Robba Rai1,2,3, Michael B Barton1,2,3, Phillip Chlap1,2,3, Gary Liney1,2,3, Carsten Brink4,5, Shalini Vinod1,2,3, Monique Heinke6, Yuvnik Trada7, Lois C Holloway1,2,3,8,9. 1. University of New South Wales, South Western Sydney Clinical School, Liverpool, New South Wales, Australia. 2. Liverpool Hospital, Liverpool and Macarthur Cancer Therapy Centre, Liverpool, New South Wales, Australia. 3. Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia. 4. Odense University Hospital, Laboratory of Radiation Physics, Department of Oncology, Odense, Denmark. 5. University of Southern Denmark, Department of Clinical Research, Odense, Denmark. 6. GenesisCare, Alexandria, New South Wales, Australia. 7. Calvary Mater Newcastle, Department of Radiation Oncology, Newcastle, New South Wales, Australia. 8. University of Wollongong, Centre of Radiation Physics, Wollongong, New South Wales, Australia. 9. University of Sydney, Institute of Medical Physics, School of Physics, Sydney, New South Wales, Australia.
Abstract
Purpose: Radiomics of magnetic resonance images (MRIs) in rectal cancer can non-invasively characterize tumor heterogeneity with potential to discover new imaging biomarkers. However, for radiomics to be reliable, the imaging features measured must be stable and reproducible. The aim of this study is to quantify the repeatability and reproducibility of MRI-based radiomic features in rectal cancer. Approach: An MRI radiomics phantom was used to measure the longitudinal repeatability of radiomic features and the impact of post-processing changes related to image resolution and noise. Repeatability measurements in rectal cancers were also quantified in a cohort of 10 patients with test-retest imaging among two observers. Results: We found that many radiomic features, particularly from texture classes, were highly sensitive to changes in image resolution and noise. About 49% of features had coefficient of variations ≤ 10 % in longitudinal phantom measurements. About 75% of radiomic features in in vivo test-retest measurements had an intraclass correlation coefficient of ≥ 0.8 . We saw excellent interobserver agreement with mean Dice similarity coefficient of 0.95 ± 0.04 for test and retest scans. Conclusions: The results of this study show that even when using a consistent imaging protocol many radiomic features were unstable. Therefore, caution must be taken when selecting features for potential imaging biomarkers.
Purpose: Radiomics of magnetic resonance images (MRIs) in rectal cancer can non-invasively characterize tumor heterogeneity with potential to discover new imaging biomarkers. However, for radiomics to be reliable, the imaging features measured must be stable and reproducible. The aim of this study is to quantify the repeatability and reproducibility of MRI-based radiomic features in rectal cancer. Approach: An MRI radiomics phantom was used to measure the longitudinal repeatability of radiomic features and the impact of post-processing changes related to image resolution and noise. Repeatability measurements in rectal cancers were also quantified in a cohort of 10 patients with test-retest imaging among two observers. Results: We found that many radiomic features, particularly from texture classes, were highly sensitive to changes in image resolution and noise. About 49% of features had coefficient of variations ≤ 10 % in longitudinal phantom measurements. About 75% of radiomic features in in vivo test-retest measurements had an intraclass correlation coefficient of ≥ 0.8 . We saw excellent interobserver agreement with mean Dice similarity coefficient of 0.95 ± 0.04 for test and retest scans. Conclusions: The results of this study show that even when using a consistent imaging protocol many radiomic features were unstable. Therefore, caution must be taken when selecting features for potential imaging biomarkers.
Authors: Ralph T H Leijenaar; Sara Carvalho; Emmanuel Rios Velazquez; Wouter J C van Elmpt; Chintan Parmar; Otto S Hoekstra; Corneline J Hoekstra; Ronald Boellaard; André L A J Dekker; Robert J Gillies; Hugo J W L Aerts; Philippe Lambin Journal: Acta Oncol Date: 2013-09-09 Impact factor: 4.089
Authors: Ke Nie; Liming Shi; Qin Chen; Xi Hu; Salma K Jabbour; Ning Yue; Tianye Niu; Xiaonan Sun Journal: Clin Cancer Res Date: 2016-05-16 Impact factor: 12.531
Authors: Joost J M van Griethuysen; Andriy Fedorov; Chintan Parmar; Ahmed Hosny; Nicole Aucoin; Vivek Narayan; Regina G H Beets-Tan; Jean-Christophe Fillion-Robin; Steve Pieper; Hugo J W L Aerts Journal: Cancer Res Date: 2017-11-01 Impact factor: 12.701
Authors: Hugo J W L Aerts; Emmanuel Rios Velazquez; Ralph T H Leijenaar; Chintan Parmar; Patrick Grossmann; Sara Carvalho; Sara Cavalho; Johan Bussink; René Monshouwer; Benjamin Haibe-Kains; Derek Rietveld; Frank Hoebers; Michelle M Rietbergen; C René Leemans; Andre Dekker; John Quackenbush; Robert J Gillies; Philippe Lambin Journal: Nat Commun Date: 2014-06-03 Impact factor: 14.919
Authors: Chintan Parmar; Ralph T H Leijenaar; Patrick Grossmann; Emmanuel Rios Velazquez; Johan Bussink; Derek Rietveld; Michelle M Rietbergen; Benjamin Haibe-Kains; Philippe Lambin; Hugo J W L Aerts Journal: Sci Rep Date: 2015-06-05 Impact factor: 4.379
Authors: Janna E van Timmeren; Ralph T H Leijenaar; Wouter van Elmpt; Jiazhou Wang; Zhen Zhang; André Dekker; Philippe Lambin Journal: Tomography Date: 2016-12