A sample of two types of (a) two-dimensional (level tracing method) and (b) three-dimensional (GrowCut method) segmentation methods. Two-dimensional segmentation was performed on the slice with the largest tumor diameter. Three-dimensional segmentation was confirmed from three directions.|@|~(^,^)~|@|Results of (a) two-dimensional features and (b) three-dimensional features estimated using the linear discriminant analysis employing two radiomic features. Through the generation of linear decision boundaries, the features allow classification of the two groups. SCC, squamous cell carcinoma; LCC, large cell carcinoma.|@|~(^,^)~|@|Nodule images that could be classified as large cell carcinoma (LCC) and squamous cell carcinoma (SCC) and those that could not. (a) In two-dimensional features, each of the two cases with the highest and lowest wHHL_RV values were displayed as SCC and cases with the highest and lowest wHHL_f_90P values were displayed as LCC. (b) In three-dimensional features, each of the two cases with the highest and lowest o_SRHGLE values were displayed as SCC and LCC.|@|~(^,^)~|@|Results of the receiver operating characteristic curve estimated using the features with the highest regression coefficient among two-dimensional features (Features_2d, dash line) and three-dimensional features (Features_3d, full line). Areas under the receiver operating characteristic curve (AUC) were analyzed using the DeLong method, and no statistical difference was found at a significance level of 0.05.

Progress in Medical Physics 2023;34:23~32 https://doi.org/10.14316/pmp.2023.34.3.23