ArticleHistopathology2020
Interobserver variation in the classification of thymic lesions including biopsies and resection specimens in an international digital microscopy panel.
Article in Histopathology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06301945 (Artificial Intelligence for Histopathological Classification and Recurrence Prediction of Thymic Epithelial Tumors), which is not on this map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Artificial Intelligence for Histopathological Classification and Recurrence Prediction of Thymic Epithelial Tumors
Who cites it
10 citing papers in PubMed, 1 citations in OpenAlex.
- Machine Learning-Based Prediction of Masaoka-Koga Stage and WHO Histological Risk Group in Thymic Epithelial Tumors Using Biomarker Combinations.Diagnostics (Basel, Switzerland) · 2026Article
- Neoadjuvant chemotherapy enabling resection of stage IVA thymoma with myasthenia gravis and systemic lupus erythematosus: a case report.Frontiers in oncology · 2026Article
- Thymic tumors: radiotherapy experience for single institute.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Article
- Artificial intelligence for diagnosis and prognosis of thymic epithelial tumors: a systematic review.Mediastinum (Hong Kong, China) · 2025Review
- Factors distinguishing thymomas from thymic squamous cell carcinoma: a proposal for diagnosis emphasizing the immunophenotype.Mediastinum (Hong Kong, China) · 2025Review
- The clinicopathological significance of thymic epithelial markers expression in thymoma and thymic carcinoma.BMC cancer · 2023Article
- Article
- CD117, BAP1, MTAP, and TdT Is a Useful Immunohistochemical Panel to Distinguish Thymoma from Thymic Carcinoma.Cancers · 2022Article
- Review
- Interobserver variation in the classification of thymic lesions including biopsies and resection specimens in an international digital microscopy panel.Histopathology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 9 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimsThymic tumours are rare in routine pathology practice. Although the World Health Organization (WHO) classification describes a number of well-defined categories, the classification remains challenging. The aim of this study was to investigate the reproducibility of the WHO classification among a large group of international pathologists with expertise in thymic pathology and by using whole slide imaging to facilitate rapid diagnostic turnover. METHODS AND
resultsThree hundred and five tumours, consisting of 90 biopsies and 215 resection specimens, were reviewed with a panel-based virtual microscopy approach by a group of 13 pathologists with expertise in thymic tumours over a period of 6 years. The specimens were classified according to the WHO 2015 classification. The data were subjected to statistical analysis, and interobserver concordance (Fleiss kappa) was calculated. All cases were diagnosed within a time frame of 2 weeks. The overall level of agreement was substantial (κ = 0.6762), and differed slightly between resection specimens (κ = 0.7281) and biopsies (κ = 0.5955). When analysis was limited to thymomas only, and they were grouped according to the European Society for Medical Oncology Clinical Practice Guidelines into B2, B3 versus A, AB, B1 and B3 versus A, AB, B1, B2, the level of agreement decreased slightly (κ = 0.5506 and κ = 0.4929, respectively). Difficulties arose in distinguishing thymoma from thymic carcinoma. Within the thymoma subgroup, difficulties in distinction were seen within the B group.
conclusionsAgreement in diagnosing thymic lesions is substantial when they are assessed by pathologists with experience of these rare tumours. Digital pathology decreases the turnaround time and facilitates access to what is essentially a multinational resource. This platform provides a template for dealing with rare tumours for which expertise is sparse.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.