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

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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
Externally publishedYes

Bibliographical note

Funding Information:
Author PL acknowledge financial support from ERC advanced grant (ERC-ADG-2015, n° 694812 - Hypoximmuno) and the QuIC-ConCePT project, which is partly funded by EFPI A companies and the Innovative Medicine Initiative Joint Undertaking (IMI JU) under Grant Agreement No. 115151 . Author PL also acknowledge financial support from the Dutch technology Foundation STW (grant n° 10696 DuCAT & n° P14-19 Radiomics STRaTegy), which is the applied science division of NWO, and the Technology Programme of the Ministry of Economic Affairs. Authors also acknowledge financial support from the SME Phase 2 (RAIL - n°673780), EUROSTARS (DART, DECIDE, COMPACT), the European Program H2020-2015-17 (ImmunoSABR - n° 733008 and PREDICT - ITN - n° 766276), Interreg V-A Euregio Meuse-Rhine (“Euradiomics”) and Kankeronderzoekfonds Limburg from the Health Foundation Limburg.

The NVALT12 is a multicenter randomized open-label parallel group phase II trial conducted by the Dutch Lung Physician Society (NVALT) and was supported by the Dutch Cancer Society under Grant UM 2010–4883.

Publisher Copyright:
© 2018 The Authors

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