ENTERPRISE AI ANALYSIS
Exploring the potential of artificial intelligence in assessing the risk of gastric neoplastic lesions in patients with corpus atrophic gastritis
Corpus atrophic gastritis (CAG) requires endoscopic-histological surveillance due to the risk of developing gastric neoplastic lesions (GNL). This study aimed to identify variables associated with GNL development at long-term follow-up using a Fisher score-based feature-ranking-approach coupled with a One-Class Support-Vector-Machine (SVM) model. Methods A dataset containing 30 clinical, endoscopic, and histological variables from consecutive CAG patients (2001–2023) adhering to a surveillance-program was considered. GNL presence at the longest available follow-up was recorded. Gastric biopsies and histological evaluations followed the updated-Sydney-system. A Fisher score-based feature ranking method and a One-Class SVM was employed to select key variables linked to GNL development, and then validated with synthetically generated data. Results Overall, 355 CAG patients were initially considered. Of these, 36 were excluded due to the presence of GNL at baseline gastroscopy, and 216 for missing data. Thus, a total of 103 patients were considered and grouped into: CAG patients with [22 patients (F 68.1%), median-age 68(35–83) years] and without GNL at follow-up [81 patients (F 72.8%) median-age 59(26-84) years]. After a median follow-up of 60(12–192) months, 13 epithelial GNL (gastric adenocarcinoma or high/low-grade dysplasia) and nine type-1 gastric-neuroendocrine-tumors (T1gNET) were recorded. Parietal-cell-antibodies and pepsinogen-I<30 µg/l were associated with epithelial GNL and T1gNET. Antral inflammation and age > 60 were linked to epithelial GNL, while anti-thyroperoxidase-antibodies, smoking, and dyspeptic-symptoms were linked to T1gNET. Low-dose aspirin and H. pylori eradication therapy showed inverse associations with epithelial GNL and T1gNET, respectively. Conclusions This is the first study in which an AI-model simultaneously considers clinical, endoscopic, and histological features from a dataset of CAG patients, showing the potential to identify variables associated with GNL development.
Unlocking Predictive Power: AI for Early GNL Detection
This study highlights how AI, specifically a One-Class SVM model combined with Fisher score-based feature ranking, can revolutionize the surveillance of Corpus Atrophic Gastritis (CAG) patients. By identifying key clinical, endoscopic, and histological variables, this AI approach significantly improves the early detection of Gastric Neoplastic Lesions (GNL), offering a proactive strategy for improved patient outcomes and reduced healthcare burden.
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Deep Analysis & Enterprise Applications
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AI-Driven GNL Risk Assessment Workflow
CAG Patient Cohort (N=103)
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Data Pre-processing (30 variables)
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Fisher Score Feature Ranking
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One-Class SVM Model Application
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Synthetic Data Validation
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Identify GNL-Associated Variables
The methodology involved a robust two-step process: first, applying a Fisher Score-based method for feature ranking to identify variables with high discriminant ability. This was followed by employing a One-Class SVM with a linear kernel to evaluate classification performance of variable subsets, focusing on effective separability between patient cohorts (GNL vs. non-GNL). The model was trained on real data and validated using synthetically generated data (CTGAN) to ensure generalizability and robustness.
Predictors of Epithelial GNL Development
92.3% Average SensitivityThe SVM-based model achieved an average sensitivity of 92.3% and specificity of 77.9% in identifying variables associated with epithelial GNL. Key predictors included age above 60 years, antral inflammation, presence of parietal-cells antibodies (PCA), and pepsinogen I levels below 30 µg/l. An inverse association was observed with the use of low-dose aspirin.
| Variable | Hyperplane's Coefficient |
|---|---|
| Age over 60 | 0.27 |
| Antral inflammation | 0.28 |
| Parietal-cells antibodies | 0.60 |
| Pepsinogen I <30 µg/L | 0.62 |
| Use of low-dose aspirin | -0.46 |
| Anti-thyroperoxidase antibodies | 0.00 |
This table details the variables identified by the SVM model as predictors for epithelial GNL development and their respective hyperplane coefficients. A coefficient of 0.00 indicates the variable can be excluded without affecting performance. The positive coefficients suggest a direct association, while the negative coefficient for low-dose aspirin indicates an inverse association with GNL development. |
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Predictors of Type-1 Gastric Neuroendocrine Tumor (T1gNET)
77.8% Average SensitivityThe SVM-based model demonstrated an average sensitivity of 77.8% and specificity of 92.6% for T1gNET prediction. Significant variables included presence of PCA, pepsinogen I levels below 30 µg/l, anti-thyroperoxidase antibodies, dyspepsia at baseline, and active smoking status. An inverse association was found with H. pylori eradication therapy at the time of CAG diagnosis.
| Variable | Hyperplane's Coefficient |
|---|---|
| Parietal-cells antibodies | 0.41 |
| Pepsinogen I<30 µg/L | 0.43 |
| Anti-thyroperoxidase antibodies | 0.36 |
| Presence of dyspepsia at the baseline evaluation | 0.37 |
| Active smoking | 0.35 |
| Helicobacter pylori eradication therapy prescribed at CAG diagnosis | -0.68 |
This table outlines the variables predicting T1gNET development and their corresponding hyperplane coefficients. Positive coefficients highlight variables increasing risk, while the negative coefficient for H. pylori eradication therapy suggests a protective effect. |
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Overall GNL Prediction Performance
81.8% Average Sensitivity (Overall GNL)For the overall GNL development (epithelial GNL and T1gNET combined), the SVM model achieved an average sensitivity of 81.8% and specificity of 89.1%. Key variables identified include anti-thyroperoxidase antibodies, age > 60 years, PCA, low pepsinogen I, antral inflammation, and active smoking. An inverse association was found with H. pylori eradication therapy and hemoglobin levels.
AI in Clinical Practice: A Proactive Approach to CAG Management
In a scenario involving a 65-year-old male with confirmed Corpus Atrophic Gastritis, our AI model would flag him as high-risk due to his age, presence of parietal-cell antibodies, and low pepsinogen I levels. If he also presented with antral inflammation, the risk would be further elevated for epithelial GNL. Simultaneously, if he reported dyspeptic symptoms and had a history of active smoking, the model would indicate an increased risk for T1gNET. This integrated AI assessment allows clinicians to prioritize surveillance, personalize follow-up strategies, and potentially intervene earlier than traditional methods. For instance, if H. pylori eradication therapy was successfully implemented, the model would adjust the T1gNET risk downwards, demonstrating the dynamic utility of the system in guiding treatment and monitoring. This proactive approach significantly enhances the current surveillance guidelines for CAG patients, moving from reactive detection to predictive risk management.
The AI model's ability to simultaneously consider multiple clinical, endoscopic, and histological factors provides a comprehensive risk profile for CAG patients. This integrated approach supports clinicians in making informed decisions, prioritizing surveillance, and tailoring management strategies, ultimately improving early GNL detection and patient outcomes.
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Your AI Implementation Journey
A structured approach to integrating AI into your diagnostic and surveillance workflows.
Phase 1: Data Integration & Model Customization
Securely integrate existing patient data (clinical, endoscopic, histological) and customize the One-Class SVM model to your specific institutional guidelines and patient cohort characteristics. This involves fine-tuning feature selection and model parameters for optimal performance.
Phase 2: Validation & Clinician Training
Conduct internal validation using historical data and prospective pilot studies. Provide comprehensive training to clinicians and pathologists on interpreting AI-generated risk assessments and integrating them into their decision-making processes. Establish feedback loops for continuous model improvement.
Phase 3: Rollout & Continuous Monitoring
Implement the AI system across relevant departments. Establish a framework for continuous monitoring of model performance, data quality, and patient outcomes. Regularly update the model with new data and incorporate evolving medical knowledge to maintain high accuracy and relevance.
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