Reproducible Radiomics Features from Multi-MRI-Scanner Test-Retest-Study: Influence on Performance and Generalizability of Models.

Autor: Wennmann M; Division of Radiology, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.; Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany., Rotkopf LT; Division of Radiology, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany., Bauer F; Division of Radiology, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany., Hielscher T; Division of Biostatistics, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany., Kächele J; Division of Medical Image Computing, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.; German Cancer Consortium (DKTK), Partner Site Heidelberg, Heidelberg, Germany., Mai EK; Heidelberg Myeloma Center, Department of Medicine, University Hospital Heidelberg, Heidelberg, Germany., Weinhold N; Heidelberg Myeloma Center, Department of Medicine, University Hospital Heidelberg, Heidelberg, Germany., Raab MS; Heidelberg Myeloma Center, Department of Medicine, University Hospital Heidelberg, Heidelberg, Germany., Goldschmidt H; Heidelberg Myeloma Center, Department of Medicine, University Hospital Heidelberg, Heidelberg, Germany.; National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany., Weber TF; Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg, Germany., Schlemmer HP; Division of Radiology, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.; National Center for Tumor Diseases (NCT) Heidelberg, Heidelberg, Germany., Delorme S; Division of Radiology, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany., Maier-Hein K; Division of Medical Image Computing, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.; German Cancer Consortium (DKTK), Partner Site Heidelberg, Heidelberg, Germany.; Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany., Neher P; Division of Medical Image Computing, German Cancer Research Center (DKFZ) Heidelberg, Heidelberg, Germany.; German Cancer Consortium (DKTK), Partner Site Heidelberg, Heidelberg, Germany.; Pattern Analysis and Learning Group, Department of Radiation Oncology, University Hospital Heidelberg, Heidelberg, Germany.
Jazyk: angličtina
Zdroj: Journal of magnetic resonance imaging : JMRI [J Magn Reson Imaging] 2024 May 11. Date of Electronic Publication: 2024 May 11.
DOI: 10.1002/jmri.29442
Abstrakt: Background: Radiomics models trained on data from one center typically show a decline of performance when applied to data from external centers, hindering their introduction into large-scale clinical practice. Current expert recommendations suggest to use only reproducible radiomics features isolated by multiscanner test-retest experiments, which might help to overcome the problem of limited generalizability to external data.
Purpose: To evaluate the influence of using only a subset of robust radiomics features, defined in a prior in vivo multi-MRI-scanner test-retest-study, on the performance and generalizability of radiomics models.
Study Type: Retrospective.
Population: Patients with monoclonal plasma cell disorders. Training set (117 MRIs from center 1); internal test set (42 MRIs from center 1); external test set (143 MRIs from center 2-8).
Field Strength/sequence: 1.5T and 3.0T; T1-weighted turbo spin echo.
Assessment: The task for the radiomics models was to predict plasma cell infiltration, determined by bone marrow biopsy, noninvasively from MRI. Radiomics machine learning models, including linear regressor, support vector regressor (SVR), and random forest regressor (RFR), were trained on data from center 1, using either all radiomics features, or using only reproducible radiomics features. Models were tested on an internal (center 1) and a multicentric external data set (center 2-8).
Statistical Tests: Pearson correlation coefficient r and mean absolute error (MAE) between predicted and actual plasma cell infiltration. Fisher's z-transformation, Wilcoxon signed-rank test, Wilcoxon rank-sum test; significance level P < 0.05.
Results: When using only reproducible features compared with all features, the performance of the SVR on the external test set significantly improved (r = 0.43 vs. r = 0.18 and MAE = 22.6 vs. MAE = 28.2). For the RFR, the performance on the external test set deteriorated when using only reproducible instead of all radiomics features (r = 0.33 vs. r = 0.44, P = 0.29 and MAE = 21.9 vs. MAE = 20.5, P = 0.10).
Conclusion: Using only reproducible radiomics features improves the external performance of some, but not all machine learning models, and did not automatically lead to an improvement of the external performance of the overall best radiomics model.
Technical Efficacy: Stage 2.
(© 2024 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)
Databáze: MEDLINE
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