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Personalized Pancreatic Tumor Growth Prediction via Group Learning

  • Ling Zhang
  • Le Lu
  • Ronald M. Summers
  • Electron Kebebew
  • Jianhua YaoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

Abstract

Tumor growth prediction, a highly challenging task, has long been viewed as a mathematical modeling problem, where the tumor growth pattern is personalized based on imaging and clinical data of a target patient. Though mathematical models yield promising results, their prediction accuracy may be limited by the absence of population trend data and personalized clinical characteristics. In this paper, we propose a statistical group learning approach to predict the tumor growth pattern that incorporates both the population trend and personalized data. In order to discover high-level features from multimodal imaging data, a deep convolutional neural network approach is developed to model the voxel-wise spatio-temporal tumor progression. The deep features are combined with the time intervals and the clinical factors to feed a process of feature selection. Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient’s tumor. Multimodal imaging data at multiple time points are used in the learning, personalization and inference stages. Our method achieves a Dice coefficient of \(86.8\%\,\pm \,3.6\%\) and RVD of \(7.9\%\,\pm \,5.4\%\) on a pancreatic tumor data set, outperforming the DSC of \(84.4\%\,\pm \,4.0\%\) and RVD \(13.9\%\,\pm \,9.8\%\) obtained by a previous state-of-the-art model-based method.

Notes

Acknowledgments

This work was supported by the Intramural Research Program at the NIH Clinical Center. The authors thank Nvidia for the TITAN X Pascal GPU donation.

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Copyright information

© Springer International Publishing AG (outside the US) 2017

Authors and Affiliations

  • Ling Zhang
    • 1
  • Le Lu
    • 1
  • Ronald M. Summers
    • 1
  • Electron Kebebew
    • 2
  • Jianhua Yao
    • 1
    Email author
  1. 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory and the Clinical Image Processing Service, Radiology and Imaging Sciences DepartmentNational Institutes of Health Clinical CenterBethesdaUSA
  2. 2.Endocrine Oncology Branch, National Cancer InstituteNational Institutes of HealthBethesdaUSA

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