Evaluation of a Deep Learning Approach to Differentiate Bowen’s Disease and Seborrheic Keratosis

Background: Some of the most common cutaneous neoplasms are Bowen’s disease and seborrheic keratosis, a malignant and a benign proliferation, respectively. These entities represent a significant fraction of a dermatopathologists’ workload, and in some cases, histological differentiation may be challenging. The potential of deep learning networks to distinguish these diseases is assessed. Methods: In total, 1935 whole-slide images from three institutions were scanned on two different slide scanners. A U-Net-based segmentation deep learning algorithm was trained on data from one of the centers to differentiate Bowen’s disease, seborrheic keratosis, and normal tissue, learning from annotations performed by dermatopathologists. Optimal thresholds for the class distinction of diagnoses were extracted and assessed on a test set with data from all three institutions. Results: We aimed to diagnose Bowen’s diseases with the highest sensitivity. A good performance was observed across all three centers, underlining the model’s robustness. In one of the centers, the distinction between Bowen’s disease and all other diagnoses was achieved with an AUC of 0.9858 and a sensitivity of 0.9511. Seborrheic keratosis was detected with an AUC of 0.9764 and a sensitivity of 0.9394. Nevertheless, distinguishing irritated seborrheic keratosis from Bowen’s disease remained challenging. Conclusions: Bowen’s disease and seborrheic keratosis could be correctly identified by the evaluated deep learning model on test sets from three different centers, two of which were not involved in training, and AUC scores > 0.97 were obtained. The method proved robust to changes in the staining solution and scanner model. We believe this demonstrates that deep learning algorithms can aid in clinical routine; however, the results should be confirmed by qualified histopathologists

T-bet+ B cells are activated by and control endogenous retroviruses through TLR-dependent mechanisms

Abstract Endogenous retroviruses (ERVs) are an integral part of the mammalian genome. The role of immune control of ERVs in general is poorly defined as is their function as anti-cancer immune targets or drivers of autoimmune disease. Here, we generate mouse-strains where Moloney-Murine Leukemia Virus tagged with GFP (ERV-GFP) infected the mouse germline. This enables us to analyze the role of genetic, epigenetic and cell intrinsic restriction factors in ERV activation and control. We identify an autoreactive B cell response against the neo-self/ERV antigen GFP as a key mechanism of ERV control. Hallmarks of this response are spontaneous ERV-GFP+ germinal center formation, elevated serum IFN-γ levels and a dependency on Age-associated B cells (ABCs) a subclass of T-bet+ memory B cells. Impairment of IgM B cell receptor-signal in nucleic-acid sensing TLR-deficient mice contributes to defective ERV control. Although ERVs are a part of the genome they break immune tolerance, induce immune surveillance against ERV-derived self-antigens and shape the host immune response