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首页> 外文期刊>Expert Systems with Application >Combining case-based reasoning with Bee Colony Optimization for dose planning in well differentiated thyroid cancer treatment
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Combining case-based reasoning with Bee Colony Optimization for dose planning in well differentiated thyroid cancer treatment

机译:将基于案例的推理与Bee Colony Optimization相结合,以在分化良好的甲状腺癌治疗中进行剂量规划

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摘要

Thyroid cancers are the most common endocrine carcinomas. Case-based reasoning (CBR) is used in this paper to describe a physician's expertise, intuition and experience when treating patients with well differentiated thyroid cancer. Various clinical parameters (the patient's diagnosis, the patient's age, the tumor size, the existence of metastases in the lymph nodes and the existence of distant metastases) influence a physician's decision-making in dose planning. The weights (importance) of these parameters are determined here with the Bee Colony Optimization (BCO) meta-heuristic. The proposed CBR-BCO model suggests the 1-131 iodine dose in radioactive iodine therapy. This approach is tested on real data from patients treated in the Department of Nuclear Medicine, Clinical Center Kragujevac, Serbia. By comparing the results that are obtained through the developed CBR-BCO model with those resulting from the physician's decision, it has been found that the developed model is highly reflective of reality.
机译:甲状腺癌是最常见的内分泌癌。本文使用基于案例的推理(CBR)来描述在治疗分化良好的甲状腺癌患者时医师的专业知识,直觉和经验。各种临床参数(患者的诊断,患者的年龄,肿瘤的大小,淋巴结转移的存在以及远处转移的存在)都会影响医生在剂量规划中的决策。这些参数的权重(重要性)是在此处通过Bee Colony Optimization(BCO)元启发式方法确定的。提议的CBR-BCO模型建议放射性碘治疗中使用1-131碘剂量。该方法已经在塞尔维亚克拉格耶瓦茨临床中心核医学科接受治疗的患者的真实数据上进行了测试。通过将通过开发的CBR-BCO模型获得的结果与医生决定得出的结果进行比较,发现开发的模型高度反映了现实。

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