Clinical trials

This study will evaluate whether an artificial intelligence (AI) system called DeepTRG can reliably assess cancer tissue removed during surgery after treatment given before surgery. It will include adults with stomach cancer or cancer at the junction between the stomach and the food pipe who have received treatment before surgery. Pathologists are doctors who examine tissue to diagnose disease. After cancer surgery, they assess how much living cancer remains and whether cancer is present in nearby lymph nodes. DeepTRG analyzes digital images of tissue slides to measure remaining cancer and describe tissue changes, such as scarring and inflammation. It combines information from the original tumor site and lymph nodes to produce a structured assessment. The main question is: \* How often can DeepTRG complete the required assessment and produce a clear result without a person correcting the AI analysis? The study will also examine: * How closely the system's measurements agree with assessments made by independent expert pathologists. * How accurately it detects remaining cancer, including small amounts of cancer and cancer in lymph nodes. * How long the analysis takes and how often it fails or produces an uncertain result. * Whether its performance differs across treatment types and cancer tissue types. This study will take place at the Fourth Hospital of Hebei Medical University in China. Eligible patients will be enrolled consecutively. Researchers will use tissue collected during the patients' planned surgery. No additional surgery or biopsy is required for this study. DeepTRG will run in the background in "silent mode." The doctors responsible for the patients' routine pathology reports and treatment decisions will not receive its results. Patients will continue to receive usual care. The AI model and its grading rules will be fixed before validation begins. Independent expert pathologists will assess the tissue without seeing the AI results. Researchers will then compare the expert assessments, routine pathology reports, and DeepTRG outputs. Cases with poor-quality images, failed analyses, or uncertain outputs will remain part of the assessment of how reliably the system works. Researchers will also collect information about subsequent treatment, cancer recurrence, and survival during follow-up. The initial analysis will focus on feasibility and measurement performance. A separate future study would be needed to determine whether using DeepTRG in clinical care improves doctors' decisions or patient outcomes.

Gastric cancer is a major global health challenge. Currently, a combination of chemotherapy and immunotherapy (PD-1 inhibitors) is frequently used before surgery to shrink tumors, a strategy known as neoadjuvant therapy. While this approach is effective for many patients, responses vary significantly, and there are currently no reliable tools to predict which patients will benefit the most before treatment begins. The PRISM-GC study aims to develop and validate a novel Artificial Intelligence (AI) system to address this need. This is a prospective, observational study that will collect data from patients diagnosed with locally advanced gastric cancer who are scheduled to receive standard neoadjuvant chemotherapy combined with immunotherapy in a real-world clinical setting. The specific choice of immunotherapy drug is determined by the treating physician and is not dictated by the study. Researchers will analyze standard preoperative CT scans and pathological tissue slides using advanced deep learning algorithms. The goal is to create a "multimodal" AI model that can accurately predict how well a tumor will respond to treatment (specifically, whether the tumor will disappear or shrink significantly). If successful, this AI tool could help doctors personalize treatment plans in the future, ensuring that each patient receives the most effective therapy while avoiding unnecessary side effects.

Gastric cancer is a leading cause of cancer-related mortality, and radical surgery remains the primary treatment. However, postoperative complications are common and can significantly impact patient recovery and quality of life. Currently, doctors lack precise tools to accurately predict which patients are at high risk for developing severe complications before surgery. This study aims to validate a novel artificial intelligence (AI) model called "DeepComp." The DeepComp model integrates clinical data with advanced radiomic features derived from routine preoperative CT scans. Specifically, it analyzes both the tumor characteristics and the patient's body composition (including skeletal muscle and fat distribution) to assess physiological reserve. In this prospective, multicenter observational study, researchers will enroll patients scheduled for gastric cancer surgery across five medical centers. The DeepComp model will be used to predict the risk of moderate-to-severe postoperative complications (Clavien-Dindo grade II or higher). These predictions will then be compared with the actual clinical outcomes observed 30 days after surgery. The goal is to determine the accuracy and reliability of the DeepComp model in a real-world clinical setting, potentially providing a powerful tool for personalized surgical risk assessment.

This study aims to develop and validate an artificial intelligence (AI) model that integrates clinical, pathological, and imaging data to predict the presence of lymph node metastasis (LNM) in patients with T1-stage gastric cancer. The study will also compare the diagnostic performance of physicians with and without AI assistance, including clinicians with varying levels of experience. The goal is to improve early decision-making and support more personalized treatment strategies for patients with early gastric cancer.

This clinical trial aims to develop a predictive model for gastric cancer (GC) peritoneal metastasis and cachexia by integrating BUB1 gene data with radiological and pathological data using advanced deep learning techniques. The study will focus on utilizing imaging genomics (radiomics) and histopathological data to identify early biomarkers for peritoneal metastasis and cachexia in GC patients. By leveraging deep learning algorithms, the project seeks to improve the accuracy and reliability of predictions, enabling earlier intervention and personalized treatment strategies. The ultimate goal is to enhance clinical decision-making and prognosis prediction in GC patients with peritoneal metastasis and cachexia.

This clinical trial aims to construct and apply a collaborative network database for gastric cancer in Hebei Province, driven by artificial intelligence (AI) technology. The project seeks to integrate clinical data, genomic information, and treatment outcomes from multiple hospitals and research centers within the region. By leveraging advanced AI algorithms, the database will facilitate comprehensive data analysis to identify novel biomarkers, optimize therapeutic strategies, and improve patient outcomes. This initiative will also support real-time data sharing and collaboration among healthcare providers, researchers, and policymakers, ultimately enhancing the overall management and treatment of gastric cancer in Hebei Province.

Brief Summary: Machine Learning-Driven Noninvasive Screening of Transcriptomics Liquid Biopsies for Early Diagnosis of Occult Peritoneal Metastases in Locally Advanced Gastric Cancer Gastric cancer, commonly known as stomach cancer, is a significant health issue worldwide, especially when it progresses to an advanced stage. One of the major challenges in treating locally advanced gastric cancer (LAGC) is the detection of occult (hidden) peritoneal metastases. These metastases are cancer cells that spread to the peritoneum (the lining of the abdominal cavity) but are not easily detectable with standard imaging techniques or during surgery. Early and accurate detection of these hidden metastases can significantly improve treatment strategies and outcomes for patients. This clinical study explores an innovative approach to tackle this problem using machine learning (ML) technology and liquid biopsies. Liquid biopsies are a noninvasive method that involves analyzing blood samples to detect cancer-related biomarkers, such as circulating tumor DNA or RNA. This study specifically focuses on the transcriptomics of liquid biopsies, which refers to the analysis of RNA molecules to understand the gene expression profiles associated with cancer. Hypothesis The hypothesis of this study is that machine learning algorithms can effectively analyze transcriptomics data from liquid biopsies to detect occult peritoneal metastases in patients with locally advanced gastric cancer. By doing so, this method could provide a noninvasive, accurate, and early diagnosis of metastases, which are otherwise difficult to identify through traditional methods. Study Design 1. Participants: The study will enroll patients diagnosed with locally advanced gastric cancer. These patients will undergo standard diagnostic and staging procedures to confirm their cancer stage and overall health status. 2. Sample Collection: Blood samples will be collected from the participants at various stages of their treatment journey. These samples will be processed to extract RNA, which will then be analyzed to obtain transcriptomic data. 3. Machine Learning Analysis: Advanced machine learning algorithms will be employed to analyze the transcriptomic data from the liquid biopsies. The algorithms will be trained to identify patterns and markers associated with occult peritoneal metastases. The models will be continuously refined and validated using a subset of the collected data to ensure accuracy and reliability. 4. Comparison with Traditional Methods: The results of the machine learning analysis will be compared with the outcomes of traditional diagnostic methods, such as imaging and surgical examinations, to evaluate the effectiveness of the ML-driven approach. 5. Outcome Measures: The primary outcome measure will be the accuracy of the machine learning models in detecting occult peritoneal metastases compared to traditional methods. Secondary measures will include the impact of early detection on treatment decisions, patient outcomes, and overall survival rates. Significance Early and accurate detection of occult peritoneal metastases in locally advanced gastric cancer is crucial for effective treatment planning. Traditional diagnostic methods often fail to identify these hidden metastases until they have progressed, limiting the treatment options and adversely affecting patient prognosis. By leveraging machine learning technology to analyze transcriptomics data from liquid biopsies, this study aims to develop a noninvasive and reliable screening tool that can detect these metastases at an earlier stage. Such an advancement could lead to several benefits, including: * Improved Treatment Planning: Early detection allows for more tailored and effective treatment strategies, potentially including more aggressive therapies or surgical interventions when necessary. * Better Patient Outcomes: With earlier and more accurate diagnosis, patients have a higher chance of receiving timely and appropriate treatments, which can improve survival rates and quality of life. * Noninvasive Screening: Liquid biopsies are less invasive than traditional biopsy methods, reducing the physical and psychological burden on patients. * Cost-Effectiveness: Early detection and treatment can potentially reduce the overall cost of care by preventing the need for more extensive and expensive treatments at later stages of the disease. Conclusion This clinical study represents a promising step forward in the fight against gastric cancer. By integrating machine learning with noninvasive liquid biopsy techniques, it aims to provide a new tool for the early detection of occult peritoneal metastases, ultimately improving outcomes for patients with locally advanced gastric cancer. The success of this study could pave the way for broader applications of machine learning in cancer diagnostics and personalized medicine.