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.
Muhammad, H., Häggström, I., Klimstra, D. S., & Fuchs, T. J. (2018). Survival modeling of pancreatic cancer with radiology using convolutional neural networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11042 LNCS, pp. 187–192). Springer Verlag. https://doi.org/10.1007/978-3-030-01045-4_23