A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT.
A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT.
复制标题
使用核支撑张量机进行肺 SBRT 远距离故障预测的初步研究
DOI：
10.1016/j.media.2018.09.004
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发表时间：
2018-12
影响因子：
10.9
通讯作者：
Wang J
中科院分区：
文献类型：
--
作者：
Li S;Yang N;Li B;Zhou Z;Hao H;Folkert MR;Iyengar P;Westover K;Choy H;Timmerman R;Jiang S;Wang J
我们开发了一种基于核支持张量机（KSTM）的模型，将来自治疗前PET和CT成像的肿瘤张量作为输入，以预测立体定向体部放射治疗（SBRT）治疗的早期非小细胞肺癌（NSCLC）的远端失败。患者队列包括110例接受SBRT治疗的早期NSCLC患者，其中25例在远端部位失败。构建三维肿瘤张量并用作基于KSTM的分类器的输入。提出了一种具有收敛性证明的KSTM迭代算法，用于训练分类器中张量各模态的权向量。与依赖于手工制作的成像特征的传统放射组学方法相比，基于KSTM的分类器使用3D成像作为输入，充分利用成像信息。基于KSTM的分类器保留了医学图像固有的3D几何结构和原始图像中的相关性，并在自适应特征张量空间中训练分类超平面。将基于KSTM的预测算法与三种传统的机器学习模型和三种放射组学方法进行了比较。对于PET和CT，基于KSTM的预测方法在本研究中基于10倍交叉验证和独立检验研究的七种方法中获得了最高的预测结果。
We developed a kernelled support tensor machine (KSTM)-based model with tumor tensors derived from pre-treatment PET and CT imaging as input to predict distant failure in early stage non-small cell lung cancer (NSCLC) treated with stereotactic body radiation therapy (SBRT). The patient cohort included 110 early stage NSCLC patients treated with SBRT, 25 of whom experienced failure at distant sites. Three-dimensional tumor tensors were constructed and used as input for the KSTM-based classifier. A KSTM iterative algorithm with a convergent proof was developed to train the weight vectors for every mode of the tensor for the classifier. In contrast to conventional radiomics approaches that rely on handcrafted imaging features, the KSTM-based classifier uses 3D imaging as input, taking full advantage of the imaging information. The KSTM-based classifier preserves the intrinsic 3D geometry structure of the medical images and the correlation in the original images and trains the classification hyper-plane in an adaptive feature tensor space. The KSTM-based predictive algorithm was compared with three conventional machine learning models and three radiomics approaches. For PET and CT, the KSTMbased predictive method achieved the highest prediction results among the seven methods investigated in this study based on 10-fold cross validation and independent testing.
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影响因子：
3.2
作者：
Abdi, Herve;Williams, Lynne J.
通讯作者：
Williams, Lynne J.
DOI：
10.1109/tpami.1979.4766921
发表时间：
1979-01-01
影响因子：
23.6
作者：
DAVIS, LS;JOHNS, SA;AGGARWAL, JK
通讯作者：
AGGARWAL, JK
影响因子：
10.6
作者：
Hao, Zhifeng;He, Lifang;Yang, Xiaowei
通讯作者：
Yang, Xiaowei
影响因子：
4.6
作者：
HOLMES, CE;RUCKDESCHEL, JC;LONG, S
通讯作者：
LONG, S
影响因子：
5
作者：
Chawla, NV;Bowyer, KW;Kegelmeyer, WP
通讯作者：
Kegelmeyer, WP