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
|
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: | Schools > Medicine Schools > Engineering Research Institutes & Centres > 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 |
Citation Data
Cited 51 times in Scopus. View in Scopus. Powered By Scopus® Data
Actions (repository staff only)
 |
Edit Item |
Dimensions
Dimensions