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Baseline radiomic signature to estimate overall survival in patients with non-small cell lung cancer

Published:January 12, 2023DOI:https://doi.org/10.1016/j.jtho.2022.12.019
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      Abstract:

      Introduction

      We aimed to define a baseline radiomic signature associated with overall survival (OS) using baseline computed-tomography (CT) images obtained from patients with non-small cell lung cancer (NSCLC) treated with nivolumab or chemotherapy.

      Methods

      The radiomics signature was developed in patients with NSCLC treated with nivolumab in CheckMate 017, 026, and 063. Nivolumab-treated patients were pooled and randomized to training, calibration, or validation sets using a 2:1:1 ratio. From baseline CT images, volume of tumor lesions was semi-automatically segmented, and 38 radiomic variables depicting tumor phenotype were extracted. Association between the radiomics signature and OS was assessed in the nivolumab-treated (validation set) and chemotherapy-treated (test set) patients in these studies.

      Results

      A baseline radiomic signature was identified using CT images obtained from 758 patients. The radiomic signature used a combination of imaging variables (spatial correlation, tumor volume in the liver, and tumor volume in mediastinal lymph nodes) to output a continuous value, ranging from 0 to 1 (from most to least favorable estimated OS). Given a threshold of 0.55, the sensitivity and specificity of the radiomic signature for predicting 3-month OS were 86% and 77.8%, respectively. The signature was identified in the training set of patients treated with nivolumab and was significantly associated (p < 0.0001) with OS in patients treated with nivolumab or chemotherapy.

      Conclusions

      The radiomic signature provides an early readout of the anticipated OS in patients with NSCLC treated with nivolumab or chemotherapy. This could provide important prognostic information and may support risk stratification in clinical trials.

      Key words

      Abbreviation list:

      AI (artificial intelligence), AUC (area under the curve), CI (confidence interval), CT (computed tomography), DWT (discrete wavelet transform), GLCM (gray-level co-occurrence matrix), HR (hazard ratio), ICI (immune checkpoint inhibitor), LN (lymph node), NA (not applicable), NSCLC (non-small cell lung cancer), OS (overall survival), PD-1 (programmed death-1), PD-L1 (programmed death ligand 1), ROC (receiver operating characteristic), SCC (squamous cell carcinoma)
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