Advertisement

Survival Modeling of Pancreatic Cancer with Radiology Using Convolutional Neural Networks

  • Hassan Muhammad
  • Ida Häggström
  • David S. Klimstra
  • Thomas J. Fuchs
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11042)

Abstract

No reliable biomarkers for early detection of pancreatic cancer are known to date but morphological signatures from non-invasive imaging might be able to close this gap. In this paper, we present a convolutional neural network-based survival model trained directly from computed tomography (CT) images. 159 CT images with associated survival data, and 3D segmentations of organ and tumor were provided by the Pancreatic Cancer Survival Prediction MICCAI grand challenge. A simple, yet novel, approach was used to convert CT slices into RGB-channel images in order to utilize pre-training of the model’s convolutional layers. The proposed model achieves a concordance index of 0.85, indicating a relationship between high-level features in CT imaging and disease progression. The ultimate hope is that these promising results translate to more personalized treatment decisions and better cancer care for patients.

Keywords

Deep learning Radiomics Survival analysis 

References

  1. 1.
    Basturk, O., et al.: A revised classification system and recommendations from the baltimore consensus meeting for neoplastic precursor lesions in the pancreas. Am. J. Surg. Pathol. 39(12), 1730 (2015)CrossRefGoogle Scholar
  2. 2.
    Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: IEEE Conference on Computer Vision and Pattern Recognition, 2009, CVPR 2009, pp. 248–255. IEEE (2009)Google Scholar
  3. 3.
    Faraggi, D., Simon, R.: A neural network model for survival data. Stat. Med. 14(1), 73–82 (1995)CrossRefGoogle Scholar
  4. 4.
    van Griethuysen, J.J., et al.: Computational radiomics system to decode the radiographic phenotype. Cancer Res. 77(21), e104–e107 (2017)CrossRefGoogle Scholar
  5. 5.
    Harrell Jr., F.E., Lee, K.L., Califf, R.M., Pryor, D.B., Rosati, R.A.: Regression modelling strategies for improved prognostic prediction. Stat. Med. 3(2), 143–152 (1984)CrossRefGoogle Scholar
  6. 6.
    Kieler, M., Unseld, M., Bianconi, D., Prager, G.: Challenges and perspectives for immunotherapy in adenocarcinoma of the pancreas: the cancer immunity cycle. Pancreas 47(2), 142–157 (2018)CrossRefGoogle Scholar
  7. 7.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  8. 8.
    Lowery, M.A., et al.: Phase II trial of veliparib in patients with previously treated BRCA-mutated pancreas ductal adenocarcinoma. Eur. J. Cancer 89, 19–26 (2018)CrossRefGoogle Scholar
  9. 9.
    Mobadersany, P., et al.: Predicting cancer outcomes from histology and genomics using convolutional networks. Proc. Natl. Acad. Sci., 201717139 (2018)Google Scholar
  10. 10.
    Wu, W., et al.: Rising trends in pancreatic cancer incidence and mortality in 2000–2014. Clin. Epidemiol. 10, 789–797 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Hassan Muhammad
    • 1
    • 2
  • Ida Häggström
    • 2
  • David S. Klimstra
    • 2
  • Thomas J. Fuchs
    • 1
    • 2
  1. 1.Weill Cornell MedicineNew YorkUSA
  2. 2.Memorial Sloan-Kettering Cancer CenterNew YorkUSA

Personalised recommendations