The pathological identification of lymph node (LN) metastasis is demanding and tedious. Although convolutional neural networks (CNNs) possess considerable potential in improving the process, the ultrahigh-resolution of whole slide images hinders the development of a clinically applicable solution. We design an artificial-intelligence-assisted LN assessment workflow to facilitate the routine counting of metastatic LNs. Unlike previous patch-based approaches, our proposed method trains CNNs by using 5-gigapixel images, obviating the need for lesion-level annotations. Trained on 5907 LN images, our algorithm identifies metastatic LNs in gastric cancer with a slide-level area under the receiver operating characteristic curve (AUC) of 0.9936. Clinical experiments reveal that the workflow significantly improves the sensitivity of micrometastasis identification (81.94% to 95.83%, P < .001) and isolated tumor cells (67.95% to 96.15%, P < .001) in a significantly shorter review time (−31.5%, P < .001). Cross-site evaluation indicates that the algorithm is highly robust (AUC = 0.9829).

NAB2‐STAT6 gene fusion drives STAT6 nuclear expression and is the pathognomonic hallmark of solitary fibrous tumors (SFTs). However, no study has systematically analyzed the clinicopathological features, STAT6 immunoexpression status, or the fusion variants of NAB2‐STAT6 in intrathoracic SFTs. Fifty‐two intrathoracic SFTs were retrieved to appraise histopathology, assess STAT6 immunoexpression, and determine NAB2‐STAT6 fusion variants by RT‐PCR. Location‐relevant histologic mimics served as controls. Thirty‐one pleura‐based, 12 mediastinal/pericardial, and nine intrapulmonary lesions were histologically categorized into eight malignant, eight atypical, and 36 conventional or cellular SFTs, including two fat‐forming and two giant cell angiofibroma‐like SFTs. STAT6 distinctively decorated the tumoral nuclei in 51 (98%) SFTs. However, no nuclear staining was observed in the histological mimics. NAB2‐STAT6 fusion was detected in 34 SFTs. Twenty‐nine (85.3%) exhibited the major NAB2ex4‐STAT6ex2/3 variant and 5 (14.7%) the minor NAB2ex6‐STAT6ex16/17. NAB2ex4‐STAT6ex2 was significantly associated with older age (P = 0.01) and pleuropulmonary tumors (P = 0.025). After a median follow‐up of 33.9 (range, 0.3–174.6) months, adverse outcomes occurred in one atypical and five malignant SFTs, including two local relapses, one intrapulmonary metastasis, and three extrathoracic metastases. Inferior disease‐free survival was univariately associated with atypical/malignant histology (P = 0.001) and a mitosis >4/10 HPFs (P = 0.0012) but was unrelated to fusion variants. In conclusion, the majority of intrathoracic SFTs exhibited STAT6 nuclear staining, and NAB2ex4‐STAT6ex2/3 was the predominant fusion type. However, clinical aggressiveness is associated with atypical/malignant histology primarily contributed by increased mitosis but was unrelated to the NAB2‐STAT6 fusion variants.

Background: The aim of this study was to assess the validity of the 7^th edition of the American Joint Committee on Cancer (AJCC) TNM system (TNM-7) for patients undergoing hepatectomy for hepatocellular carcinoma (HCC). Methods: Partial hepatectomies performed for 879 patients from 1993 to 2005 were retrospectively reviewed. Clinicopathological factors, surgical outcome, overall survival (OS), and disease-free survival (DFS) were analyzed to evaluate the predictive value of the TNM-7 staging system. Results: According to the TNM-7 system, differences in five-year survival between stages I, II, and III were statistically significant. Subgroup analysis of stage III patients revealed that the difference between stages II and IIIA was not significant (OS, p = 0.246; DFS, p = 0.105). Further stratification of stages IIIA, IIIB and IIIC also did not reveal significant differences. Cox proportional hazard models of stage III analyses identified additional clinicopathological factors affecting patient survival: lack of tumor encapsulation, aspartate aminotransferase (AST) values > 68 U/L, and blood loss > 500 mL affected DFS whereas lack of tumor encapsulation, AST values > 68 U/L, blood loss > 500 mL, and serum α-fetoprotein (AFP) values > 200 ng/mL were independent factors impairing OS. Stage III factors including tumor thrombus, satellite lesions, and tumor rupture did not appear to influence survival in the stage III subgroup. Conclusions: In terms of 5-year survival rates, the TNM-7 system is capable of stratifying post-hepatectomy HCC patients into stages I, II, and III but is unable to stratify stage III patients into stages IIIA, IIIB and IIIC. Lack of tumor encapsulation, AST values > 68 U/L, blood loss > 500 mL, and AFP values > 200 ng/mL are independent prognostic factors affecting long-term survival.

Neutrophils play essential anti-microbial and inflammatory roles in host defense, however, their activities require tight regulation as dysfunction often leads to detrimental inflammatory and autoimmune diseases. Here we show that the adhesion molecule GPR97 allosterically activates CD177-associated membrane proteinase 3 (mPR3), and in conjugation with several protein interaction partners leads to neutrophil activation in humans. Crystallographic and deletion analysis of the GPR97 extracellular region identified two independent mPR3-binding domains. Mechanistically, the efficient binding and activation of mPR3 by GPR97 requires the macromolecular CD177/GPR97/PAR2/CD16b complex and induces the activation of PAR2, a G protein-coupled receptor known for its function in inflammation. Triggering PAR2 by the upstream complex leads to strong inflammatory activation, prompting anti-microbial activities and endothelial dysfunction. The role of the complex in pathologic inflammation is underscored by the finding that both GPR97 and mPR3 are upregulated on the surface of disease-associated neutrophils. In summary, we identify a PAR2 activation mechanism that directs neutrophil activation, and thus inflammation. The PR3/CD177/GPR97/PAR2/CD16b protein complex, therefore, represents a potential therapeutic target for neutrophil-mediated inflammatory diseases.