Objective To investigate the clinical value of a combined model of enhanced CT-based entropy features and traditional imaging features for the differential diagnosis of risk status in patients with thymic epithelial tumor (TET).

Methods A retrospective analysis was conducted on the clinical data of 178 TET patients (83 males and 95 females; aged (52.7±12.4) years, ranged from 26 years to 83 years) confirmed by surgical and histopathological results at Jiangmen Central Hospital and the Fifth Affiliated Hospital of Sun Yat-sen University from October 2008 to May 2021 were retrospectively analyzed. They were divided into low-risk (A, AB, and B1 types) and high-risk (B2 and B3 types) groups according to histopathology subtypes. All patients were further divided into a training set (n=86), an internal validation set (n=51), and an external validation set (n=41). The internal and external validation sets were called the total validation set (n=92). The training and validation sets were used for the process construction and performance evaluation of the predictive model, respectively. The clinical characteristics of TET patients were recorded, and the traditional CT features of lesions were analyzed. Entropy features were extracted and selected from enhanced CT venous phase images by using software developed based on MATLAB R2016 platform. Mann-Whitney U test was used to select valuable entropy features. Extreme learning machine classification algorithm was adopted to calculate the different weights of entropy features and entropy signature value. Clinical model, entropy model, and combined model were constructed through Logistic analysis, and receiver operating characteristic curve was used to compare the diagnostic efficacy of the three predictive models.

Results A total of 83 cases were included in the low-risk group (38 males and 45 females; aged (52.8±12.4) years, ranged from 26 years to 83 years), whereas 95 cases were in the high-risk group (45 males and 50 females; aged (52.0±12.0) years, ranged from 27 years to 80 years). Univariate analysis showed that the difference in peripheral invasion between the two groups in the training set was statistically significant (χ²=5.108, P=0.024). A total of 1 680 initial entropy features were extracted, and 21 core entropy features were ultimately selected. The entropy signature value of the low-risk group in the training set was 0.519±0.21, which was significantly lower than that of the high-risk group (0.997±0.23). The difference between the two groups was statistically significant (t=−9.747, P<0.001). The area under curve (AUC) of the entropy model in the training set, internal validation set, external validation set, and total validation set were 0.929(95%CI: 0.876–0.983), 0.832(95%CI: 0.723–0.941), 0.802(95%CI: 0.666–0.939), and 0.803(95%CI: 0.715–0.890), respectively. Results of multivariate Logistic regression analysis showed that peripheral invasion (OR=6.343; 95%CI: 1.009−36.604; P=0.039) and entropy signature value (OR=20.145; 95%CI: 5.887−68.936; P<0.001) were independent risk factors for predicting TET risk status. The combined model constructed by the two together had AUCs of 0.941(95%CI: 0.894–0.987), 0.871(95%CI: 0.775–0.968), 0.819(95%CI: 0.689–0.949), and 0.840(95%CI: 0.761–0.919) in the training set, internal validation set, external validation set, and total validation set, respectively.

Conclusions The entropy feature based on enhanced chest-CT images can be used to assess the risk status of TET quantitatively. The combined model constructed by peripheral invasion and entropy signature value showed the highest diagnostic efficacy, which can accurately guide the preoperative treatment strategy of TET patients.