Portal de Periódicos da CAPES

Radiomic phenotype features predict pathological response in non-small cell lung cancer

2016; Elsevier BV; Volume: 119; Issue: 3 Linguagem: Inglês

10.1016/j.radonc.2016.04.004

1879-0887

Thibaud Coroller, Vishesh Agrawal, Vivek Narayan, Ying Hou, Patrick Großmann, Stephanie W. Lee, Raymond H. Mak, Hugo J.W.L. Aerts,

Gastric Cancer Management and Outcomes

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).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.Seven features were predictive for pathologic gross residual disease (AUC>0.6, 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).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.