Extracting massive features from images to quantify tumors provides a new insight to solve the problem that tumor heterogeneity is difficult to assess quantitatively. However, quantification of tumors by single-mode methods often has defects such as difficulty in features extraction and high computational complexity. The multimodal approach has shown effective application prospects in solving these problems. In this paper, we propose a feature fusion method based on positron emission tomography (PET) images and clinical information, which is used to obtain features for lung metastasis prediction of soft tissue sarcomas (STSs). Random forest method was adopted to select effective features by eliminating irrelevant or redundant features, and then they were used for the prediction of the lung metastasis combined with back propagation (BP) neural network. The results show that the prediction ability of the proposed model using fusion features is better than that of the model using an image or clinical feature alone. Furthermore, a good performance can be obtained using 3 standard uptake value (SUV) features of PET image and 7 clinical features, and its average accuracy, sensitivity, and specificity on all the sets can reach 92%, 91%, and 92%, respectively. Therefore, the fusing features have the potential to predict lung metastasis for STSs.

中文翻译：

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融合FDG-PET图像和临床特征预测软组织肉瘤的肺转移。

从图像中提取大量特征以量化肿瘤为解决难以定量评估肿瘤异质性的问题提供了新的见解。然而，通过单模方法对肿瘤进行量化通常具有诸如特征提取困难和计算复杂度高的缺陷。多峰方法已显示出解决这些问题的有效应用前景。在本文中，我们提出了一种基于正电子发射断层扫描（PET）图像和临床信息的特征融合方法，该方法用于获得预测软组织肉瘤（STS）肺转移的特征。通过消除不相关或多余的特征，采用随机森林法来选择有效特征，然后结合反向传播（BP）神经网络将其用于预测肺转移。结果表明，所提出的使用融合特征的模型的预测能力要优于仅使用图像或临床特征的模型的预测能力。此外，使用3个PET图像的标准摄取值（SUV）特征和7个临床特征可以获得良好的性能，并且所有组的平均准确度，灵敏度和特异性可以达到92％，91％和92％，分别。因此，融合功能有可能预测STS的肺转移。使用PET图像的3个标准摄取值（SUV）特征和7个临床特征可以获得良好的性能，并且所有组的平均准确度，敏感性和特异性分别达到92％，91％和92％。因此，融合功能有可能预测STS的肺转移。使用PET图像的3个标准摄取值（SUV）特征和7个临床特征可以获得良好的性能，并且所有组的平均准确度，灵敏度和特异性分别达到92％，91％和92％。因此，融合功能有可能预测STS的肺转移。