Radiomics and machine learning applications in rectal cancer: Current update and future perspectives

Table 1 Overview of the most widely adopted machine learning algorithms in rectal cancer imaging

Algorithm name	Description
Random forest	An ensemble method that combines multiple decision trees (a class of predictive learning models used in supervised ML) to obtain more accurate results for classification and regression tasks
Support vector machine	A linear approach used mainly for classification problems with the aim to find the best hyper plane which most accurately separate input data into two classes
Logistic regression	A classifier used to obtain the best fitting model for the relationship between multiple predictor variables and a dichotomous outcome
LASSO	A regularized regression method that performs both variable selection and regularization in order to optimally fit the resulting generalized statistical model
Naive Bayes	A classifier relying on the Bayes Theorem to model the probability of an outcome based on the strong (naive) independence assumptions between the features data
Quadratic discriminant analysis	A subtype of Dimensionality Reduction Algorithms that turn high-dimensional data into to low-dimensional data retaining the most significant features of original data for the prediction of the class label
ANN	A subgroup of ML composed of neuronal-like multi-layered networks allowing to automatically extract features without prior labelling and perform complex operations
CNN	As subset of ANN containing multiple computational hidden layers that filter and compute high-dimensional data to enhance the learning of high-level tasks (deep learning)

ANN: Artificial neural network; CNN: Convolutional neural network; LASSO: Least absolute shrinkage and selection operator; ML: Machine learning.