Extended Data Fig. 6: MSC workflow. | Nature Biomedical Engineering

Extended Data Fig. 6: MSC workflow.

From: Quantification of intratumoural heterogeneity in mice and patients via machine-learning models trained on PET–MRI data

Extended Data Fig. 6

The MSC workflow broadly comprised three analysis stages. The first and second stages utilized spectral clustering to segment the multiparametric MRI and dynamic 18F-FDG PET data, respectively. In the third stage, complementary information from both the previous steps was combined to obtain the PET/MRI maps of intratumoural heterogeneity. WMRI and WPET denote the affinity matrices that yielded the optimal clustering results for the associated modalities. The PET/MRI affinity matrix, WPET/MRI, was obtained using a convex combination of the matrices from the two views, where the parameter α regulated the influence of each view. The optimal parameters in each step were selected based on a grid search to maximize the DSC between the histology and imaging clustering maps.

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