Abstract
Image-based precision medicine aims to personalize treatment decisions based on an individual’s unique imaging features so as to improve their clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. This allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes.
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Notes
- 1.
See [24] for a review on causality in medical imaging.
- 2.
Also known as conditional average treatment effect (CATE).
- 3.
Results on other treatments can be found in the Appendix.
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Acknowledgement
This investigation was supported by the International Progressive Multiple Sclerosis Alliance (PA-1412-02420), the companies who generously provided the data: Biogen, BioMS, MedDay, Novartis, Roche/Genentech, and Teva, the Canada Institute for Advanced Research (CIFAR) AI Chairs program, the Natural Sciences and Engineering Research Council of Canada, the Multiple Sclerosis Society of Canada, Calcul Quebec, and the Digital Research Alliance of Canada (alliance.can.ca). The authors would like to thank Louis Collins and Mahsa Dadar for preprocessing the MRI data, Zografos Caramanos, Alfredo Morales Pinzon, Charles Guttmann and István Mórocz for collating the clinical data, and Sridar Narayanan. Maria-Pia Sormani for their MS expertise.
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Durso-Finley, J., Falet, JP., Mehta, R., Arnold, D.L., Pawlowski, N., Arbel, T. (2023). Improving Image-Based Precision Medicine with Uncertainty-Aware Causal Models. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14224. Springer, Cham. https://doi.org/10.1007/978-3-031-43904-9_46
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