title: The components of progression as explanatory variables for overall survival in the Response Evaluation Criteria in Solid Tumours 1.1 database.
abstract: OBJECTIVE
  Progressive disease (PD) per Response Evaluation Criteria in Solid Tumours (RECIST) 1.1 is defined as growth of measurable target lesions, presence of new lesions or unequivocal progression of non-target disease. In this manuscript we explored whether a more refined categorisation of tumour response and/or these components of progression, varying over time, can improve prediction of overall survival (OS) in the RECIST database.
  
  
  METHODS
  Data were randomly selected from 13 randomised clinical trials (3758 patients with breast, lung or colorectal cancer). A maximum of five target lesions contributed to the sum of longest diameters. At each measurement time we determined: best target response as best % improvement from baseline; tumour growth of target lesions as worst % change and worst rate of increase (mm/week) from nadir; presence of new lesions and occurrence of non-target PD. OS was analysed by tumour type using Cox regression, adjusting for baseline sum and including these parameters as time-dependent covariates.
  
  
  RESULTS
  36% of patients had new lesions, 28% non-target PD and 49% experienced target lesion growth (median strongest growth 1.5mm/week). Regardless of tumour type, presence of new lesions (hazard ratio (HR) ranging 1.5-2.3) and non-target PD (HR 1.5-2.0) were strongly associated with worse OS. The explanatory value of tumour growth for OS was low compared to the other components.
  
  
  CONCLUSIONS
  Modelling target lesion tumour growth did not show a marked improvement in OS prediction over and above the other components. These analyses enable a better understanding of the role of each component in PD evaluation. Work is ongoing to incorporate this information into an updated version of RECIST with enhanced prediction of subsequent survival.
authors.affiliation: European Organisation for Research and Treatment of Cancer (EORTC), Brussels, Belgium. Electronic address: saskia.litiere@eortc.be.
authors.forename: Saskia
authors.initials: S
authors.lastname: Litière
authors.affiliation: Department of Medical Oncology, University of Groningen, University Medical Center, Groningen, The Netherlands.
authors.forename: Elisabeth G E
authors.initials: EGE
authors.lastname: de Vries
authors.affiliation: NCIC Clinical trials group, Queens University, Kingston, Canada.
authors.forename: Lesley
authors.initials: L
authors.lastname: Seymour
authors.affiliation: Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States.
authors.forename: Dan
authors.initials: D
authors.lastname: Sargent
authors.affiliation: NCI Cancer Imaging Program, National Institutes of Health, Bethesda, MA, United States.
authors.forename: Lalitha
authors.initials: L
authors.lastname: Shankar
authors.affiliation: European Organisation for Research and Treatment of Cancer (EORTC), Brussels, Belgium.
authors.forename: Jan
authors.initials: J
authors.lastname: Bogaerts
mesh_terms: D019540:Area Under Curve; D001943:Breast Neoplasms / Q000401:mortality / Q000473:pathology / Q000628:therapy*; D015179:Colorectal Neoplasms / Q000401:mortality / Q000473:pathology / Q000628:therapy*; D016208:Databases, Factual; D018450:Disease Progression; D023381:Endpoint Determination; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000401:mortality / Q000473:pathology / Q000628:therapy*; D008297:Male; D015233:Models, Statistical; D015999:Multivariate Analysis; D016016:Proportional Hazards Models; D012372:ROC Curve; D016032:Randomized Controlled Trials as Topic; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome; D047368:Tumor Burden
keywords: Breast cancer; Colorectal cancer; Goodness-of-fit; Lung cancer; Time-dependent model; Tumour growth