Adaptive radiotherapy dose prediction on head and neck cancer patients with a 3D multi-headed U-Net deep learning architecture.

Online adaptive radiation therapy (ART) personalizes treatment plans by accounting for daily anatomical changes, requiring workflows distinct from conventional radiotherapy. Deep learning-based dose prediction models can enhance treatment planning efficiency by rapidly generating accuracy dose distributions, reducing manual trial-and-error and accelerating the overall workflow; however, most existing approaches overlook critical pre-treatment plan information-specifically, physician-defined clinical objectives tailored to individual patients. To address this limitation, we introduce the multi-headed U-Net (MHU-Net), a novel architecture that explicitly incorporates physician intent from pre-treatment plans to improve dose prediction accuracy in adaptive head and neck cancer treatments.

Virtual radiotherapy plan quality education: Perspectives from a global setting.

Purpose: Evaluation of treatment plan quality is a critical element of training for radiotherapy professionals. With the increased adoption of intensity modulated radiotherapy internationally, this training is crucial to address patient care inequity. We aim to evaluate learning outcomes from a 14-session remote training course targeting critical elements of plan quality with advanced modalities.

Development and clinical implementation of an MRI-only planning workflow featuring deep learning-based synthetic CT for prostate cancer external beam radiotherapy.

Background: In external beam radiation therapy (EBRT) for prostate cancer, both MRI and CT are typically used-CT provides electron density for dose calculation and visualization of bony anatomy and fiducial markers, while MRI offers superior soft tissue contrast. With recent advances in deep learning, synthetic CT (sCT) images can now be generated from MRI, potentially eliminating the need for separate CT scans.

Purpose: To clinically implement an MRI-only planning (MROP) workflow for both X-ray and MRI-guided systems.

Impact of daily adaptive head and neck radiotherapy on toxicity and quality-of-life: results of the DARTBOARD phase II randomized trial.

Background: The utility of adaptive radiotherapy (ART) for head and neck squamous cell carcinoma (HNSCC) remains poorly defined. Daily ART (DART) promises both anatomic adaptation and planning target volume (PTV) reduction. In this prospective trial using cone-beam computed tomography-based ART, patients with HNSCC undergoing definitive radiotherapy (RT) or chemoradiotherapy (CRT) were randomized to DART with reduced PTV margins or no ART with standard margins (image-guided RT [IGRT]).

Strategies for Offline Adaptive Biology-Guided Radiotherapy (BgRT) on a PET-Linac Platform.

: This study aims to present a structured clinical workflow for offline adaptive Biology-guided Radiotherapy (BgRT) using the RefleXion X1 PET-linac system, addressing challenges introduced by inter-treatment anatomical and biological changes. : We propose a decision tree offline adaptation framework based on real-time assessments of Activity Concentration (AC), Normalized Target Signal (NTS), and bounded dose-volume histogram (bDVH%) metrics. Three offline strategies were developed: (1) preemptive adaptation for minor changes, (2) partial re-simulation for moderate changes, and (3) full re-simulation for major anatomical or metabolic alterations.

Feasibility of Monte Carlo-based patient-specific quality assurance in 1.5 Tesla magnetic resonance-guided online adaptive radiotherapy: a multi-institutional study.

Introduction: To evaluate the feasibility of Monte Carlo (MC)-based patient-specific quality assurance (PSQA) for MR-guided online adaptive radiotherapy and to explore the potential to eliminate the post-delivery measurement-based PSQA.

Material And Methods: A total of 113 cases from two institutions, treated on MR-Linac machines, were included in the study. A customized GPU-accelerated, Monte Carlo-based secondary dose verification software (ART2Dose) was developed and integrated into the QA workflow, accounting for a 1.

Improving efficiency in lung SAbR planning using integrated tools for X-ray based adaptive radiotherapy.

Purpose: To evaluate the feasibility of translating clinical lung stereotactic ablative radiotherapy (SAbR) templates from Ethos1.1 to Ethos2.0, leveraging new features to facilitate dose fall-off and automate patient-specific beam arrangement.

Comparative Outcomes of Standard Radiation Therapy and 5-Fraction Adaptive Stereotactic Radiation Therapy in Newly Diagnosed Glioblastoma: A Propensity Score-Matched Analysis.

Purpose: Glioblastoma multiforme (GBM) is the most common and aggressive primary brain tumor in adults, with poor survival despite advancements in treatment. Adaptive stereotactic radiation therapy (RT) using a magnetic resonance imaging linear accelerator is an emerging approach for patients with newly diagnosed GBM eligible for conventional fractionation. We hypothesize that adaptive stereotactic RT can provide comparable outcomes with conventional fractionation while reducing treatment burden.

Cone Beam Computed Tomography-Guided Online Adaptive Radiation Therapy: Clinical Insights From a Nationwide Staffing Survey.

Purpose: This study aimed to provide insights into the staffing and workflow requirements for cone beam computed tomography (CBCT)-guided online adaptive radiation therapy (ART) systems to guide institutions in optimizing staffing strategies and to promote broader ART adoption.

Methods And Materials: We conducted a nationwide survey to collect data on ART program metrics, clinician roles during online treatment, and physicist staffing models, along with free-text feedback for sharing of insights and challenges. Additionally, we reviewed 26 published articles describing ART workflows across various anatomic sites and performed a literature-based timing analysis to provide further context on workflow efficiency.

Adaptive Radiation Therapy in Lymphomas: Indications, Early Experiences, and Future Directions.

Purpose: Adaptive radiation therapy (RT) allows for smaller treatment volumes and adaptation to shrinking tumors, which is particularly intriguing in lymphomas that are radiosensitive and sometimes in difficult to target locations. We hypothesize that adaptive RT may be beneficial in a variety of lymphoma cases and describe our early experience to outline indications for adaptive RT in lymphomas.

Methods And Materials: An institutional review board-approved prospective registry was reviewed to identify patients with lymphoma who were treated with cone-beam computed tomography-based online adaptive RT (oART) at our institution from 2021 to 2024.

Prior guided deep difference meta-learner for fast adaptation to stylized segmentation.

Radiotherapy treatment planning requires segmenting anatomical structures in various styles, influenced by guidelines, protocols, preferences, or dose planning needs. Deep learning-based auto-segmentation models, trained on anatomical definitions, may not match local clinicians' styles at new institutions. Adapting these models can be challenging without sufficient resources.

Advancements in Radiation Therapy Treatment Workflows for Precision Medicine: A Review and Forward Looking.

The conventional radiation therapy (RT) workflow, standardized over the past 3 decades, comprises 4 key phases: computed tomography (CT) simulation, treatment planning, quality assurance, and treatment delivery. This workflow relies heavily on the acquisition of planning CT scans for accurate 3-dimensional planning and patient positioning. Advancements in on-board 3-dimensional imaging, such as fan-beam or cone-beam CT and magnetic resonance imaging, now offer improved dosimetric accuracy directly or through synthetic CT generation, image deformation, and bulk density override techniques.

Automatic, machine-agnostic, convolution-based beam, and fluence modeling for Monte Carlo independent dose calculation.

Background: Monte Carlo (MC)-based independent dose calculation is increasingly sought after for plan- and delivery-specific quality assurance (QA) in modern radiotherapy because of its high accuracy. It is particularly valuable for online adaptive radiotherapy, where measurement-based QA solutions are impractical. However, challenges related to beam modeling, commissioning, and plan/delivery-specific fluence calculation have hindered its widespread clinical adoption.

Simulation-Omitting and Using Library Patients for Pre-Planning Online Adaptive Radiotherapy (SUPPORT): A Feasibility Study for Spine Stereotactic Ablative Radiotherapy (SAbR) Patients.

Treatment planning in the field of radiation therapy has evolved from three-dimensional (3D) planning to inverse planning and, most recently, to personalized adaptive radiotherapy (ART) [...

Feasibility and Impact of a Radiation Therapy Technologist-Driven Workflow for Cone Beam Computed Tomography Guided Partial Breast Adaptive Radiation Therapy.

Purpose: Cone beam computed tomography-based online adaptive radiation therapy (ART) allows significantly smaller planning target volume margins for patients treated with adaptive stereotactic partial breast irradiation. However, this approach places increased demands on the treatment team, particularly physicians. We hypothesize that with appropriate training, physicians' involvement at the treatment console can be reduced by delegating contouring and planning tasks to radiation therapy technologists (RTTs) with a physicist copilot without reducing treatment quality.

Establishing a Safety Net in X-Ray-Based Online Adaptive Radiation Therapy: Early Detection of Planning Deficiencies Through Upstream Physics Plan Review.

Purpose: Online adaptive radiation therapy (oART) is increasingly adopted in clinics worldwide, making robust error mitigation essential to deliver high-quality treatment. This study reports on a 1-year experience with an upstream physics plan review process aimed at early error detection and prevention of x-ray-based oART planning deficiencies.

Methods And Materials: An upstream plan review process was implemented, enabling physicists to evaluate adaptive plans before physician approval, with a focus on identifying deficiencies early and allowing time for corrective modifications.

Dosimetric Advantages of Online Adaptive Radiation Therapy for Head and Neck Squamous Cell Carcinoma: Results From a Prospective Registry Study.

Purpose: Data are limited on the feasibility and dosimetric advantages of cone beam computed tomography-based online adaptive radiation therapy (oART) in head and neck squamous cell carcinoma. In this retrospective analysis, we assessed the dosimetric outcomes in patients receiving definitive radiation therapy and treated with oART at least once during their treatment course.

Methods And Materials: We retrospectively analyzed 69 patients with head and neck squamous cell carcinoma who received definitive-intent treatment and oART using the Varian Ethos system at a single tertiary care institution between September 2021 and March 2024.

Closing the gap in plan quality: Leveraging deep-learning dose prediction for adaptive radiotherapy.

Purpose: Balancing quality and efficiency has been a challenge for online adaptive therapy. Most systems start the online re-optimization with the original planning goals. While some systems allow planners to modify the planning goals, achieving a high-quality plan within time constraints remains a common barrier.

Enhanced dose prediction for head and neck cancer artificial intelligence-driven radiotherapy based on transfer learning with limited training data.

Purpose: Training deep learning dose prediction models for the latest cutting-edge radiotherapy techniques, such as AI-based nodal radiotherapy (AINRT) and Daily Adaptive AI-based nodal radiotherapy (DA-AINRT), is challenging due to limited data. This study aims to investigate the impact of transfer learning on the predictive performance of an existing clinical dose prediction model and its potential to enhance emerging radiotherapy approaches for head and neck cancer patients.

Method: We evaluated the impact and benefits of transfer learning by fine-tuning a Hierarchically Densely Connected U-net on both AINRT and DA-AINRT patient datasets, creating Model (Study 1) and Model (Study 2).

Cone Beam Online Adaptive Radiation Therapy: A Promising Approach for Gastric Mucosa-Associated Lymphoid Tissue Lymphoma?

Purpose: Daily online adaptive radiation therapy (oART) opens the opportunity to treat gastric mucosa-associated lymphoid tissue (MALT) lymphoma with a reduced margin. This study reports our early experience of cone beam computed tomography (CBCT)-based daily oART treating gastric MALT lymphoma with breath-hold and reduced margins.

Methods And Materials: Ten patients were treated on a CBCT-based oART system.

Contour uncertainty assessment for MD-omitted daily adaptive online head and neck radiotherapy.

Background And Purpose: Daily online adaptive radiotherapy (DART) increases treatment accuracy by crafting daily customized plans that adjust to the patient's daily setup and anatomy. The routine application of DART is limited by its resource-intensive processes. This study proposes a novel DART strategy for head and neck squamous cell carcinoma (HNSCC), automizing the process by propagating physician-edited treatment contours for each fraction.

Assessing population-based to personalized planning strategies for head and neck adaptive radiotherapy.

Purpose: Optimal head-and-neck cancer (HNC) treatment planning requires accurate and feasible planning goals to meet dosimetric constraints and generate robust online adaptive treatment plans. A new x-ray-based adaptive radiotherapy (ART) treatment planning system (TPS) version 2.0 emulator includes novel methods to drive the planning process including the revised intelligent optimization engine algorithm (IOE2).

Mitigating Risks in Cone Beam Computed Tomography Guided Online Adaptive Radiation Therapy: A Preventative Reference Planning Review Approach.

Purpose: Online adaptive radiation therapy (oART) treatment planning requires evaluating the temporal robustness of reference plans and anticipating the potential changes during treatment courses that may even lead to risks unique to the adaptive workflow. This study conducted a risk analysis of the cone beam computed tomography guided adaptive workflow and is the first to assess an adaptive-specific reference planning review that mitigates risk in the planning process to prevent events and treatment deficiencies during adaptation.

Methods And Materials: A quality management team of medical physicists, residents, physicians, and radiation therapists performed a fault tree analysis and failure mode and effects analysis.

Impact of Manual Contour Editing on Plan Quality for Online Adaptive Radiation Therapy for Head and Neck Cancer.

Purpose: Online adaptive radiation therapy (oART) has high resource costs especially for head and neck (H&N) cancer, which requires recontouring complex targets and numerous organs-at-risk (OARs). Adaptive radiation therapy systems provide autocontours to help. We aimed to explore the optimal level of editing automatic contours to maintain plan quality in a cone beam computed tomography-based oART system for H&N cancer.