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.

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 / de Jong, E. E. C.; van Elmpt, W.; Rizzo, S.; Colarieti, A.; Spitaleri, G.; Leijenaar, R. T. H.; Jochems, A.; Hendriks, L. E. L.; Troost, E. G. C.; Reymen, B.; Dingemans, A. -M. C.; Lambin, P.. - In: LUNG CANCER. - ISSN 0169-5002. - 124:(2018), pp. 6-11. [10.1016/j.lungcan.2018.07.023]

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

Colarieti A.;
2018

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.
2018
CT; Prognostic model; Radiomics; Stage IV NSCLC
01 Pubblicazione su rivista::01a Articolo in rivista
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 / de Jong, E. E. C.; van Elmpt, W.; Rizzo, S.; Colarieti, A.; Spitaleri, G.; Leijenaar, R. T. H.; Jochems, A.; Hendriks, L. E. L.; Troost, E. G. C.; Reymen, B.; Dingemans, A. -M. C.; Lambin, P.. - In: LUNG CANCER. - ISSN 0169-5002. - 124:(2018), pp. 6-11. [10.1016/j.lungcan.2018.07.023]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1672456
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