Dose Planning and Radiation Optimization for Thoracic Conventional, Twice Daily, and Stereotactic Radiation Therapy: A Delphi Consensus From a National Survey of Practitioners.

Purpose: We sent surveys to a large number of radiation oncologists with active thoracic cancer practices and applied the Delphi method over 3 rounds to generate consensus dose-volume histogram metrics. We used these results to create consensus-based organs-at-risk dose constraints and target goal templates for practical implementation.

Methods And Materials: In this institutional review board-approved study, data were collected using REDCap electronic data capture on a secure server.

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.

The TRIPOD-LLM Statement: A Targeted Guideline For Reporting Large Language Models Use.

Unlabelled: Large Language Models (LLMs) are rapidly being adopted in healthcare, necessitating standardized reporting guidelines. We present TRIPOD-LLM, an extension of the TRIPOD+AI statement, addressing the unique challenges of LLMs in biomedical applications. TRIPOD-LLM provides a comprehensive checklist of 19 main items and 50 subitems, covering key aspects from title to discussion.

Three-Dimensional Deep Learning Normal Tissue Complication Probability Model to Predict Late Xerostomia in Patients With Head and Neck Cancer.

Purpose: Conventional normal tissue complication probability (NTCP) models for patients with head and neck cancer are typically based on single-value variables, which, for radiation-induced xerostomia, are baseline xerostomia and mean salivary gland doses. This study aimed to improve the prediction of late xerostomia by using 3-dimensional information from radiation dose distributions, computed tomography imaging, organ-at-risk segmentations, and clinical variables with deep learning (DL).

Methods And Materials: An international cohort of 1208 patients with head and neck cancer from 2 institutes was used to train and twice validate DL models (deep convolutional neural network, EfficientNet-v2, and ResNet) with 3-dimensional dose distribution, computed tomography scan, organ-at-risk segmentations, baseline xerostomia score, sex, and age as input.

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.

A prospective cohort study on stereotactic radiotherapy in the management of dural recurrence of olfactory neuroblastoma.

Background: Treatment for dural recurrence of olfactory neuroblastoma (ONB) is not standardized. We assess the outcomes of stereotactic body radiotherapy (SBRT) in this population.

Methods: ONB patients with dural recurrences treated between 2013 and 2022 on a prospective registry were included.

A Visualization and Radiation Treatment Plan Quality Scoring Method for Triage in a Population-Based Context.

Purpose: Our purpose was to develop a clinically intuitive and easily understandable scoring method using statistical metrics to visually determine the quality of a radiation treatment plan.

Methods And Materials: Data from 111 patients with head and neck cancer were used to establish a percentile-based scoring system for treatment plan quality evaluation on both a plan-by-plan and objective-by-objective basis. The percentile scores for each clinical objective and the overall treatment plan score were then visualized using a daisy plot.

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.

Development and Standardization of an Osteoradionecrosis Classification System in Head and Neck Cancer: Implementation of a Risk-Based Model.

Purpose: Osteoradionecrosis of the jaw (ORN) can manifest in varying severity. The aim of this study is to identify ORN risk factors and develop a novel classification to depict the severity of ORN.

Methods: Consecutive patients with head and neck cancer (HNC) treated with curative-intent intensity-modulated radiation therapy (IMRT) (≥45 Gy) from 2011 to 2017 were included.

MR-guided stereotactic radiation therapy for head and neck cancers.

Purpose: MR-guided radiotherapy (MRgRT) has the advantage of utilizing high soft tissue contrast imaging to track daily changes in target and critical organs throughout the entire radiation treatment course. Head and neck (HN) stereotactic body radiation therapy (SBRT) has been increasingly used to treat localized lesions within a shorter timeframe. The purpose of this study is to examine the dosimetric difference between the step-and-shot intensity modulated radiation therapy (IMRT) plans on Elekta Unity and our clinical volumetric modulated arc therapy (VMAT) plans on Varian TrueBeam for HN SBRT.

Neoadjuvant Chemotherapy Alone or Combined with EGFR-Directed Targeted Therapy or Anti-PD-1 Immunotherapy for Locally Advanced Lacrimal Sac and Nasolacrimal Duct Carcinomas.

Background/aims: We describe our findings in patients with locally advanced lacrimal sac and nasolacrimal duct (NLD) carcinoma who received neoadjuvant systemic therapy.

Methods: We identified patients with locally advanced primary lacrimal sac/NLD carcinoma treated with neoadjuvant systemic intravenous therapy at our institution during 2017-2019.

Results: The study included seven patients, four men and three women; the mean age was 60.

Clinical and prognostic differences in oropharyngeal squamous cell carcinoma in USA and Denmark, two HPV high-prevalence areas.

Background: Uncertainty persists regarding clinical and treatment variations crucial to consider when comparing high human papillomavirus (HPV)-prevalence oropharyngeal squamous cell carcinoma (OPSCC) cohorts for accurate patient stratification and replicability of clinical trials across different geographical areas.

Methods: OPSCC patients were included from The University of Texas MD Anderson Cancer Center (UTMDACC), USA and from The University Hospital of Copenhagen, Denmark from 2015-2020, (n = 2484). Outcomes were 3-year overall survival (OS) and recurrence-free interval (RFI).

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.