A time-series deep learning radiomics model predicted post-radiotherapy rib fractures in non-small cell lung cancer with an AUC of 0.792, sensitivity of 0.85, and specificity of 0.78.
Does the KA-TMoE deep learning model accurately predict post-radiotherapy rib fractures in NSCLC patients undergoing SBRT?
A novel time-series deep learning radiomics model (KA-TMoE) demonstrated strong predictive performance for post-radiotherapy rib fractures in NSCLC patients, enabling personalized risk assessment.
Absolute Event Rate: 0% vs 0%
Abstract Background and purpose: Rib fracture is a recognized clinical complication in medically inoperable patients with non-small cell lung cancer (NSCLC) undergoing stereotactic body radiotherapy (SBRT), leading to diminished quality of life and delayed recovery. This study aimed to develop and validate a deep learning model for predicting post-radiotherapy rib fracture using time-series CT radiomics. Material and methods: This study retrospectively collected CT scans from 67 NSCLC patients, comprising 1,605 individual ribs as separate instances. We proposed a novel Knowledge-aware Temporal Mixture of Experts (KA-TMoE) model that integrates radiomics from sequential CT scans to estimate fracture risk for each rib. Model performance was evaluated using area under the curve (AUC), sensitivity, specificity, and F1 score. Model interpretability was achieved using SHapley Additive exPlanations analysis, which attributed predictive value to each input feature. Results: The KA-TMoE model demonstrated strong predictive performance, achieving favorable AUC in the validation cohort (0.792). The DeLong test confirmed statistically significant improvements over ablation variants, underscoring the importance of integrating temporal data and domain knowledge. High sensitivity (0.85) and specificity (0.78) reflected a well-balanced trade-off, surpassing alternative approaches. Whitney U tests further supported its robustness, which showed significant differences in output distributions across cohorts. Among the top 20 most influential features, half originated from three-month postoperative radiomics, emphasizing the critical role of temporal information. Conclusion: The KA-TMoE model provides a robust, accurate framework for predicting rib fractures after SBRT in NSCLC patients. Its predictive power enables personalized risk assessment, better patient management, and optimized clinical prognosis. Citation Format: Yijun Chen, Michael Farris, Ariel Choi, Nga Thi Thanh Nguyen, Amanda Goetz, Corbin A. Helis, Fei Xing, Liang Liu, Qing Lyu, Christopher T. Whitlow, Christina K. Cramer, Michael D. Chan, Dan Bourland, Michael T. Munley, Jeffrey S Willey, Yuming Jiang. Time-series deep learning radiomics for predicting post-radiotherapy rib fractures in non-small cell lung cancer abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3732.
Chen et al. (Fri,) reported a other. A time-series deep learning radiomics model predicted post-radiotherapy rib fractures in non-small cell lung cancer with an AUC of 0.792, sensitivity of 0.85, and specificity of 0.78.