Personalized Pancreatic Tumor Growth Prediction via Group Learning
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.
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.
- 1.Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. (TIST) 2(3), 27 (2011)Google Scholar
- 2.Clatz, O., Sermesant, M., Bondiau, P.Y., Delingette, H., Warfield, S.K., Malandain, G., Ayache, N.: Realistic simulation of the 3D growth of brain tumors in MR images coupling diffusion with biomechanical deformation. TMI 24(10), 1334–1346 (2005)Google Scholar
- 3.Greenspan, H., van Ginneken, B., Summers, R.M.: Guest editorial deep learning in medical imaging: overview and future promise of an exciting new technique. TMI 35(5), 1153–1159 (2016)Google Scholar
- 6.Jia, Y.: Caffe: an open source convolutional architecture for fast feature embedding (2013). http://caffe.berkeleyvision.org/
- 7.Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: NIPS, pp. 1097–1105 (2012)Google Scholar
- 9.Wong, K.C.L., Summers, R.M., Kebebew, E., Yao, J.: Pancreatic tumor growth prediction with elastic-growth decomposition, image-derived motion, and FDM-FEM coupling. TMI 36(1), 111–123 (2017)Google Scholar
- 10.Yao, J., Wang, S., Zhu, X., Huang, J.: Imaging biomarker discovery for lung cancer survival prediction. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9901, pp. 649–657. Springer, Cham (2016). doi: 10.1007/978-3-319-46723-8_75CrossRefGoogle Scholar