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To develop a Machine Learning (ML) model that accurately estimates the patient fat-free mass (FFM) in order to personalize intravenous (i.v.) contrast volume injection and ensure reproducible target liver enhancement. A dataset of previously collected abdominal CT data from 689 adult patients referred for liver lesion characterization or cancer follow-up was utilized. The data was obtained from eleven different radiology centers, utilizing various CT vendors, spanning the period from 2018 to 2022. The dataset encompasses diverse patient characteristics and measurements, including age, gender, weight, height, Body Mass Index (BMI), Size Specific Dose Estimate (SSDE), and FFM measured with a Bioelectrical Impedance meter. A multivariate linear regression model was developed to estimate FFM. The relationships between the investigated variables and the measured FFM were examined, and the most highly correlated variables were selected for inclusion in the final model. The dataset was divided into training and test sets according to the 80/20 rule and validated using the K-fold technique. We built several multivariate linear regression models and evaluated the performance of the trained model using the testing dataset against metrics such as the GT using Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), and R-squared. The cross-validation results for the best model reveal the model's robustness in typical patient profiles within our clinical settings. Specifically, the model exhibits low MAPE (0.033+/-0.003), high R2 (0.91+/-0.02), and relatively low standard deviation of residuals RMSE (2.13+/-0.23). External validation is being performed and preliminary results confirm the validity of the implementation of the theoretical FFM estimation in our personalized algorithm for contrast injection. Our model provides reliable and efficient estimation of patient FFM, facilitating the personalization of i.v. contrast volume in adult abdominal CT examinations while eliminating the need for expensive equipment.
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