title: Evaluation of alternate categorical tumor metrics and cut points for response categorization using the RECIST 1.1 data warehouse.
abstract: OBJECTIVE
  We sought to test and validate the predictive utility of trichotomous tumor response (TriTR; complete response [CR] or partial response [PR] v stable disease [SD] v progressive disease [PD]), disease control rate (DCR; CR/PR/SD v PD), and dichotomous tumor response (DiTR; CR/PR v others) metrics using alternate cut points for PR and PD. The data warehouse assembled to guide the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 was used.
  
  
  METHODS
  Data from 13 trials (5,480 patients with metastatic breast cancer, non-small-cell lung cancer, or colorectal cancer) were randomly split (60:40) into training and validation data sets. In all, 27 pairs of cut points for PR and PD were considered: PR (10% to 50% decrease by 5% increments) and PD (10% to 20% increase by 5% increments), for which 30% and 20% correspond to the RECIST categorization. Cox proportional hazards models with landmark analyses at 12 and 24 weeks stratified by study and number of lesions (fewer than three v three or more) and adjusted for average baseline tumor size were used to assess the impact of each metric on overall survival (OS). Model discrimination was assessed by using the concordance index (c-index).
  
  
  RESULTS
  Standard RECIST cut points demonstrated predictive ability similar to the alternate PR and PD cut points. Regardless of tumor type, the TriTR, DiTR, and DCR metrics had similar predictive performance. The 24-week metrics (albeit with higher c-index point estimate) were not meaningfully better than the 12-week metrics. None of the metrics did particularly well for breast cancer.
  
  
  CONCLUSIONS
  Alternative cut points to RECIST standards provided no meaningful improvement in OS prediction. Metrics assessed at 12 weeks have good predictive performance.
authors: Sumithra J. Mandrekar, Jeffrey Meyers, Axel Grothey, and Daniel J. Sargent, Mayo Clinic, Rochester, MN; Ming-Wen An, Vassar College, Poughkeepsie, NY; and Jan Bogaerts, European Organisation for Research and Treatment of Cancer, Brussels, Belgium.
authors: Sumithra J
authors: SJ
authors: Mandrekar
authors: Ming-Wen
authors: MW
authors: An
authors: Jeffrey
authors: J
authors: Meyers
authors: Axel
authors: A
authors: Grothey
authors: Jan
authors: J
authors: Bogaerts
authors: Daniel J
authors: DJ
authors: Sargent
mesh_terms: D001943:Breast Neoplasms / Q000401:mortality / Q000556:secondary / Q000628:therapy*; D002289:Carcinoma, Non-Small-Cell Lung / Q000401:mortality / Q000473:pathology / Q000628:therapy*; D015179:Colorectal Neoplasms / Q000401:mortality / Q000473:pathology / Q000628:therapy*; D020000:Decision Support Systems, Clinical; D003661:Decision Support Techniques; D016002:Discriminant Analysis; D018450:Disease Progression; D018572:Disease-Free Survival; D005260:Female; D006801:Humans; D053208:Kaplan-Meier Estimate; D008175:Lung Neoplasms / Q000401:mortality / Q000473:pathology / Q000628:therapy*; D008297:Male; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D012074:Remission Induction; D015203:Reproducibility of Results; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome; D047368:Tumor Burden