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

Janna E. van Timmeren*, Wouter van Elmpt, Ralph T. H. Leijenaar, Bart Reymen, Rene Monshouwer, Johan Bussink, Leen Paelinck, Evelien Bogaert, Carlos De Wagter, Elamin Elhaseen, Yolande Lievens, Olfred Hansen, Carsten Brink, Philippe Lambin

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

31 Citations (Web of Science)

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. 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. 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. 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. (C) 2019 The Authors. Published by Elsevier B. V. Radiotherapy and Oncology 136 (2019) 78-85 This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Original languageEnglish
Pages (from-to)78-85
Number of pages8
JournalRadiotherapy and Oncology
Volume136
DOIs
Publication statusPublished - Jul 2019

Keywords

  • Non-small cell lung cancer
  • Radiomics
  • Cone-beam CT
  • Longitudinal
  • Overall survival
  • MULTIVARIABLE PREDICTION MODEL
  • INDIVIDUAL PROGNOSIS
  • DIAGNOSIS TRIPOD
  • FEATURES
  • REGRESSION

Cite this