ARTIFICIAL INTELLIGENCE AND PATHOLOGY

Abstract

The microscopic diagnosis of cancer remains challenging. Recent data from our group within the French (nationwide) LymphoPath network shows that 20% of diagnoses are inaccurate, with direct impact on patient care. Indeed, molecular techniques, that are not affordable for all pathology departments, have become critical for the final diagnosis. Currently, automated solutions that could help pathologists with diagnostic decisions or histological grading are lacking, and it is not reasonable to expect pathologists to be experts on rare tumours if they only see a few cases per year. Digital microscopy offers unique features which are not available in conventional optical microscopy. Assisted by dedicated software tools, digital microscopy enables dynamic and prompt access to any detail of the stained slides at several microscopic magnifications. Furthermore, the calibrated qualities and the number of discrete pixels in a digital slide allow automated image analysis and quantification by using computer vision and, in particular, deep learning approaches. Image feature extraction methods based on pixel detection or less often on object segmentation has brought much hope in improving the accuracy of the human eye. Automatic analysis of cancer whole-slide images has recently been performed to allow experts for predicting tumour classification, gene mutations and survival outcomes. However, the positioning of such technologies in routine practice is limited by the trained networks which frequently do not meet industrial constraints required for a general application such as certification, qualification and explainability (black box effect) of algorithms. This presentation will describe different approaches of machine learning aiming at classifying cancer subtypes through the analysis of histopathologic features. Advantages and drawbacks of these techniques will be discussed with a special emphasis on the risk of biased performance assessment of deep learning systems. The critical role of data sets quality will be discussed and different strategies will be envisaged to broaden the use deep neural networks. Providing that these limitations are taken into account and circumvented, it seems that artificial intelligence solutions dedicated to cancer histopathology should pave the road to precision medicine through data integration into a holistic patient dashboard for oncology care teams. Keywords: Bioinformatics; Computational and Systems Biology, Genomics, Epigenomics, and Other -Omics, Tumor Biology and Heterogeneity Conflicts of interests pertinent to the abstract P. Brousset Consultant or advisory role: Roche, MSB, Janssen Cilag laboratory Research funding: Roche, Pierre Fabre