Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer

Foley, Kieran G., Hills, Robert K. ORCID: https://orcid.org/0000-0003-0166-0062, Berthon, Beatrice, Marshall, Christopher, Parkinson, Craig ORCID: https://orcid.org/0000-0003-3454-4957, Lewis, Wyn G., Crosby, Tom D. L., Spezi, Emiliano ORCID: https://orcid.org/0000-0002-1452-8813 and Roberts, Stuart Ashley 2018. Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer. European Radiology 28 , pp. 428-436. 10.1007/s00330-017-4973-y

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Abstract

Objectives This retrospective cohort study developed a prognostic model incorporating PET texture analysis in patients with oesophageal cancer (OC). Internal validation of the model was performed. Methods Consecutive OC patients (n = 403) were chronologically separated into development (n = 302, September 2010-September 2014, median age = 67.0, males = 227, adenocarcinomas = 237) and validation cohorts (n = 101, September 2014-July 2015, median age = 69.0, males = 78, adenocarcinomas = 79). Texture metrics were obtained using a machine-learning algorithm for automatic PET segmentation. A Cox regression model including age, radiological stage, treatment and 16 texture metrics was developed. Patients were stratified into quartiles according to a prognostic score derived from the model. A p-value < 0.05 was considered statistically significant. Primary outcome was overall survival (OS). Results Six variables were significantly and independently associated with OS: age [HR =1.02 (95% CI 1.01-1.04), p < 0.001], radiological stage [1.49 (1.20-1.84), p < 0.001], treatment [0.34 (0.24–0.47), p < 0.001], log(TLG) [5.74 (1.44–22.83), p = 0.013], log(Histogram Energy) [0.27 (0.10–0.74), p = 0.011] and Histogram Kurtosis [1.22 (1.04–1.44), p = 0.017]. The prognostic score demonstrated significant differences in OS between quartiles in both the development (X2 143.14, df 3, p < 0.001) and validation cohorts (X2 20.621, df 3, p < 0.001). Conclusions This prognostic model can risk stratify patients and demonstrates the additional benefit of PET texture analysis in OC staging.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine Engineering Data Innovation Research Institute (DIURI)
Subjects:	T Technology > TA Engineering (General). Civil engineering (General)
Additional Information:	This is an open access article under the terms of the CC-BY Attribution 4.0 International license.
Publisher:	Springer Verlag (Germany)
ISSN:	0938-7994
Funders:	Engineering and Physical Sciences Research Council
Date of First Compliant Deposit:	27 June 2017
Date of Acceptance:	26 June 2017
Last Modified:	22 May 2023 18:41
URI:	https://orca.cardiff.ac.uk/id/eprint/101828

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