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About this Research Topic

Manuscript Submission Deadline 21 January 2023

Nowadays, the diagnosis and treatment of gastrointestinal cancer (DTGC) mainly depends on the clinical experience of doctors, combined with various examination reports and diagnostic data of patients for comprehensive judgment. However, different doctors have different clinical thinking, and thus there are often some deviations in the diagnostic results and therapeutic schedule. With the development of modern medicine, the use of artificial intelligence (AI) technique combined with multisource medical information for multimodal auxiliary diagnosis and treatment, is gradually becoming an important part of the research in the field of gastrointestinal cancer informatics and precision medicine. It can provide and integrate more accurate information, and offer personalized treatment for each gastrointestinal cancer patient. AI can also accelerate the discovery of new materials, which may greatly speed up the development of anti-cancer drugs. It is believed that AI will become a powerful driving force for gastrointestinal cancer diagnosis and treatment in the future and bring profound changes to medical technology. Therefore, it is necessary to comprehensively analyze the gastrointestinal cancer patient's signs, symptoms, medical test results, multi-omics data and other multimodal information together by developing new artificial intelligence methods, which will make promising contributions to the progress of gastrointestinal cancer diagnosis and treatment in the era of precision medicine.

This Research Topic aims at seeking high-quality papers from academics and industry-related researchers of artificial intelligence, to present the most recently advanced methods and applications for realizing promising diagnoses and treatments of gastrointestinal cancer. Thus, based on the intersection of biomedicine and information, and with the help of artificial intelligence technology, the biological characteristics of the full cycle evolution of gastrointestinal cancer can be revealed, the evolution law of cancer heterogeneity and the early screening mode of image pathology omics integration can be clarified, laying a theoretical foundation for accurate diagnosis and treatment of gastrointestinal cancer.

Topics of interest include, but are not limited to:
• AI Theory and Methods for DTGC
• AI with Various Therapies for Gastrointestinal Cancer
• Multimodal Analysis for DTGC
• Domain Adaption and Transfer Learning for DTGC
• Knowledge Graphs for DTGC
• Small Sample Learning for DTGC
• Uncertainty Data Analysis for DTGC
• Data Reliability Analysis for DTGC
• AI and Clinical Decision Support Systems Related to DTGC

Keywords: Tumour Diagnosis, Multimodal Data, Machine Learning, Artificial Intelligence, gastrointestinal cancer, tumour treatment


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

Nowadays, the diagnosis and treatment of gastrointestinal cancer (DTGC) mainly depends on the clinical experience of doctors, combined with various examination reports and diagnostic data of patients for comprehensive judgment. However, different doctors have different clinical thinking, and thus there are often some deviations in the diagnostic results and therapeutic schedule. With the development of modern medicine, the use of artificial intelligence (AI) technique combined with multisource medical information for multimodal auxiliary diagnosis and treatment, is gradually becoming an important part of the research in the field of gastrointestinal cancer informatics and precision medicine. It can provide and integrate more accurate information, and offer personalized treatment for each gastrointestinal cancer patient. AI can also accelerate the discovery of new materials, which may greatly speed up the development of anti-cancer drugs. It is believed that AI will become a powerful driving force for gastrointestinal cancer diagnosis and treatment in the future and bring profound changes to medical technology. Therefore, it is necessary to comprehensively analyze the gastrointestinal cancer patient's signs, symptoms, medical test results, multi-omics data and other multimodal information together by developing new artificial intelligence methods, which will make promising contributions to the progress of gastrointestinal cancer diagnosis and treatment in the era of precision medicine.

This Research Topic aims at seeking high-quality papers from academics and industry-related researchers of artificial intelligence, to present the most recently advanced methods and applications for realizing promising diagnoses and treatments of gastrointestinal cancer. Thus, based on the intersection of biomedicine and information, and with the help of artificial intelligence technology, the biological characteristics of the full cycle evolution of gastrointestinal cancer can be revealed, the evolution law of cancer heterogeneity and the early screening mode of image pathology omics integration can be clarified, laying a theoretical foundation for accurate diagnosis and treatment of gastrointestinal cancer.

Topics of interest include, but are not limited to:
• AI Theory and Methods for DTGC
• AI with Various Therapies for Gastrointestinal Cancer
• Multimodal Analysis for DTGC
• Domain Adaption and Transfer Learning for DTGC
• Knowledge Graphs for DTGC
• Small Sample Learning for DTGC
• Uncertainty Data Analysis for DTGC
• Data Reliability Analysis for DTGC
• AI and Clinical Decision Support Systems Related to DTGC

Keywords: Tumour Diagnosis, Multimodal Data, Machine Learning, Artificial Intelligence, gastrointestinal cancer, tumour treatment


Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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