Feature selection andradiomics
signature building
The radiomics signature (RSI) included a total of 13 categories, such as
Laa, Va and Trans (refer to section
2.2 for details). Moving on to feature selection and radiomics signature
building, we employed six machine learning algorithms, including
Gradient Boosting, Support Vector Machine, AdaBoost, Random Forest,
K-Nearest Neighbor, and Neural Network, to build a radiomics signatures
index (RSI) that could independently predict disease-free survival (DFS)
based on the phenotypic characteristics of CT and PET images. The
nonlinear survival model was utilized to generate a new feature by
predicting survival outcomes via multiple machine learning.
Subsequently, repeated 10-fold
cross-validation was used to evaluate the superiority of the trained
model, and the Random Forest algorithm was found to obtain a higher AUC
(Table 2). The results showed that the AUC value obtained by the random
forest model was 0.8587 (95% CI: 0.8421-0.8753). Notably, the
prediction accuracy of the model was 0.8529
(95% CI: 0.7968-0.8985). The
importance indicators and sorting results of features in the Random
Forest model were illustrated (Fig. 1), and the Random Forest algorithm
was employed to extract the corresponding radiomics signatures index
(RSI) from the imaging data of each patient.
Table 2 Machine learning outcomes of RSI.