Radiomics-based discriminant analysis of principal components to stratify the treatment response of lung metastases following stereotactic body radiation therapy

• Published in: Physica medica 2024-05, Vol.121, p.103340-103340, Article 103340
• Main Authors: Cilla, Savino, Deodato, Francesco, Romano, Carmela, Macchia, Gabriella, Buwenge, Milly, Morganti, Alessio G.
• Format: Article
• Language: English
• Subjects: Discriminant analysis; Lung metastases; Radiomics; SBRT
• ISSN: 1120-1797; 1724-191X
• DOI: 10.1016/j.ejmp.2024.103340

•Lung metastases are highly heterogeneous in terms of genomic expression and associated stroma and vasculature.•Radiomics can capture intra- and inter-lesion tumor heterogeneity.•DAPC was performed to describe the clusters of “radiomically” related lesions.•DAPC provided an optimal discrimination between different treatment responses after SBRT. Discriminant analysis of principal components (DAPC) was introduced to describe the clusters of genetically related individuals focusing on the variation between the groups of individuals. Borrowing this approach, we evaluated the potential of DAPC for the evaluation of clusters in terms of treatment response to SBRT of lung lesions using radiomics analysis on pre-treatment CT images. 80 pulmonary metastases from 56 patients treated with SBRT were analyzed. Treatment response was stratified as complete, incomplete and null responses. For each lesion, 107 radiomics features were extracted using the PyRadiomics software. The concordance correlation coefficients (CCC) between the radiomics features obtained by two segmentations were calculated. DAPC analysis was performed to infer the structure of “radiomically” related lesions for treatment response assessment. The DAPC was performed using the “adegenet” package for the R software. The overall mean CCC was 0.97 ± 0.14. The analysis yields 14 dimensions in order to explain 95 % of the variance. DAPC was able to group the 80 lesions into the 3 different clusters based on treatment response depending on the radiomics features characteristics. The first Linear Discriminant achieved the best discrimination of individuals into the three pre-defined groups. The greater radiomics loadings who contributed the most to the treatment response differentiation were associated with the “sphericity”, “correlation” and “maximal correlation coefficient” features. This study demonstrates that a DAPC analysis based on radiomics features obtained from pretreatment CT is able to provide a reliable stratification of complete, incomplete or null response of lung metastases following SBRT.