Systemic amyloidosis is characterized by the accumulation of insoluble proteins in tissues including heart, kidney, liver and any other organs. Light chain amyloidosis is the most common type of primary amyloidosis. And it is generally considered to be the plasma cell dysfunction. Given its pathogenesis, it may affect any organ system. Thus, clinical presentation is variable and delayed diagnosis is common. Given these diagnostic difficulties, we presented a systemic amyloidosis presented as liver dysfunction.

The first edition of ‘A Standardized Pathology Report for Gastric Cancer’ was initiated by the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists and published 17 years ago. Since then, significant advances have been made in the pathologic diagnosis, molecular genetics, and management of gastric cancer (GC). To reflect those changes, a committee for publishing a second edition of the report was formed within the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists. This second edition consists of two parts: standard data elements and conditional data elements.The standard data elements contain the basic pathologic findings and items necessary to predict the prognosis of GC patients, and they are adequate for routine surgical pathology service. Other diagnostic and prognostic factors relevant to adjuvant therapy, including molecular biomarkers, are classified as conditional data elements to allow each pathologist to selectively choose items appropriate to the environment in their institution. We trust that the standardized pathology report will be helpful for GC diagnosis and facilitate large-scale multidisciplinary collaborative studies.

The first edition of ‘A Standardized Pathology Report for Gastric Cancer’ was initiated by the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists and published 17 years ago. Since then, significant advances have been made in the pathologic diagnosis, molecular genetics, and management of gastric cancer (GC). To reflect those changes, a committee for publishing a second edition of the report was formed within the Gastrointestinal Pathology Study Group of the Korean Society of Pathologists. This second edition consists of two parts: standard data elements and conditional data elements. The standard data elements contain the basic pathologic findings and items necessary to predict the prognosis of GC patients, and they are adequate for routine surgical pathology service. Other diagnostic and prognostic factors relevant to adjuvant therapy, including molecular biomarkers, are classified as conditional data elements to allow each pathologist to selectively choose items appropriate to the environment in their institution. We trust that the standardized pathology report will be helpful for GC diagnosis and facilitate large-scale multidisciplinary collaborative studies.

Autoimmune hepatitis (AIH) is a chronic, autoimmune disease of the liver that occurs when the body’s immune system attacks liver cells, causing the liver to be inflamed. AIH is one of the manifestations of a coronavirus disease 2019 (COVID-19), as well as an adverse event occurring after vaccination against severe acute respiratory syndrome coronavirus 2 (SARSCoV-2). Few cases of AIH have been described after vaccination with two messenger RNA (mRNA)-based vaccines—BTN162b2 (Pfizer-BioNTech) and mRNA-1273 (Moderna)—against SARS-CoV-2. Herein, we report a case of AIH occurring after Pfizer-BioNTech COVID-19 vaccine. A 27-year-old female presented with jaundice and hepatomegaly, appearing 14 days after receiving the second dose of Pfizer-BioNTech vaccine. Her laboratory results showed abnormal liver function with high total immunoglobulin G level. She was diagnosed with AIH with histologic finding and successfully treated with oral prednisolone. We report an AIH case after COVID-19 vaccination in Korea.

Although the understanding of appendiceal mucinous neoplasms (AMNs) and their relationship with disseminated peritoneal mucinous disease have advanced, the diagnosis, classification, and treatment of AMNs are still confusing for pathologists and clinicians. The Gastrointestinal Pathology Study Group of the Korean Society of Pathologists (GPSG-KSP) proposed a multicenter study and held a workshop for the “Standardization of the Pathologic Diagnosis of the Appendiceal Mucinous Neoplasm” to overcome the controversy and potential conflicts. The present article is focused on the diagnostic criteria, terminologies, tumor grading, pathologic staging, biologic behavior, treatment, and prognosis of AMNs and disseminated peritoneal mucinous disease. In addition, GPSG-KSP proposes a checklist of standard data elements of appendiceal epithelial neoplasms to standardize pathologic diagnosis. We hope the present article will provide pathologists with updated knowledge on how to handle and diagnose AMNs and disseminated peritoneal mucinous disease.

Although the understanding of appendiceal mucinous neoplasms (AMNs) and their relationship with disseminated peritoneal mucinous disease have advanced, the diagnosis, classification, and treatment of AMNs are still confusing for pathologists and clinicians. The Gastrointestinal Pathology Study Group of the Korean Society of Pathologists (GPSG-KSP) proposed a multicenter study and held a workshop for the “Standardization of the Pathologic Diagnosis of the Appendiceal Mucinous Neoplasm” to overcome the controversy and potential conflicts. The present article is focused on the diagnostic criteria, terminologies, tumor grading, pathologic staging, biologic behavior, treatment, and prognosis of AMNs and disseminated peritoneal mucinous disease. In addition, GPSG-KSP proposes a checklist of standard data elements of appendiceal epithelial neoplasms to standardize pathologic diagnosis. We hope the present article will provide pathologists with updated knowledge on how to handle and diagnose AMNs and disseminated peritoneal mucinous disease.

The first edition of the ‘Standardized Pathology Report for Colorectal Cancer,’ which was developed by the Gastrointestinal Pathology Study Group (GIP) of the Korean Society of Pathologists, was published 13 years ago. Meanwhile, there have been many changes in the pathologic diagnosis of colorectal cancer (CRC), pathologic findings included in the pathology report, and immunohistochemical and molecular pathology required for the diagnosis and treatment of colorectal cancer. In order to reflect these changes, we (GIP) decided to make the second edition of the report. The purpose of this standardized pathology report is to provide a practical protocol for Korean pathologists, which could help diagnose and treat CRC patients. This report consists of “standard data elements” and “conditional data elements.” Basic pathologic findings and parts necessary for prognostication of CRC patients are classified as “standard data elements,” while other prognostic factors and factors related to adjuvant therapy are classified as “conditional data elements” so that each institution could select the contents according to the characteristics of the institution. The Korean version is also provided separately so that Korean pathologists can easily understand and use this report. We hope that this report will be helpful in the daily practice of CRC diagnosis.

Background: Liver biopsy is the essential method to diagnose non-alcoholic steatohepatitis (NASH), but histological features of NASH are too subjective to achieve reproducible diagnoses in early stages of disease. We aimed to identify the key histological features of NASH and devise a scoring model for diagnosis. Methods: Thirteen pathologists blindly assessed 12 histological factors and final histological diagnoses (‘not-NASH,’ ‘borderline,’ and ‘NASH’) of 31 liver biopsies that were diagnosed as non-alcoholic fatty liver disease (NAFLD) or NASH before and after consensus. The main histological parameters to diagnose NASH were selected based on histological diagnoses and the diagnostic accuracy and agreement of 12 scoring models were compared for final diagnosis and the NAFLD Activity Score (NAS) system. Results: Inter-observer agreement of final diagnosis was fair (κ = 0.25) before consensus and slightly improved after consensus (κ = 0.33). Steatosis at more than 5% was the essential parameter for diagnosis. Major diagnostic factors for diagnosis were fibrosis except 1C grade and presence of ballooned cells. Minor diagnostic factors were lobular inflammation ( ≥ 2 foci/ × 200 field), microgranuloma, and glycogenated nuclei. All 12 models showed higher inter-observer agreement rates than NAS and post-consensus diagnosis (κ = 0.52–0.69 vs. 0.33). Considering the reproducibility of factors and practicability of the model, summation of the scores of major (× 2) and minor factors may be used for the practical diagnosis of NASH. Conclusions A scoring system for the diagnosis of NAFLD would be helpful as guidelines for pathologists and clinicians by improving the reproducibility of histological diagnosis of NAFLD.

Background: Immunohistochemistry (IHC) has played an essential role in the diagnosis of hematolymphoid neoplasms. However, IHC interpretations can be challenging in daily practice, and exponentially expanding volumes of IHC data are making the task increasingly difficult. We therefore developed a machine-learning expert-supporting system for diagnosing lymphoid neoplasms. Methods: A probabilistic decision-tree algorithm based on the Bayesian theorem was used to develop mobile application software for iOS and Android platforms. We tested the software with real data from 602 training and 392 validation cases of lymphoid neoplasms and compared the precision hit rates between the training and validation datasets. Results: IHC expression data for 150 lymphoid neoplasms and 584 antibodies was gathered. The precision hit rates of 94.7% in the training data and 95.7% in the validation data for lymphomas were not statistically significant. Results in most B-cell lymphomas were excellent, and generally equivalent performance was seen in T-cell lymphomas. The primary reasons for lack of precision were atypical IHC profiles for certain cases (e.g., CD15-negative Hodgkin lymphoma), a lack of disease-specific markers, and overlapping IHC profiles of similar diseases. Conclusions Application of the machine-learning algorithm to diagnosis precision produced acceptable hit rates in training and validation datasets. Because of the lack of origin- or disease- specific markers in differential diagnosis, contextual information such as clinical and histological features should be taken into account to make proper use of this system in the pathologic decision-making process.

Purpose: The most recent 2017 World Health Organization (WHO) classification of pancreatic neuroendocrine neoplasms (PanNENs) has refined the three-tiered 2010 scheme by separating grade 3 pancreatic neuroendocrine tumors (G3 PanNETs) from poorly differentiated pancreatic neuroendocrine carcinomas (PanNECs). However, differentiating between G3 Pan- NETs and PanNECs is difficult in clinical practice. Materials and Methods: Eighty-two surgically resected PanNENs were collected from 16 institutions and reclassified according to the 2017 WHO classification based on the histological features and proliferation index (mitosis and Ki-67). Immunohistochemical stains for ATRX, DAXX, retinoblastoma, p53, Smad4, p16, and MUC1 were performed for 15 high-grade PanNENs. Results: Re-classification resulted in 20 G1 PanNETs (24%), 47 G2 PanNETs (57%), eight G3 well-differentiated PanNETs (10%), and seven poorly differentiated PanNECs (9%). PanNECs showed more frequent diffuse nuclear atypia, solid growth patterns and apoptosis, less frequent organoid growth and regular vascular patterns, and absence of low-grade PanNET components than PanNETs. The Ki-67 index was significantly higher in PanNEC (58.2%± 15.1%) compared to G3 PanNET (22.6%±6.1%, p < 0.001). Abnormal expression of any two of p53, p16, MUC1, and Smad4 could discriminate PanNECs from G3 PanNETs with 100% specificity and 87.5% sensitivity. Conclusion Histological features supporting the diagnosis of PanNECs over G3 PanNETs were the absence of a low-grade PanNET component in the tumor, the presence of diffuse marked nuclear atypia, solid growth pattern, frequent apoptosis and markedly increased proliferative activity with homogeneous Ki-67 labeling. Immunohistochemical stains for p53, p16, MUC1, and Smad4 may be helpful in distinguishing PanNECs from G3 PanNETs in histologically ambiguous cases, especially in diagnostic practice when only small biopsied tissues are available.