title: Radiomic phenotype features predict pathological response in non-small cell lung cancer.
abstract: 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), 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).
  
  
  
  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.
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA. Electronic address: tcoroller@lroc.harvard.edu.
authors.forename: Thibaud P
authors.initials: TP
authors.lastname: Coroller
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
authors.forename: Vishesh
authors.initials: V
authors.lastname: Agrawal
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
authors.forename: Vivek
authors.initials: V
authors.lastname: Narayan
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
authors.forename: Ying
authors.initials: Y
authors.lastname: Hou
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
authors.forename: Patrick
authors.initials: P
authors.lastname: Grossmann
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
authors.forename: Stephanie W
authors.initials: SW
authors.lastname: Lee
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
authors.forename: Raymond H
authors.initials: RH
authors.lastname: Mak
authors.affiliation: Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
authors.forename: Hugo J W L
authors.initials: HJ
authors.lastname: Aerts
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging / Q000473:pathology / Q000628:therapy*; D059248:Chemoradiotherapy; D005260:Female; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology / Q000628:therapy*; D008297:Male; D008875:Middle Aged; D020360:Neoadjuvant Therapy; D014057:Tomography, X-Ray Computed / Q000379:methods*; D047368:Tumor Burden
keywords: Biomarkers; NSCLC; Pathological response; Quantitative imaging; Radiomics