original and externally validated discriminatory estimates. Among the models, the SSIGN score performed best (0.688; 95% CI, 0.686 to 0.689), and the UISS model performed worst (0.556; 95% CI, 0.555 to 0.557). Compared with the 2002 TNM staging system (C-index, 0.60), most models only marginally outperformed standard staging. Importantly, all models, including TNM, demonstrated statistically significant variability in their predictive ability over time and were most useful within the first 2 years after diagnosis.CONCLUSIONIn RCC, as in many other solid malignancies, clinicians rely on retrospective prediction tools to guide patient care and clinical trial selection and largely overestimate their predictive abilities. We used prospective collected adjuvant trial data to validate existing RCC prediction models and demonstrate a sharp decrease in the predictive ability of all models compared with their previous retrospective validations. Accordingly, we recommend prospective validation of any predictive model before implementing it into clinical practice and clinical trial design.
引用
收藏
页码:2062 / +
页数:11
相关论文
共 30 条
  • [1] The Eighth Edition AJCC Cancer Staging Manual: Continuing to build a bridge from a population-based to a more "personalized" approach to cancer staging
    Amin, Mahul B.
    Greene, Frederick L.
    Edge, Stephen B.
    Compton, Carolyn C.
    Gershenwald, Jeffrey E.
    Brookland, Robert K.
    Meyer, Laura
    Gress, Donna M.
    Byrd, David R.
    Winchester, David P.
    [J]. CA-A CANCER JOURNAL FOR CLINICIANS, 2017, 67 (02) : 93 - 99
  • [2] [Anonymous], 2015, User's Guide to the Medical Literature
  • [3] [Anonymous], 2018, PFIZER NEWS 0410
  • [4] C-statistic: A brief explanation of its construction, interpretation and limitations
    Caetano, S. J.
    Sonpavde, G.
    Pond, G. R.
    [J]. EUROPEAN JOURNAL OF CANCER, 2018, 90 : 130 - 132
  • [5] A preoperative clinical prognostic model for non-metastatic renal cell carcinoma
    Cindolo, L
    de la Taille, A
    Messina, G
    Romis, L
    Abbou, CC
    Altieri, V
    Rodriguez, A
    Patard, JJ
    [J]. BJU INTERNATIONAL, 2003, 92 (09) : 901 - 905
  • [6] DENOIX P F, 1953, Acta Unio Int Contra Cancrum, V9, P769
  • [7] An outcome prediction model for patients with clear cell renal cell carcinoma treated with radical nephrectomy based on tumor stage, size, grade and necrosis: The SSIGN score
    Frank, I
    Blute, ML
    Cheville, JC
    Lohse, CM
    Weaver, AL
    Zincke, H
    [J]. JOURNAL OF UROLOGY, 2002, 168 (06) : 2395 - 2400
  • [8] Greene F.L., 2002, AJCC Cancer Staging Handbook: TNM Classification of Malignant Tumors, V6th, P323
  • [9] Adjuvant sunitinib or sorafenib for high-risk, non-metastatic renal-cell carcinoma (ECOG-ACRIN E2805): a double-blind, placebo-controlled, randomised, phase 3 trial
    Haas, Naomi B.
    Manola, Judith
    Uzzo, Robert G.
    Flaherty, Keith T.
    Wood, Christopher G.
    Kane, Christopher
    Jewett, Michael
    Dutcher, Janice P.
    Atkins, Michael B.
    Pins, Michael
    Wilding, George
    Cella, David
    Wagner, Lynne
    Matin, Surena
    Kuzel, Timothy M.
    Sexton, Wade J.
    Wong, Yu-Ning
    Choueiri, Toni K.
    Pili, Roberto
    Puzanov, Igor
    Kohli, Manish
    Stadler, Walter
    Carducci, Michael
    Coomes, Robert
    DiPaola, Robert S.
    [J]. LANCET, 2016, 387 (10032) : 2008 - 2016
  • [10] Harrell FE, 1996, STAT MED, V15, P361, DOI 10.1002/(SICI)1097-0258(19960229)15:4<361::AID-SIM168>3.0.CO