Deep learning combining imaging, dose and clinical data for predicting bowel toxicity after pelvic radiotherapy.

ABSTRACT:

Background and purpose

A comprehensive understanding of radiotherapy toxicity requires analysis of multimodal data. However, it is challenging to develop a model that can analyse both 3D imaging and clinical data simultaneously. In this study, a deep learning model is proposed for simultaneously analysing computed tomography scans, dose distributions, and clinical metadata to predict toxicity, and identify the impact of clinical risk factors and anatomical regions.

Materials and methods

: A deep model based on multiple instance learning with feature-level fusion and attention was developed. The study used a dataset of 313 patients treated with 3D conformal radiation therapy and volumetric modulated arc therapy, with heterogeneous cohorts varying in dose, volume, fractionatio

SUBMITTER: Elhaminia B 

PROVIDER: S-EPMC11880715 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature