Imperial College London
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ProfessorAndreaRockall

Faculty of Medicine, Department of Surgery & Cancer

Clinical Chair in Radiology
 
 
 
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Contact

 

a.rockall

 
 
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Location

 

ICTEM buildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Li:2023:10.1002/jmri.28544,
author = {Li, X and Marcus, D and Russell, J and Aboagye, E and Ellis, L and Sheeka, A and Park, W-HE and Bharwani, N and Ghaem-Maghami, S and Rockall, A},
doi = {10.1002/jmri.28544},
journal = {Journal of Magnetic Resonance Imaging},
pages = {1922--1933},
title = {An integrated clinical-MR radiomics model to estimate survival time in patients with endometrial cancer},
url = {http://dx.doi.org/10.1002/jmri.28544},
volume = {57},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Background:Determination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning.Purpose:To identify clinical features and imaging signatures on T2-weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects.Study Type:Retrospective.Population:Four hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years).Field Strength/Sequence:1.5-T and 3-T scanners with sagittal T2-weighted spin echo sequence.Assessment:Tumor regions were manually segmented on T2-weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets.Statistical Tests:A likelihood-ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model.Results:Three radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respective
AU  - Li,X
AU  - Marcus,D
AU  - Russell,J
AU  - Aboagye,E
AU  - Ellis,L
AU  - Sheeka,A
AU  - Park,W-HE
AU  - Bharwani,N
AU  - Ghaem-Maghami,S
AU  - Rockall,A
DO  - 10.1002/jmri.28544
EP  - 1933
PY  - 2023///
SN  - 1053-1807
SP  - 1922
TI  - An integrated clinical-MR radiomics model to estimate survival time in patients with endometrial cancer
T2  - Journal of Magnetic Resonance Imaging
UR  - http://dx.doi.org/10.1002/jmri.28544
UR  - http://hdl.handle.net/10044/1/101761
VL  - 57
ER  -
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