Automated treatment planning with deep reinforcement learning for head-and-neck (HN) cancer intensity modulated radiation therapy (IMRT).

To develop a deep reinforcement learning (DRL) agent to self-interact with the treatment planning system to automatically generate intensity modulated radiation therapy (IMRT) treatment plans for head-and-neck (HN) cancer with consistent organ-at-risk (OAR) sparing performance.With IRB approval, one hundred and twenty HN patients receiving IMRT were included. The DRL agent was trained with 20 patients.

Understanding and modeling human-AI interaction of artificial intelligence tool in radiation oncology clinic using deep neural network: a feasibility study using three year prospective data.

Artificial intelligence (AI) based treatment planning tools are being implemented in clinic. However, human interactions with such AI tools are rarely analyzed. This study aims to comprehend human planner's interaction with the AI planning tool and incorporate the analysis to improve the existing AI tool.

AAPM Task Group 334: A guidance document to using radiotherapy immobilization devices and accessories in an MR environment.

Use of magnetic resonance (MR) imaging in radiation therapy has increased substantially in recent years as more radiotherapy centers are having MR simulators installed, requesting more time on clinical diagnostic MR systems, or even treating with combination MR linear accelerator (MR-linac) systems. With this increased use, to ensure the most accurate integration of images into radiotherapy (RT), RT immobilization devices and accessories must be able to be used safely in the MR environment and produce minimal perturbations. The determination of the safety profile and considerations often falls to the medical physicist or other support staff members who at a minimum should be a Level 2 personnel as per the ACR.

Computed Tomography-Based Radiomics for Long-Term Prognostication of High-Risk Localized Prostate Cancer Patients Received Whole Pelvic Radiotherapy.

Given the high death rate caused by high-risk prostate cancer (PCa) (>40%) and the reliability issues associated with traditional prognostic markers, the purpose of this study is to investigate planning computed tomography (pCT)-based radiomics for the long-term prognostication of high-risk localized PCa patients who received whole pelvic radiotherapy (WPRT). This is a retrospective study with methods based on best practice procedures for radiomics research. Sixty-four patients were selected and randomly assigned to training ( = 45) and testing ( = 19) cohorts for radiomics model development with five major steps: pCT image acquisition using a Philips Big Bore CT simulator; multiple manual segmentations of clinical target volume for the prostate (CTV) on the pCT images; feature extraction from the CTV using PyRadiomics; feature selection for overfitting avoidance; and model development with three-fold cross-validation.

NRG Oncology Assessment of Artificial Intelligence Deep Learning-Based Auto-segmentation for Radiation Therapy: Current Developments, Clinical Considerations, and Future Directions.

Deep learning neural networks (DLNN) in Artificial intelligence (AI) have been extensively explored for automatic segmentation in radiotherapy (RT). In contrast to traditional model-based methods, data-driven AI-based models for auto-segmentation have shown high accuracy in early studies in research settings and controlled environment (single institution). Vendor-provided commercial AI models are made available as part of the integrated treatment planning system (TPS) or as a stand-alone tool that provides streamlined workflow interacting with the main TPS.

Effect of Time-Restricted Eating on Weight Loss in Adults With Type 2 Diabetes: A Randomized Clinical Trial.

Importance: Time-restricted eating (TRE) has become increasingly popular, yet longer-term randomized clinical trials have not evaluated its efficacy and safety in patients with type 2 diabetes (T2D).

Objective: To determine whether TRE is more effective for weight reduction and glycemic control than daily calorie restriction (CR) or a control condition in adults with T2D.

Design, Setting, And Participants: This 6-month, parallel-group, randomized clinical trial was performed between January 25, 2022, and April 1, 2023, at the University of Illinois Chicago.

Radiation-induced acoustic signal denoising using a supervised deep learning framework for imaging and therapy monitoring.

Radiation-induced acoustic (RA) imaging is a promising technique for visualizing the invisible radiation energy deposition in tissues, enabling new imaging modalities and real-time therapy monitoring. However, RA imaging signal often suffers from poor signal-to-noise ratios (SNRs), thus requiring measuring hundreds or even thousands of frames for averaging to achieve satisfactory quality. This repetitive measurement increases ionizing radiation dose and degrades the temporal resolution of RA imaging, limiting its clinical utility.

Time-Restricted Eating Without Calorie Counting for Weight Loss in a Racially Diverse Population : A Randomized Controlled Trial.

Background: Time-restricted eating (TRE), without calorie counting, has become a popular weight loss strategy, yet long-term randomized trials evaluating its efficacy are limited.

Objective: To determine whether TRE is more effective for weight control and cardiometabolic risk reduction compared with calorie restriction (CR) or control.

Design: 12-month randomized controlled trial.

Radiation-induced Acoustic Signal Denoising using a Supervised Deep Learning Framework for Imaging and Therapy Monitoring.

Radiation-induced acoustic (RA) imaging is a promising technique for visualizing radiation energy deposition in tissues, enabling new imaging modalities and real-time therapy monitoring. However, it requires measuring hundreds or even thousands of averages to achieve satisfactory signal-to-noise ratios (SNRs). This repetitive measurement increases ionizing radiation dose and degrades the temporal resolution of RA imaging, limiting its clinical utility.

Input feature design and its impact on the performance of deep learning models for predicting fluence maps in intensity-modulated radiation therapy.

. Deep learning (DL) models for fluence map prediction (FMP) have great potential to reduce treatment planning time in intensity-modulated radiation therapy (IMRT) by avoiding the lengthy inverse optimization process. This study aims to improve the rigor of input feature design in a DL-FMP model by examining how different designs of input features influence model prediction performance.

3Ddose verification in prostate proton therapy with deep learning-based proton-acoustic imaging.

Dose delivery uncertainty is a major concern in proton therapy, adversely affecting the treatment precision and outcome. Recently, a promising technique, proton-acoustic (PA) imaging, has been developed to provide real-time3D dose verification. However, its dosimetry accuracy is limited due to the limited-angle view of the ultrasound transducer.

Purposeful irradiation of the epidural space to enhance local control without compromising cord sparing in spine radiosurgery.

Purpose: The epidural space is a frequent site of cancer recurrence after spine stereotactic radiosurgery (SSRS). This may be due to microscopic disease in the epidural space which is underdosed to obey strict spinal cord dose constraints. We hypothesized that the epidural space could be purposefully irradiated to prescription dose levels, potentially reducing the risk of recurrence in the epidural space without increasing toxicity.

Technical note: Determining the applicability of a clinical knowledge-based learning model via prospective outlier detection.

Background: Knowledge-based planning (KBP) is increasingly implemented clinically because of its demonstrated ability to improve treatment planning efficiency and reduce plan quality variations. However, cases with large dose-volume histogram (DVH) prediction uncertainties may still need manual adjustments by the planner to achieve high plan quality.

Purpose: The purpose of this study is to develop a data-driven method to detect patients with high prediction uncertainties so that intentional effort is directed to these patients.

Artificial intelligence applications in intensity modulated radiation treatment planning: an overview.

Artificial intelligence (AI) refers to methods that improve and automate challenging human tasks by systematically capturing and applying relevant knowledge in these tasks. Over the past decades, a number of approaches have been developed to address different types and needs of system intelligence ranging from search strategies to knowledge representation and inference to robotic planning. In the context of radiation treatment planning, multiple AI approaches may be adopted to improve the planning quality and efficiency.

Assessing the robustness of artificial intelligence powered planning tools in radiotherapy clinical settings-a phantom simulation approach.

Background: Artificial intelligence (AI) based radiotherapy treatment planning tools have gained interest in automating the treatment planning process. It is essential to understand their overall robustness in various clinical scenarios. This is an existing gap between many AI based tools and their actual clinical deployment.

A data-driven approach to optimal beam/arc angle selection for liver stereotactic body radiation therapy treatment planning.

Background: Stereotactic body radiation therapy (SBRT) for liver cancer has shown promising therapeutic effects. Effective treatment relies not only on the precise delivery provided by image-guided radiation therapy (IGRT) but also high dose gradient formed around the treatment volume to spare functional liver tissue, which is highly dependent on the beam/arc angle selection. In this study, we aim to develop a decision support model to learn human planner's beam navigation approach for beam angle/arc angle selection for liver SBRT.

Insights of an AI agent via analysis of prediction errors: a case study of fluence map prediction for radiation therapy planning.

We have previously reported an artificial intelligence (AI) agent that automatically generates intensity-modulated radiation therapy (IMRT) plans via fluence map prediction, by-passing inverse planning. This AI agent achieved clinically comparable quality for prostate cases, but its performance on head-and-neck patients leaves room for improvement. This study aims to collect insights of the deep-learning-based (DL-based) fluence map prediction model by systematically analyzing its prediction errors.

Introducing matrix sparsity with kernel truncation into dose calculations for fluence optimization.

Deep learning algorithms for radiation therapy treatment planning automation require large patient datasets and complex architectures that often take hundreds of hours to train. Some of these algorithms require constant dose updating (such as with reinforcement learning) and may take days. When these algorithms rely on commerical treatment planning systems to perform dose calculations, the data pipeline becomes the bottleneck of the entire algorithm's efficiency.

Deep Learning-Based Fluence Map Prediction for Pancreas Stereotactic Body Radiation Therapy With Simultaneous Integrated Boost.

Purpose: Treatment planning for pancreas stereotactic body radiation therapy (SBRT) is a challenging task, especially with simultaneous integrated boost treatment approaches. We propose a deep learning (DL) framework to accurately predict fluence maps from patient anatomy and directly generate intensity modulated radiation therapy plans.

Methods And Materials: The framework employs 2 convolutional neural networks (CNNs) to sequentially generate beam dose prediction and fluence map prediction, creating a deliverable 9-beam intensity modulated radiation therapy plan.

A Novel Machine Learning Model for Dose Prediction in Prostate Volumetric Modulated Arc Therapy Using Output Initialization and Optimization Priorities.

Treatment planning for prostate volumetric modulated arc therapy (VMAT) can take 5-30 min per plan to optimize and calculate, limiting the number of plan options that can be explored before the final plan decision. Inspired by the speed and accuracy of modern machine learning models, such as residual networks, we hypothesized that it was possible to use a machine learning model to bypass the time-intensive dose optimization and dose calculation steps, arriving directly at an estimate of the resulting dose distribution for use in multi-criteria optimization (MCO). In this study, we present a novel machine learning model for predicting the dose distribution for a given patient with a given set of optimization priorities.

Clinical Experience With Machine Learning-Based Automated Treatment Planning for Whole Breast Radiation Therapy.

Purpose: The machine learning-based automated treatment planning (MLAP) tool has been developed and evaluated for breast radiation therapy planning at our institution. We implemented MLAP for patient treatment and assessed our clinical experience for its performance.

Methods And Materials: A total of 102 patients of breast or chest wall treatment plans were prospectively evaluated with institutional review board approval.

Knowledge Models as Teaching Aid for Training Intensity Modulated Radiation Therapy Planning: A Lung Cancer Case Study.

Artificial intelligence (AI) employs knowledge models that often behave as a black-box to the majority of users and are not designed to improve the skill level of users. In this study, we aim to demonstrate the feasibility that AI can serve as an effective teaching aid to train individuals to develop optimal intensity modulated radiation therapy (IMRT) plans. The training program is composed of a host of training cases and a tutoring system that consists of a front-end visualization module powered by knowledge models and a scoring system.

An artificial intelligence-driven agent for real-time head-and-neck IMRT plan generation using conditional generative adversarial network (cGAN).

Purpose: To develop an artificial intelligence (AI) agent for fully automated rapid head-and-neck intensity-modulated radiation therapy (IMRT) plan generation without time-consuming dose-volume-based inverse planning.

Methods: This AI agent was trained via implementing a conditional generative adversarial network (cGAN) architecture. The generator, PyraNet, is a novel deep learning network that implements 28 classic ResNet blocks in pyramid-like concatenations.