Longitudinal radiomics of cone-beam CT images from non-small cell lung cancer patients: Evaluation of the added prognostic value for overall survival and locoregional recurrence

Abstract

Background and purpose: The prognostic value of radiomics for non-small cell lung cancer (NSCLC) patients has been investigated for images acquired prior to treatment, but no prognostic model has been developed that includes the change of radiomic features during treatment. Therefore, the aim of this study was to investigate the potential added prognostic value of a longitudinal radiomics approach using cone-beam computed tomography (CBCT) for NSCLC patients.

Materials and methods: This retrospective study includes a training dataset of 141 stage I-IV NSCLC patients and three external validation datasets of 94, 61 and 41 patients, all treated with curative intended (chemo)radiotherapy. The change of radiomic features extracted from CBCT images was summarized as the slope of a linear regression. The CBCT slope-features and CT-extracted features were used as input for a Cox proportional hazards model. Moreover, prognostic performance of clinical parameters was investigated for overall survival and locoregional recurrence. Model performances were assessed using the Kaplan-Meier curves and c-index.

Results: The radiomics model contained only CT-derived features and reached a c-index of 0.63 for overall survival and could be validated on the first validation dataset. No model for locoregional recurrence could be developed that validated on the validation datasets. The clinical parameters model could not be validated for either overall survival or locoregional recurrence.

Conclusion: In this study we could not confirm our hypothesis that longitudinal CBCT-extracted radiomic features contribute to improved prognostic information. Moreover, performance of baseline radiomic features or clinical parameters was poor, probably affected by heterogeneity within and between datasets.

Keywords: Cone-beam CT; Longitudinal; Non-small cell lung cancer; Overall survival; Radiomics.

Fig. 1

Kaplan Meier curves showing the overall survival (A) and locoregional recurrence (B) of all datasets.

Fig. 2

Schematic overview of the feature selection process applied to Dataset 1. The initial 2317 features were initially reduced to 2254 features by a clean-up (see text) and the CBCT features were successively reduced based on time variance (see text). Finally the combined set of features (2777) are modeled either after removable based on correlations or by use of PCA in order to investigate model stabilities.

Fig. 3

C-indices of the prognostic models identified for overall survival (left) and locoregional recurrence (right). The combination was either Datasets 2, 3 and 4 (model 1.1) or Datasets 2 and 3 (models 1.2, 3.1 and 3.3). Models 1.1 and 1.2 contain only radiomic features and models 3.1 and 3.2 contain only clinical parameters. For models 2.1 and 2.2 no prognostic features could not be identified.

Fig. 4

Kaplan Meier curves of model 1.1 containing three CT features to predict overall survival. A) Training: Dataset 1, B) Validation 1: Dataset 2, C) Validation 2: Dataset 3, D) Validation 3: Dataset 4. For Datasets 2 and 4, there were no censored data up to 4 years of follow-up.