Interobserver variation in the classification of thymic tumours - A multicentre study using the WHO classification system
Department of Pathology, Leeds Teaching Hospital NHS Trust, Leeds, UK. Histopathology
(Impact Factor: 3.45).
09/2008; 53(2):218-23. DOI: 10.1111/j.1365-2559.2008.03088.x
To test the reproducibility of the current World Health Organization (WHO) classification of thymic epithelial tumours and to determine the level of interobserver variation within a group of pathologists, all with experience and expertise in thoracic pathology.
Ninety-five thymic tumours were circulated to a group of 17 pathologists in the UK and The Netherlands over a 1-year period. Participants were asked to classify them according to WHO criteria. The diagnoses were subjected to statistical analysis and kappa values calculated. The overall level of agreement was moderate (kappa 0.45). When the categories were reduced in number by creating two groups, (A + AB + B1 + B2 and B3 + C), the level of agreement increased to 0.62. An alternative grouping (A + AB + B1 and B2 + B3 + C) increased it slightly further. The best agreement was in tumour types A and AB. Difficulties arose in distinguishing B1 tumours from B2 tumours and B2 tumours from B3 tumours.
Although the WHO system describes a number of well-defined tumour types with clear diagnostic criteria, the overall level of agreement was moderate and improved if some groups were amalgamated.
Figures in this publication
Available from: Philipp Albert Schnabel
- "Thymomas comprise a spectrum of unique thymic epithelial tumors that generally show intratumoral thymopoiesis. They are subdivided into WHO type A, AB, B1, B2, and B3 thymomas (1, 2), but this classification has been challenged by some authors (3, 4). Thymic carcinoma also show a spectrum subtypes that resemble analogously called extrathymic carcinomas (TCs) (2, 5). "
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ABSTRACT: The molecular pathogenesis of thymomas and thymic carcinomas (TCs) is poorly understood and results of adjuvant therapy are unsatisfactory in case of metastatic disease and tumor recurrence. For these clinical settings, novel therapeutic strategies are urgently needed. Recently, limited sequencing efforts revealed that a broad spectrum of genes that play key roles in various common cancers are rarely affected in thymomas and TCs, suggesting that other oncogenic principles might be important. This made us re-analyze historic expression data obtained in a spectrum of thymomas and thymic squamous cell carcinomas (TSCCs) with a custom-made cDNA microarray. By cluster analysis, different anti-apoptotic signatures were detected in type B3 thymoma and TSCC, including overexpression of BIRC3 in TSCCs. This was confirmed by qRT-PCR in the original and an independent validation set of tumors. In contrast to several other cancer cell lines, the BIRC3-positive TSCC cell line, 1889c showed spontaneous apoptosis after BIRC3 knock-down. Targeting apoptosis genes is worth testing as therapeutic principle in TSCC.
Frontiers in Oncology 12/2013; 3:316. DOI:10.3389/fonc.2013.00316
Available from: Diponkar Banerjee
- "To this day pathologists everywhere in the world rely upon haematoxylin and eosin (H&E) labelled tissue sections to diagnose cancer using a compound light microscope. Diagnostic criteria and current classification systems in clinical use for human cancers remain largely morphology based [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26], with their attendant problems with interobserver variability [27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45]. Notable exceptions are the classification systems of neoplasms of haematopoietic and lymphopoietic systems, which are now heavily reliant upon ancillary technologies . "
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ABSTRACT: Human cancers are still diagnosed and classified using the light microscope. The criteria are based upon morphologic observations by pathologists and tend to be subject to interobserver variation. In preoperative biopsies of non-small cell lung cancers, the diagnostic concordance, even amongst experienced pulmonary pathologists, is no better than a coin-toss. Only 25% of cancer patients, on average, benefit from therapy as most therapies do not account for individual factors that influence response or outcome. Unsuccessful first line therapy costs Canada CAN$1.2 billion for the top 14 cancer types, and this extrapolates to $90 billion globally. The availability of accurate drug selection for personalized therapy could better allocate these precious resources to the right therapies. This wasteful situation is beginning to change with the completion of the human genome sequencing project and with the increasing availability of targeted therapies. Both factors are giving rise to attempts to correlate tumor characteristics and response to specific adjuvant and neoadjuvant therapies. Static cancer classification and grading systems need to be replaced by functional classification systems that not only account for intra- and inter- tumor heterogeneity, but which also allow for the selection of the correct chemotherapeutic compounds for the individual patient. In this review, the examples of lung and breast cancer are used to illustrate the issues to be addressed in the coming years, as well as the emerging technologies that have great promise in enabling personalized therapy.
Cancers 06/2010; 2(2):1066-1091. DOI:10.3390/cancers2021066
Available from: Tiziano De Giacomo
- "Thus, from type A to type C, there is a clear deterioration of prognosis; A, AB, B1 and B2 show a progressively worse outcome; B3 (the old 'well-differentiated thymic carcinoma') is more aggressive and shows intermediate survival, while patients with type C lesions present the worse outcome, with poor survival and high recurrence rate. Although the WHO system describes a number of welldefined tumour types with clear diagnostic criteria, the overall level of agreement is moderate with recognised intraand inter-observer discrepancies ; however, it could be improved if some groups are amalgamated. A meta-analysis "
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ABSTRACT: Thymoma and thymic carcinoma are an extremely heterogeneous group of neoplastic lesions with an exceedingly wide spectrum of morphologic appearances. They show different presentations with a variable and unpredictable evolution ranging from an indolent non-invasive attitude to a highly infiltrative and metastasising one. Prognosis can be predicted on the basis of a number of variables, mainly staging, the WHO histological pattern and diameter of the tumour. Complete surgical resection is certainly the gold standard to achieve cure. However, especially in patients with lesions at advanced stage, complete resection may be difficult and recurrence often occurs; at these stages, disease-free long-term survival may be difficult to be accomplished. Chemo- and radiotherapy protocols have been designed to complete surgical treatment and improve results in inoperable patients as well, based on the reported sensitivity of thymic tumours to these treatment modalities. The integration of clinical staging and histology, with the new histogenetic morphological classification, has contributed to design multimodality treatment protocols that help to improve prognosis. Induction therapy can now be applied before surgery in patients with tumours considered inoperable, improving resectability and outcome without adding morbidity and mortality to the surgical procedure. This newly developed approach helps to reduce the recurrence rate and to ameliorate disease-free survival. New therapies are now being evaluated as for many other tumours; however, they still need confirmation in prospective randomised studies. In the future, integrated treatment modality should be incorporated in a standardised approach that goes from a careful assessment of histology, staging and lymph node status, and a constructive and non-empirical co-operation between medical and radiation oncologists, pathologists and thoracic surgeons.
European journal of cardio-thoracic surgery: official journal of the European Association for Cardio-thoracic Surgery 08/2009; 37(1):13-25. DOI:10.1016/j.ejcts.2009.05.038 · 3.30 Impact Factor
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