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首页> 外文会议>IEEE International Conference on Systems, Man, and Cybernetics >A quantitative histogram-based approach to predict treatment outcome for Soft Tissue Sarcomas using pre- and post-treatment MRIs
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A quantitative histogram-based approach to predict treatment outcome for Soft Tissue Sarcomas using pre- and post-treatment MRIs

机译：基于定量直方图的方法，使用治疗前和治疗后MRI预测软组织肉瘤的治疗结果

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The goal of this paper is to show the use of data mining techniques to predict the Soft Tissue Sarcoma (STS) tumor progression. STS are cancers which occur in different parts of the body such as fat, muscle and nerves. The lack of effective treatments and the difficulty in predicting treatment response make them challenging for physicians, and has likely slowed the evolution of new therapeutic agents. To design a prediction model, we propose a novel quantitative histogram-based method to analyze the difference in histograms obtained from pre and post-treatment multi-modality magnetic resonance images. Here, we used Radiomics techniques as a non-invasive method for outcome prediction. This study could help physicians identify distinctive patterns within each tumor to find more patient-specific treatments. We demonstrated the new approach on two practical tasks: tumor recurrence prediction (metastasis) and rate of necrosis prediction. Our learned model shows 87.79% prediction accuracy for metastasis with a 0.73 AUC and 82.22% prediction accuracy for necrosis with a 0.65 AUC.
机译：本文的目的是展示使用数据挖掘技术来预测软组织肉瘤（STS）肿瘤的进展。 STS是发生在身体不同部位（例如脂肪，肌肉和神经）的癌症。缺乏有效的治疗方法以及难以预测治疗反应的方法，使它们对医生具有挑战性，并可能减缓了新治疗剂的发展。为了设计预测模型，我们提出了一种基于定量直方图的新颖方法来分析从治疗前和治疗后多模态磁共振图像中获得的直方图差异。在这里，我们将Radiomics技术用作结果预测的非侵入性方法。这项研究可以帮助医生确定每种肿瘤内的独特模式，以找到更多针对患者的治疗方法。我们在两个实际任务上展示了这种新方法：肿瘤复发预测（转移）和坏死率预测。我们的学习模型显示0.73 AUC的转移预测准确度为87.79％，坏死0.65 AUC的转移准确度为82.22％。

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