Clinical radiomics-based machine learning versus three-dimension convolutional neural network analysis for differentiation of thymic epithelial tumors from other prevascular mediastinal tumors on chest computed tomography scan

Abstract

PurposeTo compare the diagnostic performance of radiomic analysis with machine learning (ML) model with a convolutional neural network (CNN) in differentiating thymic epithelial tumors (TETs) from other prevascular mediastinal tumors (PMTs).MethodsA retrospective study was performed in patients with PMTs and undergoing surgical resection or biopsy in National Cheng Kung University Hospital, Tainan, Taiwan, E-Da Hospital, Kaohsiung, Taiwan, and Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan between January 2010 and December 2019. Clinical data including age, sex, myasthenia gravis (MG) symptoms and pathologic diagnosis were collected. The datasets were divided into UECT (unenhanced computed tomography) and CECT (enhanced computed tomography) for analysis and modelling. Radiomics model and 3D CNN model were used to differentiate TETs from non-TET PMTs (including cyst, malignant germ cell tumor, lymphoma and teratoma). The macro F1-score and receiver operating characteristic (ROC) analysis were performed to evaluate the prediction models.ResultIn the UECT dataset, there were 297 patients with TETs and 79 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 83.95%, ROC-AUC = 0.9117) had better performance than the 3D CNN model (macro F1-score = 75.54%, ROC-AUC = 0.9015). In the CECT dataset, there were 296 patients with TETs and 77 patients with other PMTs. The performance of radiomic analysis with machine learning model using LightGBM with Extra Tree (macro F1-Score = 85.65%, ROC-AUC = 0.9464) had better performance than the 3D CNN model (macro F1-score = 81.01%, ROC-AUC = 0.9275).ConclusionOur study revealed that the individualized prediction model integrating clinical information and radiomic features using machine learning demonstrated better predictive performance in the differentiation of TETs from other PMTs at chest CT scan than 3D CNN model.

Therapeutic Decision Making in Prevascular Mediastinal Tumors Using CT Radiomics and Clinical Features: Upfront Surgery or Pretreatment Needle Biopsy?

“…Generally speaking, deep learning models outperformed the ML models; however, deep learning models are more complex and require more parameters, huge amounts of data, and high computational cost. In our recent study, we found that the radiomics-based ML model had superior performance compared to the 3D conventional neural network (CNN) model within a limited dataset [29]. Due to the limited sample size, the present study focused on the development of ML models using both clinical features and radiomics.…”

“…Compared with other clinical-radiomics model studies that limited specific PMT histology types [27][28][29], this study did not set restrictions on PMT subtypes, including 12 different PMT subtypes. In addition, unlike other clinical-radiomics model studies aimed to improve differential diagnosis [27][28][29], the developed voting ensemble ML model may be utilized as a clinical decision support system to help the selection of initial management for patients with PMTs.…”

“…The least absolute shrinkage and selection operator (LASSO) regression analysis is a feature selection method based on a linear regression model. Both the 851 radiomic features and all the above clinical data were entered into the LASSO regression for variable selection, as previously described [28,29]. The extracted radiomic features reflect subtle characteristics of MPTs in images, such as the sphericity, diameter, homogeneity, and calcification.…”

“…Liu et al [26] reported that a radiomics-based ML model for differentiating anterior mediastinal cysts from thymomas had a sensitivity of 89.3%, while the sensitivity of the ML model could be improved to 92.3% if both clinical and radiological features were used to construct an ML model. Several clinical-radiomics models have been developed to predict pathological subtypes of PMTs [27], to differentiate low-risk from high-risk thymomas [28] and to distingue TETs from other PMT subtypes [29], thereby facilitating clinical diagnosis. However, no clinicalradiomics models have been developed to predict the selection of the initial management between direct surgery and CNB.…”

18F-FDG PET radiomics-based machine learning model for differentiating pathological subtypes in locally advanced cervical cancer

“…Furthermore, researchers have successfully achieved good discriminative performance in distinguishing low-grade and high-grade meningiomas using the LightGBM algorithm for both radiomics and deep learning models(Yang et al 2022). Similarly, Chang et al constructed LightGBM and convolutional neural network (CNN) models based on non-contrast CT and enhanced images to differentiate thymic epithelial tumors from other anterior mediastinal tumors(Chang et al 2023). The results demonstrated that the LightGBM model outperformed the CNN model in both the non-contrast CT dataset and the enhanced CT dataset.…”