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Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients: Evaluation of the added prognostic value for overall survival and locoregional recurrence.

Authors :
van Timmeren JE
van Elmpt W
Leijenaar RTH
Reymen B
Monshouwer R
Bussink J
Paelinck L
Bogaert E
De Wagter C
Elhaseen E
Lievens Y
Hansen O
Brink C
Lambin P
Source :
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology [Radiother Oncol] 2019 Jul; Vol. 136, pp. 78-85. Date of Electronic Publication: 2019 Apr 11.
Publication Year :
2019

Abstract

Background and Purpose: The prognostic value of radiomics for non-small cell lung cancer (NSCLC) patients has been investigated for images acquired prior to treatment, but no prognostic model has been developed that includes the change of radiomic features during treatment. Therefore, the aim of this study was to investigate the potential added prognostic value of a longitudinal radiomics approach using cone-beam computed tomography (CBCT) for NSCLC patients.<br />Materials and Methods: This retrospective study includes a training dataset of 141 stage I-IV NSCLC patients and three external validation datasets of 94, 61 and 41 patients, all treated with curative intended (chemo)radiotherapy. The change of radiomic features extracted from CBCT images was summarized as the slope of a linear regression. The CBCT slope-features and CT-extracted features were used as input for a Cox proportional hazards model. Moreover, prognostic performance of clinical parameters was investigated for overall survival and locoregional recurrence. Model performances were assessed using the Kaplan-Meier curves and c-index.<br />Results: The radiomics model contained only CT-derived features and reached a c-index of 0.63 for overall survival and could be validated on the first validation dataset. No model for locoregional recurrence could be developed that validated on the validation datasets. The clinical parameters model could not be validated for either overall survival or locoregional recurrence.<br />Conclusion: In this study we could not confirm our hypothesis that longitudinal CBCT-extracted radiomic features contribute to improved prognostic information. Moreover, performance of baseline radiomic features or clinical parameters was poor, probably affected by heterogeneity within and between datasets.<br /> (Copyright © 2019 The Authors. Published by Elsevier B.V. All rights reserved.)

Details

Language :
English
ISSN :
1879-0887
Volume :
136
Database :
MEDLINE
Journal :
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Publication Type :
Academic Journal
Accession number :
31015133
Full Text :
https://doi.org/10.1016/j.radonc.2019.03.032