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

Figure 4.

Figure 4

Univariate CCTA-radiomics feature analysis for predicting IVOCT-MC. Manhattan plot of PCAT-LOI (A) and PCAT-Vessel (B) of the mean AUCs for the identification of coronary vessels with IVOCT-MC. The number of radiomic features with AUC > 0.5 for identification of coronary vessels with IVOCT-MC was 195/293 (66.6%) for PCAT-LOI and 203/341 (59.5%) for PCAT-Vessel. Of 195 radiomic features with AUC > 0.5 from the PCAT-LOI, 31 (15.9%) were shape features, 27 (13.9%) were first-order statistics, and 137 (70.3%) were texture-based features (GLCM: 16, GLDM: 26, GLRLM: 19, GLSZM: 63, NGTDM: 13). Of 203 radiomic features from the PCAT-Vessel, 32 (15.8%) were shape features, 19 (9.4%) were first-order statistics, and 152 (74.9%) were texture-based features (GLCM: 24, GLDM: 28, GLRLM: 21, GLSZM: 67, NGTDM: 12).