title: Automated detection of circulating tumor cells with naive Bayesian classifiers.
abstract: Personalized medicine is a modern healthcare approach where information on each person's unique clinical constitution is exploited to realize early disease intervention based on more informed medical decisions. The application of diagnostic tools in combination with measurement evaluation that can be performed in a reliable and automated fashion plays a key role in this context. As the progression of various cancer diseases and the effectiveness of their treatments are related to a varying number of tumor cells that circulate in blood, the determination of their extremely low numbers by liquid biopsy is a decisive prognostic marker. To detect and enumerate circulating tumor cells (CTCs) in a reliable and automated fashion, we apply methods from machine learning using a naive Bayesian classifier (NBC) based on a probabilistic generative mixture model. Cells are collected with a functionalized medical wire and are stained for fluorescence microscopy so that their color signature can be used for classification through the construction of Red-Green-Blue (RGB) color histograms. Exploiting the information on the fluorescence signature of CTCs by the NBC does not only allow going beyond previous approaches but also provides a method of unsupervised learning that is required for unlabeled training data. A quantitative comparison with a state-of-the-art support vector machine, which requires labeled data, demonstrates the competitiveness of the NBC method.
authors.affiliation: Applied Systems Biology, Leibniz Institute for Natural Product Research and Infection Biology, Hans-Knöll-Institute (HKI), Jena, Germany; Frankfurt Institute for Advanced Studies (FIAS), Goethe-University Frankfurt, Frankfurt am Main, Germany.
authors.forename: Carl-Magnus
authors.initials: CM
authors.lastname: Svensson
authors.forename: Solveigh
authors.initials: S
authors.lastname: Krusekopf
authors.forename: Jörg
authors.initials: J
authors.lastname: Lücke
authors.forename: Marc
authors.initials: M
authors.lastname: Thilo Figge
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D055088:Early Detection of Cancer; D006801:Humans; D009369:Neoplasms / Q000175:diagnosis* / Q000473:pathology; D009360:Neoplastic Cells, Circulating / Q000473:pathology* / Q000648:ultrastructure; D010363:Pattern Recognition, Automated / Q000379:methods; D057285:Precision Medicine; D060388:Support Vector Machine
keywords: Gaussian mixture model; biomedical image processing; cancer detection; support vector machines