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bioRxiv · 10.1101/681577

A novel imaging biomarker for survival prediction in EGFR-mutated NSCLC patients treated with TKI

Abstract

EGFR-mutated non-small cells lung carcinoma are treated with Tyrosine Kinase Inhibitors (TKI). Very often, the disease is only responding for a while before relapsing. TKI efficacy in the long run is therefore challenging to evaluate. Our objective is to derive a new imaging biomarker that could offer better insights on the disease response to treatment. This study includes 17 patients diagnosed as EGFR-mutated non-small cell lung cancer and exposed to an EGFR-targeting TKI. The early response to treatment is evaluated with 3 computed tomography (CT) scans of the primitive tumor (one before the TKI introduction and two after). Using our knowledge of the disease, an imaging biomarker based on the tumor heterogeneity evolution between the first and the third exams is defined and computed using a novel mathematical model calibrated on patient data. Defining the overall survival as the time between the introduction of the TKI treatment and the patient death, we obtain a statistically significant correlation between the overall survival and our imaging marker (p = 0.009). Using the ROC curve, the patients are separated into two populations hence the comparison of the survival curves is statistically significant (p = 0.025). Initial state of the tumor seems to have a role for the prognosis of the response to TKI treatment. More precisely, the imaging marker - defined using only the CT scan before the TKI introduction - allows us to determine a first classification of the population which is refined over time using the imaging marker as more CT scans become available. This exploratory study leads us to think that it is possible to obtain a survival assessment using only few CT scans of the primary tumor.

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Collin, A., Groza, V., Missenard, L., Chomy, F., Colin, T., Palussiere, J., Saut, O.. 2019-06-24. A novel imaging biomarker for survival prediction in EGFR-mutated NSCLC patients treated with TKI. https://doi.org/10.1101/681577

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