Analysis of CT Features and Quantitative Texture Analysis in Patients with Thymic Tumors: Correlation with Grading and Staging
Overview
Authors
Affiliations
Objectives: To evaluate potential relationship between qualitative CT features, quantitative texture analysis (QTA), histology, WHO staging, Masaoka classification and myasthenic syndrome in patients with thymic tumors.
Materials And Methods: Sixteen patients affected by histologically proven thymic tumors were retrospectively included in the study population. Clinical information, with special regard to myasthenic syndrome and serological positivity of anti-AchR antibodies, were recorded. Qualitative CT evaluation included the following parameters: (a) location; (b) tumor edges; (c) necrosis; (d) pleural effusion; (e) metastases; (f) chest wall infiltration; (g) tumor margins. QTA included evaluation of "Mean" (M), "Standard Deviation" (SD), "Kurtosis" (K), "Skewness" (S), "Entropy" (E), "Shape from Texture" (TX_sigma) and "average of positive pixels" (MPP). Pearson-Rho test was used to evaluate the relationship of continuous non-dichotomic parameters, whereas Mann-Whitney test was used for dichotomic parameters.
Results: Histological evaluation demonstrated thymoma in 12 cases and thymic carcinoma in 4 cases. Tumor necrosis was significantly correlated with QTA Mean (p = 0.0253), MPP (p = 0.0417), S (p = 0.0488) and K (p = 0.0178). WHO staging was correlated with Mean (p = 0.0193), SD (p = 0.0191) and MPP (p = 0.0195). Masaoka classification was correlated with Mean (p = 0.0322), MPP (p = 0.0315), skewness (p = 0.0433) and Kurtosis (p = 0.0083). Myasthenic syndrome was significantly associated with Mean (p = 0.0211) and MPP (p = 0.0261), whereas tumor size was correlated with Mean (p = 0.0241), entropy (p = 0.0177), MPP (p = 0.0468), skewness (p = 0.009) and Kurtosis (p = 0.006).
Conclusion: Our study demonstrates significant relationship between radiomics parameters, histology, grading and clinical manifestations of thymic tumors.
Yuan Y, Zhang H, Xu W, Dong D, Gao P, Zhang C Radiol Oncol. 2025; 59(1):69-78.
PMID: 40014788 PMC: 11867572. DOI: 10.2478/raon-2025-0016.
Xia H, Yu J, Nie K, Yang J, Zhu L, Zhang S Cancer Imaging. 2024; 24(1):163.
PMID: 39609913 PMC: 11603948. DOI: 10.1186/s40644-024-00808-2.
Jovanovic M, Stefanovic A, Sarac D, Kovac J, Jankovic A, Saponjski D Cancers (Basel). 2023; 15(24).
PMID: 38136387 PMC: 10742259. DOI: 10.3390/cancers15245840.
Chen X, Feng B, Xu K, Chen Y, Duan X, Jin Z Eur Radiol. 2023; 33(10):6804-6816.
PMID: 37148352 DOI: 10.1007/s00330-023-09690-1.
Chang C, Tang E, Wei Y, Lin C, Wu F, Wu M Front Oncol. 2023; 13:1105100.
PMID: 37143945 PMC: 10151670. DOI: 10.3389/fonc.2023.1105100.