Cancer Genetics and Epigenetics 2024, Vol.12, No.1, 37-46 http://www.medscipublisher.com/index.php/cge 42 of surgical plans. Additionally, MRI can provide crucial information about the involvement of adjacent organs, which is essential for evaluating the feasibility of surgery and selecting the most appropriate treatment strategy. 4.3 Advantages, limitations, and future trends of the technology Despite the significant potential demonstrated by PET/CT and MRI in the diagnosis of gastric cancer, they also have certain limitations. For instance, PET/CT may face challenges in distinguishing between inflammation and tumors, as both can exhibit areas of increased metabolic activity (Pijl et al., 2021). Additionally, PET/CT has limited resolution and may not detect extremely small tumors. While MRI provides excellent soft tissue contrast, it requires longer examination times, demands higher requirements of tolerance and cooperation for patients, and has limitations related to metallic implants. Future trends aim to enhance the accuracy and resolution of these technologies, reducing instances of misdiagnosis and missed diagnoses. With technological advancements, we can anticipate higher-resolution images and improved capabilities for detecting tumors with greater precision. Furthermore, the integration of artificial intelligence may enhance the efficiency and accuracy of image analysis, making diagnoses faster and more precise. 5 Application of Artificial Intelligence and Machine Learning in Gastric Cancer Diagnosis 5.1 Application of artificial intelligence in imaging analysis In recent years, the application of artificial intelligence (AI) in the field of medical imaging has emerged as a frontier in healthcare technological innovation. Particularly in the early diagnosis of gastric cancer, AI technology has demonstrated significant potential. Through deep learning and neural networks, AI can analyze complex imaging data, assisting physicians in more accurately identifying tumors and lesions. For example, the use of AI algorithms to process endoscopic images of the stomach can aid in the identification of minute cancerous or precancerous lesions, presenting a significant challenge to traditional visual observation (Yu, 2020). One of the significant advantages of AI in gastric cancer diagnosis is its capability to handle and analyze large volumes of data. By learning from thousands of cases through medical imaging, AI systems can "learn" to identify different stages and types of gastric cancer, even detecting subtle differences that may be imperceptible to the naked eye. Additionally, AI technology can assist in reducing misdiagnosis and missed diagnosis rates, thereby enhancing the accuracy and efficiency of diagnosis. However, the application of AI in gastric cancer imaging analysis also faces several challenges. Firstly, the accuracy of algorithms heavily relies on the quality and quantity of training data. Moreover, AI systems may encounter difficulties when dealing with atypical cases or rare types of gastric cancer. Overcoming these challenges requires ongoing technological innovation and broader clinical trials. 5.2 The role of machine learning in data parsing and molecular marker screening In the early diagnosis of gastric cancer, the discovery and validation of molecular biomarkers are crucial steps. Machine learning technology plays an increasingly important role in this process. Machine learning algorithms can handle large-scale genomics, proteomics, and metabolomics data, identifying specific molecular patterns and biomarkers associated with gastric cancer from these datasets. Through these algorithms, researchers can expedite the discovery process of potential biomarker candidates from vast datasets. Additionally, machine learning can assist in optimizing the combination of biomarkers, enhancing the sensitivity and specificity of diagnostic tests. For instance, by analyzing gene expression patterns in different patients, machine learning can help identify which combinations of gene mutations or protein expressions are most likely associated with the occurrence of gastric cancer. However, the application of machine learning in this field also faces challenges. The quality and diversity of data are crucial for the effectiveness of algorithms. Inaccurate or biased data may lead to erroneous conclusions. Furthermore, even when relevant molecular biomarkers are identified, their application in clinical practice requires time and further validation.
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