Zeng, Jing

Abstract Introduction: Radiotherapy (RT)-induced acute esophagitis (AE) is a common side effect in lung cancer patients receiving RT, which can significantly impact their quality of life. This highlights the need for a predictive model that can estimate AE risk in advance using pretreatment imaging data. However, collecting sufficient data for model development is often resource-intensive and costly. Additionally, acquiring a homogeneous training dataset (e.g., from a single center or modality) is not always feasible. This study aimed to develop an automated artificial intelligence-based 3D vision foundation model (VFM) combining standard-of-care planning computed tomography (pCT) and planned radiation dose maps to predict grade II or higher AE. Materials and methods: This study included 246 patients with non-small cell lung cancer who underwent either image-guided radiation treatment (IMRT) or proton-beam radiation therapy. The endpoint was grade two or higher AE (33% positive and 67% negative). The IMRT group consisted of 182 patients from a single center, whereas the proton therapy group included 64 patients from the Proton Collaborative Group trial from 11 different institutions. For each patient, pCT, dose maps, and radiotherapy segmentations were available. The VFM was created using a transformer pretrained using a large number of unlabeled volumetric 3D CTs from patients with varied diseases through a self-supervised learning approach, which extracts useful features directly from images. Next, the pretrained encoder was combined with fully connected classification layers and fine-tuned using stratified 5-fold cross-validation with 20 patients set aside for testing. We developed two models: a CT only and CT + dose model. Model performance was assessed using the area under the curve (AUC), specificity, and sensitivity. Results: In cross-validation, adding dose information to CT (CT+Dose) improved model performance, increasing AUC from 0.72 ± 0.08 to 0.76 ± 0.10 and specificity from 0.82 ± 0.09 to 0.87 ± 0.04, while sensitivity remained the same. On the independent test set, CT+Dose showed a marked improvement over CT only (AUC: 0.82 vs. 0.59; sensitivity: 0.57 vs. 0.14), with a slight decrease in specificity (0.77 vs. 1.00). Conclusion: A VFM model combining CT and radiation dose showed the capability to predict AE more accurately with higher specificity. Further studies on larger cohorts of testing patients are planned to assess model generalization. Citation Format: Chloe Min Seo Choi, Jue Jiang, Nikhil Mankuzhy, John Chang, Jing Zeng, Carlos Vargas, James Urbanic, Isabella Choi, Mark Mcdonald, James Gray, Joseph Deasy, Maria Thor, Charles Simone, Harini Veeraraghavan. Prediction of radiotherapy-Induced esophagitis in non-small cell lung cancer using a 3D vision foundation model [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Artificial Intelligence and Machine Learning; 2025 Jul 10-12; Montreal, QC, Canada. Philadelphia (PA): AACR; Clin Cancer Res 2025;31(13_Suppl):Abstract nr B022.

Abstract BACKGROUND Standard of care treatment for recurrent malignant glioma after prior radiotherapy (RT) is not well established. Proton therapy (PT) is increasingly used for reirradiation (ReRT) at the time of recurrence; however, treatment outcomes, toxicity, and prognostic factors for patients treated with PT-ReRT remain poorly defined. METHODS The prospective, multi-institutional Proton Collaborative Group (PCG) registry was queried for patients with malignant glioma who previously received RT and underwent PT-ReRT between 7/2011-12/2023. Overall survival (OS) and progression-free survival (PFS) were assessed using the Kaplan-Meier method, and Cox proportional hazards regression was used for uni- and multivariate analysis (UVA and MVA). Dose was calculated using equivalent dose in 2-Gy fractions (EQD2). RESULTS 143 consecutive patients were identified. Median follow-up was 11.2 months and median time interval (TI) from prior RT (median 58.5 Gy) to PT-ReRT (median 44.6 Gy) was 42.4 months. Primary histology was glioblastoma (n=74), astrocytoma (n=35), and oligodendroglioma (n=34). Median PFS and OS were 8.1 and 11.2 months, respectively. On UVA, improved OS was associated with oligodendroglioma (HR 0.37) and astrocytoma histology (HR 0.53) compared to glioblastoma, TI >60 months (HR 0.2), CNS WHO Grade 2 compared to 4 (HR 0.42), and ECOG performance status 0 (HR 0.47). On MVA, improved OS remained associated only with oligodendroglioma (HR 0.42) and TI >60 months (HR 0.49). Acute and Late Grade 3 toxicity occurred in 7% and 4% of patients. Acute Grade 3 toxicity was associated with poor performance status (HR 16.15). CONCLUSIONS In the largest series of glioma PT-ReRT reported to date, retreatment appears well tolerated with variable outcomes based on clinical prognostic factors. Toxicity rates were acceptable compared to historical photon-based literature despite a high median prescription dose.