Interpretable deep learning model to predict lymph node metastasis in early gastric cancer using whole slide images.

In early gastric cancer (EGC), the presence of lymph node metastasis (LNM) is a crucial factor for determining the treatment options. Endoscopic resection is used for treatment of EGC with minimal risk of LNM. However, owing to the lack of definitive criteria for identifying patients who require additional surgery, some patients undergo unnecessary additional surgery. Considering that histopathologic patterns are significant factor for predicting lymph node metastasis in gastric cancer, we aimed to develop a machine learning algorithm which can predict LNM status using hematoxylin and eosin (H&E)-stained images. The images were obtained from several institutions. Our pipeline comprised two sequential approaches including a feature extractor and a risk classifier. For the feature extractor, a segmentation network (DeepLabV3+) was trained on 243 WSIs across three datasets to differentiate each histological subtype. The risk classifier was trained with XGBoost using 70 morphological features inferred from the trained feature extractor. The trained segmentation network, the feature extractor, achieved high performance, with pixel accuracies of 0.9348 and 0.8939 for the internal and external datasets in patch level, respectively. The risk classifier achieved an overall AUC of 0.75 in predicting LNM status. Remarkably, one of the datasets also showed a promising result with an AUC of 0.92. This is the first multi-institution study to develop machine learning algorithm for predicting LNM status in patients with EGC using H&E-stained histopathology images. Our findings have the potential to improve the selection of patients who require surgery among those with EGC showing high-risk histological features.

Prognostic Significance of EGFR, HER2, and c-Met Overexpression in Surgically Treated Patients with Adenocarcinoma of the Ampulla of Vater.

Adenocarcinoma of the ampulla of Vater (AAC) is a rare malignancy with heterogeneous tumors arising from various histologic subtypes, necessitating new therapeutic strategies. This study examines epidermal growth factor receptor (EGFR), human epidermal growth factor receptor 2 (HER2), and c-Met expression in AAC, given their potential as druggable targets. Among 87 patients who underwent curative resection, EGFR overexpression was found in 87.4%, HER2 in 11.5%, and c-Met in 50%. EGFR overexpression was more common in the pancreatobiliary subtype (p = 0.018) and associated with a higher histologic grade (p = 0.008). HER2 did not correlate with clinicopathological features, while c-Met was more common in node-negative groups (p = 0.004) and often co-expressed with EGFR (p = 0.049). EGFR-positive patients had worse disease-free (HR = 2.89; 95% CI, 1.35-6.20; p = 0.061) and overall survival (HR = 6.89; 95% CI, 2.94-16.2; p = 0.026) than EGFR-negative patients. HER2-positive AAC showed a trend towards shorter survival, although not statistically significant, and c-Met had no impact on survival outcomes. In the context of systemic disease, survival outcomes did not vary according to EGFR, HER2, and c-Met expression, but the HER2-positive group showed a trend towards inferior progression-free survival (HR = 1.90; 95% CI, 0.56-6.41; p = 0.166). This study underscores the potential of EGFR, HER2, and c-Met as targets for personalized therapy in AAC, warranting further research to evaluate targeted treatments.

Consistent efficacy of hepatic artery infusion chemotherapy irrespective of PD­L1 positivity in unresectable hepatocellular carcinoma.

Atezolizumab/bevacizumab is the first line of treatment for unresectable hepatocellular carcinoma (HCC), combining immune checkpoint inhibitor and anti-VEGF monoclonal antibodies. Hepatic arterial infusion chemotherapy (HAIC) is administered when the above-described combination fails to confer sufficient clinical benefit. The present study aimed to explore the association between tumor programmed cell death-ligand 1 (PD-L1) positivity and HAIC response. A total of 40 patients with HCC who had undergone HAIC with available biopsy samples obtained between January 2020 and May 2023 were retrospectively enrolled. Tumor response, progression-free survival (PFS), disease control rate (DCR) and overall survival (OS) were evaluated. PD-L1 expression in tumor samples was assessed using a combined positivity score. The response rates of HAIC-treated patients with advanced HCC after failure of atezolizumab/bevacizumab combination therapy were recorded. OS (P=0.9717) and PFS (P=0.4194) did not differ between patients with and without PD-L1 positivity. The objective response rate (P=0.7830) and DCR (P=0.7020) also did not differ based on PD-L1 status. In conclusion, the current findings highlight the consistent efficacy of HAIC, regardless of PD-L1 positivity.

Molecular Classification of Breast Cancer Using Weakly Supervised Learning.

Purpose: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era in which weakly supervised learning leveraging whole-slide images has gained prominence in developing deep learning models because this approach alleviates the need for extensive manual annotation. Weakly supervised learning was employed to classify the molecular subtypes of breast cancer. Methods: Our approach capitalizes on two whole-slide image datasets: one consisting of breast cancer cases from the Korea University Guro Hospital (KG) and the other originating from The Cancer Genomic Atlas dataset (TCGA). Furthermore, we visualized the inferred results using an attention-based heat map and reviewed the histomorphological features of the most attentive patches. Results: The KG+TCGA-trained model achieved an area under the receiver operating characteristics value of 0.749. An inherent challenge lies in the imbalance among subtypes. Additionally, discrepancies between the two datasets resulted in different molecular subtype proportions. To mitigate this imbalance, we merged the two datasets, and the resulting model exhibited improved performance. The attentive patches correlated well with widely recognized histomorphologic features. The triple-negative subtype has a high incidence of high-grade nuclei, tumor necrosis, and intratumoral tumor-infiltrating lymphocytes. The luminal A subtype showed a high incidence of collagen fibers. Conclusions: The artificial intelligence (AI) model based on weakly supervised learning showed promising performance. A review of the most attentive patches provided insights into the predictions of the AI model. AI models can become invaluable screening tools that reduce costs and workloads in practice.

Colon cancer: the 2023 Korean clinical practice guidelines for diagnosis and treatment.

Colorectal cancer is the third most common cancer in Korea and the third leading cause of death from cancer. Treatment outcomes for colon cancer are steadily improving due to national health screening programs with advances in diagnostic methods, surgical techniques, and therapeutic agents.. The Korea Colon Cancer Multidisciplinary (KCCM) Committee intends to provide professionals who treat colon cancer with the most up-to-date, evidence-based practice guidelines to improve outcomes and help them make decisions that reflect their patients' values and preferences. These guidelines have been established by consensus reached by the KCCM Guideline Committee based on a systematic literature review and evidence synthesis and by considering the national health insurance system in real clinical practice settings. Each recommendation is presented with a recommendation strength and level of evidence based on the consensus of the committee.

Interpretation of PD-L1 expression in gastric cancer: summary of a consensus meeting of Korean gastrointestinal pathologists.

Nivolumab plus chemotherapy in the first-line setting has demonstrated clinical efficacy in patients with human epidermal growth factor receptor 2-negative advanced or metastatic gastric cancer, and is currently indicated as a standard treatment. Programmed death-ligand 1 (PD-L1) expression is an important biomarker for predicting response to anti-programmed death 1/PD-L1 agents in several solid tumors, including gastric cancer. In the CheckMate-649 trial, significant clinical improvements were observed in patients with PD-L1 combined positive score (CPS) ≥ 5, determined using the 28-8 pharmDx assay. Accordingly, an accurate interpretation of PD-L1 CPS, especially at a cutoff of 5, is important. The CPS method evaluates both immune and tumor cells and provides a comprehensive assessment of PD-L1 expression in the tumor microenvironment of gastric cancer. However, CPS evaluation has several limitations, one of which is poor interobserver concordance among pathologists. Despite these limitations, clinical indications relying on PD-L1 CPS are increasing. In response, Korean gastrointestinal pathologists held a consensus meeting for the interpretation of PD-L1 CPS in gastric cancer. Eleven pathologists reviewed 20 PD-L1 slides with a CPS cutoff close to 5, stained with the 28-8 pharmDx assay, and determined the consensus scores. The issues observed in discrepant cases were discussed. In this review, we present cases of gastric cancer with consensus PD-L1 CPS. In addition, we briefly touch upon current practices and clinical issues associated with assays used for the assessment of PD-L1 expression in gastric cancer.

Assessing risk stratification in long-term outcomes of rectal neuroendocrine tumors following endoscopic resection: a multicenter retrospective study.

OBJECTIVES: While endoscopic resection of rectal neuroendocrine tumors (NETs) has significantly increased, long-term data on risk factors for recurrence are still lacking. Our aim is to analyze the long-term outcomes of patients with rectal NETs after endoscopic resection through risk stratification. METHODS: In this multicenter retrospective study, we included patients who underwent endoscopic resection of rectal NETs from 2009 to 2018 and were followed for ≥12 months at five university hospitals. We classified the patients into three risk groups according to the clinicopathological status of the rectal neuroendocrine tumors: low, indeterminate, and high. The high-risk group was defined if the tumors have any of the followings: size ≥ 10 mm, lymphovascular invasion, muscularis propria or deeper invasion, positive resection margins, or mitotic count ≥2/10. RESULTS: A total of 346 patients were included, with 144 (41.6%), 121 (35.0%), and 81 (23.4%) classified into the low-, indeterminate-, and high-risk groups, respectively. Among the high-risk group, seven patients (8.6%) received salvage treatment 28 (27-67) days after the initial endoscopic resection, with no reported extracolonic recurrence. Throughout the follow-up period, 1.1% (4/346) of patients experienced extracolonic recurrences at 56.5 (54-73) months after the initial endoscopic resection. Three of these patients (75%) were in the high-risk group and did not undergo salvage treatment. The risk of extracolonic recurrence was significantly higher in the high-risk group compared to the other groups (p = 0.039). CONCLUSION: Physicians should be concerned about the possibility of metastasis during long-term follow-up of high-risk patients and consider salvage treatment.

Accuracy of preoperative T2 gallbladder tumor localization and the adequate surgical resection.

INTRODUCTION: The agreement between the radiologic and histopathologic tumor locations in T2 gallbladder cancer is critical. There is no consensus regarding the extent of curative resection by tumor locations. METHODS: Between January 2010 and December 2019, a consecutive series of 118 patients with pathological T2 gallbladder cancer who underwent surgery were retrospectively analyzed in terms of the accordance between radiologic and histopathologic tumor locations, the extents of hepatic resection and the numbers of harvested lymph nodes. Radical resection was defined as liver resection with harvesting of at least four lymph nodes. RESULTS: The accuracy of preoperative tumor localization was only 68%. After radical resection, the 5-year overall survival (OS) was 59.4%; after nonradical resection, the figure was 46.1% (p = 0.092). In subanalyses, the 5-year OS was marginally better for patients who underwent liver resection or from whom at least four lymph nodes were harvested than those who did not undergo liver resection or from whom three or fewer lymph nodes were harvested (58.2% vs. 39.4%, p = 0.072; 59.9% vs. 50.0%, p = 0.072, respectively). In patients with peritoneal side tumor, the 5-year OSs of those who did and did not undergo liver resection were 67% and 41.2%, respectively (p = 0.028). In multivariate analysis, perineural invasion and radical resection were independently prognostic of OS. CONCLUSION: The accuracy of preoperative tumor localization was 68%. Hepatic resection, lymph node dissection harvesting of at least four lymph nodes are required for curative resection for gallbladder cancer, regardless of tumor location.

Deep Learning for the Pathologic Diagnosis of Hepatocellular Carcinoma, Cholangiocarcinoma, and Metastatic Colorectal Cancer.

Diagnosing primary liver cancers, particularly hepatocellular carcinoma (HCC) and cholangiocarcinoma (CC), is a challenging and labor-intensive process, even for experts, and secondary liver cancers further complicate the diagnosis. Artificial intelligence (AI) offers promising solutions to these diagnostic challenges by facilitating the histopathological classification of tumors using digital whole slide images (WSIs). This study aimed to develop a deep learning model for distinguishing HCC, CC, and metastatic colorectal cancer (mCRC) using histopathological images and to discuss its clinical implications. The WSIs from HCC, CC, and mCRC were used to train the classifiers. For normal/tumor classification, the areas under the curve (AUCs) were 0.989, 0.988, and 0.991 for HCC, CC, and mCRC, respectively. Using proper tumor tissues, the HCC/other cancer type classifier was trained to effectively distinguish HCC from CC and mCRC, with a concatenated AUC of 0.998. Subsequently, the CC/mCRC classifier differentiated CC from mCRC with a concatenated AUC of 0.995. However, testing on an external dataset revealed that the HCC/other cancer type classifier underperformed with an AUC of 0.745. After combining the original training datasets with external datasets and retraining, the classification drastically improved, all achieving AUCs of 1.000. Although these results are promising and offer crucial insights into liver cancer, further research is required for model refinement and validation.

Large gastric hamartomatous inverted polyp accompanied by advanced gastric cancer: A case report.

BACKGROUND: Gastric hamartomatous inverted polyps (GHIPs) are benign polyps of the gastric submucosal layer. Currently there are 52 reported cases in the English literature. According to a literature review, approximately 27% of GHIPs show a coexisting carcinoma. CASE SUMMARY: A 66-year-old man was referred to our institution with ulcerative lesions detected on esophagogastroduodenoscopy (EGD) during a regular check-up. Other medical findings were nonspecific. The lesions had borderline histologic features that could not exclude malignancy and were followed up with three EGDs and biopsies at intervals of 3 mo. The latest biopsy was revealed as an adenocarcinoma. A total gastrectomy was performed to remove the tumor. The surgical specimen revealed a 6.9 cm × 4.5 cm sized GHIP with a coexisting 1.6 cm sized well-differentiated adenocarcinoma which extended to the muscularis propria. The malignancy did not originate from the GHIP but showed an overlap. CONCLUSION: A large GHIP, which was unusually presented as an ulcerative lesion, was surgically removed, and was accompanied by advanced gastric cancer. Regular follow-up and thorough examinations of ulcerative lesions with equivocal biopsy have resulted in appropriate diagnosis and treatment. Therefore, aggressive intervention may be beneficial if GHIP is suspected.

Histologic subtype-based evaluation of recurrence and survival outcomes in patients with adenocarcinoma of the ampulla of Vater.

Patients with ampulla of Vater adenocarcinoma exhibit diverse outcomes, likely since these malignancies can originate from any of the three converging epithelia at this site. Such variability presents difficulties in clinical decision-making processes and in devising therapeutic approaches. In this study, the potential clinical value of histomolecular phenotypes was determined by integrating histopathological analysis with protein expression (MUC1, CDX2, CK20, and MUC2), in a cohort of 87 patients diagnosed with stage IB to III ampulla of Vater adenocarcinoma who underwent curative surgical resection. Of the 87 patients, 54 were classified as pancreato-biliary (PB) subtype and 33 as intestinal subtype. The median follow-up time for all patients was 32.8 months (95% CI, 25.3-49.2). Patients with a histomolecular PB phenotype (CDX2 negative, MUC1 positive, MUC2 negative, and irrespective of the CK20 results) were associated with poor prognostic outcomes in both disease-free survival (DFS) (HR = 1.81; 95% CI, 1.04-3.17; p = 0.054) and overall survival (OS) (HR = 2.01; 95% CI, 1.11-3.66; p = 0.039) compared to those with histomolecular intestinal carcinomas. Patients with the PB subtype were more likely to have local recurrence alone (11 of 37, 29.7%) compared to those with the intestinal subtype (1 of 15, 6.7%). In the context of systemic disease, a notably greater proportion of patients exhibiting elevated carbohydrate antigen 19-9 levels were observed in the PB subtype compared to the intestinal subtype (p = 0.024). In the cohort of 38 patients who received first-line palliative chemotherapy, a diminished median overall survival (OS) was observed in the PB group compared to the intestinal group (10.3 vs. 28.3 months, HR = 2.47; 95% CI, 1.23-4.95; p = 0.025). By integrating histopathologic and molecular criteria, we can identify distinct and clinically relevant histomolecular phenotypes in adenocarcinomas of the ampulla of Vater, which could have considerable impact on existing therapeutic approaches.

Unravelling interobserver variability in gastrointestinal glandular neoplasia: a contemporary study of US and Korean pathologists.

AIMS: Interobserver variability in the assessment of gastric neoplasia biopsies between most Western and Eastern (predominantly represented by Japanese in the literature) pathologists has been documented. It is unknown if such variability exists between the US and Korean pathologists in the current era. METHODS: Ten gastrointestinal (GI) pathologists from the USA (n=5) and South Korea (n=5) evaluated 100 scanned images of gastric (n=50) and colorectal (n=50) neoplasia biopsies and answered multiple questionnaires. Consensus was defined as the answer chosen by the majority. Cohen's (κc) and Fleiss' kappa (κf) values were calculated between the consensus of the two groups and among the raters, respectively. RESULTS: Both groups reached a consensus in the majority of cases (74%-100%) with slight to perfect intergroup (κc=0.049-1.000) and no to substantial intragroup (κf=-0.083 to 0.660) agreements. For gastric neoplasia, Korean pathologists relied heavily on cytoarchitectural atypia, whereas the US pathologists focused on stromal invasion when diagnosing adenocarcinoma. For colorectal neoplasia, the Korean pathologists identified concurrent intramucosal carcinoma when diagnosing invasive adenocarcinoma, while the presence of desmoplasia was a prerequisite for the diagnosis of invasive adenocarcinoma for the US pathologists. CONCLUSIONS: For GI neoplasia biopsy interpretation, the diagnostic approach of Korean pathologists is similar to that of Eastern/Japanese pathologists. Consensus outperformed kappa statistics in capturing the magnitude of inter-rater and intergroup reliability, highlighting the potential benefit of consensus meetings to decrease the gap between Western and Eastern diagnostic approaches.

Single-center study on clinicopathological and typical molecular pathologic features of metastatic brain tumor.

BACKGROUND: The metastatic brain tumor is the most common brain tumor. The aim of this study was to demonstrate the clinicopathological and molecular pathologic features of brain metastases (BM). METHODS: A total of 269 patients were diagnosed with BM through surgical resection at Seoul St. Mary's Hospital from January 2010 to March 2020. We reviewed the clinicopathological features and molecular status of primary and metastatic brain tissues using immunohistochemistry and molecular pathology results. RESULTS: Among 269 patients, 139 males and 130 females were included. The median age of primary tumor was 58 years (range, 13 to 87 years) and 86 patients (32.0%) had BM at initial presentation. Median BM free interval was 28.0 months (range, 1 to 286 months). The most frequent primary site was lung 46.5% (125/269), and followed by breast 15.6% (42/269), colorectum 10.0% (27/269). Epidermal growth factor receptor (EGFR) mutation was found in 50.8% (32/63) and 58.0% (40/69) of lung primary and BM, respectively. In both breast primary and breast cancer with BM, luminal B was the most frequent subtype at 37.9% (11/29) and 42.9% (18/42), respectively, followed by human epidermal growth factor receptor 2 with 31.0% (9/29) and 33.3% (14/42). Triple-negative was 20.7% (6/29) and 16.7% (7/42), and luminal A was 10.3% (3/29) and 7.1% (3/42) of breast primary and BM, respectively. In colorectal primary and colorectal cancer with BM, KRAS mutation was found in 76.9% (10/13) and 66.7% (2/3), respectively. CONCLUSIONS: We report the clinicopathological and molecular pathologic features of BM that can provide useful information for understanding the pathogenesis of metastasis and for clinical trials based on the tumor's molecular pathology.

Erratum: Korean Practice Guidelines for Gastric Cancer 2022: An Evidence-based, Multidisciplinary Approach.

This corrects the article on p. 3 in vol. 23, PMID: 36750993.

Cancer-specific functional profiling in microsatellite-unstable (MSI) colon and endometrial cancers using combined differentially expressed genes and biclustering analysis.

Microsatellite-unstable (MSI) cancers have distinct genetic and clinical features from microsatellite-stable cancers, but the molecular functional differences between MSI cancers originating from different tissues or organs have not been well studied because the application of usual differentially expressed gene (DEG) analysis is error-prone, producing too many noncancer-specific normally functioning genes. To maximize therapeutic efficacy, biomarkers reflecting cancer-specific differences between MSI cancers of different tissue origins should be identified. To identify functional differences between MSI colon and endometrial cancers, we combined DEG analysis and biclustering instead of DEG analysis alone and refined functionally relevant biclusters reflecting genuine functional differences between the 2 tumors. Specifically, using The Cancer Genome Atlas and genome-tissue expression as data sources, gene ontology (GO) enrichment tests were performed after routinely identifying DEGs between the 2 tumors with the exclusion of DEGs identified in their normal counterparts. Cancer-specific biclusters and associated enriched GO terms were obtained by biclustering with enrichment tests for the preferences for cancer type (either colon or endometrium) and GO enrichment tests for each cancer-specific bicluster, respectively. A novel childness score was developed to select functionally relevant biclusters among cancer-specific biclusters based on the extent to which the enriched GO terms of the biclusters tended to be child terms of the enriched GO terms in DEGs. The selected biclusters were tested using survival analysis to validate their clinical significance. We performed multiple sequential analyses to produce functionally relevant biclusters from the RNA sequencing data of MSI colon and endometrial cancer samples and their normal counterparts. We identified 3066 cancer-specific DEGs. Biclustering analysis revealed 153 biclusters and 41 cancer-specific biclusters were selected using Fisher exact test. A mean childness score over 0.6 was applied as the threshold and yielded 8 functionally relevant biclusters from cancer-specific biclusters. Functional differences appear to include gland cavitation and the TGF-ß receptor, G protein, and cytokine pathways. In the survival analysis, 6 of the 8 functionally relevant biclusters were statistically significant. By attenuating noise and applying a synergistic contribution of DEG results, we refined candidate biomarkers to complement tissue-specific features of MSI tumors.

Prognostic value of soluble PD-L1 and exosomal PD-L1 in advanced gastric cancer patients receiving systemic chemotherapy.

The prognostic role of soluble PD-L1 (sPD-L1) and exosomal PD-L1 (exoPD-L1) in patients with gastric cancer (GC) receiving systemic chemotherapy remains unelucidated. Thus, we examined their prognostic significance in patients with advanced GC. Blood samples were obtained from 99 patients with advanced GC receiving first-line chemotherapy. Serum-derived exosomes were isolated by centrifugation and polymer precipitation. The correlation between serum-derived exoPD-L1, plasma sPD-L1, immune-related markers, and circulating immune cells was evaluated. Patients were divided into two groups according to pretreatment sPD-L1 and exoPD-L1 levels: low sPD-L1 and high sPD-L1 groups, low exoPD-L1 and high exoPD-L1 groups. Patients with low sPD-L1 level before treatment (< 9.32 pg/mL) showed significantly better overall survival (OS) and progression-free survival (PFS) than those with high sPD-L1 level (≥ 9.32 pg/mL). The low exoPD-L1 group (< 10.21 pg/mL) showed a tendency of longer PFS than the high exoPD-L1 group (≥ 10.21 pg/mL). Pretreatment sPD-L1 was an independent prognostic factor for OS in multivariate analysis. exoPD-L1 was associated with systemic inflammation markers, immunomodulatory cytokines, and T cells, while sPD-L1 was associated with tumor markers. Pretreatment plasma-derived sPD-L1 level could be used as a prognostic marker for patients receiving cytotoxic chemotherapy. Serum-derived exoPD-L1 may reflect the immunosuppressive state of patients with advanced GC.

Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers.

PURPOSE: Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-consuming and not universally available. Artificial intelligence (AI) has shown the potential to determine a wide range of genetic mutations on histologic image analysis. Here, we assessed the status of mutation prediction AI models on histologic images by a systematic review. METHODS: A literature search using the MEDLINE, Embase and Cochrane databases was conducted in August 2021. The articles were shortlisted by titles and abstracts. After a full-text review, publication trends, study characteristic analysis and comparison of performance metrics were performed. RESULTS: Twenty-four studies were found mostly from developed countries, and their number is increasing. The major targets were gastrointestinal, genitourinary, gynecological, lung and head and neck cancers. Most studies used the Cancer Genome Atlas, with a few using an in-house dataset. The area under the curve of some of the cancer driver gene mutations in particular organs was satisfactory, such as 0.92 of BRAF in thyroid cancers and 0.79 of EGFR in lung cancers, whereas the average of all gene mutations was 0.64, which is still suboptimal. CONCLUSION: AI has the potential to predict gene mutations on histologic images with appropriate caution. Further validation with larger datasets is still required before AI models can be used in clinical practice to predict gene mutations.

A Standardized Pathology Report for Gastric Cancer: 2nd Edition.

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.