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Deep learning of circulating tumour cells


Circulating tumour cells (CTCs) found in the blood of cancer patients are a promising biomarker in precision medicine. However, their use is currently hindered by their low frequency, tedious manual scoring and extensive cell heterogeneities. Those challenges limit the effectiveness of classical machine-learning methods for automated CTC analysis. Here, we combine autoencoding convolutional neural networks with advanced visualization techniques. This provides a very informative view on the data that opens the way for new biomedical research questions. We unravel hidden information in the raw image data of fluorescent images of blood samples enriched for CTCs. Our network classifies fluorescent images of single cells in five different classes with an accuracy, sensitivity and specificity of over 96%, and the obtained CTC counts predict the overall survival of cancer patients as well as state-of-the-art manual counts. Moreover, our network excelled in identifying different important subclasses of objects. Deep learning was faster and superior to classical image analysis approaches and enabled the identification of new biological phenomena.

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Fig. 1: Overview of the analysis workflow used.
Fig. 2: Comparison of the t-SNE maps of the latent space for a standard and an autoencoder CNN.
Fig. 3: Identification of new cell classes based on the 2D t-SNE map.
Fig. 4: Validation of the classification.
Fig. 5: Morphological heterogeneity of CTCs.
Fig. 6: Kaplan–Meier plots comparing the predictive value of CellSearch, standard CNN and autoencoder CNN scores.
Fig. 7: Overview of the different results obtained with our framework.
Fig. 8: Visualization of the CNN architecture used.

Data availability

A subset of our training and validation set based only on cell line samples and the corresponding weight set is available at Patient data from clinical studies cannot be shared publicly. Raw data to reproduce Figs. 2, 3, 4, 5 and 6 can be shared upon request.

Code availability

The accompanying code for this manuscript is publicly available at


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L.Z., S.d.W., K.A. and L.T. acknowledge support by EUFP7 programme #305341 CTCTrap. L.Z., G.v.D., K.A., L.T. and C.B. acknowledge support by IMI EU programme #115749 CANCER-ID. C.B. acknowledges support by EU-H2020 project NoMADS #777826. Y.B. and C.B. acknowledge support by the SACAMIR project of TKI Life Science & Health. A.N. and L.T. acknowledge support by NWO Applied and Engineering Sciences project Cancer-ID #14190.

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The conception and design of the work described herein was made by L.L.Z., L.W.M.M.T. and C.B. L.L.Z., G.v.D., A.N., S.d.W., K.C.A. and J.F.S. acquired the data and generated the ground truth set, and L.L.Z. and A.N. performed the data analysis and interpretation of the results. The code used in this work was created by L.L.Z. and Y.E.B. The first draft of the paper was written by L.L.Z., L.W.M.M.T. and C.B. L.L.Z., Y.E.B., G.v.D., A.N., S.d.W., K.C.A., S.A.v.G., L.W.M.M.T. and C.B. commented on and edited the manuscript.

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Correspondence to Christoph Brune.

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Zeune, L.L., Boink, Y.E., van Dalum, G. et al. Deep learning of circulating tumour cells. Nat Mach Intell 2, 124–133 (2020).

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