Radiomic phenotype features predict pathological response in non-small cell lung cancer - PubMed (original) (raw)
Radiomic phenotype features predict pathological response in non-small cell lung cancer
Thibaud P Coroller et al. Radiother Oncol. 2016 Jun.
Abstract
Background and purpose: Radiomics can quantify tumor phenotype characteristics non-invasively by applying advanced imaging feature algorithms. In this study we assessed if pre-treatment radiomics data are able to predict pathological response after neoadjuvant chemoradiation in patients with locally advanced non-small cell lung cancer (NSCLC).
Materials and methods: 127 NSCLC patients were included in this study. Fifteen radiomic features selected based on stability and variance were evaluated for its power to predict pathological response. Predictive power was evaluated using area under the curve (AUC). Conventional imaging features (tumor volume and diameter) were used for comparison.
Results: Seven features were predictive for pathologic gross residual disease (AUC>0.6, p-value<0.05), and one for pathologic complete response (AUC=0.63, p-value=0.01). No conventional imaging features were predictive (range AUC=0.51-0.59, p-value>0.05). Tumors that did not respond well to neoadjuvant chemoradiation were more likely to present a rounder shape (spherical disproportionality, AUC=0.63, p-value=0.009) and heterogeneous texture (LoG 5mm 3D - GLCM entropy, AUC=0.61, p-value=0.03).
Conclusion: We identified predictive radiomic features for pathological response, although no conventional features were significantly predictive. This study demonstrates that radiomics can provide valuable clinical information, and performed better than conventional imaging features.
Keywords: Biomarkers; NSCLC; Pathological response; Quantitative imaging; Radiomics.
Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.
Conflict of interest statement
Conflict of Interest Statement: None
Figures
Figure 1
Radiomic analysis workflow description: A) Lung primary tumor site was manually contoured from treatment planning images (shown in green on CT image on the left and the 3D mask on the right). B) All images were subsequently resampled and from those contours the radiomic features describing tumor phenotype were extracted using three feature groups: Shape, Statistic and Textural features, with and without Wavelet and Laplacian of Gaussian filtering. C) Finally, association between radiomic features and the clinical outcomes were investigated for image-based biomarkers discovery.
Figure 2
AUC of Radiomic features and conventional volumetric imaging features for A) poor responders (gross residual disease) vs. good responders (pathologic complete response and microscopic residual disease) and B) pathological complete responders vs. non-complete responders (microscopic and gross residual disease). Predicting power was reported as proportional or disproportionate to the risk of experiencing the response as the feature value is increasing. Features reported with a “*” are significant from random (Noether test, p-value <0.05). Legend colors indicates feature group.
Figure 3
Comparison of multivariate models for A) Gross residual disease and B) complete pathological response. AUC from the validation is reported from the Cross-Validation (100 iterations, 80% training and 20% validation) for each model. Comparison between models were done using a permutation test. A “*” is reported if the model performance is significantly greater than the other, else “ns” for non-significant.
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