Applicability of a prognostic CT-based radiomic signature model trained on stage I-III non-small cell lung cancer in stage IV non-small cell lung cancer

Evelyn E. C. de Jong*, Wouter van Elmpt, Stefania Rizzo, Anna Colarieti, Gianluca Spitaleri, Ralph T. H. Leijenaar, Arthur Jochems, Lizza E. L. Hendriks, Esther G. C. Troost, Bart Reymen, Anne-Marie C. Dingemans, Philippe Lambin*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

22 Citations (Web of Science)

Abstract

Objectives: Recently it has been shown that radiomic features of computed tomography (CT) have prognostic information in stage I-III non-small cell lung cancer (NSCLC) patients. We aim to validate this prognostic radiomic signature in stage IV adenocarcinoma patients undergoing chemotherapy.

Materials and methods: Two datasets of chemo-naive stage IV adenocarcinoma patients were investigated, dataset 1: 285 patients with CTs performed in a single center; dataset 2: 223 patients included in a multicenter clinical trial. The main exclusion criteria were EGFR mutation or unknown mutation status and non-delineated primary tumor. Radiomic features were calculated for the primary tumor. The c-index of cox regression was calculated and compared to the signature performance for overall survival (OS).

Results: In total CT scans from 195 patients were eligible for analysis. Patients having a prognostic index (PI) lower than the signature median (n = 92) had a significantly better OS than patients with a PI higher than the median (n = 103, HR 1.445, 95% CI 1.07-1.95, p = 0.02, c-index 0.576, 95% CI 0.527-0.624).

Conclusion: The radiomic signature, derived from daily practice CT scans, has prognostic value for stage IV NSCLC, however the signature performs less than previously described for stage I-III NSCLC stages. In the future, machine learning techniques can potentially lead to a better prognostic imaging based model for stage IV NSCLC.

Original languageEnglish
Pages (from-to)6-11
Number of pages6
JournalLung Cancer
Volume124
DOIs
Publication statusPublished - Oct 2018

Keywords

  • Stage IV NSCLC
  • Prognostic model
  • Radiomics
  • CT
  • TEXTURE ANALYSIS
  • FEATURES
  • REPRODUCIBILITY
  • CARCINOMA
  • SURVIVAL
  • IMAGES
  • RADIOTHERAPY

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