Utility of a deep learning model and a clinical model for predicting bleeding after endoscopic submucosal dissection in patients with early gastric cancer

Characteristics of early gastric tumors with different differentiation and predictors of long-term outcomes after endoscopic submucosal dissection

BACKGROUND

Gastric cancer is a common malignant tumor of the digestive tract, and endoscopic submucosal dissection (ESD) is the preferred treatment for early-stage gastric cancer. The analysis of the epidemiological characteristics of gastric mucosal tumors with different differentiation degrees and the influencing factors of long-term ESD efficacy may have certain significance for revealing the development of gastric cancer and ESD.

AIM

To analyze the features of gastric mucosal tumors at different differentiation levels, and to explore the prognostic factors of ESD.

METHODS

We retrospectively studied 301 lesions in 285 patients at The Second Affiliated Hospital of Xi'an Jiaotong University from 2014 to 2021, according to the latest Japanese guidelines (sixth edition), and divided them into low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and differentiated and undifferentiated early carcinoma. They are followed up by endoscopy, chest and abdominal computed tomography at 3, 6 and 12 months after ESD. We compared clinicopathologic characteristics, ESD efficacy, and complications with different degrees of differentiation, and analyzed the related factors associated with ESD.

RESULTS

HGIN and differentiated carcinoma patients were significantly older compared with LGIN patients (P < 0.001) and accounted for more 0-IIc (P < 0.001), atrophic gastritis was common (P < 0.001), and irregular microvascular patterns (IMVPs) and demarcation lines (DLs) were more obvious (P < 0.001). There was more infiltration in the undifferentiated carcinoma tissue (P < 0.001), more abnormal folds and poorer mucosal peristalsis (P < 0.001), and more obvious IMVPs, irregular microsurface patterns and DLs (P < 0.05) than in the LGIN and HGIN tissues. The disease-free survival rates at 2, 5, and 8 years after ESD were 95.0%, 90.1%, and 86.9%, respectively. Undifferentiated lesions (HR 5.066), white moss (HR 7.187), incomplete resection (HR 3.658), and multiple primary cancers (HR 2.462) were significantly associated with poor prognosis.

CONCLUSION

Differentiations of gastric mucosal tumors have different epidemiological and endoscopic characteristics, which are closely related to the safety and efficacy of ESD.

Risk factors for postoperative bleeding following endoscopic submucosal dissection in early gastric cancer: A systematic review and meta-analysis

BACKGROUND

Early gastric cancer (EGC) presents a significant challenge in surgical management, particularly concerning postoperative bleeding following endoscopic submucosal dissection. Understanding the risk factors associated with postoperative bleeding is crucial for improving patient outcomes.

METHODS

Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, a systematic review and meta-analysis were conducted across PubMed, Embase, Web of Science, and the Cochrane Library without publication date restrictions. The inclusion criteria encompassed observational studies and randomized controlled trials focusing on EGC patients undergoing endoscopic submucosal dissection and their risk factors for postoperative bleeding. The Newcastle-Ottawa Scale was utilized for quality assessment. The effect size was calculated using random or fixed-effects models based on the observed heterogeneity. We assessed the heterogeneity between studies and conducted a sensitivity analysis.

RESULTS

In our meta-analysis, 6 studies involving 4868 EGC cases were analyzed. The risk of postoperative bleeding was notably increased with intraoperative ulcer detection (odds ratio: 1.97, 95% confidence interval [CI]: 1.03-3.76, I2 = 61.0%, P = .025) and antithrombotic medication use (odds ratio: 2.02, 95% CI: 1.16-3.51, I2 = 57.2%, P = .039). Lesion resection size showed a significant mean difference (5.16, 95% CI: 2.97-7.98, P < .01), and longer intraoperative procedure time was associated with increased bleeding risk (mean difference: 11.69 minutes, 95% CI: 1.82-26.20, P < .05). Sensitivity analysis affirmed the robustness of these findings, and publication bias assessment indicated no significant bias.

CONCLUSIONS

In EGC treatment, the risk of post-endoscopic submucosal dissection bleeding is intricately linked to factors like intraoperative ulcer detection, antithrombotic medication use, the extent of lesion resection, and the length of the surgical procedure. These interwoven risk factors necessitate careful consideration and integrated management strategies to enhance patient outcomes and safety in EGC surgeries.

Early gastric cancer recurrence after endoscopic submucosal dissection: Not to be ignored!

This editorial comments on the article "Efficacy of multi-slice spiral computed tomography in evaluating gastric cancer recurrence after endoscopic submucosal dissection". We focus on the importance of paying more attention to post-endoscopic submucosal dissection (ESD) gastric cancer recurrence in patients with early gastric cancer (EGC) and how to manage it effectively. ESD has been a well-known treatment and the mainstay for EGC, with the advantages of less invasion and fewer complications when compared with traditional surgical procedures. Despite a lower local recurrence rate after ESD, the problem of postoperative recurrence in patients with EGC has become increasingly non-ignorable with the global popularization of ESD technology and the increasing number of post-ESD patients.

Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology

Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently, artificial intelligence approaches, particularly machine learning and deep learning, are rapidly reshaping the full spectrum of clinical management for gastric cancer. Machine learning is formed from computers running repeated iterative models for progressively improving performance on a particular task. Deep learning is a subtype of machine learning on the basis of multilayered neural networks inspired by the human brain. This review summarizes the application of artificial intelligence algorithms to multi-dimensional data including clinical and follow-up information, conventional images (endoscope, histopathology, and computed tomography (CT)), molecular biomarkers, etc. to improve the risk surveillance of gastric cancer with established risk factors; the accuracy of diagnosis, and survival prediction among established gastric cancer patients; and the prediction of treatment outcomes for assisting clinical decision making. Therefore, artificial intelligence makes a profound impact on almost all aspects of gastric cancer from improving diagnosis to precision medicine. Despite this, most established artificial intelligence-based models are in a research-based format and often have limited value in real-world clinical practice. With the increasing adoption of artificial intelligence in clinical use, we anticipate the arrival of artificial intelligence-powered gastric cancer care.