Efficacy of ultrasound endoscopy with artificial intelligence for the differential diagnosis of non-gastric gastrointestinal stromal tumors

An artificial intelligence model utilizing endoscopic ultrasonography for differentiating small and micro gastric stromal tumors from gastric leiomyomas

BACKGROUND

Gastric stromal tumors (GSTs) and gastric leiomyomas (GLs) represent the primary subtypes of gastric submucosal tumors (SMTs) characterized by distinct biological characteristics and treatment modalities. The accurate differentiation between GSTs and GLs poses a significant clinical challenge. Recent advancements in artificial intelligence (AI) leveraging endoscopic ultrasonography (EUS) have demonstrated promising results in the categorization of larger-diameter SMTs (> 2.0 cm). However, the diagnostic capacity of AI models for micro-diameter SMTs (< 1.0 cm) remains uncertain due to limited imaging features. This study seeks to develop a specialized diagnostic model utilizing EUS images to differentiate small and micro GSTs from GLs effectively.

METHODS

In this study, a dataset comprising 358 EUS images of GSTs or GLs was utilized for training the EUS-AI model. Subsequently, 216 EUS images were allocated for validation purposes, with 159 images in validation set 1 (micro SMTs: tumor diameter < 1.0 cm) and 216 images in validation set 2 (small SMTs: tumor diameter < 2.0 cm). The diagnostic performance of the EUS-AI model for individual tumors was assessed by consolidating the diagnostic outcomes of the corresponding images. Comparative analyses were conducted between the diagnostic outcomes of endoscopists, clinical signatures, and those of the EUS-AI models.

RESULTS

The EUS-AI models were developed using DenseNet201, ResNet50, and VGG19 architectures. Among the three models, the ResNet50 model demonstrated superior performance on EUS images, achieving area under the curve (AUC) values of 0.938, 0.832, and 0.841 in the training set, validation set 1, and validation set 2, respectively. By combining predictions from multiple images for each tumor, the diagnostic efficacy of ResNet50 was further enhanced, resulting in AUCs of 0.994, 0.911, and 0.915 in the aforementioned sets. In comparison, both clinical signatures and endoscopists exhibited notably lower AUC values than those obtained with the EUS-AI model.

CONCLUSIONS

The EUS-AI model utilizing ResNet50 architecture effectively discriminates between micro GSTs and GLs from both image-centric and tumor-centric perspectives. Demonstrating superior diagnostic efficiency compared to clinical models and assessments by endoscopists, the EUS-AI model serves as a valuable tool for clinicians in precisely distinguishing small and micro GSTs from GLs before surgery.

The application of the combination between artificial intelligence and endoscopy in gastrointestinal tumors

Gastrointestinal (GI) tumors have always been a major type of malignant tumor and a leading cause of tumor‐related deaths worldwide. The main principles of modern medicine for GI tumors are early prevention, early diagnosis, and early treatment, with early diagnosis being the most effective measure. Endoscopy, due to its ability to visualize lesions, has been one of the primary modalities for screening, diagnosing, and treating GI tumors. However, a qualified endoscopist often requires long training and extensive experience, which to some extent limits the wider use of endoscopy. With advances in data science, artificial intelligence (AI) has brought a new development direction for the endoscopy of GI tumors. AI can quickly process large quantities of data and images and improve diagnostic accuracy with some training, greatly reducing the workload of endoscopists and assisting them in early diagnosis. Therefore, this review focuses on the combined application of endoscopy and AI in GI tumors in recent years, describing the latest research progress on the main types of tumors and their performance in clinical trials, the application of multimodal AI in endoscopy, the development of endoscopy, and the potential applications of AI within it, with the aim of providing a reference for subsequent research.

Endoscopic Ultrasound and Gastric Sub-Epithelial Lesions: Ultrasonographic Features, Tissue Acquisition Strategies, and Therapeutic Management

Background and objectives: Subepithelial lesions (SELs) of the gastrointestinal (GI) tract present a diagnostic challenge due to their heterogeneous nature and varied clinical manifestations. Usually, SELs are small and asymptomatic; generally discovered during routine endoscopy or radiological examinations. Currently, endoscopic ultrasound (EUS) is the best tool to characterize gastric SELs. Materials and methods: For this review, the research and the study selection were conducted using the PubMed database. Articles in English language were reviewed from August 2019 to July 2024. Results: This review aims to summarize the international literature to examine and illustrate the progress in the last five years of endosonographic diagnostics and treatment of gastric SELs. Conclusions: Endoscopic ultrasound is the preferred option for the diagnosis of sub-epithelial lesions. In most of the cases, EUS-guided tissue sampling is mandatory; however, ancillary techniques (elastography, CEH-EUS, AI) may help in both diagnosis and prognostic assessment.

Applications and Prospects of Artificial Intelligence-Assisted Endoscopic Ultrasound in Digestive System Diseases

Endoscopic ultrasound (EUS) has emerged as a widely utilized tool in the diagnosis of digestive diseases. In recent years, the potential of artificial intelligence (AI) in healthcare has been gradually recognized, and its superiority in the field of EUS is becoming apparent. Machine learning (ML) and deep learning (DL) are the two main AI algorithms. This paper aims to outline the applications and prospects of artificial intelligence-assisted endoscopic ultrasound (EUS-AI) in digestive diseases over the past decade. The results demonstrated that EUS-AI has shown superiority or at least equivalence to traditional methods in the diagnosis, prognosis, and quality control of subepithelial lesions, early esophageal cancer, early gastric cancer, and pancreatic diseases including pancreatic cystic lesions, autoimmune pancreatitis, and pancreatic cancer. The implementation of EUS-AI has opened up new avenues for individualized precision medicine and has introduced novel diagnostic and treatment approaches for digestive diseases.

Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis: A systematic review and meta-analysis

BACKGROUND

Subepithelial lesions (SELs) are gastrointestinal tumors with heterogeneous malignant potential. Endoscopic ultrasonography (EUS) is the leading method for evaluation, but without histopathological analysis, precise differentiation of SEL risk is limited. Artificial intelligence (AI) is a promising aid for the diagnosis of gastrointestinal lesions in the absence of histopathology.

AIM

To determine the diagnostic accuracy of AI-assisted EUS in diagnosing SELs, especially lesions originating from the muscularis propria layer.

METHODS

Electronic databases including PubMed, EMBASE, and Cochrane Library were searched. Patients of any sex and > 18 years, with SELs assessed by EUS AI-assisted, with previous histopathological diagnosis, and presented sufficient data values which were extracted to construct a 2 × 2 table. The reference standard was histopathology. The primary outcome was the accuracy of AI for gastrointestinal stromal tumor (GIST). Secondary outcomes were AI-assisted EUS diagnosis for GIST vs gastrointestinal leiomyoma (GIL), the diagnostic performance of experienced endoscopists for GIST, and GIST vs GIL. Pooled sensitivity, specificity, positive, and negative predictive values were calculated. The corresponding summary receiver operating characteristic curve and post-test probability were also analyzed.

RESULTS

Eight retrospective studies with a total of 2355 patients and 44154 images were included in this meta-analysis. The AI-assisted EUS for GIST diagnosis showed a sensitivity of 92% [95% confidence interval (CI): 0.89-0.95; P < 0.01), specificity of 80% (95%CI: 0.75-0.85; P < 0.01), and area under the curve (AUC) of 0.949. For diagnosis of GIST vs GIL by AI-assisted EUS, specificity was 90% (95%CI: 0.88-0.95; P = 0.02) and AUC of 0.966. The experienced endoscopists' values were sensitivity of 72% (95%CI: 0.67-0.76; P < 0.01), specificity of 70% (95%CI: 0.64-0.76; P < 0.01), and AUC of 0.777 for GIST. Evaluating GIST vs GIL, the experts achieved a sensitivity of 73% (95%CI: 0.65-0.80; P < 0.01) and an AUC of 0.819.

CONCLUSION

AI-assisted EUS has high diagnostic accuracy for fourth-layer SELs, especially for GIST, demonstrating superiority compared to experienced endoscopists' and improving their diagnostic performance in the absence of invasive procedures.

Natural Course of Asymptomatic Upper Gastrointestinal Subepithelial Lesion of 2 cm or Less in Size

There is limited evidence of a natural course of an upper gastrointestinal (UGI)-subepithelial lesion (SEL) of 2 cm or less in size. This study aims to determine the natural course of UGI-SELs and find the risk factors of the endoscopic and endoscopic ultrasonography (EUS) findings associated with an increase in size. The medical records of 2539 patients with UGI-SELs between 2004 and 2016 were reviewed retrospectively. A total of 672 SELs of 2 cm or less in size were analyzed through EUS and followed up for at least 36 months. The mean follow-up duration was 68 months (range: 36-190 months), and 97 SELs (14.4%) showed an increase in size with a mean increase rate of 1.2 mm/year. Initial size (aOR 1.03, 95% confidence interval (CI) 1.01-1.06), an endoscopic finding of a hemorrhagic spot (aOR 3.13, 95% CI 1.14-8.60), and an EUS finding of a lesion in the fourth layer (aOR 1.87, 95% CI (1.21-2.88) were related to an increase in size. An endoscopic finding of translucidity (aOR 0.28, 95% CI (0.10-0.76) and an EUS finding of calcification (aOR 0.30, 95% CI 0.09-0.95) were inversely related to an increase in size. There was no death related to UGI-SELs during the follow-up. While most UGI-SELs of 2 cm or less in size showed no significant size change and favorable prognosis, an individualized follow-up strategy needs to be considered in case of the presence of hemorrhagic spots and lesions in the fourth layer.