Machine learning classifies cancer
Brain tumours are often classified by visual assessment of tumour cells, yet such diagnoses can vary depending on the observer. Machine-learning methods to spot molecular patterns could improve cancer diagnosis.
Accurate diagnosis is essential for appropriate disease treatment. A core technique used to diagnose brain cancer today is the microscope-based analysis of tumour samples on glass slides, termed histology. However, this requires the appraisal of subtle cellular alterations, which in some cases may lead to different classifications for a given sample by different individuals. Nowadays, technological developments enable vast amounts of molecular data to be obtained and assessed for a tumour without the need for such subjective diagnostics. Machine-based-learning approaches are being developed to aid the diagnosis of clinical samples, and in a paper in Nature, Capper et al.1 report such a method for classifying brain tumours on the basis of molecular patterns.
In 1926, a publication entitled A Classification of the Tumors of the Glioma Group on a Histo-Genetic Basis with a Correlated Study of Prognosis2 by neurosurgeons Percival Bailey and Harvey Cushing provided early insight into the development, cellular characteristics and clinical consequences of glioma, a type of cancer of the central nervous system (CNS). The book’s title was prophetic and ambitious, given that the microscope-based diagnostic approach they advocated was not common then. The authors’ ideas were ahead of their time — for example, the word ‘histo-genetic’ in the book’s title points to a link between cellular changes and genetics. Bailey and Cushing’s obsessive attention to detail allowed them to identify gross and microscopic tumour features that correlated with clinical outcomes, and the book reported the classification of 14 types of tumour.
Today, many brain tumours are identified by analysis of both histological and molecular features3–5. The identification6,7 of biologically relevant, tumour-type-defining and clinically informative genetic alterations in brain tumours prompted the World Health Organization (WHO) to update its diagnostic guidelines for certain brain tumours in 2016 to recommend an integrative diagnostic approach that combines both histology and molecular information8,9. However, diagnoses that rely predominantly on histology remain common for many types of rare tumour, owing to a lack of molecular identifiers. Yet histological diagnoses face many challenges, including possible cellular variations in tumours that are a mosaic of cells containing different genetic alterations, or the fact that similar histological features can be shared by many different types of brain tumour. Questions remain about how well histological similarity reflects tumour similarity, given that tumours that have similar histology can progress in different ways, and tumours that have contrasting histology can progress in the same way.
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