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首页> 美国卫生研究院文献>Technology in Cancer Research Treatment >Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling:A Pelvic Case Study
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Incorporating Case-Based Reasoning for Radiation Therapy Knowledge Modeling:A Pelvic Case Study

机译：将基于案例的推理纳入放射治疗知识建模：骨盆病例研究

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Knowledge models in radiotherapy capture the relation between patient anatomy and dosimetry to provide treatment planning guidance. When treatment schemes evolve, existing models struggle to predict accurately. We propose a case-based reasoning framework designed to handle novel anatomies that are of same type but vary beyond original training samples. A total of 105 pelvic intensity-modulated radiotherapy cases were analyzed. Eighty cases were prostate cases while the other 25 were prostate-plus-lymph-node cases. We simulated 4 scenarios: Scarce scenario, Semiscarce scenario, Semiample scenario, and Ample scenario. For the Scarce scenario, a multiple stepwise regression model was trained using 85 cases (80 prostate, 5 prostate-plus-lymph-node). The proposed workflow started with evaluating the feature novelty of new cases against 5 training prostate-plus-lymph-node cases using leverage statistic. The case database was composed of a 5-case dose atlas. Case-based dose prediction was compared against the regression model prediction using sum of squared residual. Mean sum of squared residual of case-based and regression predictions for the bladder of 13 identified outliers were 0.174 ± 0.166 and 0.459 ± 0.508, respectively (P = .0326). For the rectum, the respective mean sum of squared residuals were 0.103 ± 0.120 and 0.150 ± 0.171 for case-based and regression prediction (P = .1972). By retaining novel cases, under theAmple scenario, significant statistical improvement was observed over the Scarce scenario(P = .0398) for the bladder model. We expect that the incorporation ofcase-based reasoning that judiciously applies appropriate predictive models could improveoverall prediction accuracy and robustness in clinical practice.
机译：放射治疗中的知识模型可捕获患者解剖结构与剂量学之间的关系，以提供治疗计划指导。随着治疗方案的发展，现有模型难以准确预测。我们提出了一个基于案例的推理框架，该框架旨在处理相同类型但不同于原始训练样本的新颖解剖结构。共分析了105例盆腔调强放疗病例。 80例为前列腺病例，而其他25例为前列腺加淋巴结病例。我们模拟了4个场景：稀缺场景，半稀缺场景，Semiample场景和Ample场景。对于稀缺场景，使用85例（80个前列腺，5个前列腺加淋巴结）训练了多步逐步回归模型。拟议的工作流程首先使用杠杆统计数据针对5个训练的前列腺加淋巴结病例评估新病例的特征新颖性。病例数据库由5例剂量图集组成。使用残差平方和将基于案例的剂量预测与回归模型预测进行比较。基于病例的案例和回归预测的13个已识别异常值的膀胱的残差平方和分别为0.174±0.166和0.459±0.508（P = .0326）。对于直肠，基于病例和回归预测的残差平方平均分别为0.103±0.120和0.150±0.171（P = .1972）。通过保留新颖的案例，情景充裕，与稀缺情景相比，统计显着改善（P = .0398）对于膀胱模型。我们希望成立明智地应用适当的预测模型的基于案例的推理可以改善临床实践中的总体预测准确性和鲁棒性。

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