Rong, Yi

Abstract Background While the radiation dose to left anterior descending (LAD) artery is associated with overall survival (OS) in NSCLC patients, manual segmentation is labor‐intensive. Several auto‐segmentation models were developed on average 4DCT with promising geometric agreement, yet their feasibility in clinical outcome analysis on lung CT remains unclear. Purpose This study evaluates whether automated LAD segmentation can reproduce, at the group level, the established dose‐survival association in a multi‐institutional clinical trial dataset. Methods Three deep learning‐based auto‐segmentation (DLAS) models for automatic segmentation of the LAD coronary artery were applied to 460 patients from the NRG Oncology/RTOG 0617 dataset. Kaplan–Meier curves and Cox proportional hazard ratio (HR) were calculated using each model's segmentation. The LAD V15 Gy was used to stratify patients into high‐and low‐risk groups, and the cut‐off values were determined by minimizing mean absolute error (MAE) between DLAS‐derived and manual OS curves. Results All three models demonstrated significantly different OS curves between the high risk and low risk groups ( p < 0.05). The HR was 1.40 (95% CI: 1.02–1.93; p = 0.0381; MAE = 7.1%), 1.32 (95% CI: 1.03–1.68; p = 0.0258; MAE = 9.6%), and 1.37 (95% CI: 1.03–1.84; p = 0.0331; MAE = 7.9%) for the three models using a common V15 Gy ≥ 10% cut‐off. After optimizing the cut‐off, one model aligned well with what was derived from manual segmentation (MAE = 4.3%). Conclusion Among the evaluated models, one DL model showed the strongest performance of survival groups separation and achieved group‐level overall survival stratification comparable to that previously reported using manual contours in the multi‐institutional cohort. Further validation with paired manual contours and pre‐specified cutoffs is warranted.

Abstract Background The left anterior descending (LAD) coronary artery is a critical organ‐at‐risk in thoracic radiotherapy. In thoracic stereotactic body radiation therapy (SBRT), sharp dose gradients and poor LAD contrast on 4DCT scans introduce uncertainty in LAD dose assessment. Methods We evaluated the performance of an AI‐generated LAD region structure as a surrogate for manual LAD contours in early‐stage NSCLC. We used deep inspiration breath hold (DIBH) scans from 49 patients with manually delineated LADs; planning risk volume contours were expanded by 3 mm. Then, 96 lung SBRT dose maps were registered by matching the left anterior portion of the heart contours. Dice similarity coefficient (DSC), inclusion ratios (IR), dose–volume histogram (DVH) metrics, linear regression, and linear mixed models were used to compare the structures. The correlation of the AI LAD region and LAD dose to mean heart dose (MHD) was assessed. Results The AI LAD region showed strong correlation with manual LAD contours for maximum dose (D1%, R 2 = 0.93) and volume receiving ≥10 Gy (V10Gy, R 2 = 0.81). The planning risk volume also demonstrated high correlation to the AI LAD region. MHD was a poor predictor of LAD dose ( R 2 ≈0.4). Additional dose metric correlations were assessed. Discussion To investigate dose volume effects to the LAD during radiotherapy for stage I NSCLC, a robust segmentation paradigm must be established. It must be easily implementable on typical imaging acquired during treatment, with an emphasis on clinical translatability. Our model has shown that the AI LAD region has a strong correlation with LAD dose under typical lung SBRT doses and supports its use for further investigation into LAD dose volume constraints during lung SBRT. Conclusion AI LAD region provides a clinically feasible and scalable alternative to manual LAD contouring for dose estimation in lung SBRT. Its strong correlation with key dose metrics validates its use in large‐scale retrospective and prospective studies.

TPS6138 Background: Patients with early-stage oral cavity cancer (OCC; T1-2N0M0; AJCC 8th edition) have a 20-30% risk of occult nodal metastases despite clinical and radiographic evaluations. Standard-of-care treatment for most patients includes elective neck dissection (END), which requires surgical removal of the regional cervical lymph nodes, even though 70-80% of these necks are disease-free. Sentinel lymph node biopsy (SLN Bx), a less invasive procedure, could assess the first echelon lymph nodes as an alternative to END, potentially reducing morbidity and costs. A pivotal clinical trial comparing SLN Bx to END (NCT#04333537) is underway to inform efforts to establish optimal disease management for early-stage OCC. Methods: To assess the efficacy of SLN Bx in this population, we activated an international, multi-institutional, prospective phase II/III trial in July 2020, randomizing patients to two surgical arms: SLN Bx and END. A node-negative 18 F-FDG PET/CT imaging biomarker study with centralized read was required before randomization. OCC patients with a positive PET/CT remained in a registry to compare imaging findings with final neck pathology. Given the current evidence on morbidity for SLN Bx versus END, the phase II was designed to determine whether the change in patient-reported neck and shoulder function and related quality of life (QOL) from baseline to 6 months after surgery, using the Neck Dissection Impairment Index (NDII), showed a signal of superiority of SLN Bx compared to END (minimum important difference ³ 7.5; one-sided a = 0.10; 90% power). As of December 2024, 261 patients had been randomized and 203 were analyzed for the “Go/No-Go” decision to move forward into phase III. The phase III portion is a non-inferiority (NI) trial with disease-free survival (DFS) as the primary endpoint (NI margin hazard ratio 1.34 based on a 5% absolute difference in 2-year DFS; one-sided a = 0.05; 80% power, and two interim looks). The change in NDII from baseline to 6 months after surgery is a hierarchical co-primary endpoint for phase III. Phase III opened in October 2025 upon receiving a “Go” signal from phase II. Target accrual for phase III is 686 node-negative PET/CT patients, including those randomized in phase II (425 additional patients). In addition to sites requiring radiotherapy and imaging credentialing, quality assurance will include central pathology review of all negative SLN Bx cases and surgeon credentialing through an education course with SLN Bx and END case review by the surgical co-chairs. A surgical quality assurance working group will review all trial SLN Bx and END procedures. As of 01/12/26, 351 patients have been screened, and 273 of the planned 686 have been randomized. Clinical trial information: NCT#04333537 .

Abstract Additive manufacturing (AM), commonly referred to as 3D printing, has been broadly used in the field of medical imaging and radiotherapy. In medical imaging applications, patient‐specific anatomic models have been used to aid in preoperative planning, patient communication, imaging technique assessment and optimization, and medical education. Within the field of radiation therapy, 3D printing finds significance in fabricating patient‐specific immobilization devices, custom bolus devices, anthropomorphic phantoms, brachytherapy devices, and electron therapy devices. Consistent with standard medical physics practices there is a need to establish quality assurance and quality control (QA/QC) protocols, tolerances, and testing frequencies. The diversity of 3D printing technologies and products poses challenges for medical adoption and thus requires guidelines in workflow standardization and establishment of testing procedures. This report is centered around providing comprehensive vendor‐neutral implementation guidelines and recommendations for QC and QA processes. The scope of this report encompasses diverse requirements within medical imaging and radiation therapy settings, serving both clinical and research purposes. Brief overview of 3D printing technology, common materials, and potential clinical applications is provided. QA team, infrastructure, measurement techniques and challenges are discussed. QA processes for each step in 3D printing are discussed in great detail and baseline and action limits are recommended. This report also touched on regulatory considerations. Key recommendations are summarized at the end of the report.