Deep Learning-Enabled Diagnosis of Liver Adenocarcinoma.

BACKGROUND & AIMS: Diagnosis of adenocarcinoma in the liver is a frequent scenario in routine pathology and has a critical impact on clinical decision making. However, rendering a correct diagnosis can be challenging, and often requires the integration of clinical, radiologic, and immunohistochemical information. We present a deep learning model (HEPNET) to distinguish intrahepatic cholangiocarcinoma from colorectal liver metastasis, as the most frequent primary and secondary forms of liver adenocarcinoma, with clinical grade accuracy using H&E-stained whole-slide images. METHODS: HEPNET was trained on 714,589 image tiles from 456 patients who were randomly selected in a stratified manner from a pool of 571 patients who underwent surgical resection or biopsy at Heidelberg University Hospital. Model performance was evaluated on a hold-out internal test set comprising 115 patients and externally validated on 159 patients recruited at Mainz University Hospital. RESULTS: On the hold-out internal test set, HEPNET achieved an area under the receiver operating characteristic curve of 0.994 (95% CI, 0.989-1.000) and an accuracy of 96.522% (95% CI, 94.521%-98.694%) at the patient level. Validation on the external test set yielded an area under the receiver operating characteristic curve of 0.997 (95% CI, 0.995-1.000), corresponding to an accuracy of 98.113% (95% CI, 96.907%-100.000%). HEPNET surpassed the performance of 6 pathology experts with different levels of experience in a reader study of 50 patients (P = .0005), boosted the performance of resident pathologists to the level of senior pathologists, and reduced potential downstream analyses. CONCLUSIONS: We provided a ready-to-use tool with clinical grade performance that may facilitate routine pathology by rendering a definitive diagnosis and guiding ancillary testing. The incorporation of HEPNET into pathology laboratories may optimize the diagnostic workflow, complemented by test-related labor and cost savings.

STAT1 and STAT3 Exhibit a Crosstalk and Are Associated with Increased Inflammation in Hepatocellular Carcinoma.

Liver cancers, which are mostly hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA), are very aggressive tumors with poor prognosis. Therapeutic options with curative intent are largely limited to surgery and available systemic therapies show limited benefit. Signal transducer and activator of transcription 1 (STAT1) and 3 (STAT3) are key transcription factors activated by pro-inflammatory cytokines such as interferon-γ (IFN-γ) and interleukin-6 (IL-6). In this study, we combined in vitro cell culture experiments and immunohistochemical analyses of human HCC (N = 124) and CCA (N = 138) specimens. We observed that in the absence of STAT3, IL-6 induced the activation of STAT1 and its target genes suggesting that IL-6 derived from the tumor microenvironment may activate both STAT1 and STAT3 target genes in HCC tumor cells. In addition, STAT1 and STAT3 were highly activated in a subset of HCC, which exhibited a high degree of infiltrating CD8- and FOXP3-positive immune cells and PD-L1 expression. Our results demonstrate that STAT1 and STAT3 are expressed and activated in HCC and tumor infiltrating immune cells. In addition, HCC cases with high STAT1 and STAT3 expression also exhibited a high degree of immune cell infiltration, suggesting increased immunological tolerance.

The Transmembrane Receptor TIRC7 Identifies a Distinct Subset of Immune Cells with Prognostic Implications in Cholangiocarcinoma.

Cholangiocarcinoma (CCA) is a heterogeneous malignancy with a dismal prognosis. Therapeutic options are largely limited to surgery and conventional chemotherapy offers limited benefit. As immunotherapy has proven highly effective in various cancer types, we have undertaken a quantitative immunohistopathological assessment of immune cells expressing the immunoinhibitory T cell immune response cDNA 7 receptor (TIRC7), an emerging immunoinhibitory receptor, in a cohort of 135 CCA patients. TIRC7+ immune cells were present in both the tumor epithelia and stroma in the majority of CCA cases with the highest levels found in intrahepatic CCA. While intraepithelial density of TIRC7+ immune cells was decreased compared to matched non-neoplastic bile ducts, stromal quantity was higher in the tumor samples. Tumors exhibiting signet ring cell or adenosquamous morphology were exclusively associated with an intraepithelial TIRC7+ phenotype. Survival analysis showed intraepithelial TIRC7+ immune cell density to be a highly significant favorable prognosticator in intrahepatic but not proximal or distal CCA. Furthermore, intraepithelial TIRC7+ immune cell density correlated with the number of intraepithelial CD8+ immune cells and with the total number of CD4+ immune cells. Our results suggest the presence and prognostic relevance of TIRC7+ immune cells in CCA and warrant further functional studies on its pharmacological modulation.

The Pathology of Severe COVID-19-Related Lung Damage.

BACKGROUND: The histomorphological changes of lung damage in severe coronavirus disease 2019 (COVID-19) have not yet been adequately characterized. In this article, we describe the sequence of pathological changes in COVID-19 and discuss the implications for approaches to treatment. METHODS: Standardized autopsies were performed on thirteen patients who had died of COVID-19. The findings were analyzed together with clinical data from the patients' medical records. RESULTS: Most (77%) of the deceased patients were men. Their median age at death was 78 years (range, 41-90). Most of them had major pre-existing chronic diseases, most commonly arterial hypertension. The autopsies revealed characteristic COVID-19-induced pathological changes in the lungs, which were regarded as the cause of death in most patients. The main histological finding was sequential alveolar damage, apparently due in large measure to focal capillary microthrombus formation. Alveolar damage leads to the death of the patient either directly or by the induction of pulmonary parenchymal fibrosis. Diffuse lung damage was seen exclusively in invasively ventilated patients. CONCLUSION: Autopsies are crucial for the systematic assessment of new diseases such as COVID-19: they provide a basis for further investigations of disease mechanisms and for the devising of potentially effective modes of treatment. The autopsy findings suggest that focal damage of the microvascular pulmonary circulation is a main mechanism of lethal lung disease due to the SARS-CoV-2 virus. It may also be a cause of persistent lung damage in patients who recover from severe COVID-19.