Kieran G Foley1, Zhenwei Shi2, Philip Whybra3, Petros Kalendralis2, Ruben Larue2, Maaike Berbee2, Meindert N Sosef4, Craig Parkinson3, John Staffurth5, Tom D L Crosby6, Stuart Ashley Roberts7, Andre Dekker2, Leonard Wee2, Emiliano Spezi8. 1. Division of Cancer & Genetics, School of Medicine, Cardiff University, UK. Electronic address: foleykg@cardiff.ac.uk. 2. Department of Radiation Oncology (MAASTRO Clinic), GROW School for Oncology and Development Biology, Maastricht University Medical Centre, The Netherlands. 3. School of Engineering, Cardiff University, UK. 4. Zuyderland Medisch Centrum, Heerlen-Sittard-Geleen, The Netherlands. 5. Division of Cancer & Genetics, School of Medicine, Cardiff University, UK; Velindre Cancer Centre, Cardiff, UK. 6. Velindre Cancer Centre, Cardiff, UK. 7. Department of Clinical Radiology, University Hospital of Wales, Cardiff, UK. 8. School of Engineering, Cardiff University, UK; Zuyderland Medisch Centrum, Heerlen-Sittard-Geleen, The Netherlands.
Abstract
AIM: Enhanced prognostic models are required to improve risk stratification of patients with oesophageal cancer so treatment decisions can be optimised. The primary aim was to externally validate a published prognostic model incorporating PET image features. Transferability of the model was compared using only clinical variables. METHODS: This was a Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis (TRIPOD) type 3 study. The model was validated against patients treated with neoadjuvant chemoradiotherapy according to the Neoadjuvant chemoradiotherapy plus surgery versus surgery alone for oesophageal or junctional cancer (CROSS) trial regimen using pre- and post-harmonised image features. The Kaplan-Meier method with log-rank significance tests assessed risk strata discrimination. A Cox proportional hazards model assessed model calibration. Primary outcome was overall survival (OS). RESULTS: Between 2010 and 2015, 449 patients were included in the development (n = 302), internal validation (n = 101) and external validation (n = 46) cohorts. No statistically significant difference in OS between patient quartiles was demonstrated in prognostic models incorporating PET image features (X2 = 1.42, df = 3, p = 0.70) or exclusively clinical variables (age, disease stage and treatment; X2 = 1.19, df = 3, p = 0.75). The calibration slope β of both models was not significantly different from unity (p = 0.29 and 0.29, respectively). Risk groups defined using only clinical variables suggested differences in OS, although these were not statistically significant (X2 = 0.71, df = 2, p = 0.70). CONCLUSION: The prognostic model did not enable significant discrimination between the validation risk groups, but a second model with exclusively clinical variables suggested some transferable prognostic ability. PET harmonisation did not significantly change the results of model validation. Crown
AIM: Enhanced prognostic models are required to improve risk stratification of patients with oesophageal cancer so treatment decisions can be optimised. The primary aim was to externally validate a published prognostic model incorporating PET image features. Transferability of the model was compared using only clinical variables. METHODS: This was a Transparent Reporting of a multivariate prediction model for Individual Prognosis Or Diagnosis (TRIPOD) type 3 study. The model was validated against patients treated with neoadjuvant chemoradiotherapy according to the Neoadjuvant chemoradiotherapy plus surgery versus surgery alone for oesophageal or junctional cancer (CROSS) trial regimen using pre- and post-harmonised image features. The Kaplan-Meier method with log-rank significance tests assessed risk strata discrimination. A Cox proportional hazards model assessed model calibration. Primary outcome was overall survival (OS). RESULTS: Between 2010 and 2015, 449 patients were included in the development (n = 302), internal validation (n = 101) and external validation (n = 46) cohorts. No statistically significant difference in OS between patient quartiles was demonstrated in prognostic models incorporating PET image features (X2 = 1.42, df = 3, p = 0.70) or exclusively clinical variables (age, disease stage and treatment; X2 = 1.19, df = 3, p = 0.75). The calibration slope β of both models was not significantly different from unity (p = 0.29 and 0.29, respectively). Risk groups defined using only clinical variables suggested differences in OS, although these were not statistically significant (X2 = 0.71, df = 2, p = 0.70). CONCLUSION: The prognostic model did not enable significant discrimination between the validation risk groups, but a second model with exclusively clinical variables suggested some transferable prognostic ability. PET harmonisation did not significantly change the results of model validation. Crown