Fig 1.
Flowchart of patient inclusion.
Fig 2.
Pipeline of radiomics workflow.
(a) Four alternative combinations of the three input feature sets were demonstrated in type-1 radiomics models with multi-sequence inputs. (b) Five R models built from one of five sequences containing including T1, T2, CE, diffusion, and ADC features generated type-2 radiomics model with ensemble learning approach.
Table 1.
Demographic data of soft tissue tumors in included patients.
Fig 3.
ROC curves and F1 scores of type-1 and type-2 radiomic models.
(a) R-T, (b) R-C, (c) R-D, (d) R-A and (e) ensemble models showed average AUC values of 0.752, 0.756, 0.750, 0.749 and 0.774, respectively. The AUC of (e) type-2 radiomics model was superior to those of (a, b, c and d) all type-1 radiomics models, but the difference was not significant.
Table 2.
Selected radiomic features after Lasso regression in the type-1 radiomics model.
Table 3.
Diagnostic performance of the type-1 and type-2 radiomics models.