Fast estimation of patient-specific organ doses from abdomen and head CT examinations without segmenting internal organs using machine learning models.

BACKGROUND: Computed Tomography (CT) imaging is essential for disease detection but carries a risk of cancer due to X-ray exposure. Typically, assessing this risk requires segmentation of the internal organ contours to predict organ doses, which hinders its clinical application. This study introduces a method that uses support vector regression (SVR) models trained on skin outline radiomic features to predict organ doses without organ segmentation, thus streamlining the process for clinical use.

METHODS: CT scans of the head and abdomen were used to extract radiomic features of the skin outline. These features were used as inputs, with organ doses from Monte Carlo simulations as benchmarks to train the SVR models for predicting organ doses. The accuracy of the models was evaluated using the mean absolute percentage error (MAPE) and coefficient of determination (R2).

RESULTS: The results showed a high precision in dose prediction for various organs, including the brain (MAPE: 1.5%, R2: 0.9), eyes (MAPE: 5%, R2: 0.84), lens (MAPE: 5%, R2: 0.82), bowel (MAPE: 6%, R2: 0.84), kidneys (MAPE: 7.5%, R2: 0.7), and liver (MAPE: 8%, R2: 0.67). Internal organ disturbances had a minimal impact on accuracy.

CONCLUSIONS: The SVR models efficiently predicted patient-specific organ doses from CT scans, offering a user-friendly tool for rapid segmentation-free dose prediction. This innovation can significantly enhance clinical efficiency and accessibility in predicting patient-specific organ doses using CT.