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Knowledge Modeling for the Outcome of Brain Stereotactic Radiosurgery.

机译：脑立体定向放射外科手术结果的知识建模。

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摘要

Purpose: To build a model that will predict the survival time for patients that were treated with stereotactic radiosurgery for brain metastases using support vector machine (SVM) regression.;Methods and Materials: This study utilized data from 481 patients, which were equally divided into training and validation datasets randomly. The SVM model used a Gaussian RBF function, along with various parameters, such as the size of the epsilon insensitive region and the cost parameter (C) that are used to control the amount of error tolerated by the model. The predictor variables for the SVM model consisted of the actual survival time of the patient, the number of brain metastases, the graded prognostic assessment (GPA) and Karnofsky Performance Scale (KPS) scores, prescription dose, and the largest planning target volume (PTV). The response of the model is the survival time of the patient. The resulting survival time predictions were analyzed against the actual survival times by single parameter classification and two-parameter classification. The predicted mean survival times within each classification were compared with the actual values to obtain the confidence interval associated with the model's predictions. In addition to visualizing the data on plots using the means and error bars, the correlation coefficients between the actual and predicted means of the survival times were calculated during each step of the classification.;Results: The number of metastases and KPS scores, were consistently shown to be the strongest predictors in the single parameter classification, and were subsequently used as first classifiers in the two-parameter classification. When the survival times were analyzed with the number of metastases as the first classifier, the best correlation was obtained for patients with 3 metastases, while patients with 4 or 5 metastases had significantly worse results. When the KPS score was used as the first classifier, patients with a KPS score of 60 and 90/100 had similar strong correlation results. These mixed results are likely due to the limited data available for patients with more than 3 metastases or KPS scores of 60 or less.;Conclusions: The number of metastases and the KPS score both showed to be strong predictors of patient survival time. The model was less accurate for patients with more metastases and certain KPS scores due to the lack of training data.
机译：目的：建立一个模型，以预测通过支持向量机（SVM）回归接受立体定向放射外科手术治疗脑转移的患者的生存时间。；方法和材料：本研究利用了481例患者的数据，将其平均分为随机训练和验证数据集。 SVM模型使用了高斯RBF函数，以及各种参数，例如ε不敏感区域的大小和成本参数（C），这些参数用于控制模型所允许的误差量。 SVM模型的预测变量包括患者的实际存活时间，脑转移瘤的数量，预后评估分级（GPA）和卡诺夫斯基绩效量表（KPS）分数，处方剂量以及最大计划目标量（PTV） ）。该模型的响应是患者的生存时间。通过单参数分类和两参数分类，将所得的生存时间预测与实际生存时间进行了比较。将每个分类内的预测平均生存时间与实际值进行比较，以获得与模型预测相关的置信区间。除了使用均值和误差条将图上的数据可视化外，在分类的每个步骤中还计算了生存时间的实际和预测均值之间的相关系数。结果：转移的数量和KPS分数一致在单参数分类中显示为最强的预测变量，随后在两参数分类中用作第一分类。当以转移数作为第一分类器分析生存时间时，具有3个转移的患者获得最佳相关性，而具有4个或5个转移的患者的结果明显差。当将KPS分数用作第一分类器时，KPS分数分别为60和90/100的患者具有相似的强相关性结果。这些结果的混合可能是由于对于3个以上转移或KPS得分小于或等于60的患者可用的数据有限。结论：转移的数目和KPS得分均是患者生​​存时间的有力预测指标。由于缺乏训练数据，该模型对于转移较多且KPS评分较高的患者的准确性较低。

著录项

  • 作者

    Hauck, Jillian E.;

  • 作者单位

    Duke University.;

  • 授予单位 Duke University.;
  • 学科 Nuclear physics and radiation.;Oncology.;Medical imaging.
  • 学位 M.S.
  • 年度 2016
  • 页码 57 p.
  • 总页数 57
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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