This study aims to develop a neural network-based method for predicting patient-specific organ doses from chest CT scans, utilizing hybrid patient size vectors for enhanced computational efficiency, accuracy, and generality. A dataset of 705 chest CT scans was retrospectively analyzed to construct predictive models for organ dose estimation. The proposed approach employs high dimensional hybrid vectors to represent patient size, combining muti-slice parameters regarding lateral dimension, anteroposterior dimension, and water-equivalent diameter (D