Estimating patient-specific organ doses from head and abdominal CT scans via machine learning with optimized regulation strength and feature quantity

Abstract

Purpose

This study aims to investigate estimation of patient-specific organ doses from CT scans via radiomics feature-based SVR models with training parameter optimization, and maximize SVR models’ predictive accuracy and robustness via fine-tuning regularization parameter and input feature quantities.

Methods

CT images from head and abdominal scans underwent processing using DeepViewer®, an auto-segmentation tool for defining regions of interest (ROIs) of their organs. Radiomics features were extracted from the CT data and ROIs. Benchmark organ doses were then calculated through Monte Carlo (MC) simulations. SVR models, utilizing these extracted radiomics features as inputs for model training, were employed to predict patient-specific organ doses from CT scans. The trained SVR models underwent optimization by adjusting parameters for the input radiomics feature quantity and regulation parameter, resulting in appropriate configurations for accurate patient-specific organ dose predictions.

Results

The C values of 5 and 10 have made the SVR models arrive at a saturation state for the head and abdominal organs. The SVR models’ MAPE and R² strongly depend on organ types. The appropriate parameters respectively are C = 5 or 10 coupled with input feature quantities of 50 for the brain and 200 for the left eye, right eye, left lens, and right lens. the appropriate parameters would be C = 5 or 10 accompanying input feature quantities of 80 for the bowel, 50 for the left kidney, right kidney, and 100 for the liver.

Conclusions

Performance optimization of selecting appropriate combinations of input feature quantity and regulation parameters can maximize the predictive accuracy and robustness of radiomics feature-based SVR models in the realm of patient-specific organ dose predictions from CT scans.