Machine Learning-driven Noninvasive Screening of Transcriptomics Liquid Biopsies for Early Diagnosis of Occult Peritoneal Metastases in Locally Advanced Gastric Cancer
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.
• Diagnosis of Locally Advanced Gastric Cancer (LAGC): Patients must have a confirmed diagnosis of locally advanced gastric cancer, as determined by standard diagnostic procedures such as imaging and histopathological examination.
• Age: Participants must be adults aged 18 years or older. Consent: Patients must be able to provide informed consent to participate in the study.
• Adequate Organ Function: Participants should have adequate bone marrow, liver, and kidney function as defined by specific laboratory criteria (e.g., specific levels of hemoglobin, platelet count, liver enzymes, and creatinine clearance).
• Performance Status: Patients should have an Eastern Cooperative Oncology Group (ECOG) performance status of 0 to 2, indicating they are fully active, restricted in physically strenuous activity but ambulatory, or capable of all self-care but unable to carry out any work activities.
• Willingness to Provide Blood Samples: Participants must be willing to provide blood samples at specified time points throughout the study.
• Previous Treatment: Patients who have received prior treatments for gastric cancer (e.g., chemotherapy, radiation therapy, or surgery) may be included, provided there is a sufficient washout period as determined by the study protocol.