Abstract
Currently, approximately 150 different brain tumour types are defined by the WHO. Recent endeavours to exploit machine learning and deep learning methods for supporting more precise diagnostics based on the histological tumour appearance have been hampered by the relative paucity of accessible digital histopathological datasets. While freely available datasets are relatively common in many medical specialties such as radiology and genomic medicine, there is still an unmet need regarding histopathological data. Thus, we digitized a significant portion of a large dedicated brain tumour bank based at the Division of Neuropathology and Neurochemistry of the Medical University of Vienna, covering brain tumour cases from 1995–2019. A total of 3,115 slides of 126 brain tumour types (including 47 control tissue slides) have been scanned. Additionally, complementary clinical annotations have been collected for each case. In the present manuscript, we thoroughly discuss this unique dataset and make it publicly available for potential use cases in machine learning and digital image analysis, teaching and as a reference for external validation.
Highlights
Brain tumours account for a large fraction of years of potential life lost as compared with tumours from other sites[1], and have a significant negative impact on patients’ quality of life[2]
While the majority of tumours is diagnosed solely based on histopathology, an integrated approach is mandatory for 19 tumour types
Diagnostic algorithms trained on DNA methylation data have been shown to significantly increase diagnostic accuracy[6]
Summary
Background & SummaryBrain tumours account for a large fraction of years of potential life lost as compared with tumours from other sites[1], and have a significant negative impact on patients’ quality of life[2]. Current diagnostic guidelines published by the WHO define approximately 150 distinct brain tumour types and assign grades I to IV, based on malignancy and potential to malignant transformation or progression They are mainly differentiated by their histopathological phenotypes and molecular alterations[4]. Most available histopathology data such as those available through TCGA8, IvyGAP9,10 or TCIA11 focus on only a few diagnostic entities They mostly consist of digitized fresh frozen tissue sections, which feature relatively poor tissue morphology as compared to formalin-fixed and paraffin-embedded tissues. Still, even with these limited data, computational algorithms have been successfully trained - amongst others - for survival prediction[12], detection of tumour-infiltrating lymphocytes[13], and assessments of tumour microvessels[14]. Larger datasets encompassing an even wider range of brain tumours and featuring improved cellular and morphological characteristics are necessary to further develop these algorithms and extend their applicability to the entire spectrum of brain tumour types
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