Regional Lymph Node Dissection as an Additional Treatment Option to Endoscopic Resection for Expanded Indications in Gastric Cancer: a Prospective Cohort Study

Versatile and Tunable Performance of PVA/PAM Tridimensional Hydrogel Models for Tissues and Organs: Augmenting Realism in Advanced Surgical Training

The increasing complexity and difficulty of surgical procedures have led to a rise in medical errors within clinical settings in recent years. Gastrointestinal diseases, in particular, present significant medical challenges and impose substantial economic burdens, underscoring the urgent need for experiential, high-fidelity gastrointestinal surgical training tools. This study leverages patient-specific computed tomography (CT) and magnetic resonance imaging (MRI) data, combined with 3D printed manufacturing, to develop hydrogel organ models with tunable performance and tissue-mimicking softness. These properties are achieved by regulating the freeze-thaw cycles, cross-linking agents, and the concentration of incorporated antibacterial nanoparticles in DN hydrogels. Through the application of indirect 3D printing and the "sacrificial material method", we successfully fabricate organ tissues such as the stomach, intestines, and blood vessels with high precision. In ex vivo surgical training demonstrations, these tissue-like soft hydrogels provide an effective platform for preoperative simulation and surgical training in digestive surgery, accommodating various surgical procedures and accurately simulating intraoperative bleeding. The development of advanced bionic organ models with specific and tunable characteristics based on DN hydrogels is poised to significantly advance surgical training, medical device testing, and the reform of medical education.

Evaluation of the endoscopic cure criteria in patients undergoing surgery for early gastric cancer

BACKGROUND AND OBJECTIVES

Gastric cancer (GC) prognosis is influenced by the extent of the tumor, lymph node involvement (LNM), and metastasis. Endoscopic resection (ER) or gastrectomy with lymphadenectomy are standard treatments for early GC (EGC). This study evaluated LNM frequency according to eCura categories, clinicopathological characteristics, disease-free (DFS), and overall (OS) survival rates.

METHODS

We included EGC patients who underwent curative gastrectomy between 2009 and 2020 from our single-center database. Anatomopathological and clinical reports were reviewed to analyze eCura categories.

RESULTS

We included 160 EGC patients who underwent gastrectomy with eCura categories A, B, and C, comprising 26.3%, 13.8%, and 60%, respectively. Baseline clinical characteristics showed no intergroup disparities. LNM incidence for A, B, and C was 4.8%, 18.2%, and 19.8%. When evaluating the criteria for ER and its association with eCura categories, we found that 95.2% of eCura A and 100% of eCura B patients had classic or expanded criteria for ER. On the other hand, 97.9% of eCura C patients were referred to surgical resection. Multivariate analysis demonstrated that lymphatic (OR = 5.57, CI95% = 1.45-21.29, p = 0.012) and perineural (OR = 15.8, CI95% = 1.39-179.88, p = 0.026) invasions were associated with a higher risk of LNM. No significant differences in DFS or OS were found among eCura categories.

CONCLUSION

The eCura categories were associated with the occurrence of LNM. In most patients, those with classic and expanded indication criteria for ER were classified as eCura A and B.

A retrospective analysis based on multiple machine learning models to predict lymph node metastasis in early gastric cancer

Background

Endoscopic submucosal dissection has become the primary option of treatment for early gastric cancer. However, lymph node metastasis may lead to poor prognosis. We analyzed factors related to lymph node metastasis in EGC patients, and we developed a construction prediction model with machine learning using data from a retrospective series.

Methods

Two independent cohorts’ series were evaluated including 305 patients with EGC from China as cohort I and 35 patients from Spain as cohort II. Five classifiers obtained from machine learning were selected to establish a robust prediction model for lymph node metastasis in EGC.

Results

The clinical variables such as invasion depth, histologic type, ulceration, tumor location, tumor size, Lauren classification, and age were selected to establish the five prediction models: linear support vector classifier (Linear SVC), logistic regression model, extreme gradient boosting model (XGBoost), light gradient boosting machine model (LightGBM), and Gaussian process classification model. Interestingly, all prediction models of cohort I showed accuracy between 70 and 81%. Furthermore, the prediction models of the cohort II exhibited accuracy between 48 and 82%. The areas under curve (AUC) of the five models between cohort I and cohort II were between 0.736 and 0.830.

Conclusions

Our results support that the machine learning method could be used to predict lymph node metastasis in early gastric cancer and perhaps provide another evaluation method to choose the suited treatment for patients.

The First Systematic Gastroscopy Training Program for Surgeons in Korea

BACKGROUND

Endoscopic evaluation of the stomach is essential for preoperative planning and post-surgical surveillance for various diseases of the stomach, including malignancy. The gastroscopy education program for surgeons is currently in its infancy and is not systematically organized in Korea. This study aimed to introduce the first systematic gastroscopy education program for surgeons in Korea.

METHODS

The gastroscopy education program entitled "Gastroscopy School for Surgeons (GSS)" comprised of theoretical education, dry lab hands-on training, and clinical practice. All participants were beginners without any gastroscopy experience. Clinical practice started after the completion of the theoretical and dry lab training. The gastroscopy practices utilized simple luminal observation, biopsy, localization using clips or dye injection, and limited therapeutic gastroscopy. The educational performances and surveys from 33 participants were analyzed.

RESULTS

The participants consisted of surgical residents, general surgeons, gastrointestinal-specialized surgeons, and physicians. Participants performed a total of 2,272 gastroscopies, 2,008 of which were post-gastrectomy cases. Currently, of the 33 participants, 7 (21.2%) of the participants performed gastroscopy regularly, and 7 (21.2%) occasionally. According to the self-reported survey, one participant assessed their current gastroscopic technique to be at the expert level, and 25 (75.8%) at a proficient level. All participants considered gastroscopy education for surgeons to be necessary, and 28 (84.8%) stated that systematic education is not currently provided in Korea.

CONCLUSION

We introduced the first systematic gastroscopy education program for surgeons in Korea, namely the GSS, which is practical and meets clinical needs. More training centers are needed to expand gastroscopy training among Korean surgeons.