Darryl E Wright1, Sovanlal Mukherjee1, Anurima Patra2, Hala Khasawneh1, Panagiotis Korfiatis1, Garima Suman1, Suresh T Chari3,4, Yogish C Kudva5, Timothy L Kline1, Ajit H Goenka6. 1. Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA. 2. Department of Radiology, Tata Medical Center, Kolkata, 700160, India. 3. Department of Gastroenterology, Hepatology and Nutrition, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd, Houston, TX, 77030, USA. 4. Department of Gastroenterology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA. 5. Department of Endocrinology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA. 6. Department of Radiology, Mayo Clinic, 200 First Street SW, Charlton 1, Rochester, MN, 55905, USA. goenka.ajit@mayo.edu.
Abstract
PURPOSE: To determine if pancreas radiomics-based AI model can detect the CT imaging signature of type 2 diabetes (T2D). METHODS: Total 107 radiomic features were extracted from volumetrically segmented normal pancreas in 422 T2D patients and 456 age-matched controls. Dataset was randomly split into training (300 T2D, 300 control CTs) and test subsets (122 T2D, 156 control CTs). An XGBoost model trained on 10 features selected through top-K-based selection method and optimized through threefold cross-validation on training subset was evaluated on test subset. RESULTS: Model correctly classified 73 (60%) T2D patients and 96 (62%) controls yielding F1-score, sensitivity, specificity, precision, and AUC of 0.57, 0.62, 0.61, 0.55, and 0.65, respectively. Model's performance was equivalent across gender, CT slice thicknesses, and CT vendors (p values > 0.05). There was no difference between correctly classified versus misclassified patients in the mean (range) T2D duration [4.5 (0-15.4) versus 4.8 (0-15.7) years, p = 0.8], antidiabetic treatment [insulin (22% versus 18%), oral antidiabetics (10% versus 18%), both (41% versus 39%) (p > 0.05)], and treatment duration [5.4 (0-15) versus 5 (0-13) years, p = 0.4]. CONCLUSION: Pancreas radiomics-based AI model can detect the imaging signature of T2D. Further refinement and validation are needed to evaluate its potential for opportunistic T2D detection on millions of CTs that are performed annually.
PURPOSE: To determine if pancreas radiomics-based AI model can detect the CT imaging signature of type 2 diabetes (T2D). METHODS: Total 107 radiomic features were extracted from volumetrically segmented normal pancreas in 422 T2D patients and 456 age-matched controls. Dataset was randomly split into training (300 T2D, 300 control CTs) and test subsets (122 T2D, 156 control CTs). An XGBoost model trained on 10 features selected through top-K-based selection method and optimized through threefold cross-validation on training subset was evaluated on test subset. RESULTS: Model correctly classified 73 (60%) T2D patients and 96 (62%) controls yielding F1-score, sensitivity, specificity, precision, and AUC of 0.57, 0.62, 0.61, 0.55, and 0.65, respectively. Model's performance was equivalent across gender, CT slice thicknesses, and CT vendors (p values > 0.05). There was no difference between correctly classified versus misclassified patients in the mean (range) T2D duration [4.5 (0-15.4) versus 4.8 (0-15.7) years, p = 0.8], antidiabetic treatment [insulin (22% versus 18%), oral antidiabetics (10% versus 18%), both (41% versus 39%) (p > 0.05)], and treatment duration [5.4 (0-15) versus 5 (0-13) years, p = 0.4]. CONCLUSION: Pancreas radiomics-based AI model can detect the imaging signature of T2D. Further refinement and validation are needed to evaluate its potential for opportunistic T2D detection on millions of CTs that are performed annually.
Authors: Linda C Chu; Seyoun Park; Satomi Kawamoto; Daniel F Fouladi; Shahab Shayesteh; Eva S Zinreich; Jefferson S Graves; Karen M Horton; Ralph H Hruban; Alan L Yuille; Kenneth W Kinzler; Bert Vogelstein; Elliot K Fishman Journal: AJR Am J Roentgenol Date: 2019-04-23 Impact factor: 3.959
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