KA-TMoE: A Deep Learning Method Using Time-Series Computed Tomography Radiomics to Predict Rib Fractures after
Yijun Chen1, Michael Farris1, Ariel R Choi1
1Department of Radiation Oncology, Wake Forest University School of Medicine, Winston-Salem, North Carolina.
Purpose:
Rib fracture is a recognized clinical complication in medically inoperable patients with non-small cell lung cancer (NSCLC) undergoing stereotactic body radiation therapy (SBRT), leading to diminished quality of life and delayed recovery. There remains an unmet need for reliable tools to predict rib-fracture risk to support individualized prognosis. This study aimed to develop and validate a deep learning model for predicting post-SBRT rib fractures using time-series computed tomography (CT) radiomics.
Methods And Materials:
This retrospective study collected CT scans from 3 timepoints in 67 patients with NSCLC, comprising over 1600 individual ribs. We proposed a novel Knowledge-aware Temporal Mixture-of-Experts (KA-TMoE) model that integrates longitudinal CT radiomics with radiomic grouping knowledge to estimate fracture risk at the rib level. Model performance and interpretability were evaluated.
Results:
The KA-TMoE model demonstrated favorable predictive performance in the validation cohort, achieving an area under the receiver operating characteristic curve (AUC) of 0.792. Exploratory DeLong testing was generally consistent with the observed performance differences between KA-TMoE and the ablation variants, suggesting that both longitudinal information and radiomics-grouping knowledge contributed to model performance. Mann-Whitney U tests demonstrated significant differences in model output distributions across cohorts. Time-to-event analysis showed that the model-predicted high-risk group had a higher risk of fracture than the low-risk group (hazard ratio = 10.82; P < .001). Multivariable logistic analysis showed that the KA-TMoE output remained independently associated with fracture risk in the validation cohort (odds ratio = 12.05; P = .002). Decision curve analysis demonstrated potential net benefit across clinically relevant thresholds. Features from all 3 timepoints contributed to the model's decision-making, highlighting the importance of temporal information.
Conclusion:
KA-TMoE showed potential as a preliminary rib-level risk-stratification framework for predicting post-SBRT rib fractures in patients with NSCLC. It may support earlier personalized risk stratification, closer surveillance, and timely supportive evaluation.
