Deep learning discriminates thymic epithelial tumors' histological subtypes using digital pathology.

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: 91.1% accuracy for clinically relevant 3-group classification; 77.7% for 6-class WHO classification

• Primary Outcome: Diagnostic accuracy for TET histological classification

• Nnt Or Sensitivity: 100% sensitivity for thymic carcinoma detection; 94.6% accuracy for thymic carcinoma

• Confidence Interval: Not reported

Clinical Application

Requires digital pathology infrastructure and H&E whole-slide imaging capability, increasingly available in Australian tertiary centers Highly relevant for Australian practice given limited thoracic pathology expertise outside major centers; could support telepathology networks and improve diagnostic consistency across regional hospitals Patients with suspected thymic epithelial tumors requiring histological classification, particularly in non-expert pathology settings

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 nonexpert 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. MATERIALS AND METHODS: We trained a deep learning-based model using hematoxylin-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; and 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, and 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 nonspecialized settings.

References

1. 1.Sacco, M., Pietroluongo, E., Di Lello, A., Marino, M., McGeough, A., Esposito, A., Sharma, R., Husain, A. N., Arif, Q., Elsebaie, M. A., Pearson, A. T., Dolezal, J. M., & Garassino, M. C. (2026). Deep learning discriminates thymic epithelial tumors' histological subtypes using digital pathology. Annals of Oncology, 37(4). https://doi.org/10.1016/j.annonc.2025.12.003