Dosiomics-guided deep learning for radiation esophagitis prediction in lung cancer: optimal region of interest definition via multi-branch fusion auxiliary learning

Background

Accurate delineation of regions of interest (ROIs) is critical for feature extraction and selection in radiomics-based prediction models.

Purpose

To develop a combined dosiomics and deep learning (DL) model for predicting grade ≥ 2 radiation esophagitis (RE) in lung cancer patients undergoing radiotherapy, we propose a multi-task auxiliary learning approach to define accurate and objective ROIs based on radiation dose distribution (RDD) images.

Materials and Methods

Lung cancer patients who underwent radiotherapy were gathered retrospectively from hospital 1 (January 2020 and December 2022) for model development. Two external validation sets were obtained retrospectively from hospital 2 (January 2021 and December 2022) and hospital 3 (January 2022 and December 2023), respectively. A dosiomics-guided deep learning (DGD) network using multi-task auxiliary learning to define accurate and objective ROIs was introduced by integrating dosiomic features with high-dimensional DL features for RE prediction.

Results

This study enrolled 488 patients from three hospitals: 235 in the training set, 101 in the internal validation set, 57 in the external validation set 1 and 95 in the external validation set 2, respectively. The dosiomics −guided ResNet34 combined with contrastive learning and auxiliary segmentation module achieved the best AUCs of 0.88 [95% CI: 0.76–0.95], 0.82 [95% CI: 0.65–0.96], 0.83 [95% CI: 0.74–0.92] in the internal validation set, external validation set 1, and external validation set 2, respectively.

Conclusion

The proposed DGD model leverages multi-task auxiliary learning to automatically define ROIs and effectively predict RE in lung cancer patients undergoing radiotherapy.