Table 3:

Advantages and disadvantages of procedures for the reduction and selection of representative features in medical studies with radiomics.

Procedure	Ref.	Advantages	Disadvantages
Fisher scoring	69	Rapid and guaranteed convergence. Easy to interpret. Generates standard errors of all parameters estimates.	Computations become intensive in complex models. Difficult to determine the expected value of the Hessian matrix associated with the difficulty of identifying the appropriate sampling distribution.
Principal Component Analysis (PCA)	70	Unsupervised and simple technique. Nonparametric. Not computation-intensive. Does not require large amounts of data.	It is necessary to normalize the data before applying PCA to mitigate scale effects. Difficult to evaluate the covariance matrix accurately.
Linear Discriminant Analysis (LDA)	71	Easy and intuitive to use and understand. Maximizes the separation between classes while minimizing dispersion within the class.	Only models relationships between linear dependent and independent variables. Very sensitive to the anomalies in the data.
Least Absolute Shrinkage and Selection Operator (LASSO)	72	Reduces and selects variables simultaneously for better prediction and model interpretation.	Tends to select more covariates than expected, promoting a conflict between the correct selection and the optimal prediction.