A feasibility study using a quantum cascade laser-based infrared microscope for the rapid and label-free classification of colorectal cancer tissues is presented. Infrared imaging is a reliable, robust, automated, and operator-independent tissue classification method that has been used for differential classification of tissue thin sections identifying tumorous regions. However, long acquisition time by the so far used FT-IR-based microscopes hampered the clinical translation of this technique. Here, the used quantum cascade laser-based microscope provides now infrared images for precise tissue classification within few minutes. We analyzed 110 patients with UICC-Stage II and III colorectal cancer, showing 96% sensitivity and 100% specificity of this label-free method as compared to histopathology, the gold standard in routine clinical diagnostics. The main hurdle for the clinical translation of IR-Imaging is overcome now by the short acquisition time for high quality diagnostic images, which is in the same time range as frozen sections by pathologists.

Since 2016, large nested urothelial carcinoma (LNUC) has been included within the WHO classification of urothelial tumors. Limited reports with mainly small case series have confirmed the malignant behavior of LNUC despite its bland morphological appearance. We evaluated, for the first time, markers for new immunooncological or targeted therapies including FGFR3 mutational status and PD-L1 status, the frequency of TERT-promoter mutations and the molecular subtype in a cohort of 25 LNUC using SNaPshot analysis and immunohistochemistry. Of the 25 cases, 17 were pure LNUC, with 13 showing an additional exophytic papillary/papillary-like component. Seven mixed LNUCs presented areas of classical nested variant urothelial carcinoma (NVUC) and one showed a component of conventional urothelial carcinoma. Of the 17 evaluable pure LNUCs, 16 were FGFR3-mutated with identical mutations in their concomitant papillary/papillary-like components. An FGFR3 mutation was found in 1/7 evaluable mixed LNUCs combined with NVUC. TERT-promoter mutations were detected in 86.7% pure and 83.3% mixed tumors. Immunohistochemistry revealed a luminal phenotype; PD-L1 was negative in the majority of tumor cells and tumor-associated immune cells. Pure LNUC is a prime example of a luminal, FGFR3-mutated, mostly PD-L1-negative tumor. In contrast, FGFR3 mutations seem to be rare in mixed LNUC, which may indicate a different pathway of tumor development.

Challenging histopathological diagnostics in cancer include microsatellite instability-high (MSI-H) colorectal cancer (CRC), which occurs in 15% of early-stage CRC and is caused by a deficiency in the mismatch repair system. The diagnosis of MSI-H cannot be reliably achieved by visual inspection of a hematoxylin and eosin stained thin section alone, but additionally requires subsequent molecular analysis. Time- and sample-intensive immunohistochemistry with subsequent fragment length analysis is used. The aim of the presented feasibility study is to test the ability of quantum cascade laser (QCL)-based infrared (IR) imaging as an alternative diagnostic tool for MSI-H in CRC. We analyzed samples from 100 patients with sporadic CRC UICC stage II and III. Forty samples were used to develop the random forest classifier and 60 samples to verify the results on an independent blinded dataset. Specifically, 100% sensitivity and 93% specificity were achieved based on the independent 30 MSI-H- and 30 microsatellite stable (MSS)-patient validation cohort. This showed that QCL-based IR imaging is able to distinguish between MSI-H and MSS for sporadic CRC - a question that goes beyond morphological features - based on the use of spatially resolved infrared spectra used as biomolecular fingerprints.

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.