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Enhancing radiotherapy for gastric cancer: A systematic review on the role of predictive models in clinical decision-making
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Sh. Chen   , Sh. Wei , Y. Wang , L. Chen , Zh. Lin |
| Department of Urological Oncology Surgery, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fujian Province, China , hxsf803@163.com |
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Abstract: (50 Views) |
Predictive models have become essential tools in modern oncology, significantly advancing gastric cancer management—one of the most prevalent and lethal cancers worldwide. This systematic review investigates the application of predictive models derived from public databases, emphasizing their role in improving radiotherapy outcomes. Various modeling techniques are explored, including statistical methods like logistic regression and Cox proportional hazards models, machine learning approaches such as random forests and support vector machines, and deep learning models like convolutional and recurrent neural networks. These models contribute to early detection, prognosis estimation, treatment response prediction, and tumor classification. Notably, in the context of radiotherapy, predictive models enhance tumor delineation, assist in selecting optimal radiation doses, and forecast individual treatment responses, reducing toxicity and improving precision. Other key clinical applications include molecular subtyping, biomarker discovery, and image-based diagnostics, especially through endoscopic and histopathological image analysis. These applications support the development of personalized treatment regimens and improve long-term patient outcomes. Despite their promise, several challenges remain, including inconsistent or imbalanced data, limited interpretability of complex algorithms, and concerns regarding clinical trust and AI transparency. Addressing these issues requires the development of high-quality standardized databases, stronger data-sharing frameworks, the adoption of federated learning methods, and the integration of explainable AI models into clinical workflows. This review concludes that predictive models, when properly validated and implemented, hold substantial potential to transform gastric cancer radiotherapy by enabling more tailored, data-driven treatment strategies, ultimately improving survival rates and quality of life for patients. |
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| Keywords: Stomach neoplasms, radiotherapy, predictive value of tests, machine learning, clinical decision-making. |
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Full-Text [PDF 1215 kb]
(7 Downloads)
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Type of Study: Review article |
Subject:
Radiation Biology
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