Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes
Objectives: Radiomic features have demonstrated encouraging results for non-invasive detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data has led to poor generalizability. Here, we assessed the influence of different MRI-intensity normalization techniques on the...
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| Main Authors: | , , , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2023
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| In: |
European radiology
Year: 2024, Volume: 34, Issue: 4, Pages: 2782-2790 |
| ISSN: | 1432-1084 |
| DOI: | 10.1007/s00330-023-10034-2 |
| Online Access: | Verlag, kostenfrei, Volltext: https://doi.org/10.1007/s00330-023-10034-2
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| Author Notes: | Martha Foltyn-Dumitru, Marianne Schell, Aditya Rastogi, Felix Sahm, Tobias Kessler, Wolfgang Wick, Martin Bendszus, Gianluca Brugnara, Philipp Vollmuth |
| Summary: | Objectives: Radiomic features have demonstrated encouraging results for non-invasive detection of molecular biomarkers, but the lack of guidelines for pre-processing MRI-data has led to poor generalizability. Here, we assessed the influence of different MRI-intensity normalization techniques on the performance of radiomics-based models for predicting molecular glioma subtypes. Methods: Preoperative MRI-data from n = 615 patients with newly diagnosed glioma and known isocitrate dehydrogenase (IDH) and 1p/19q status were pre-processed using four different methods: no normalization (naive), N4 bias field correction (N4), N4 followed by either WhiteStripe (N4/WS), or z-score normalization (N4/z-score). A total of 377 Image-Biomarker-Standardisation-Initiative-compliant radiomic features were extracted from each normalized data, and 9 different machine-learning algorithms were trained for multiclass prediction of molecular glioma subtypes (IDH-mutant 1p/19q codeleted vs. IDH-mutant 1p/19q non-codeleted vs. IDH wild type). External testing was performed in public glioma datasets from UCSF (n = 410) and TCGA (n = 160). Results: Support vector machine yielded the best performance with macro-average AUCs of 0.84 (naive), 0.84 (N4), 0.87 (N4/WS), and 0.87 (N4/z-score) in the internal test set. Both N4/WS and z-score outperformed the other approaches in the external UCSF and TCGA test sets with macro-average AUCs ranging from 0.85 to 0.87, replicating the performance of the internal test set, in contrast to macro-average AUCs ranging from 0.19 to 0.45 for naive and 0.26 to 0.52 for N4 alone. Conclusion: Intensity normalization of MRI data is essential for the generalizability of radiomic-based machine-learning models. Specifically, both N4/WS and N4/z-score approaches allow to preserve the high model performance, yielding generalizable performance when applying the developed radiomic-based machine-learning model in an external heterogeneous, multi-institutional setting. Clinical relevance statement: Intensity normalization such as N4/WS or N4/z-score can be used to develop reliable radiomics-based machine learning models from heterogeneous multicentre MRI datasets and provide non-invasive prediction of glioma subtypes. |
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| Item Description: | Vorab online veröffentlicht: 06. September 2023 Gesehen am 27.10.2023 |
| Physical Description: | Online Resource |
| ISSN: | 1432-1084 |
| DOI: | 10.1007/s00330-023-10034-2 |