Linda Bianchini1, João Santinha2,3, Nuno Loução4, Mário Figueiredo3, Francesca Botta5, Daniela Origgi5, Marta Cremonesi6, Enrico Cassano7, Nikolaos Papanikolaou2, Alessandro Lascialfari8. 1. Department of Physics, Università degli Studi di Milano and INSTM RU, Milan, Italy. 2. Computational Clinical Imaging Group, Center for the Unknown (CCU), Champalimaud Foundation, Lisbon, Portugal. 3. Instituto de Telecomunicações, Instituto Superior Técnico, University of Lisbon, Lisbon, Portugal. 4. Philips Healthcare, Lisbon, Portugal. 5. Medical Physics Unit, IEO, European Institute of Oncology IRCSS, Milan, Italy. 6. Radiation Research Unit, IEO, European Institute of Oncology IRCSS, Milan, Italy. 7. Breast Imaging Division, IEO, European Institute of Oncology IRCSS, Milan, Italy. 8. Department of Physics, Università degli Studi di Pavia and INSTM RU, Pavia, Italy.
Abstract
PURPOSE: To investigate the repeatability and reproducibility of radiomic features extracted from MR images and provide a workflow to identify robust features. METHODS: T2 -weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of features were assessed by the intraclass correlation coefficient and the concordance correlation coefficient, respectively, and by the within-subject coefficient of variation, considering repeated acquisitions with and without phantom repositioning, and with different scanner and acquisition parameters. The features showing intraclass correlation coefficient or concordance correlation coefficient >0.9 were selected, and their dependence on shape information (Spearman's ρ > 0.8) analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities of images. RESULTS: From 944 two-dimensional features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. A much lower range (11.2% to 85.4%) was obtained after phantom repositioning. Three-dimensional extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. In addition, 82.4% to 94.9% of the features showed excellent agreement when extracted from images acquired with echo times 5 ms apart, but decreased with increasing echo-time intervals, and 90.7% of the features exhibited excellent reproducibility for changes in pulse repetition time. Of nonshape features, 2.0% was identified as providing only shape information. CONCLUSION: We showed that radiomic features are affected by MRI protocols and propose a general workflow to identify repeatable, reproducible, and informative radiomic features to ensure robustness of clinical studies.
PURPOSE: To investigate the repeatability and reproducibility of radiomic features extracted from MR images and provide a workflow to identify robust features. METHODS: T2 -weighted images of a pelvic phantom were acquired on three scanners of two manufacturers and two magnetic field strengths. The repeatability and reproducibility of features were assessed by the intraclass correlation coefficient and the concordance correlation coefficient, respectively, and by the within-subject coefficient of variation, considering repeated acquisitions with and without phantom repositioning, and with different scanner and acquisition parameters. The features showing intraclass correlation coefficient or concordance correlation coefficient >0.9 were selected, and their dependence on shape information (Spearman's ρ > 0.8) analyzed. They were classified for their ability to distinguish textures, after shuffling voxel intensities of images. RESULTS: From 944 two-dimensional features, 79.9% to 96.4% showed excellent repeatability in fixed position across all scanners. A much lower range (11.2% to 85.4%) was obtained after phantom repositioning. Three-dimensional extraction did not improve repeatability performance. Excellent reproducibility between scanners was observed in 4.6% to 15.6% of the features, at fixed imaging parameters. In addition, 82.4% to 94.9% of the features showed excellent agreement when extracted from images acquired with echo times 5 ms apart, but decreased with increasing echo-time intervals, and 90.7% of the features exhibited excellent reproducibility for changes in pulse repetition time. Of nonshape features, 2.0% was identified as providing only shape information. CONCLUSION: We showed that radiomic features are affected by MRI protocols and propose a general workflow to identify repeatable, reproducible, and informative radiomic features to ensure robustness of clinical studies.
Authors: Zhong-Wei Chen; Huan-Ming Xiao; Xinjian Ye; Kun Liu; Rafael S Rios; Kenneth I Zheng; Yi Jin; Giovanni Targher; Christopher D Byrne; Junping Shi; Zhihan Yan; Xiao-Ling Chi; Ming-Hua Zheng Journal: Hepatobiliary Surg Nutr Date: 2022-04 Impact factor: 7.293
Authors: Matteo Ferro; Ottavio de Cobelli; Gennaro Musi; Francesco Del Giudice; Giuseppe Carrieri; Gian Maria Busetto; Ugo Giovanni Falagario; Alessandro Sciarra; Martina Maggi; Felice Crocetto; Biagio Barone; Vincenzo Francesco Caputo; Michele Marchioni; Giuseppe Lucarelli; Ciro Imbimbo; Francesco Alessandro Mistretta; Stefano Luzzago; Mihai Dorin Vartolomei; Luigi Cormio; Riccardo Autorino; Octavian Sabin Tătaru Journal: Ther Adv Urol Date: 2022-07-04