Thibaud P Coroller1, Vishesh Agrawal2, Vivek Narayan2, Ying Hou2, Patrick Grossmann2, Stephanie W Lee2, Raymond H Mak2, Hugo J W L Aerts3. 1. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA. Electronic address: tcoroller@lroc.harvard.edu. 2. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Harvard Medical School, Boston, USA. 3. 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.
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.
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 NSCLCpatients 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.
Authors: Thomas E Stinchcombe; David E Morris; Dominic T Moore; John H Bechtel; Jan S Halle; Andrea Mears; Katherine Deschesne; Julian G Rosenman; Mark A Socinski Journal: Lung Cancer Date: 2006-02-24 Impact factor: 5.705
Authors: Philippe Lambin; Emmanuel Rios-Velazquez; Ralph Leijenaar; Sara Carvalho; Ruud G P M van Stiphout; Patrick Granton; Catharina M L Zegers; Robert Gillies; Ronald Boellard; André Dekker; Hugo J W L Aerts Journal: Eur J Cancer Date: 2012-01-16 Impact factor: 9.162
Authors: Thomas Perrin; Abhishek Midya; Rikiya Yamashita; Jayasree Chakraborty; Tome Saidon; William R Jarnagin; Mithat Gonen; Amber L Simpson; Richard K G Do Journal: Abdom Radiol (NY) Date: 2018-12
Authors: Valerio Nardone; Paolo Tini; Christophe Nioche; Maria Antonietta Mazzei; Tommaso Carfagno; Giuseppe Battaglia; Pierpaolo Pastina; Roberta Grassi; Lucio Sebaste; Luigi Pirtoli Journal: Radiol Med Date: 2018-01-24 Impact factor: 3.469
Authors: Emmanuel Rios Velazquez; Chintan Parmar; Ying Liu; Thibaud P Coroller; Gisele Cruz; Olya Stringfield; Zhaoxiang Ye; Mike Makrigiorgos; Fiona Fennessy; Raymond H Mak; Robert Gillies; John Quackenbush; Hugo J W L Aerts Journal: Cancer Res Date: 2017-05-31 Impact factor: 12.701