Pleomorphic carcinoma of the lung: Prognostic models of semantic, radiomics and combined features from CT and PET/CT in 85 patients. (2021)

• Record Type: Journal Article
• Authors: Kim, Chohee; Cho, Hwan-ho; Choi, Joon Young; Franks, Teri J.; Han, Joungho; Choi, Yeonu; Lee, Se-Hoon; Park, Hyunjin; Lee, Kyung Soo
• Is Part Of: European journal of radiology open. Volume 8(2021)
• Publication Date: 2021
• Subjects: C-index Concordance index -- CRS Combined risk score -- DL Deep learning -- GCLM Gray-level co-occurrence matrix -- HR Hazard ration -- ICC Intra-class correlation -- ISZM Intensity size zone matrix -- KRAS Kirsten rat sarcoma viral oncogene homolog -- LASSO Least absolute shrinkage and selection operator -- LDA Low density area -- MRI Magnetic resonance imaging -- MTV Metabolic tumor volume -- PC Pleomorphic carcinoma -- PET/CT Positron emission tomography/Computed tomography -- ROI Region of interest -- RRS Radiomics risk score -- SRS Semantic risk score -- SUVavg Average standardized uptake value -- SUVmax Maximum standardized uptake value -- TLG Total lesion glycolysis -- VOI Volume of interest; Lung -- Non-small cell carcinoma -- Pleomorphic carcinoma -- Prognosis -- Radiomics; Medical radiology -- Periodicals; 616.075705
• DOI: 10.1016/j.ejro.2021.100351
• Languages: English
• ISSNs: 2352-0477

Abstract

Abstract: Introduction: To demonstrate semantic, radiomics, and the combined risk models related to the prognoses of pulmonary pleomorphic carcinomas (PCs). Methods: We included 85 patients (M:F = 71:14; age, 35–88 [mean, 63 years]) whose imaging features were divided into training (n = 60) and test (n = 25) sets. Nineteen semantic and 142 radiomics features related to tumors were computed. Semantic risk score (SRS) model was built using the Cox-least absolute shrinkage and selection operator (LASSO) approach. Radiomics risk score (RRS) from CT and PET features and combined risk score (CRS) adopting both semantic and radiomics features were also constructed. Risk groups were stratified by the median of the risk scores of the training set. Survival analysis was conducted with the Kaplan-Meier plots. Results: Of 85 PCs, adenocarcinoma was the most common epithelial component found in 63 (73 %) tumors. In SRS model, four features were stratified into high- and low-risk groups (HR, 4.119; concordance index ([C-index], 0.664) in the test set. In RRS model, five features helped improve the stratification (HR, 3.716; C-index, 0.591) and in CRS model, three features helped perform the best stratification (HR, 4.795; C-index, 0.617). The two significant features of CRS models were the SUVmax and the histogram feature of energy ([CT Firstorder Energy]). Conclusion: In PCs of the lungs, the combined model leveraging semantic and radiomics features provides a better prognosis compared to using semantic and radiomics features separately. The high SUVmax of solid portion (CT Firstorder Energy) of tumors is associated with poor prognosis in lung PCs.