We defined two STIR-based radiomic features to be used as an alternative to the conventional textural features of WF1 and WF2. We use these new features as the only covariates in the implementation of ML algorithms to test whether the prediction performance of ML models could be improved over those obtained by the previously described workflows. Firstly, we applied the same segmentation method of FSHD patients on the pre-processed STIR images of each healthy control (HC). In particular, six contiguous HCs slices of mid-calf region were segmented in order to ensure a robust pixel statistics of the grayscale intensity distributions. Then, two reference limits, Upper Limit (UL) and Lower Limit (LL), were defined as follows. Inspired by Dahlqvist et al. (41 (
link)), UL was defined for each calf muscle through the extraction of a pixel-wise histogram of signal intensity distribution from all slices. The six muscle-wise UL were set at the mean μ of the associated pixels-intensity distribution added to 2 standard deviation (S.D.) σ:
with
i indexing the six calf muscles.
Due to non-uniform fat suppression of STIR sequence, LL was calculated as a representative value of fat signal intensity. Therefore, subcutaneous fat (average thickness at medial level of HCs was about 10.5 mm) was manually drawn in HCs slices to ensure the extraction of LL feature. In particular, from subcutaneous fat ROI of all slices the pixel-wise histogram of signal intensity distribution was extracted. Subsequently, the LL was set as the mode of the distribution. In this way, we could calculate a more realistic fat intensity representative value, limiting the contribution of blood vessels present in the subcutaneous fat, which tend to shift the mean value of the associated distribution toward greater value due to the hyperintesity STIR signal of the blood.
Moreover, the obtained LL and muscle-wise UL coefficients were set as the reference limits to quantify, for every FSHD patient, fat infiltration grade (FFG) and muscle edema grade (MEG) by expressing the number of pixels below LL and above UL as a percentage of the total pixels in each calf muscle. FFG and MEG were then used as covariates in ML models to predict FF and wT2, respectively. Particularly, muscle-wise FFG and MEG values were separately collected into datasets according to calf muscles and neuromuscular biomarker and used as input for machine learning algorithms.
As described in
WF1, we implemented both parametric and non-parametric models using the k-folds cross validation as a resampling approach.
WF3 brought the advantage of testing the prediction accuracy of neuromuscular biomarkers with two features that were easy to compute by means of a stand-alone Python routine, without going through commercial texture software and any dimensionality reduction techniques.
Colelli G., Barzaghi L., Paoletti M., Monforte M., Bergsland N., Manco G., Deligianni X., Santini F., Ricci E., Tasca G., Mira A., Figini S, & Pichiecchio A. (2023). Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease. Frontiers in Neurology, 14, 1105276.