{"id":7434,"date":"2024-03-02T07:09:29","date_gmt":"2024-03-02T07:09:29","guid":{"rendered":"https:\/\/amnewsworld.com\/?p=7434"},"modified":"2024-03-02T07:09:29","modified_gmt":"2024-03-02T07:09:29","slug":"impact-of-signal-intensity-normalization-of-mri-on-the-generalizability-of-radiomic-based-prediction-of-molecular-glioma-subtypes","status":"publish","type":"post","link":"https:\/\/www.amnewsworld.com\/fi\/impact-of-signal-intensity-normalization-of-mri-on-the-generalizability-of-radiomic-based-prediction-of-molecular-glioma-subtypes\/","title":{"rendered":"Impact of signal intensity normalization of MRI on the generalizability of radiomic-based prediction of molecular glioma subtypes"},"content":{"rendered":"

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.<\/p>\n

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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.<\/p>\n

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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.<\/p>\n

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