Saskia Litière1, Elisabeth G E de Vries2, Lesley Seymour3, Dan Sargent4, Lalitha Shankar5, Jan Bogaerts6. 1. European Organisation for Research and Treatment of Cancer (EORTC), Brussels, Belgium. Electronic address: saskia.litiere@eortc.be. 2. Department of Medical Oncology, University of Groningen, University Medical Center, Groningen, The Netherlands. 3. NCIC Clinical trials group, Queens University, Kingston, Canada. 4. Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, United States. 5. NCI Cancer Imaging Program, National Institutes of Health, Bethesda, MA, United States. 6. European Organisation for Research and Treatment of Cancer (EORTC), Brussels, Belgium.
Abstract
PURPOSE: 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. CONCLUSION: 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.
PURPOSE: 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. CONCLUSION: 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: Saskia Litière; Gaëlle Isaac; Elisabeth G E De Vries; Jan Bogaerts; Alice Chen; Janet Dancey; Robert Ford; Stephen Gwyther; Otto Hoekstra; Erich Huang; Nancy Lin; Yan Liu; Sumithra Mandrekar; Lawrence H Schwartz; Lalitha Shankar; Patrick Therasse; Lesley Seymour Journal: J Clin Oncol Date: 2019-03-12 Impact factor: 44.544
Authors: Viktor Grünwald; Rana R McKay; Katherine M Krajewski; Daniel Kalanovic; Xun Lin; Julia J Perkins; Ronit Simantov; Toni K Choueiri Journal: Eur Urol Date: 2015-01-07 Impact factor: 20.096
Authors: Christopher Twelves; Javier Cortes; Peter A Kaufman; Louise Yelle; Ahmad Awada; Terri A Binder; Martin Olivo; James Song; Joyce A O'Shaughnessy; Maria Jove; Edith A Perez Journal: Breast Cancer Res Date: 2015-12-09 Impact factor: 6.466