Mankuzhy, Nikhil P.

Abstract Purpose: Knowledge of prognostic factors and long-term survival in patients with diffuse hemispheric glioma, H3 G34–mutant (DHG, H3 G34), remains limited in this tumor with a poor prognosis. Experimental Design: This retrospective, multi-institutional study investigated prognostic variables for patients with DHG, H3 G34, and their association with progression-free survival (PFS) and overall survival (OS). Uni- and multivariable Cox proportional hazard models were applied with multiple imputed datasets. Results: A total of 153 patients (142 G34R, 9 G34V, 2 via DNA methylation) were included. The median age at diagnosis was 17 years (range, 2–45). Initial gross/near total resection (GTR/NTR) was achieved in 43% of patients. Radiation was given in 91% (85% focal irradiation), and initial chemotherapy was given in 87% [70% temozolomide-based (TMZ), 25% TMZ/lomustine, 5% non-TMZ]. Median OS was 24 months [interquartile range (IQR), 22–28] with a median PFS of 14 months (IQR, 12–19). Twelve patients (8%) were found to be long-term survivors (≥5 years). Exploratory multivariable analysis showed that adjuvant radiotherapy [HR, 0.076; 95% confidence interval (CI), 0.033–0.17] and achieving GTR/NTR compared with < NTR (HR, 0.51; 95% CI, 0.33–0.78) were associated with improved PFS. Multivariable analysis showed improved OS with increasing age at diagnosis (HR, 0.70; 95% CI, 0.57–0.87), initial radiotherapy (HR, 0.38; 95% CI, 0.15–0.96), and initial GTR/NTR compared with < NTR (HR, 0.60; 95% CI, 0.37–0.97). Conclusions: This cohort highlights prognostic factors for patients with DHG, H3 G34, and describes relapse patterns and therapy approaches. Clinical trials and prospective registries are needed to improve outcomes.

This cohort study evaluates the use of definitive rhinectomy and adjuvant radiotherapy with or without chemotherapy for patients who declined rhinectomy or had unresectable disease.

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.