Advances in and the Applicability of Machine Learning-Based Screening and Early Detection Approaches for Cancer: A Primer.

Despite the efforts of the past decades, cancer is still among the key drivers of global mortality. To increase the detection rates, screening programs and other efforts to improve early detection were initiated to cover the populations at a particular risk for developing a specific malignant condition. These diagnostic approaches have, so far, mostly relied on conventional diagnostic methods and have made little use of the vast amounts of clinical and diagnostic data that are routinely being collected along the diagnostic pathway. Practitioners have lacked the tools to handle this ever-increasing flood of data. Only recently, the clinical field has opened up more for the opportunities that come with the systematic utilisation of high-dimensional computational data analysis. We aim to introduce the reader to the theoretical background of machine learning (ML) and elaborate on the established and potential use cases of ML algorithms in screening and early detection. Furthermore, we assess and comment on the relevant challenges and misconceptions of the applicability of ML-based diagnostic approaches. Lastly, we emphasise the need for a clear regulatory framework to responsibly introduce ML-based diagnostics in clinical practice and routine care.

Automated spheroid generation, drug application and efficacy screening using a deep learning classification: a feasibility study.

The last two decades saw the establishment of three-dimensional (3D) cell cultures as an acknowledged tool to investigate cell behaviour in a tissue-like environment. Cells growing in spheroids differentiate and develop different characteristics in comparison to their two-dimensionally grown counterparts and are hence seen to exhibit a more in vivo-like phenotype. However, generating, treating and analysing spheroids in high quantities remains labour intensive and therefore limits its applicability in drugs and compound research. Here we present a fully automated pipetting robot that is able to (a) seed hanging drops from single cell suspensions, (b) treat the spheroids formed in these hanging drops with drugs and (c) analyse the viability of the spheroids by an image-based deep learning based convolutional neuronal network (CNN). The model is trained to classify between 'unaffected', 'mildly affected' and 'affected' spheroids after drug exposure. All corresponding spheroids are initially analysed by viability flow cytometry analysis to build a labelled training set for the CNN to subsequently reduce the number of misclassifications. Hence, this approach allows to efficiently examine the efficacy of drug combinatorics or new compounds in 3D cell culture. Additionally, it may provide a valuable instrument to screen for new and individualized systemic therapeutic strategies in second and third line treatment of solid malignancies using patient derived primary cells.