A pilot study using kernelled support tensor machine for distant failure prediction in lung SBRT

Shulong Li, Ning Yang, Bin Li, Zhiguo Zhou, Hongxia Hao, Michael R. Folkert, Puneeth Iyengar, Kenneth Westover, Hak Choy, Robert Timmerman, Steve Jiang, Jing Wang

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


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 KSTM-based predictive method achieved the highest prediction results among the seven methods investigated in this study based on 10-fold cross validation and independent testing.

Original languageEnglish (US)
Pages (from-to)106-116
Number of pages11
JournalMedical Image Analysis
StatePublished - Dec 2018


  • Medical imaging
  • Radiomics
  • SBRT
  • Support tensor machine

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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