Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes

European Society of Radiology study has reveals that intensity normalization approaches like WhiteStripe and Z-score normalization are foundational elements that significantly contribute to the robustness and generalizability of radiomic-based machine learning models. Specifically, while intensity normalization was not relevant when applying the developed radiomic-based machine-learning model to homogeneous data from the same institution, it was crucial to preserve the model performance when used in an external heterogeneous, multi-institutional setting.

 

With the help of radiomics, standard medical images can be transformed into detailed, high-dimensional data sets that go beyond what the eye can see. The typical workflow of radiomic projects involves a series of sequential processes, including image registration, intensity normalization, and segmentation of the region of interest. While there is a general agreement on the essential steps, consensus on the best preprocessing methods remains elusive. Numerous works have highlighted the promising role of radiomics in the field of neuro-oncology, such as in predicting patient survival, evaluating treatment responses, and pinpointing key biomarkers.

 

They have uncover the importance of MRI signal intensity normalization to address the generalization gap of radiomic-based machine learning models and thereby facilitate its clinical translation with their work.

Key points

• MRI-intensity normalization increases the stability of radiomics-based models and leads to better generalizability.
• Intensity normalization did not appear relevant when the developed model was applied to homogeneous data from the same institution.
• Radiomic-based machine learning algorithms are a promising approach for simultaneous classification of IDH and 1p/19q status of glioma.

Credit to: Martha Foltyn-Dumitru, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg,                       Germany.

Philipp Vollmuth, Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.

 

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