Exploring Quantitative MRI Biomarkers of Head and Neck Post-Radiation Lymphedema and Fibrosis: Post Hoc Analysis of a Prospective Trial.

Background: Quantifying head and neck lymphedema and fibrosis (HN-LEF) is crucial in the investigation and management of treatment sequelae in head and neck cancer (HNC).

Methods: The T1- and T2-weighted MRI signal intensity (SI) was examined in relation to HN-LEF categories per physical/tactile examination (No-LEF, A-B = edema, C = edema + fibrosis, D = fibrosis), and MRI structural volumes were examined in relation to a novel 10-point HN-LEF score in the intraoral and submental regions.

Results: We identified differences in ranks among HN-LEF categories in relation to the MRI SI (A-B and C are higher than D and No-LEF for T2 SI, and A-B is the highest for T1).

Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge.

Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention.

Optimal Timing of Organs-at-Risk-Sparing Adaptive Radiation Therapy for Head- and-Neck Cancer under Re-planning Resource Constraints.

Background And Purpose: Prior work on adaptive organ-at-risk (OAR)-sparing radiation therapy has typically reported outcomes based on fixed-number or fixed-interval re-planning, which represent one-size-fits-all approaches and do not account for the variable progression of individual patients' toxicities. The purpose of this study was to determine the personalized optimal timing for re-planning in adaptive OAR-sparing radiation therapy, considering limited re-planning resources, for patients with head and neck cancer (HNC).

Materials And Methods: A novel Markov decision process (MDP) model was developed to determine optimal timing of re-planning based on the patient's expected toxicity, characterized by normal tissue complication probability (NTCP), for four toxicities.

Artificial intelligence uncertainty quantification in radiotherapy applications - A scoping review.

Background/purpose: The use of artificial intelligence (AI) in radiotherapy (RT) is expanding rapidly. However, there exists a notable lack of clinician trust in AI models, underscoring the need for effective uncertainty quantification (UQ) methods. The purpose of this study was to scope existing literature related to UQ in RT, identify areas of improvement, and determine future directions.