https://orcid.org/0000-0002-3437-5703
Figures & data
Figure 1 Flow diagram of patient inclusion.
Abbreviations: SRCC, signet-ring cell carcinoma; AC, adenocarcinoma.
Figure 2 Texture analysis software program. (A) 67-year-old man, AC; (B) 59-year-old man, SRCC.
Table 1 Clinical Features and CT Texture of SRCC and AC
Table 2 Diagnostic Performance of Clinical Features and CT Texture for Differentiating SRCC from AC
Figure 3 Radiomics feature selection using the LASSO regression. LASSO, least absolute shrinkage and selection operator. (A) Tuning parameter (λ) selection in the LASSO logistic model. The binominal deviance curve was generated vs log (λ). The minimum criteria for tenfold cross-validation were applied to λ selection. The optimal values of the LASSO tuning parameter (λ) are indicated by the dotted vertical lines. (B) The vertical line corresponds to the number of iterations in lasso, and the independent variable nonzero coefficients are selected.
Figure 4 The ROC analysis of Perc.01%3D, Perc.10%3D and s(1,0,0) SumAverg for differentiation SRCC from AC.
Abbreviation: ROC, receiver operating characteristic.
Figure 5 Texture analysis based on portal venous phase image to distinguish SRCC from AC. 1 for AC, 2 for SRCC. The difference between “l” and “2” is obvious, indicating excellent texture analysis and discrimination ability.
