[en] Radiomics-the high throughput extraction of quantitative features from medical images and their correlation with clinical and biological endpoints- is the subject of active and extensive research. Although the field shows promise, the generalizability of radiomic signatures is affected significantly by differences in scan acquisition and reconstruction settings. Previous studies reported on the sensitivity of radiomic features (RFs) to test-retest variability, inter-observer segmentation variability, and intra-scanner variability. A framework involving robust radiomics analysis and the application of a post-reconstruction feature harmonization method using ComBat was recently proposed to address these challenges. In this study, we investigated the reproducibility of RFs across different scanners and scanning parameters using this framework. We analysed thirteen scans of a ten-layer phantom that were acquired differently. Each layer was subdivided into sixteen regions of interest (ROIs), and the scans were compared in a pairwise manner, resulting in seventy-eight different scenarios. Ninety-one RFs were extracted from each ROI. As hypothesized, we demonstrate that the reproducibility of a given RF is not a constant but is dependent on the heterogeneity found in the data under analysis. The number (%) of reproducible RFs varied across the pairwise scenarios investigated, having a wide range between 8 (8.8%) and 78 (85.7%) RFs. Furthermore, in contrast to what has been previously reported, and as hypothesized in the robust radiomics analysis framework, our results demonstrate that ComBat cannot be applied to all RFs but rather on a percentage of those-the "ComBatable" RFs-which differed depending on the data being harmonized. The number (%) of reproducible RFs following ComBat harmonization varied across the pairwise scenarios investigated, ranging from 14 (15.4%) to 80 (87.9%) RFs, and was found to depend on the heterogeneity in the data. We conclude that the standardization of image acquisition protocols remains the cornerstone for improving the reproducibility of RFs, and the generalizability of the signatures developed. Our proposed approach helps identify the reproducible RFs across different datasets.
Disciplines :
Radiology, nuclear medicine & imaging
Author, co-author :
Ibrahim, Abdalla ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands ; Division of Nuclear Medicine and Oncological Imaging, Department of Medical Physics, University Hospital of Liège and GIGA CRC-in vivo imaging, University of Liège, Liege, Belgium ; Department of Nuclear Medicine and Comprehensive Diagnostic Centre Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
Refaee, Turkey; The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University, Maastricht, The Netherlands ; Faculty of Applied Medical Sciences, Department of Diagnostic Radiology, Jazan University, Jazan, Saudi Arabia
Leijenaar, Ralph T H; Oncoradiomics SA, Liege, Belgium
Primakov, Sergey; The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University, Maastricht, The Netherlands ; Department of Nuclear Medicine and Comprehensive Diagnostic Centre Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
HUSTINX, Roland ; Centre Hospitalier Universitaire de Liège - CHU > > Service médical de médecine nucléaire et imagerie onco
Mottaghy, Felix M; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands ; Department of Nuclear Medicine and Comprehensive Diagnostic Centre Aachen (CDCA), University Hospital RWTH Aachen University, Aachen, Germany
Woodruff, Henry C; The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
Maidment, Andrew D A; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States of America
Lambin, Philippe ; Université de Liège - ULiège > Département des sciences cliniques ; The D-Lab, Department of Precision Medicine, GROW- School for Oncology, Maastricht University, Maastricht, The Netherlands ; Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre+, Maastricht, The Netherlands
Language :
English
Title :
The application of a workflow integrating the variable reproducibility and harmonizability of radiomic features on a phantom dataset.
ERC - European Research Council [BE] EU - European Union [BE] Interreg EMR - Interreg Euregio Meuse-Rhin [NL] KWF - Kankerbestrijding [NL] UM - Universiteit Maastricht [NL]
Funding text :
Authors acknowledge financial support from ERC advanced grant (ERC-ADG-2015 n˚694812 - Hypoximmuno), ERC-2020-PoC: 957565-AUTO.DISTINCT. Authors also acknowledge financial support from SME Phase 2 (RAIL n˚673780), EUROSTARS (DART, DECIDE), the European Union’s Horizon 2020 research and innovation programme under grant agreement:
ImmunoSABR n˚ 733008, MSCA-ITN-PREDICT n˚766276, CHAIMELEON n˚ 952172, EuCanImage n˚952103, TRANSCAN Joint Transnational Call 2016
(JTC2016 CLEARLY n˚ UM 2017-8295) and Interreg V-A Euregio Meuse-Rhine (EURADIOMICS˚ EMR4). Authors further acknowledge financial support by the Dutch Cancer Society (KWF Kankerbestrijding), Project number 12085/2018-2, and Maastricht-Liege Imaging
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