A radiomics model to predict the invasiveness of thymic epithelial tumors based on contrast‑enhanced computed tomography

Abstract

In the present study, we aimed to construct a radiomics model using contrast-enhanced computed tomography (CT) to predict the pathological invasiveness of thymic epithelial tumors (TETs). We retrospectively reviewed the records of 179 consecutive patients (89 females) with histologically confirmed TETs from two hospitals. The 82 low-and 97 high-risk TETs were assigned to training (90 tumors), internal validation (49 tumors) and external validation (40 tumors) cohorts. Radiomics features extracted from preoperative contrast-enhanced chest CT were selected using least absolute shrinkage and selection operator logistic regression. Three prediction models were developed using multivariate logistic regression analysis. Their performance and clinical utility were assessed using receiver operating characteristic curves and the DeLong test, respectively. Eight radiomics features with non-zero coefficients were used to develop a radiomics score, which significantly differed between low-and high-risk TETs (P<0.001). The subjective finding, infiltration, was independently associated with high-risk TETs. Prediction models based on infiltration alone, the radiomics signature alone, and both these parameters showed diagnostic accuracies of 72.2% [area under curve (AUC), 0.731; 95% confidence interval (CI): 0.627-0.819; sensitivity, 85.7%; specificity, 60.4%], 88.9% (AUC, 0.944; 95% CI: 0.874-0.981; sensitivity, 92.9%; specificity, 85.4%), and 90.0% (AUC, 0.953; 95% CI: 0.887-0.987; sensitivity, 92.9%; specificity, 87.5%), respectively. Decision-curve analysis showed that the combined model added more net benefit than the single-parameter models. In conclusion, a radiomics signature based on contrast-enhanced CT has the potential to differentiate between low-and high-risk TETs. The model incorporating the radiomics signature and subjective finding may facilitate the individualized, preoperative prediction of the pathological invasiveness of TETs.

CT radiomics facilitates more accurate diagnosis of COVID-19 pneumonia: compared with CO-RADS

“…and J.L., with 9 and 10 years of experience in thoracic imaging, respectively). The interobserver agreement was assessed by using Cohen kappa test, where 0-0.2 was slight agreement, 0.21-0.40 fair agreement, 0.41-0.60 moderate agreement, 0.61-0.80 substantial agreement, and 0.81-1.00 almost perfect agreement [19]. The discriminatory power for the three methods was compared.…”

CT imaging-based machine learning model: a potential modality for predicting low-risk and high-risk groups of thymoma: “Impact of surgical modality choice”

“…Similar to this textural studies, radiomics models have already been used to stage tumors and predict lymph node metastasis and prognosis [ 20 – 23 ]. A few studies have used radiomics models to predict the pathological invasiveness of TETs [ 24 ]. Although some previous studies have demonstrated that a textural analysis based on CT images can be used to differentiate high-risk TETs from low-risk TETs, they only analyzed 2D textural features, and their sample sizes were small [ 25 , 26 ].…”

CT Imaging-based Machine Learning Model: A Potential Modality for Predicting Low-Risk and High-Risk Groups of Thymoma “Impact of surgical modality choice”

“…Radiomics models have already been used to stage tumors and predict lymph node metastasis and prognosis [19][20][21][22]. A few studies have used radiomics models to predict the pathological invasiveness of TETs [23]. Although some previous studies have demonstrated that a textural analysis based on CT images can be used to differentiate high-risk TETs from low-risk TETs, they only analyzed 2D textural features, and their sample sizes were small [24,25].…”