Research on the Histological Features and Pathological Types of Gastric Adenocarcinoma With Mucinous Differentiation

From guidelines to radiology practice: navigating the 2023 ASCO guidelines for advanced gastroesophageal cancer and beyond

The American Society of Clinical Oncology (ASCO) updated the guidelines for the treatment of advanced gastroesophageal (GE) cancer in 2023, signifying a major shift towards targeted therapeutics and precision medicine. This article serves as an imaging-based review of recent developments in the care of patients with GE cancer. We cover the epidemiology, the developing treatment paradigms, and the imaging assessment of GE malignancy. In addition, this review aims to familiarize radiologists with the unique adverse effects pertaining to therapeutics, surgeries, radiation therapies, and associated imaging corollaries. A case-based approach will be used to both explore the efficacy of modern treatments and demonstrate their adverse effects, such as chemotherapy-associated pneumonitis, radiation esophagitis, and anastomotic failure. With this comprehensive exploration of gastroesophageal cancer, radiologists will be equipped with the essential tools to inform the treatment decisions made by medical oncologists, radiation oncologists, and surgical oncologists in the new era of precision medicine.

Histogenetic insights and genetic landscape of fibromatosis-like undifferentiated gastric carcinoma: a focused study

BACKGROUND

The aim of this study was to elucidate the histogenesis and genetic underpinnings of fibromatosis-like undifferentiated gastric carcinoma (FLUGC), a rare pathological entity.

METHOD

Through a detailed analysis of seven cases, including histopathological evaluation, CTNNB1 gene mutation screening, human epidermal growth factor receptor 2 (HER2) protein level quantification, and HER2 gene amplification assessment to identify the pathological and molecular characteristics of FLUGC.

RESULTS

Of the seven patients in this study, five were male and two were female (age: 39-73 years). Four patients presented with lesions in the gastric antrum and three had lesions in the lateral curvature of the stomach. Histopathologically, over 90% of the tumor consisted of aggressive fibromatosis-like tissue, including proliferating spindle fibroblasts and myofibroblasts and varying amounts of collagenous fibrous tissues. Undifferentiated cancer cells, accounting for less than 10%, were dispersed among the aggressive fibromatosis-like tissues. These cells were characterized by their small size and were relatively sparse without glandular ducts or nested mass-like structures. Immunophenotyping results showed positive expression of CKpan, CDX2, villin, and p53 in undifferentiated cancer cells; positive expression of vimentin in aggressive fibromatosis-like tissue; positive cytoplasmic expression of β-catenin; and focal cytoplasmic positive expression of smooth muscle actin (SMA). Genetic analysis did not reveal any mutations in the CTNNB1 gene test, nor was there amplification in the HER2 gene fluorescence in situ hybridization (FISH) test. Additionally, the Epstein-Barr encoding region (EBER) of in situ hybridization was negative; and the mismatch repair (MMR) protein was positive. Programmed cell death-1 (PD-1) was < 1-5%; programmed cell death ligand 1 (PD-L1): TPS = 1-4%, CPS = 3-8.

CONCLUSION

The study highlights the significance of CTNNB1, HER2, EBER, and MMR as pivotal genetic markers in FLUGC, underscoring their relevance for diagnosis and clinical management. The rarity and distinct pathological features of FLUGC emphasize the importance of accurate diagnosis to prevent underdiagnosis or misdiagnosis and to raise awareness within the medical community.

Unveiling the secrets of gastrointestinal mucous adenocarcinoma survival after surgery with artificial intelligence: A population-based study

BACKGROUND

Research on gastrointestinal mucosal adenocarcinoma (GMA) is limited and controversial, and there is no reference tool for predicting postoperative survival.

AIM

To investigate the prognosis of GMA and develop predictive model.

METHODS

From the Surveillance, Epidemiology, and End Results database, we collected clinical information on patients with GMA. After random sampling, the patients were divided into the discovery (70% of the total, for model training), validation (20%, for model evaluation), and completely blind test cohorts (10%, for further model evaluation). The main assessment metric was the area under the receiver operating characteristic curve (AUC). All collected clinical features were used for Cox proportional hazard regression analysis to determine factors influencing GMA's prognosis.

RESULTS

This model had an AUC of 0.7433 [95% confidence intervals (95%CI): 0.7424-0.7442] in the discovery cohort, 0.7244 (GMA: 0.7234-0.7254) in the validation cohort, and 0.7388 (95%CI: 0.7378-0.7398) in the test cohort. We packaged it into Windows software for doctors' use and uploaded it. Mucinous gastric adenocarcinoma had the worst prognosis, and these were protective factors of GMA: Regional nodes examined [hazard ratio (HR): 0.98, 95%CI: 0.97-0.98, P < 0.001)] and chemotherapy (HR: 0.62, 95%CI: 0.58-0.66, P < 0.001).

CONCLUSION

The deep learning-based tool developed can accurately predict the overall survival of patients with GMA postoperatively. Combining surgery, chemotherapy, and adequate lymph node dissection during surgery can improve patient outcomes.

Unveiling the secrets of gastrointestinal mucous adenocarcinoma survival after surgery with artificial intelligence: A population-based study

BACKGROUND

Research on gastrointestinal mucosal adenocarcinoma (GMA) is limited and controversial, and there is no reference tool for predicting postoperative survival.

AIM

To investigate the prognosis of GMA and develop predictive model.

METHODS

From the Surveillance, Epidemiology, and End Results database, we collected clinical information on patients with GMA. After random sampling, the patients were divided into the discovery (70% of the total, for model training), validation (20%, for model evaluation), and completely blind test cohorts (10%, for further model evaluation). The main assessment metric was the area under the receiver operating characteristic curve (AUC). All collected clinical features were used for Cox proportional hazard regression analysis to determine factors influencing GMA’s prognosis.

RESULTS

This model had an AUC of 0.7433 [95% confidence intervals (95%CI): 0.7424-0.7442] in the discovery cohort, 0.7244 (GMA: 0.7234-0.7254) in the validation cohort, and 0.7388 (95%CI: 0.7378-0.7398) in the test cohort. We packaged it into Windows software for doctors’ use and uploaded it. Mucinous gastric adenocarcinoma had the worst prognosis, and these were protective factors of GMA: Regional nodes examined [hazard ratio (HR): 0.98, 95%CI: 0.97-0.98, P < 0.001)] and chemotherapy (HR: 0.62, 95%CI: 0.58-0.66, P < 0.001).

CONCLUSION

The deep learning-based tool developed can accurately predict the overall survival of patients with GMA postoperatively. Combining surgery, chemotherapy, and adequate lymph node dissection during surgery can improve patient outcomes.

Prognostic value of lymph node density on cancer staging system for gastric cancer without distal metastasis: a population-based analysis of SEER database

Background

Accurate tumor staging is the cornerstone of tumor treatment. Current tumor staging system for gastric cancer (GC) is based on regional positive lymph nodes while ignoring the total number of examined lymph nodes. We aim to assess the prognostic value of lymph node density (LND), the ratio of positive nodes to the total number examined nodes, in GC without distal metastasis.

Methods

Clinical information of patients with histologically confirmed GC and without distal metastasis was identified from the Surveillance, Epidemiology, and End Results (SEER) database between 2010 and 2015. The X-Tile software was used to identify the ideal prognosis-related cutoff point for LND. The prognostic value of LND on cancer-specific survival (CSS) and overall survival (OS) was assessed in Cox regression models. Subgroup analysis stratified by LND was performed on current lymph node staging system to further explore the interaction between LND and current lymph node staging system.

Results

A total of 4281 participants were identified from the SEER database for the final analysis. The optimal prognosis-related cutoff values of LND were calculated as 0.1 and 0.4, and LND was divided into three levels: LND1 (< 0.1), LND2 (> = 0.1, < 0.4), and LND3 (> = 0.4). LND3 was associated with worse CSS and OS in GC patients. Compared to patients with LND1, those with LND2 and LND3 had 2.43 (HR = 2.43, 95% CI 2.09–2.84, P < 0.001) and 4.69 (HR = 4.69, 95% CI 4.02–5.48, P < 0.001) folds increase in mortality in CSS, respectively. Similar results were found in the evaluation of OS in GC patients. Subgroup analysis stratified by LND also found that patients in the same current lymph node stage still had different prognosis due to the different LND levels after adjustment for other prognosis-related covariates (all P values < 0.001).

Conclusion

LND is an independent prognostic factor for GC without distal metastasis. In the current lymph node staging system, LND has potential value in further accurately classifying GC patients without distal metastasis.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02795-9.