Luis Ricardo de la O Arevalo

Introduction:Traditional NTCP models for predicting radiation-induced pneumonitis (RP) in lung cancer patients often rely on simplified dose characteristics, such as the mean lung dose, neglecting the spatial heterogeneity of dose distributions. This study introduces a deep learning (DL)-based NTCP model that integrates spatial dose information with clinical and anatomical data to improve RP prediction in loco-regionally advanced-stage lung cancer patients undergoing radiotherapy.Methods:A single-institute, retrospective cohort of 1,056 patients treated between 2013 and 2024 was used. RP was defined as CTCAE grade ≥2 within 6 or 12 months from the start of radiotherapy. The dataset was randomly split into a development and independent test set, stratified by RP incidence and immunotherapy status. A ResNet18-based DL model was trained using five-fold cross-validation. Inputs included planning CT, 3D dose distribution, lung minus gross tumour volume (Lungs-GTV) segmentation, and clinical variables (age, gender, smoking history, pulmonary comorbidities, immunotherapy, chemotherapy, surgical interventions, dose per fraction). Model performance was compared to a recent published RP NTCP model. To improve model interpretability, attentions maps generated using Grad-Cam++ highlighted regions of interest.Results:The DL-based NTCP model achieved enhanced performance on the independent test set (AUC=0.79, 95%CI[0.72-0.85]) compared to the reference model (AUC=0.76, 95%CI[0.70-0.83]) for predicting RP within 12 months. Attention maps mainly highlighted low-to-mid dose regions in the ipsilateral lung for higher RP prediction risk.Conclusion:Incorporating spatial dose information via a ResNet18-based DL model enhances RP prediction over traditional NTCP models.

**Abstract text** Stereotactic body radiotherapy (SBRT) is the standard approach for patients with early stage lung cancer who are inoperable bec | POSTER

Image segmentation is an essential process in modern radiotherapy. Both the tumors and organs at risk have to be segmented to optimize the radiot |