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Open Access 01-01-2018 | Oncology

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

Authors: Kieran G Foley, Robert K. Hills, Beatrice Berthon, Christopher Marshall, Craig Parkinson, Wyn G. Lewis, Tom D. L. Crosby, Emiliano Spezi, Stuart Ashley Roberts

Published in: European Radiology | Issue 1/2018

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.

Key points

• PET texture analysis adds prognostic value to oesophageal cancer staging.

• Texture metrics are independently and significantly associated with overall survival.

• A prognostic model including texture analysis can help risk stratify patients.

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Title: Development and validation of a prognostic model incorporating texture analysis derived from standardised segmentation of PET in patients with oesophageal cancer
Authors: Kieran G Foley
Robert K. Hills
Beatrice Berthon
Christopher Marshall
Craig Parkinson
Wyn G. Lewis
Tom D. L. Crosby
Emiliano Spezi
Stuart Ashley Roberts
Publication date: 01-01-2018
Publisher: Springer Berlin Heidelberg
Published in: European Radiology / Issue 1/2018
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-017-4973-y

ACC 2024 Congress

Springer Medicine

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

Abstract

Objectives

Methods

Results

Conclusions

Key points

ACC 2024 Congress

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key points

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