ArticleBMC cancer2024
Deep learning for risk stratification of thymoma pathological subtypes based on preoperative CT images.
Article in BMC cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Multi-modal prediction model for thymic epithelial tumors: Enhancing surgical decisions and recurrence risk assessment from CT datasets.European journal of radiology open · 2026Article
- Preoperative prediction of thymoma risk classification with machine learning-based computed tomography radiomics features.World journal of surgical oncology · 2026Article
- Deep Learning-Assisted Detection and Classification of Thymoma Tumors in CT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Machine Learning in Myasthenia Gravis: A Systematic Review of Prognostic Models and AI-Assisted Clinical Assessments.Diagnostics (Basel, Switzerland) · 2025Review
- Article
- Multi-dimensional interpretable deep learning-radiomics based on intra-tumoral and spatial habitat for preoperative prediction of thymic epithelial tumours risk categorisation.Acta oncologica (Stockholm, Sweden) · 2025Article
- Preoperative multiclass classification of thymic mass lesions based on radiomics and machine learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review.Mediastinum (Hong Kong, China) · 2025Review
- Machine learning-based radiomics analysis in enhancing CT for predicting pathological subtypes and WHO staging of thymic epithelial tumors: a multicenter study.American journal of cancer research · 2025Article
- Detection of Thymoma Disease Using mRMR Feature Selection and Transformer Models.Diagnostics (Basel, Switzerland) · 2024Article
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Authors and funding
5 authors.
Funding
Abstract
objectivesThis study aims to develop an innovative, deep model for thymoma risk stratification using preoperative CT images. Current algorithms predominantly focus on radiomic features or 2D deep features and require manual tumor segmentation by radiologists, limiting their practical applicability.
methodsThe deep model was trained and tested on a dataset comprising CT images from 147 patients (82 female; mean age, 54 years ± 10) who underwent surgical resection and received subsequent pathological confirmation. The eligible participants were divided into a training cohort (117 patients) and a testing cohort (30 patients) based on the CT scan time. The model consists of two stages: 3D tumor segmentation and risk stratification. The radiomic model and deep model (2D) were constructed for comparative analysis. Model performance was evaluated through dice coefficient, area under the curve (AUC), and accuracy.
resultsIn both the training and testing cohorts, the deep model demonstrated better performance in differentiating thymoma risk, boasting AUCs of 0.998 and 0.893 respectively. This was compared to the radiomic model (AUCs of 0.773 and 0.769) and deep model (2D) (AUCs of 0.981 and 0.760). Notably, the deep model was capable of simultaneously identifying lesions, segmenting the region of interest (ROI), and differentiating the risk of thymoma on arterial phase CT images. Its diagnostic prowess outperformed that of the baseline model.
conclusionsThe deep model has the potential to serve as an innovative decision-making tool, assisting on clinical prognosis evaluation and the discernment of suitable treatments for different thymoma pathological subtypes. KEY POINTS: • This study incorporated both tumor segmentation and risk stratification. • The deep model, using clinical and 3D deep features, effectively predicted thymoma risk. • The deep model improved AUCs by 16.1pt and 17.5pt compared to radiomic model and deep model (2D) respectively.
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