Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Review
. 2025 Sep 26:9:27.
doi: 10.21037/med-25-34. eCollection 2025.

Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review

Anna Salut Esteve Domínguez  1 , Marlou Dimmers  2 , Ties A Mulders  3 , Daphne Dumoulin  2 , Dirk De Ruysscher  4   5 , Stephanie Peeters  4 , Jan von der Thüsen  1 , Farhan Akram  1
Affiliations
Review

Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review

Anna Salut Esteve Domínguez et al. Mediastinum. .

Abstract

Background: Thymic epithelial tumors (TETs) are rare, but they are the most common tumors in the anterior mediastinum. The use of artificial intelligence (AI) in the medical field is rapidly advancing. Especially in a rare disease such as TET, AI can help to improve existing care and foster new innovations. However, there are many different AI models with various implications. This systematic review aims to give an overview of recent studies on AI applications for the diagnosis and prognosis of TETs.

Methods: In this systematic review, six electronic databases were searched for research papers with the keywords "thymoma", "thymic epithelial tumors", "thymic carcinoma", "artificial intelligence", "machine learning" and "deep learning". The screening was performed by two reviewers and conflicts were resolved by a third reviewer.

Results: After removal of duplicates, 582 articles were included. Of these, 65 articles were eligible for inclusion after screening. AI models are used mainly for distinguishing between different mediastinal tumor types (n=21), risk stratification (n=26), surgical planning (n=7) and for assisting in subtyping (n=4). For prognostic purposes, AI is used for predicting clinical outcome as well as the chance of metastasis or prognosis (n=7). Models using combined clinical and radiomics data performed better than models with a single type of data. Some of the AI models outperformed physicians or successfully supported physicians in their workflow.

Conclusions: Many AI models have been studied in the context of the diagnosis and prognosis of TETs. Even though results are promising, external validation is often lacking, and sample sizes are small. Therefore, most models are not yet ready for clinical implementation. Further research on AI models with larger datasets and external validation is necessary, with careful consideration of the risk of data leakage.

Keywords: Thymic epithelial tumors (TETs); artificial intelligence (AI); diagnosis; prognosis.

PubMed Disclaimer

Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://med.amegroups.com/article/view/10.21037/med-25-34/coif). “The Series Dedicated to the 14th International Thymic Malignancy Interest Group Annual Meeting (ITMIG 2024)” was commissioned by the editorial office without any funding or sponsorship. J.v.d.T. serves as an unpaid editorial board member of Mediastinum from May 2024 to December 2025. D.D. reported consulting fees from MSD, Amgen, Roche, BMS, Astra Zeneca, and Pfizer. D.D.R. reported grants or contracts from various entities, indicating institutional financial interests without personal financial gain from organizations such as AstraZeneca, BMS, Beigene, Philips, Olink, and Eli Lilly, where his involvement includes research grants, support, and advisory board participation. S.P. reported Hanarth grant financed the INTHYM project, on AI for histopathological classification and recurrence prediction of TET. The authors have no other conflicts of interest to declare.

Figures

Figure 1
Figure 1
Different subsets of TETs, classified according to the WHO classification (H&E, magnification 10×). TET, thymic epithelial tumor; WHO, World Health Organization.
Figure 2
Figure 2
Pipeline illustrating TET analysis using clinical, radiology, pathology, omics, or combined multi-omics data. It includes diagnosis (tumor type, risk stratification, surgical planning, subtyping) and prognosis (recurrence, survival). TET, thymic epithelial tumor.
Figure 3
Figure 3
Flowchart of the study according to the PRISMA statement. AI, artificial intelligence; CINAHL, Cumulative Index to Nursing and Allied Health Literature; PRISMA, Preferred Reporting Items for Systematic Reviews and meta-analysis; TET, thymic epithelial tumor.
See this image and copyright information in PMC

References

    1. International Agency for Research on Cancer, World Health Organization, International Academy of Pathology. Thoracic tumours. 5th ed. IARC; 2021.
    1. Hsu CH, Chan JK, Yin CH, et al. Trends in the incidence of thymoma, thymic carcinoma, and thymic neuroendocrine tumor in the United States. PLoS One 2019;14:e0227197. 10.1371/journal.pone.0227197 - DOI - PMC - PubMed
    1. Travis WD, Brambilla E, Burke AP, et al. Introduction to The 2015 World Health Organization Classification of Tumors of the Lung, Pleura, Thymus, and Heart. J Thorac Oncol 2015;10:1240-2. 10.1097/JTO.0000000000000663 - DOI - PubMed
    1. Wolf JL, van Nederveen F, Blaauwgeers H, et al. Interobserver variation in the classification of thymic lesions including biopsies and resection specimens in an international digital microscopy panel. Histopathology 2020;77:734-41. 10.1111/his.14167 - DOI - PMC - PubMed
    1. Roden AC, Yi ES, Jenkins SM, et al. Reproducibility of 3 histologic classifications and 3 staging systems for thymic epithelial neoplasms and its effect on prognosis. Am J Surg Pathol 2015;39:427-41. 10.1097/PAS.0000000000000391 - DOI - PubMed

LinkOut - more resources