Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

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

Foley, Kieran, Hills, Robert Kerrin, Berthon, Beatrice, Marshall, Christopher, Parkinson, Craig, Lewis, Wyn G, Crosby, Tom DL, Spezi, Emiliano and Roberts, Stuart A 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 (1) , pp. 428-436. 10.1007/s00330-017-4973-y

[img]
Preview
PDF - Published Version
Available under License Creative Commons Attribution.

Download (2MB) | Preview

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: Data Innovation Research Institute (DIURI)
Engineering
Medicine
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: Springer Verlag
ISSN: 0938-7994
Funders: Engineering and Physical Sciences Research Council
Date of First Compliant Deposit: 27 June 2017
Date of Acceptance: 27 June 2017
Last Modified: 19 Oct 2019 02:47
URI: http://orca.cf.ac.uk/id/eprint/101828

Citation Data

Cited 15 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item

Downloads

Downloads per month over past year

View more statistics