Contrast-enhanced CT-based radiomics model for differentiating risk subgroups of thymic epithelial tumors

Abstract

Background To validate a contrast-enhanced CT (CECT)-based radiomics model (RM) for differentiating various risk subgroups of thymic epithelial tumors (TETs). Methods A retrospective study was performed on 164 patients with TETs who underwent CECT scans before treatment. A total of 130 patients (approximately 79%, from 2012 to 2018) were designated as the training set, and 34 patients (approximately 21%, from 2019 to 2021) were designated as the testing set. The analysis of variance and least absolute shrinkage and selection operator algorithm methods were used to select the radiomics features. A logistic regression classifier was constructed to identify various subgroups of TETs. The predictive performance of RMs was evaluated based on receiver operating characteristic (ROC) curve analyses. Results Two RMs included 16 and 13 radiomics features to identify three risk subgroups of traditional risk grouping [low-risk thymomas (LRT: Types A, AB and B1), high-risk thymomas (HRT: Types B2 and B3), thymic carcinoma (TC)] and improved risk grouping [LRT* (Types A and AB), HRT* (Types B1, B2 and B3), TC], respectively. For traditional risk grouping, the areas under the ROC curves (AUCs) of LRT, HRT, and TC were 0.795, 0.851, and 0.860, respectively, the accuracy was 0.65 in the training set, the AUCs were 0.621, 0.754, and 0.500, respectively, and the accuracy was 0.47 in the testing set. For improved risk grouping, the AUCs of LRT*, HRT*, and TC were 0.855, 0.862, and 0.869, respectively, and the accuracy was 0.72 in the training set; the AUCs were 0.778, 0.716, and 0.879, respectively, and the accuracy was 0.62 in the testing set. Conclusions CECT-based RMs help to differentiate three risk subgroups of TETs, and RM established according to improved risk grouping performed better than traditional risk grouping.

Multimodal modeling with low-dose CT and clinical information for diagnostic artificial intelligence on mediastinal tumors: a preliminary study

“…Our findings contribute to the growing body of evidence supporting the application of deep learning and ViT in medical imaging 6 7 34. Unlike previous studies focusing solely on histological classification,27–31 34 our approach extends to the critical aspect of surgical determination. The potential for minimally invasive and cost-effective decision-making aligns with current trends in medical practice and economics.…”

“…The strengths of our study include the novel application of the ViT in mediastinal tumours and the innovative focus on surgical determination rather than mere classification 31. Our findings can serve as an extension of prior advancements within the realm of deep learning and radiomics.…”

“…Quantitative radiomic analysis based on CT, MRI and PET/CT has shown good diagnostic performance in differentiating tumour subtypes, staging, invasiveness and risk classification of TETs 27. Many studies have used radiomics to evaluate the risk of TETs, the most prevalent primary tumour in the anterior mediastinum, accounting for approximately 50% of all mediastinal tumours 28–31. Deep learning is a subfield of machine learning that focuses on training artificial neural networks with multiple layers to learn and extract complex patterns and representations from data 32.…”

“…To date, most mediastinal tumour machine learning studies have classified the histological subtypes of TET 27–31 34. Decisions regarding surgical intervention for mediastinal tumours are not limited solely to malignant lesions; some lesions, even benign ones, require surgical resection because of their relationship with the surrounding organs.…”

Quantitative CT parameters combined with preoperative systemic inflammatory markers for differentiating risk subgroups of thymic epithelial tumors

“…Thymic epithelial tumors (TETs), originating from thymic epithelial cells, are the most common primary neoplasms of the anterior mediastinum [ 1 , 2 ], accounting for approximately 47% of cases [ 3 ]. According to the World Health Organization (WHO) classification, TETs are classified into six subtypes: A, AB, B1, B2, B3 and thymic carcinoma (TC), which reflect the oncologic behavior and prognostic features of TETs based on the morphology of epithelial cells and the ratio of epithelial cells to lymphocytes [ 4 – 8 ].…”