Stephen S F Yip1, Chintan Parmar2, John Kim3, Elizabeth Huynh2, Raymond H Mak2, Hugo J W L Aerts4. 1. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA. Electronic address: stephen_yip@dfci.harvard.edu. 2. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA. 3. Department of Radiology, University of Michigan Health System, Ann Arbor, MI, USA. 4. Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA; Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA.
Abstract
PURPOSE: PET-based radiomic features have demonstrated great promises in predicting genetic data. However, various experimental parameters can influence the feature extraction pipeline, and hence, Here, we investigated how experimental settings affect the performance of radiomic features in predicting somatic mutation status in non-small cell lung cancer (NSCLC) patients. METHODS: 348 NSCLC patients with somatic mutation testing and diagnostic PET images were included in our analysis. Radiomic feature extractions were analyzed for varying voxel sizes, filters and bin widths. 66 radiomic features were evaluated. The performance of features in predicting mutations status was assessed using the area under the receiver-operating-characteristic curve (AUC). The influence of experimental parameters on feature predictability was quantified as the relative difference between the minimum and maximum AUC (δ). RESULTS: The large majority of features (n=56, 85%) were significantly predictive for EGFR mutation status (AUC≥0.61). 29 radiomic features significantly predicted EGFR mutations and were robust to experimental settings with δOverall<5%. The overall influence (δOverall) of the voxel size, filter and bin width for all features ranged from 5% to 15%, respectively. For all features, none of the experimental designs was predictive of KRAS+ from KRAS- (AUC≤0.56). CONCLUSION: The predictability of 29 radiomic features was robust to the choice of experimental settings; however, these settings need to be carefully chosen for all other features. The combined effect of the investigated processing methods could be substantial and must be considered. Optimized settings that will maximize the predictive performance of individual radiomic features should be investigated in the future.
PURPOSE: PET-based radiomic features have demonstrated great promises in predicting genetic data. However, various experimental parameters can influence the feature extraction pipeline, and hence, Here, we investigated how experimental settings affect the performance of radiomic features in predicting somatic mutation status in non-small cell lung cancer (NSCLC) patients. METHODS: 348 NSCLCpatients with somatic mutation testing and diagnostic PET images were included in our analysis. Radiomic feature extractions were analyzed for varying voxel sizes, filters and bin widths. 66 radiomic features were evaluated. The performance of features in predicting mutations status was assessed using the area under the receiver-operating-characteristic curve (AUC). The influence of experimental parameters on feature predictability was quantified as the relative difference between the minimum and maximum AUC (δ). RESULTS: The large majority of features (n=56, 85%) were significantly predictive for EGFR mutation status (AUC≥0.61). 29 radiomic features significantly predicted EGFR mutations and were robust to experimental settings with δOverall<5%. The overall influence (δOverall) of the voxel size, filter and bin width for all features ranged from 5% to 15%, respectively. For all features, none of the experimental designs was predictive of KRAS+ from KRAS- (AUC≤0.56). CONCLUSION: The predictability of 29 radiomic features was robust to the choice of experimental settings; however, these settings need to be carefully chosen for all other features. The combined effect of the investigated processing methods could be substantial and must be considered. Optimized settings that will maximize the predictive performance of individual radiomic features should be investigated in the future.
Authors: Athanasios K Anagnostopoulos; Anastasios Gaitanis; Ioannis Gkiozos; Emmanouil I Athanasiadis; Sofia N Chatziioannou; Konstantinos N Syrigos; Dimitris Thanos; Achilles N Chatziioannou; Nikolaos Papanikolaou Journal: Cancers (Basel) Date: 2022-03-25 Impact factor: 6.639
Authors: Marta Ferreira; Pierre Lovinfosse; Johanne Hermesse; Marjolein Decuypere; Caroline Rousseau; François Lucia; Ulrike Schick; Caroline Reinhold; Philippe Robin; Mathieu Hatt; Dimitris Visvikis; Claire Bernard; Ralph T H Leijenaar; Frédéric Kridelka; Philippe Lambin; Patrick E Meyer; Roland Hustinx Journal: Eur J Nucl Med Mol Imaging Date: 2021-03-26 Impact factor: 9.236