Noncirrhotic portal hypertension has historically been described using heterogeneous and region-specific terminology—such as idiopathic portal hypertension (IPH), noncirrhotic portal fibrosis (NCPF), obliterative portal venopathy, and nodular regenerative hyperplasia—leading to substantial variability in diagnosis, reporting, and international research collaboration. Differences in guideline definitions from major societies (AASLD, EASL, and APASL), together with the presence of characteristic histologic lesions in patients without clinically overt portal hypertension, have further complicated disease classification. To address these challenges, a large, multisociety, international initiative was convened to harmonize nomenclature and diagnostic criteria. Representatives from liver, pathology, and pediatric hepatology societies across the Americas, Europe, and Asia participated in a structured consensus process that included specialized working groups and external Delphi validation. The initiative produced a globally harmonized and implementable diagnostic framework. Consensus was reached that the terms porto-sinusoidal vascular disorder (PSVD) and NCPF may be used interchangeably when identical diagnostic criteria are applied, and that they should be written as PSVD or NCPF . The diagnosis was defined as fundamentally clinicopathological, requiring integrated assessment. Core principles include the need for a high-quality liver biopsy (≥10 mm), mandatory exclusion of cirrhosis, and systematic exclusion of specific alternative conditions. Importantly, the consensus recognizes that PSVD or NCPF may be diagnosed even without clinical portal hypertension and may coexist with other liver diseases, provided cirrhosis is excluded. Standardized major and minor histologic criteria were developed collaboratively by expert pathologists and externally validated. Features of portal hypertension were harmonized into specific and nonspecific categories applicable to routine clinical practice. An integrated diagnostic scoring system incorporating histology, clinical features, associated conditions, and concommitant etiologies was developed and validated using the Delphi method. This consensus provides the first internationally endorsed, unified framework for the diagnosis of PSVD or NCPF. Its global implementation is expected to reduce diagnostic variability, improve comparability across regions, and facilitate the development of robust, internationally harmonized clinical and translational research cohorts.
TREM2-Expressing Multinucleated Giant Macrophages Are a Biomarker of Good Prognosis in Head and Neck Squamous Cell Carcinoma
Abstract Patients with head and neck squamous cell carcinomas (HNSCC) often have poor outcomes due to suboptimal risk management and treatment strategies; yet integrating novel prognostic biomarkers into clinical practice is challenging. Here, we report the presence of multinucleated giant cells (MGC)—a type of macrophages—in tumors from patients with HNSCC, which are associated with a favorable prognosis in treatment-naive and preoperative chemotherapy–treated patients. Importantly, MGC density increased in tumors following preoperative therapy, suggesting a role of these cells in the antitumoral response. To enable clinical translation of MGC density as a prognostic marker, we developed a deep-learning model to automate its quantification on routinely stained pathological whole slide images. Finally, we used spatial transcriptomic and proteomic approaches to describe the MGC-related tumor microenvironment and observed an increase in central memory CD4 T cells. We defined an MGC-specific signature resembling to TREM2-expressing mononuclear tumor-associated macrophages, which colocalized in keratin tumor niches. Significance: Novel individual biomarkers are needed to guide therapeutic decisions for patients with head and neck cancer. We report for the first time, granulomas of TREM2-expressing multinucleated giant macrophages in keratin-rich tumor niches, as a biomarker of favorable prognosis and developed a deep-learning model to automate its quantification on routinely stained pathological slides.