Exploring the variability of radiomic features of lung cancer lesions on unenhanced and contrast-enhanced chest CT imaging. (February 2021)

• Record Type: Journal Article
• Authors: Tamponi, Matteo; Crivelli, Paola; Montella, Rino; Sanna, Fabrizio; Gabriele, Domenico; Poggiu, Angela; Sanna, Enrico; Marini, Piergiorgio; Meloni, Giovanni B; Sverzellati, Nicola; Conti, Maurizio
• Is Part Of: Physica medica. Volume 82(2021)
• Page Start: 321
• Page End: 331
• Publication Date: 2021-02
• Subjects: Radiomics -- CT imaging -- Lung cancer -- Contrast medium -- Features stability -- Gini's coefficient and Mackin's index; Medical physics -- Periodicals; Biophysics -- Periodicals; Biophysics -- Periodicals; Imagerie médicale -- Périodiques; Radiothérapie -- Périodiques; Rayons X -- Sécurité -- Mesures -- Périodiques; Physique -- Périodiques; Médecine -- Périodiques; 610.153
• DOI: 10.1016/j.ejmp.2021.02.014
• Languages: English
• ISSNs: 1120-1797

Abstract

Highlights: The behavior of radiomic features on unenhanced and contrast-enhanced chest CT imaging is evaluated. Several radiomic features were affected by high variability when moving from unenhanced to contrast-enhanced CT imaging. A subset of four stable features was isolated that produces the same tumor lesion partition on both types of images. The Gini's coefficient proved effective to outline the discrimination power of radiomic features on both types of images. Abstract: Purpose: The aim of this methods work is to explore the different behavior of radiomic features resulting by using or not the contrast medium in chest CT imaging of non-small cell lung cancer. Methods: Chest CT scans, unenhanced and contrast-enhanced, of 17 patients were selected from images collected as part of the staging process. The major T1-T3 lesion was contoured through a semi-automatic approach. These lesions formed the lesion phantoms to study features behavior. The stability of 94 features of the 3D-Slicer package Radiomics was analyzed. Feature discrimination power was quantified by means of Gini's coefficient. Correlation between distance matrices was evaluated through Mantel statistic. Heatmap, cluster and silhouette plots were applied to find well-structured partitions of lesions. Results: The Gini's coefficient evidenced a low discrimination power, <0.05, for four features and a large discrimination power, around 0.8, for five features. About 90% of features was affected by the contrast medium, masking tumor lesions variability; thirteen features only were found stable. On 8178 combinations of stable features, only one group of four features produced the same partition of lesions with the silhouette width greater than 0.51, both on unenhanced and contrast-enhanced images. Conclusions: Gini's coefficient highlighted the features discrimination power in both CT series. Many features were sensitive to the use of the contrast medium, masking the lesions intrinsic variability. Four stable features produced, on both series, the same partition of cancer lesions with reasonable structure; this may merit being objects of further validation studies and interpretative investigations.