Survival modeling of pancreatic cancer with radiology using convolutional neural networks Conference Paper

  


Authors: Muhammad, H.; Häggström, I.; Klimstra, D. S.; Fuchs, T. J.
Title: Survival modeling of pancreatic cancer with radiology using convolutional neural networks
Conference Title: International Workshop on Computational Precision Medicine (CPM 2018) held in conjunction with the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
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. © Springer Nature Switzerland AG 2018.
Keywords: survival analysis; computerized tomography; medical imaging; disease progression; diagnosis; patient treatment; medical computing; diseases; image segmentation; survival prediction; non-invasive imaging; neural networks; ultrasonic applications; deep learning; radiomics; convolution; convolutional neural network; high-level features; morphological signatures
Journal Title Lecture Notes in Computer Science
Volume: 11042
Conference Dates: 2018 Sep 16
Conference Location: Granada, Spain
ISBN: 0302-9743
Publisher: Springer  
Location: Cham, Switzerland
Date Published: 2018-01-01
Start Page: 187
End Page: 192
Language: English
DOI: 10.1007/978-3-030-01045-4_23
PROVIDER: scopus
DOI/URL:

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85054410488&doi=10.1007%2f978-3-030-01045-4_23&partnerID=40&md5=92d838f9c234c3ffb30fd6ed7d01fc02
Notes: Conference Paper -- Export Date: 1 November 2018 -- Source: Scopus
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MSK Authors
  1. David S Klimstra
    978 Klimstra
  2. Thomas Fuchs
    29 Fuchs
  3. Ida Haeggstroem
    11 Haeggstroem
  4. Hassan Muhammad
    4 Muhammad
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