External validation of a prognostic model incorporating quantitative PET image features in oesophageal cancer

Kieran G. Foley; Zhenwei Shi; Philip Whybra; Petros Kalendralis; Ruben Larue; Maaike Berbee; Meindert N. Sosef; Craig Parkinson; John Staffurth; Tom D. L. Crosby; Stuart Ashley Roberts; Andre Dekker; Leonard Wee; Emiliano Spezi

doi:10.1016/j.radonc.2018.10.033

External validation of a prognostic model incorporating quantitative PET image features in oesophageal cancer

Kieran G. Foley^*, Zhenwei Shi, Philip Whybra, Petros Kalendralis, Ruben Larue, Maaike Berbee, Meindert N. Sosef, Craig Parkinson, John Staffurth, Tom D. L. Crosby, Stuart Ashley Roberts, Andre Dekker, Leonard Wee, Emiliano Spezi

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

90 Downloads (Pure)

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 amultivariate 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 (X-2 = 1.42, df = 3, p = 0.70) or exclusively clinical variables (age, disease stage and treatment; X-2 = 1.19, df = 3, p = 0.75). The calibration slope b 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 (X-2 = 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 Copyright (C) 2018 Published by Elsevier B.V. All rights reserved.

Original language	English
Pages (from-to)	205-212
Number of pages	8
Journal	Radiotherapy and Oncology
Volume	133
DOIs	https://doi.org/10.1016/j.radonc.2018.10.033
Publication status	Published - Apr 2019

Keywords

Oesophageal cancer
Positron-emission tomography
Radiomics
Survival
Prognosis
TEXTURE ANALYSIS
HETEROGENEITY
SEGMENTATION
RADIOMICS
VOLUMES

Access to Document

10.1016/j.radonc.2018.10.033

Full TextFinal published version, 928 KBLicence: Taverne

Cite this

Foley, K. G., Shi, Z., Whybra, P., Kalendralis, P., Larue, R., Berbee, M., Sosef, M. N., Parkinson, C., Staffurth, J., Crosby, T. D. L., Roberts, S. A., Dekker, A., Wee, L., & Spezi, E. (2019). External validation of a prognostic model incorporating quantitative PET image features in oesophageal cancer. Radiotherapy and Oncology, 133, 205-212. https://doi.org/10.1016/j.radonc.2018.10.033

@article{ef70a0cded1e4cfc9825224353191587,

title = "External validation of a prognostic model incorporating quantitative PET image features in oesophageal cancer",

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 amultivariate 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 (X-2 = 1.42, df = 3, p = 0.70) or exclusively clinical variables (age, disease stage and treatment; X-2 = 1.19, df = 3, p = 0.75). The calibration slope b 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 (X-2 = 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 Copyright (C) 2018 Published by Elsevier B.V. All rights reserved.",

keywords = "Oesophageal cancer, Positron-emission tomography, Radiomics, Survival, Prognosis, TEXTURE ANALYSIS, HETEROGENEITY, SEGMENTATION, RADIOMICS, VOLUMES",

author = "Foley, {Kieran G.} and Zhenwei Shi and Philip Whybra and Petros Kalendralis and Ruben Larue and Maaike Berbee and Sosef, {Meindert N.} and Craig Parkinson and John Staffurth and Crosby, {Tom D. L.} and Roberts, {Stuart Ashley} and Andre Dekker and Leonard Wee and Emiliano Spezi",

note = "Funding Information: The authors wish to acknowledge the contributions of Professor Robert K Hills who developed the original prognostic model, Professor Wyn G Lewis who helped with the STAGE cohort data collection, Professor Christopher Marshall (Director of the Positron-Emission Tomography Imaging Centre (PETIC) in Cardiff and members of the South-East Wales Upper GI Cancer MDT committee. Institutional review board approval was obtained. The data that have been used in this study are confidential and cannot be shared. The study was partially funded by a UK Tenovus Cancer Care Grant (TIG2016/04). The authors declare that they have no competing interests. KF, AR, LW and ES conceived and designed the study. RL, MB, MS, PK and TC collected the data. ZS, PW, CP and PK preformed the data analysis. KF, LW, JS, TC and AD drafted the manuscript. All authors read and approved the final manuscript. Funding Information: The study was partially funded by a UK Tenovus Cancer Care Grant ( TIG2016/04 ). Publisher Copyright: {\textcopyright} 2018",

year = "2019",

month = apr,

doi = "10.1016/j.radonc.2018.10.033",

language = "English",

volume = "133",

pages = "205--212",

journal = "Radiotherapy and Oncology",

issn = "0167-8140",

publisher = "Elsevier Ireland Ltd",

}

Foley, KG, Shi, Z, Whybra, P, Kalendralis, P, Larue, R, Berbee, M, Sosef, MN, Parkinson, C, Staffurth, J, Crosby, TDL, Roberts, SA, Dekker, A , Wee, L & Spezi, E 2019, 'External validation of a prognostic model incorporating quantitative PET image features in oesophageal cancer', Radiotherapy and Oncology, vol. 133, pp. 205-212. https://doi.org/10.1016/j.radonc.2018.10.033

TY - JOUR

T1 - External validation of a prognostic model incorporating quantitative PET image features in oesophageal cancer

AU - Foley, Kieran G.

AU - Shi, Zhenwei

AU - Whybra, Philip

AU - Kalendralis, Petros

AU - Larue, Ruben

AU - Berbee, Maaike

AU - Sosef, Meindert N.

AU - Parkinson, Craig

AU - Staffurth, John

AU - Crosby, Tom D. L.

AU - Roberts, Stuart Ashley

AU - Dekker, Andre

AU - Wee, Leonard

AU - Spezi, Emiliano

N1 - Funding Information: The authors wish to acknowledge the contributions of Professor Robert K Hills who developed the original prognostic model, Professor Wyn G Lewis who helped with the STAGE cohort data collection, Professor Christopher Marshall (Director of the Positron-Emission Tomography Imaging Centre (PETIC) in Cardiff and members of the South-East Wales Upper GI Cancer MDT committee. Institutional review board approval was obtained. The data that have been used in this study are confidential and cannot be shared. The study was partially funded by a UK Tenovus Cancer Care Grant (TIG2016/04). The authors declare that they have no competing interests. KF, AR, LW and ES conceived and designed the study. RL, MB, MS, PK and TC collected the data. ZS, PW, CP and PK preformed the data analysis. KF, LW, JS, TC and AD drafted the manuscript. All authors read and approved the final manuscript. Funding Information: The study was partially funded by a UK Tenovus Cancer Care Grant ( TIG2016/04 ). Publisher Copyright: © 2018

PY - 2019/4

Y1 - 2019/4

N2 - 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 amultivariate 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 (X-2 = 1.42, df = 3, p = 0.70) or exclusively clinical variables (age, disease stage and treatment; X-2 = 1.19, df = 3, p = 0.75). The calibration slope b 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 (X-2 = 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 Copyright (C) 2018 Published by Elsevier B.V. All rights reserved.

AB - 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 amultivariate 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 (X-2 = 1.42, df = 3, p = 0.70) or exclusively clinical variables (age, disease stage and treatment; X-2 = 1.19, df = 3, p = 0.75). The calibration slope b 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 (X-2 = 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 Copyright (C) 2018 Published by Elsevier B.V. All rights reserved.

KW - Oesophageal cancer

KW - Positron-emission tomography

KW - Radiomics

KW - Survival

KW - Prognosis

KW - TEXTURE ANALYSIS

KW - HETEROGENEITY

KW - SEGMENTATION

KW - RADIOMICS

KW - VOLUMES

U2 - 10.1016/j.radonc.2018.10.033

DO - 10.1016/j.radonc.2018.10.033

M3 - Article

C2 - 30424894

SN - 0167-8140

VL - 133

SP - 205

EP - 212

JO - Radiotherapy and Oncology

JF - Radiotherapy and Oncology

ER -