No abstract

Artificial intelligence (AI) algorithms continue to rival human performance on a variety of clinical tasks, while their actual impact on human diagnosticians, when incorporated into clinical workflows, remains relatively unexplored. In this study, we developed a deep learning-based assistant to help pathologists differentiate between two subtypes of primary liver cancer, hepatocellular carcinoma and cholangiocarcinoma, on hematoxylin and eosin-stained whole-slide images (WSI), and evaluated its effect on the diagnostic performance of 11 pathologists with varying levels of expertise. Our model achieved accuracies of 0.885 on a validation set of 26 WSI, and 0.842 on an independent test set of 80 WSI. Although use of the assistant did not change the mean accuracy of the 11 pathologists (p = 0.184, OR = 1.281), it significantly improved the accuracy (p = 0.045, OR = 1.499) of a subset of nine pathologists who fell within well-defined experience levels (GI subspecialists, non-GI subspecialists, and trainees). In the assisted state, model accuracy significantly impacted the diagnostic decisions of all 11 pathologists. As expected, when the model's prediction was correct, assistance significantly improved accuracy (p = 0.000, OR = 4.289), whereas when the model's prediction was incorrect, assistance significantly decreased accuracy (p = 0.000, OR = 0.253), with both effects holding across all pathologist experience levels and case difficulty levels. Our results highlight the challenges of translating AI models into the clinical setting, and emphasize the importance of taking into account potential unintended negative consequences of model assistance when designing and testing medical AI-assistance tools.npj Digital Medicine (2020) 3:23 ; https://doi.

For many solid tumors, surgical resection remains the gold standard and tumor-involved margins are associated with poor clinical outcomes. Near-infrared (NIR) fluorescence imaging using molecular agents has shown promise for imaging during resection. However, for cancers with difficult imaging conditions, surgical value may lie in tumor mapping of surgical specimens. We thus evaluated a novel approach for real-time, intraoperative tumor margin assessment. Twenty-one adult patients with biopsy-confirmed squamous cell carcinoma arising from the head and neck (HNSCC) scheduled for standard-of-care surgery were enrolled. Cohort 1 ( = 3) received panitumumab-IRDye800CW at an intravenous microdose of 0.06 mg/kg, cohort 2A ( = 5) received 0.5 mg/kg, cohort 2B ( = 7) received 1 mg/kg, and cohort 3 ( = 6) received 50 mg. Patients were followed 30 days postinfusion and adverse events were recorded. Imaging was performed using several closed- and wide-field devices. Fluorescence was histologically correlated to determine sensitivity and specificity. imaging demonstrated tumor-to-background ratio (TBR) of 2 to 3, compared with specimen imaging TBR of 5 to 6. We obtained clear differentiation between tumor and normal tissue, with a 3-fold signal difference between positive and negative specimens ( < 0.05). We achieved high correlation of fluorescence intensity with tumor location with sensitivities and specificities >89%; fluorescence predicted distance of tumor tissue to the cut surface of the specimen. This novel method of detecting tumor-involved margins in surgical specimens using a cancer-specific agent provides highly sensitive and specific, real-time, intraoperative surgical navigation in resections with complex anatomy, which are otherwise less amenable to image guidance. This study demonstrates that fluorescence can be used as a sensitive and specific method of guiding surgeries for head and neck cancers and potentially other cancers with challenging imaging conditions, increasing the probability of complete resections and improving oncologic outcomes. .

Molecular characterization of cell types using single-cell transcriptome sequencing is revolutionizing cell biology and enabling new insights into the physiology of human organs. We created a human reference atlas comprising nearly 500,000 cells from 24 different tissues and organs, many from the same donor. This atlas enabled molecular characterization of more than 400 cell types, their distribution across tissues, and tissue-specific variation in gene expression. Using multiple tissues from a single donor enabled identification of the clonal distribution of T cells between tissues, identification of the tissue-specific mutation rate in B cells, and analysis of the cell cycle state and proliferative potential of shared cell types across tissues. Cell type–specific RNA splicing was discovered and analyzed across tissues within an individual.

B cells in human food allergy have been studied predominantly in the blood. Little is known about IgE+ B cells or plasma cells in tissues exposed to dietary antigens. We characterized IgE+ clones in blood, stomach, duodenum, and esophagus of 19 peanut-allergic patients, using high-throughput DNA sequencing. IgE+ cells in allergic patients are enriched in stomach and duodenum, and have a plasma cell phenotype. Clonally related IgE+ and non-IgE–expressing cell frequencies in tissues suggest local isotype switching, including transitions between IgA and IgE isotypes. Highly similar antibody sequences specific for peanut allergen Ara h 2 are shared between patients, indicating that common immunoglobulin genetic rearrangements may contribute to pathogenesis. These data define the gastrointestinal tract as a reservoir of IgE+ B lineage cells in food allergy.