Impact of retrieved lymph node count on short-term complications in patients with gastric cancer

Indocyanine Green (ICG) in Robotic Gastrectomy: A Retrospective Review of Lymphadenectomy Outcomes for Gastric Cancer

Radical gastrectomy is essential for gastric cancer treatment. While guidelines advise dissecting at least 16 lymph nodes, some research suggests over 30 nodes might be beneficial. This study assessed ICG-guided robotic gastrectomy's effectiveness in thorough lymph node dissection. We analyzed data from 393 stage II or III gastric cancer patients treated at Seoul St. Mary's Hospital from 2016-2022. Patients were categorized into conventional laparoscopy (G1, n = 288), ICG-guided laparoscopy (G2, n = 61), and ICG-guided robotic surgery (G3, n = 44). Among 391 patients, 308 (78.4%) achieved proper lymphadenectomy. The ICG-robotic group (G3) showed the highest success rate at 90.9%. ICG-guided robotic surgery was a significant predictor for achieving proper lymphadenectomy, with an odds ratio of 3.151. In conclusion, ICG-robotic gastrectomy improves lymphadenectomy outcomes in selected gastric cancer cases, indicating a promising surgical approach for the future.

MORBIDITY AND SURVIVAL AFTER PERIOPERATIVE CHEMOTHERAPY IN GASTRIC CANCER: A STUDY USING THE BECKER'S CLASSIFICATION AND REGRESSION

BACKGROUND

Gastric cancer is an aggressive neoplasm with a poor prognosis. The multimodal approach with perioperative chemotherapy is currently the recommended treatment for patients with locally advanced gastric cancer. This treatment induces a histopathological response expressed either through the degree of regression of the primary tumor or of the lymph nodes or through yTNM staging. Despite its advantages, there are still doubts regarding the effects of chemotherapy on postoperative morbidity and mortality.

AIMS

This study aims to evaluate the impact of perioperative chemotherapy and its effect on anatomopathological results and postoperative morbidity and on patient survival.

METHODS

This is an observational retrospective study on 134 patients with advanced gastric cancer who underwent perioperative chemotherapy and curative radical surgery. The degree of histological regression of the primary tumor was evaluated according to Becker's criteria; the proportion of regressed lymph nodes was determined, and postoperative complications were evaluated according to the Clavien-Dindo classification. Survival times were compared between the groups using Kaplan-Meier curves and the Mantel-Cox log-rank test.

RESULTS

In all, 22.3% of the patients were classified as good responders and 75.9% as poor responders. This variable was not correlated with operative morbidity (p=1.68); 64.2% of patients had invaded lymph nodes and 46.3% had regressed lymph nodes; and 49.4% had no lymphatic invasion and 61.9% had no signs of venous invasion. Postoperative complications occurred in 30.6% of the patients. The group of good responders had an average survival of 56.0 months and the group of poor responders had 34.0 months (p=0.17).

CONCLUSION

Perioperative chemotherapy induces regression in both the primary tumor and lymph nodes. The results of the operative morbidity were similar to those described in the literature. However, although the group of good responders showed better survival, this value was not significant. Therefore, further studies are needed to evaluate the importance of the degree of lymph node regression and its impact on the survival of these patients.

Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology

Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.

Clinical significance of the number of retrieved lymph nodes in early gastric cancer with submucosal invasion

The prognosis of early gastric cancer (EGC) with submucosal invasion is favorable; however, several cases of recurrence have been reported even after curative gastrectomy. This study aimed to investigate risk factors and evaluate the clinical significance of the number of retrieved lymph nodes (LNs) in EGC with submucosal invasion. We retrospectively analyzed the data of 443 patients with gastric cancer with submucosal invasion after curative gastrectomy for recurrent risk factors. Recurrence was observed in 22 of the 443 gastric cancer patients with submucosal invasion. In the univariate analysis, the risk factors for recurrence were the number of retrieved LNs ≤ 25 and node metastasis. In the multivariate analysis, retrieved LNs ≤ 25 (hazard ratio [HR] = 5.754, P-value = .001) and node metastasis (HR = 3.031, P-value = .029) were independent risk factors for recurrence after curative gastrectomy. Body mass index was related to retrieved LNs ≤ 25 in univariate and multivariate analyses (HR = .510, P = .002). The number of retrieved LNs and node metastases were independent risk factors for EGC with submucosal invasion. For EGC with submucosal invasion, retrieved LNs > 25 are necessary for appropriate diagnosis and treatment.

Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images

BACKGROUND

Traditional diagnosis methods for lymph node metastases are labor-intensive and time-consuming. As a result, diagnostic systems based on deep learning (DL) algorithms have become a hot topic. However, current research lacks testing with sufficient data to verify performance. The aim of this study was to develop and test a deep learning system capable of identifying lymph node metastases.

METHODS

921 whole-slide images of lymph nodes were divided into two cohorts: training and testing. For lymph node quantification, we combined Faster RCNN and DeepLab as a cascade DL algorithm to detect regions of interest. For metastatic cancer identification, we fused Xception and DenseNet-121 models and extracted features. Prospective testing to verify the performance of the diagnostic system was performed using 327 unlabeled images. We further validated the proposed system using Positive Predictive Value (PPV) and Negative Predictive Value (NPV) criteria.

RESULTS

We developed a DL-based system capable of automated quantification and identification of metastatic lymph nodes. The accuracy of lymph node quantification was shown to be 97.13%. The PPV of the combined Xception and DenseNet-121 model was 93.53%, and the NPV was 97.99%. Our experimental results show that the differentiation level of metastatic cancer affects the recognition performance.

CONCLUSIONS

The diagnostic system we established reached a high level of efficiency and accuracy of lymph node diagnosis. This system could potentially be implemented into clinical workflow to assist pathologists in making a preliminary screening for lymph node metastases in gastric cancer patients.