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. 2023 Mar 14;10(3):360. doi: 10.3390/bioengineering10030360

Figure 3.

Figure 3

Univariate CCTA-feature analysis for predicting IVOCT-TCFA. Manhattan plot of PCAT-LOI (A) and PCAT-Vessel (B) of the mean AUCs for the identification of coronary vessels with IVOCT-TCFA. The number of radiomic features with AUC > 0.5 for identification of coronary vessels with IVOCT-TCFA was 147/293 (50.2%) for PCAT-LOI and 99/341 (29.0%) for PCAT-Vessel. Of the 147 PCAT-LOI-radiomics features with AUC > 0.5, 14 (9.5%) were shape features, 21 (14.3%) were first-order statistics, and 112 (76.2%) were texture-based features (GLCM: 18, GLDM: 21, GLRLM: 16, GLSZM: 51, NGTDM: 6). Of the 99 PCAT-Vessel-radiomics features with AUC > 0.5, eight (8.1%) were shape features, thirteen (13.1%) were first-order statistics, and seventy-eight (78.8%) were texture-based features (GLCM: 11, GLDM: 16, GLRLM: 19, GLSZM: 27, NGTDM: 5).