Prog Med Phys.  2023 Sep;34(3):28-32. 10.14316/pmp.2023.34.3.23.

Influence of Two-Dimensional and Three-Dimensional Acquisitions of Radiomic Features for Prediction Accuracy

• i Fukui R
• Matsuura R
• Kida K
• Goto S

• KMID: 2546688
• DOI: http://doi.org/10.14316/pmp.2023.34.3.23

Abstract

Purpose
In radiomics analysis, to evaluate features, and predict genetic characteristics and survival time, the pixel values of lesions depicted in computed tomography (CT) and magnetic resonance imaging (MRI) images are used. CT and MRI offer three-dimensional images, thus producing three-dimensional features (Features_3d) as output. However, in reports, the superiority between Features_3d and two-dimensional features (Features_2d) is distinct. In this study, we aimed to investigate whether a difference exists in the prediction accuracy of radiomics analysis of lung cancer using Features_2d and Features_3d.
Methods
A total of 38 cases of large cell carcinoma (LCC) and 40 cases of squamous cell carcinoma (SCC) were selected for this study. Two- and three-dimensional lesion segmentations were performed. A total of 774 features were obtained. Using least absolute shrinkage and selection operator regression, seven Features_2d and six Features_3d were obtained.
Results
Linear discriminant analysis revealed that the sensitivities of Features_2d and Features_3d to LCC were 86.8% and 89.5%, respectively. The coefficients of determination through multiple regression analysis and the areas under the receiver operating characteristic curve (AUC) were 0.68 and 0.70 and 0.93 and 0.94, respectively. The P-value of the estimated AUC was 0.87.
Conclusions
No difference was found in the prediction accuracy for LCC and SCC between Features_2d and Features_3d.

Keyword

Radiomics; Computed tomography; Lung cancer; Least absolute shrinkage and selection operator; Linear discriminant analysis