Fast and label-free automated detection of microsatellite status in early colon cancer using artificial intelligence integrated infrared imaging.

PURPOSE: Microsatellite instability (MSI) due to mismatch repair (MMR) defects accounts for 15-20% of colon cancers (CC). MSI testing is currently standard of care in CC with immunohistochemistry of the four MMR proteins representing the gold standard. Instead, label-free quantum cascade laser (QCL) based infrared (IR) imaging combined with artificial intelligence (AI) may classify MSI/microsatellite stability (MSS) in unstained tissue sections user-independently and tissue preserving. METHODS: Paraffin-embedded unstained tissue sections of early CC from patients participating in the multicentre AIO ColoPredict Plus (CPP) 2.0 registry were analysed after dividing into three groups (training, test, and validation). IR images of tissue sections using QCL-IR microscopes were classified by AI (convolutional neural networks [CNN]) using a two-step approach. The first CNN (modified U-Net) detected areas of cancer while the second CNN (VGG-Net) classified MSI/MSS. End-points were area under receiver operating characteristic (AUROC) and area under precision recall curve (AUPRC). RESULTS: The cancer detection in the first step was based on 629 patients (train n = 273, test n = 138, and validation n = 218). Resulting classification AUROC was 1.0 for the validation dataset. The second step classifying MSI/MSS was performed on 547 patients (train n = 331, test n = 69, and validation n = 147) reaching AUROC and AUPRC of 0.9 and 0.74, respectively, for the validation cohort. CONCLUSION: Our novel label-free digital pathology approach accurately and rapidly classifies MSI vs. MSS. The tissue sections analysed were not processed leaving the sample unmodified for subsequent analyses. Our approach demonstrates an AI-based decision support tool potentially driving improved patient stratification and precision oncology in the future.

A framework for falsifiable explanations of machine learning models with an application in computational pathology.

In recent years, deep learning has been the key driver of breakthrough developments in computational pathology and other image based approaches that support medical diagnosis and treatment. The underlying neural networks as inherent black boxes lack transparency and are often accompanied by approaches to explain their output. However, formally defining explainability has been a notorious unsolved riddle. Here, we introduce a hypothesis-based framework for falsifiable explanations of machine learning models. A falsifiable explanation is a hypothesis that connects an intermediate space induced by the model with the sample from which the data originate. We instantiate this framework in a computational pathology setting using hyperspectral infrared microscopy. The intermediate space is an activation map, which is trained with an inductive bias to localize tumor. An explanation is constituted by hypothesizing that activation corresponds to tumor and associated structures, which we validate by histological staining as an independent secondary experiment.

The tripeptide KdPT ameliorates ongoing psoriasis-like skin inflammation in murine and human skin.

Psoriasis is a chronic inflammatory disease appearing as scaly erythematous cutaneous lesions, which are characterized by parakeratosis and acanthosis as well as the infiltration of immune cells, such as T helper-1 and T helper-17 cells. Here, we demonstrated that KdPT, a tripeptide structurally related to the C-terminal amino acids of alpha-melanocyte-stimulating hormone, which was previously shown to exhibit anti-inflammatory effects in intestinal inflammation, ameliorated ongoing disease in the mouse model of imiquimod-induced psoriasis-like skin inflammation and in the small xenotransplant mouse model of psoriasis. We could show that systemic KdPT treatment significantly reduced hyperkeratosis and acanthosis in murine as well as human skin. Moreover, KdPT upregulated Foxp3 in CD4+ T cells from mice and from peripheral blood of individuals with psoriasis and decreased the expression of type 1 inflammatory cytokines, indicating that the beneficial effect of KdPT was, at least in part, mediated by the induction of functional regulatory T cells that suppressed the activation of pathogenic CD4+ IFN-γ+ and CD4+ IL-17+ T cells. Thus, these data might suggest KdPT as a potential novel therapeutic alternative for the treatment of psoriasis.