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 and Machine Learning in Cancer Pain: A Systematic Review.

Background/objectives: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer.

Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer," "Pain," "Pain Management," "Analgesics," "Artificial Intelligence," "Machine Learning," and "Neural Networks" published up to September 7, 2023.

Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning.

Background: Radiotherapy is a core treatment modality for oropharyngeal cancer (OPC), where the primary gross tumor volume (GTVp) is manually segmented with high interobserver variability. This calls for reliable and trustworthy automated tools in clinician workflow. Therefore, accurate uncertainty quantification and its downstream utilization is critical.

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.

Biodegradable suture development-based albumin composites for tissue engineering applications.

Recent advancements in the field of biomedical engineering have underscored the pivotal role of biodegradable materials in addressing the challenges associated with tissue regeneration therapies. The spectrum of biodegradable materials presently encompasses ceramics, polymers, metals, and composites, each offering distinct advantages for the replacement or repair of compromised human tissues. Despite their utility, these biomaterials are not devoid of limitations, with issues such as suboptimal tissue integration, potential cytotoxicity, and mechanical mismatch (stress shielding) emerging as significant concerns.

Longitudinal changes in the carotid arteries of head and neck cancer patients following radiation therapy: Results from a prospective serial imaging biomarker characterization study.

Introduction: We prospectively evaluated morphologic and functional changes in the carotid arteries of patients treated with unilateral neck radiation therapy (RT) for head and neck cancer.

Methods: Bilateral carotid artery duplex studies were performed at 0, 3, 6, 12, 18 months and 2, 3, 4, and 5 years following RT. Intima media thickness (IMT); global and regional circumferential, as well as radial strain, arterial elasticity, stiffness, and distensibility were calculated.

Comparison of Machine Leaning Models for Prediction of Acute Pain Severity and On-Treatment Opioid Utilization in Oral Cavity and Oropharyngeal Cancer Patients Receiving Radiation Therapy: Exploratory Analysis from a Large-Scale Retrospective Cohort.

Background: Acute pain is a common and debilitating symptom experienced by oral cavity and oropharyngeal cancer (OC/OPC) patients undergoing radiation therapy (RT). Uncontrolled pain can result in opioid overuse and increased risks of long-term opioid dependence. The specific aim of this exploratory analysis was the prediction of severe acute pain and opioid use in the acute on-treatment setting, to develop risk-stratification models for pragmatic clinical trials.

Artificial Intelligence and Machine Learning in Cancer Related Pain: A Systematic Review.

Background/objective: Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer.

Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023.

Weekly Intra-Treatment Diffusion Weighted Imaging Dataset for Head and Neck Cancer Patients Undergoing MR-linac Treatment.

Radiation therapy (RT) is a crucial treatment for head and neck squamous cell carcinoma (HNSCC), however it can have adverse effects on patients' long-term function and quality of life. Biomarkers that can predict tumor response to RT are being explored to personalize treatment and improve outcomes. While tissue and blood biomarkers have limitations, imaging biomarkers derived from magnetic resonance imaging (MRI) offer detailed information.

Multi-organ spatial stratification of 3-D dose distributions improves risk prediction of long-term self-reported severe symptoms in oropharyngeal cancer patients receiving radiotherapy: development of a pre-treatment decision support tool.

Purpose: Identify Oropharyngeal cancer (OPC) patients at high-risk of developing long-term severe radiation-associated symptoms using dose volume histograms for organs-at-risk, via unsupervised clustering.

Material And Methods: All patients were treated using radiation therapy for OPC. Dose-volume histograms of organs-at-risk were extracted from patients' treatment plans.

Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases.

Background: Demand for head and neck cancer (HNC) radiotherapy data in algorithmic development has prompted increased image dataset sharing. Medical images must comply with data protection requirements so that re-use is enabled without disclosing patient identifiers. Defacing, i.

Application of simultaneous uncertainty quantification for image segmentation with probabilistic deep learning: Performance benchmarking of oropharyngeal cancer target delineation as a use-case.

Background: Oropharyngeal cancer (OPC) is a widespread disease, with radiotherapy being a core treatment modality. Manual segmentation of the primary gross tumor volume (GTVp) is currently employed for OPC radiotherapy planning, but is subject to significant interobserver variability. Deep learning (DL) approaches have shown promise in automating GTVp segmentation, but comparative (auto)confidence metrics of these models predictions has not been well-explored.

International Multi-Specialty Expert Physician Preoperative Identification of Extranodal Extension n Oropharyngeal Cancer Patients using Computed Tomography: Prospective Blinded Human Inter-Observer Performance Evaluation.

Importance: Extranodal extension (pENE) is a critical prognostic factor in oropharyngeal cancer (OPC) that drives therapeutic disposition. Determination of pENE from radiological imaging has been associated with high inter-observer variability. However, the impact of clinician specialty on human observer performance of imaging-detected extranodal extension (iENE) remains poorly understood.