ARRS 2022 Abstracts

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1664. Radiomics-Based Machine Learning (ML) Classifier for Detection of Type-2 Diabetes Mellitus (T2DM) on Standard-of-Care Abdomen CTs
Authors * Denotes Presenting Author
  1. Darryl Wright *; Mayo Clinic
  2. Anurima Patra; Mayo Clinic
  3. Garima Suman; Mayo Clinic
  4. Hala Khasawneh; Mayo Clinic
  5. Panagiotis Korfiatis; Mayo Clinic
  6. Timothy Kline; Mayo Clinic
  7. Ajit Goenka; Mayo Clinic
Objective:
Globally, around 185 million people living with T2DM have silent disease. Our hypothesis was that T2DM-associated pancreatic imaging phenotype could be characterized with quantitative radiomics. Our purpose was to determine if CT radiomics-based ML classifier could differentiate patients with T2DM versus control subjects.

Materials and Methods:
Volumetric pancreas segmentation using 3D Slicer was done on a dataset [portal venous CTs, slice thickness (ST) = 5mm] with morphologically normal pancreas in 422 patients with T2DM (mean age=63 years; male: female=0.8:1) and 456 controls (mean age=64 years; male: female=1.1:1). Data were randomly split into training (n=600; 300 each of T2DM and controls) and test sets (n=278; 122 T2DM, 156 controls). Pre-processing included: modified soft tissue window (level 50 HU, width 500 HU); rescaling to range 0-255; resampling to 0.75 x 0.75 x 2-mm (in-plane x ST). Using a bin width of 25,107 features (shape, first order, and texture features) were extracted using PyRadiomics. Multiple feature selection methods were tested. Four classifiers: logistic regression, SVMs, random forests, and XGBoost - were built using selected features. Parameters were chosen through a 3-fold cross-validation grid search on the training set. Model with the highest performance was evaluated on the independent test set.

Results:
Mean (range) age of T2DM and control cohorts in the test set was 63 (32-90) and 64 (35-89) years, respectively. Mean (range) T2DM duration was 4.6 (0-15.7) years. Patients with T2DM were on insulin (n=16, 13%), oral antidiabetics (n=25, 20.5%), both (n=49, 40%), or no medications (n=32, 26%). In cross validation, XGBoost model (n_estimators=100, gamma=0.1, max_depth=4, lambda=10, learning_rate=0.01), which was based on the top 10 features selected according to the ANOVA F-value, had the highest performance [accuracy: 0.673 (0.012), area under the curve (AUC) 0.691 (0.025)]. On the test set, the model correctly classified 73 (60%) T2DM CT scans and 97 (62.2%) controls, which translated to accuracy and AUC of 0.61 (0.02) and 0.65 (0.014), respectively. Model’s performance was similar across genders (p=0.6), CT STs (accuracy 58% on ST <3 mm vs 64% on ST >3 mm; p=0.4) and vendors (accuracy 59.2% on CTs from Siemens vs 76% on CTs from others; p=0.1). Mean (range) T2DM duration (in years) was similar for correctly classified [4.5 (0-15.4)] vs misclassified CTs [4.8 (0-15.7)] (p=0.8). There was no difference in the distribution of correctly classified vs. misclassified T2DM CT scans with regards to type of antidiabetic medication: insulin (22% vs. 8%), oral antidiabetics (9% vs 18%), both (41% vs 39%) (p=0.6). Mean (range) duration (in years) of antidiabetic medications was also similar for correctly classified [5.4 (0-15)] vs misclassified CTs [5 (0-13)] (p=0.4).

Conclusion:
ML classifier based on radiomics extracted from a morphologically normal pancreas could differentiate patients with T2DM vs unaffected subjects. Prospective validation on enriched multicenter datasets could offer the prospect of opportunistic CT detection of undiagnosed or unsuspected T2DM.