Deep Learning Discriminates Thymic Epithelial Tumors Histological Subtypes Using Digital Pathology

Matteo Sacco, Erica Pietroluongo, Anna Di Lello, Mirella Marino, Alexander McGeough, Alessandra Esposito, Rishi Sharma, Aliya N. Husain, Qudsia Arif, Maha A.T. Elsebaie, Alexander T. Pearson, James M. Dolezal, Marina Chiara Garassino,
Deep Learning Discriminates Thymic Epithelial Tumors Histological Subtypes Using Digital Pathology,
Annals of Oncology,
2025,
,
ISSN 0923-7534,
https://doi.org/10.1016/j.annonc.2025.12.003.
(https://www.sciencedirect.com/science/article/pii/S0923753425063173)
Abstract: Background
Thymic epithelial tumors (TETs) are rare malignancies that pose significant diagnostic challenges due to their heterogeneous histological patterns and substantial interobserver variability in classification. Despite standardized World Health Organization (WHO) classification criteria, diagnostic concordance remains suboptimal, particularly in non-expert settings, where second-opinion reviews lead to diagnostic reclassification in up to 57% of cases. Deep learning may offer a tool to reduce diagnostic variability and improve the consistency of histological classification.
Methods
We trained a deep learning-based model using hematoxylin and eosin (H&E) whole-slide images from The Cancer Genome Atlas as a training dataset. The model incorporated a novel hierarchical loss function designed to reflect clinically relevant tumor groupings based on treatment strategies and patient outcomes. We validated the model on 112 consecutive cases from the University of Chicago, with diagnoses confirmed by an expert thoracic pathologist. Model performances were evaluated using both a three-group hierarchical scheme and the six-class WHO classification.
Results
In the clinically relevant hierarchical three-group classification (As: A+AB; Bs: B1+B2+B3; Thymic Carcinoma), the model achieved an accuracy of 91.1% with Cohen’s κ= 0.859, indicating almost perfect agreement. In the six-class classification (A, AB, B1, B2, B3, Thymic Carcinoma), the accuracy was 77.7% with κ = 0.716. The model demonstrated 100% sensitivity and 94.6% accuracy for thymic carcinoma detection. Notably, 60% of misclassifications occurred within the same clinical management group, thereby limiting their impact on therapeutic decision-making.
Conclusion
This deep learning model demonstrates strong potential as a diagnostic tool for TETs classification, particularly in settings with limited thoracic pathology expertise. The high sensitivity for thymic carcinoma detection and robust performance across different tissue processing conditions suggest its clinical applicability for improving diagnostic consistency and supporting pathological decision-making in both specialized and non-specialized settings.
Keywords: Thymic epithelial tumors; Thymoma; Thymic carcinoma; Histologic classification; Interobserver variability; Digital pathology; Deep learning; Whole-slide images; Hierarchical loss; Decision support; External validation