Learning-Based Radiomic Prediction of Type 2 Diabetes Mellitus Using Image-Derived Phenotypes

Michael S Yao, Allison Chae, Matthew Timothy MacLean, Anurag Verma, Jeffrey T. Duda, James Gee, Drew Torigian, Daniel James Rader, Charles Kahn, Walter R Witschey, Hersh Sagreiya

Keywords: Tabular Data, Disease Prediction, Machine Learning for Health, Out-of-Domain Generalization, Classification

TL;DR: Tabular learning models can predict patient diabetes risk from a combination of noninvasive physical examination and CT imaging data.

Abstract: Early diagnosis of Type 2 Diabetes Mellitus (T2DM) is crucial to enable timely therapeutic interventions and lifestyle modifications. As medical imaging data become more widely available for many patient populations, we sought to investigate whether image-derived phenotypic data could be leveraged in tabular learning classifier models to predict T2DM incidence without the use of invasive blood lab measurements. We show that both neural network and decision tree models that use image-derived phenotypes can predict patient T2DM status with recall scores as high as 87.6%. We also propose the novel use of these same architectures as 'SynthA1c encoders' that are able to output interpretable values mimicking blood hemoglobin A1C empirical lab measurements. Finally, we demonstrate that T2DM risk prediction model sensitivity to small perturbations in input vector components can be used to predict performance on covariates sampled from previously unseen patient populations.

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