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首页> 外文会议>International Conference on Algorithmic Applications in Management >Model Establishment and Algorithm Research of Tumor Marker Combination Prediction for Colorectal Cancer
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Model Establishment and Algorithm Research of Tumor Marker Combination Prediction for Colorectal Cancer

机译：大肠癌肿瘤标志物组合预测模型的建立和算法研究

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Colorectal Cancer (CRC) is one of the most common malignant tumors of digestive tract in the world; the incidence of CRC is increasing year by year. With the development of early screening, the improvement of surgical technique and the application of new treatment methods such as targeted therapy and immunother-apy, the mortality rate of patients with colorectal cancer has decreased obviously, but the long-term therapeutic effect remains suboptimal. Early detection, early diagnosis and early treatment are still the main treatment strategies for CRC. Currently, effective tumor markers are the primary basis for analysis and reference for clinical decision making in CRC treatment. Due to the large variety and quantity of data, the sensitivity and specificity between tumor marker data are difficult to determine, which brings some inconvenience to treatment. We use two-layer neural network's self-organizing-mapping (SOM) model and unsuperyised learning algorithms to map high-dimensional data to a two-dimensional topology, allowing the relationships between the data to be visually represented, which can help doctors better judge the prognosis of CRC and monitor the recurrence and metastasis of CRC from the relationship between the data.
机译：大肠癌（CRC）是世界上最常见的消化道恶性肿瘤之一。 CRC的发病率逐年增加。随着早期筛查技术的发展，外科技术的进步以及靶向治疗和免疫疗法等新治疗方法的应用，结直肠癌患者的死亡率明显下降，但长期治疗效果仍然欠佳。早期发现，早期诊断和早期治疗仍然是CRC的主要治疗策略。当前，有效的肿瘤标志物是进行CRC治疗的临床决策分析和参考的主要依据。由于数据种类繁多，难以确定肿瘤标志物数据之间的敏感性和特异性，给治疗带来一定的不便。我们使用两层神经网络的自组织映射（SOM）模型和非超级学习算法将高维数据映射到二维拓扑，从而可以直观地表示数据之间的关系，从而可以帮助医生更好地判断从数据之间的关系出发，探讨CRC的预后并监测CRC的复发和转移。

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