Dosimetry in MRgPT: Impact of magnetic fields on TLD dose response during proton irradiation.

Background: Proton beam therapy, when integrated with MRI guidance, presents complex dosimetric challenges due to interactions with magnetic fields. Prior research has emphasized the nuanced impact of magnetic fields on dosimetry. For thermoluminescent dosimeters (TLDs) the electron-return effect, alongside small air cavities surrounding the pellets, can lead to nonuniform dose distributions.

Automation of ePROMs in radiation oncology and its impact on patient response and bias.

Purpose: This study evaluates the impact of integrating a novel, in-house developed electronic Patient-Reported Outcome Measures (ePROMs) tool with a commercial Oncology Information System (OIS) on patient response rates and potential biases in real-world data science applications.

Materials And Methods: We designed an ePROMs tool using the NodeJS web application framework, automatically sending e-mail questionnaires to patients based on their treatment schedules in the OIS. The tool is used across various treatment sites to collect PROMs data in a real-world setting.

Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data.

Background And Purpose: Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies.

Biochemical control in intermediate- and high-risk prostate cancer after EBRT with and without brachytherapy boost.

Purpose: External beam radiotherapy (EBRT) with or without brachytherapy boost (BTB) has not been compared in prospective studies using guideline-recommended radiation dose and recommended androgen-deprivation therapy (ADT). In this multicenter retrospective analysis, we compared modern-day EBRT with BTB in terms of biochemical control (BC) for intermediate-risk (IR) and high-risk (HR) prostate cancer.

Methods: Patients were treated for primary IR or HR prostate cancer during 1999-2019 at three high-volume centers.

Metastases-directed local therapies (MDT) beyond genuine oligometastatic disease (OMD): Indications, endpoints and the role of imaging.

To further personalise treatment in metastatic cancer, the indications for metastases-directed local therapy (MDT) and the biology of oligometastatic disease (OMD) should be kept conceptually apart. Both need to be vigorously investigated. Tumour growth dynamics - growth rate combined with metastatic seeding efficiency - is the single most important biological feature determining the likelihood of success of MDT in an individual patient, which might even be beneficial in slowly developing polymetastatic disease.

The role of artificial intelligence in informed patient consent for radiotherapy treatments-a case report.

Recent advancements in large language models (LMM; e.g., ChatGPT (OpenAI, San Francisco, California, USA)) have seen widespread use in various fields, including healthcare.

Clinical Implementation and Evaluation of Auto-Segmentation Tools for Multi-Site Contouring in Radiotherapy.

Background And Purpose: Tools for auto-segmentation in radiotherapy are widely available, but guidelines for clinical implementation are missing. The goal was to develop a workflow for performance evaluation of three commercial auto-segmentation tools to select one candidate for clinical implementation.

Materials And Methods: One hundred patients with six treatment sites (brain, head-and-neck, thorax, abdomen, and pelvis) were included.

Increasing Quality and Efficiency of the Radiotherapy Treatment Planning Process by Constructing and Implementing a Workflow-Monitoring Application.

Purpose: To demonstrate how the efficiency of the treatment planning processes of a university radiation oncology department (2,500 new patients/year) could be improved by constructing and implementing a workflow-monitoring application.

Methods: A web-based application was developed in house, which enhanced the process management tools of the clinic's oncology information system. The application calculates the days left for the next task in the treatment planning process and visualizes the information on a browser-based whiteboard.

EviGUIDE - a tool for evidence-based decision making in image-guided adaptive brachytherapy for cervical cancer.

Purpose: To develop a novel decision-support system for radiation oncology that incorporates clinical, treatment and outcome data, as well as outcome models from a large clinical trial on magnetic resonance image-guided adaptive brachytherapy (MR-IGABT) for locally advanced cervical cancer (LACC).

Methods: A system, called EviGUIDE, was developed that combines dosimetric information from the treatment planning system, patient and treatment characteristics, and established tumor control probability (TCP), and normal tissue complication probability (NTCP) models, to predict clinical outcome of radiotherapy treatment of LACC. Six Cox Proportional Hazards models based on data from 1341 patients of the EMBRACE-I study have been integrated.

Generating deliverable DICOM RT treatment plans for prostate VMAT by predicting MLC motion sequences with an encoder-decoder network.

Background: Deep learning-based auto-planning is an active research field; however, for some tasks a treatment planning system (TPS) is still required.

Purpose: To introduce a deep learning-based model generating deliverable DICOM RT treatment plans that can be directly irradiated by a linear accelerator (LINAC). The model was based on an encoder-decoder network and can predict multileaf collimator (MLC) motion sequences for prostate VMAT radiotherapy.

Tools for large-scale data analytics of an international multi-center study in radiation oncology for cervical cancer.

Purpose: To develop and implement a software that enables centers, treating patients with state-of-the-art radiation oncology, to compare their patient, treatment, and outcome data to a reference cohort, and to assess the quality of their treatment approach.

Materials And Methods: A comprehensive data dashboard was designed, which al- lowed holistic assessment of institutional treatment approaches. The software was tested in the ongoing EMBRACE-II study for locally advanced cervical cancer.

On the sensitivity of PROMs during breast radiotherapy.

Purpose: To investigate the sensitivity of patient-reported outcome measures (PROMs) to detect treatment-related side effects in patients with breast cancer undergoing external beam photon radiotherapy.

Methods: As part of daily clinical care, an in-house developed PROM tool was used to assess side effects in patients during a) whole-breast irradiation (WBI) to 40 Gy, b) WBI with a sequential boost of 10 Gy, and c) partial-breast irradiation (PBI) to 40 Gy.

Results: 414 patients participated in this prospective study between October 2020 and January 2022, with 128 patients (31 %) receiving WBI, 241 (58 %) receiving WBI followed by a sequential boost, and 50 patients (12 %) receiving PBI.

OpenKBP-Opt: an international and reproducible evaluation of 76 knowledge-based planning pipelines.

To establish an open framework for developing plan optimization models for knowledge-based planning (KBP).Our framework includes radiotherapy treatment data (i.e.

Accelerating and improving radiochromic film calibration by utilizing the dose ratio in photon and proton beams.

Purpose: Radiochromic films are versatile 2D dosimeters with high-resolution and near tissue equivalence. To assure high precision and accuracy, a time-consuming calibration process is required. To improve the time efficiency, a novel calibration method utilizing the ratio of the same dose profile measured at different monitor units (MUs) is introduced and tested in a proton and photon beam.

Neural network-assisted automated image registration for MRI-guided adaptive brachytherapy in cervical cancer.

Purpose: In image-guided adaptive brachytherapy (IGABT) a quantitative evaluation of the dosimetric changes between fractions due to anatomical variations, can be implemented via rigid registration of images from subsequent fractions based on the applicator as a reference structure. With available treatment planning systems (TPS), this is a manual and time-consuming process. The aim of this retrospective study was to automate this process.

Can Generative Adversarial Networks help to overcome the limited data problem in segmentation?

Purpose: For image translational tasks, the application of deep learning methods showed that Generative Adversarial Network (GAN) architectures outperform the traditional U-Net networks, when using the same training data size. This study investigates whether this performance boost can also be expected for segmentation tasks with small training dataset size.

Materials/methods: Two models were trained on varying training dataset sizes ranging from 1-100 patients: a) U-Net and b) U-Net with patch discriminator (conditional GAN).

Technical Note: Dose prediction for radiation therapy using feature-based losses and One Cycle Learning.

Purpose: To present the technical details of the runner-up model in the open knowledge-based planning (OpenKBP) challenge for the dose-volume histogram (DVH) stream. The model was designed to ensure simple and reproducible training, without the necessity of costly advanced generative adversarial network (GAN) techniques.

Methods: The model was developed based on the OpenKBP challenge dataset, consisting of 200 and 40 head-and-neck patients for training and validation, respectively.

An MR-only acquisition and artificial intelligence based image-processing protocol for photon and proton therapy using a low field MR.

Objective: Recent developments on synthetically generated CTs (sCT), hybrid MRI linacs and MR-only simulations underlined the clinical feasibility and acceptance of MR guided radiation therapy. However, considering clinical application of open and low field MR with a limited field of view can result in truncation of the patient's anatomy which further affects the MR to sCT conversion. In this study an acquisition protocol and subsequent MR image stitching is proposed to overcome the limited field of view restriction of open MR scanners, for MR-only photon and proton therapy.

Latent space manipulation for high-resolution medical image synthesis via the StyleGAN.

Introduction: This paper explores the potential of the StyleGAN model as an high-resolution image generator for synthetic medical images. The possibility to generate sample patient images of different modalities can be helpful for training deep learning algorithms as e.g.

Investigating conditional GAN performance with different generator architectures, an ensemble model, and different MR scanners for MR-sCT conversion.

Recent developments in magnetic resonance (MR) to synthetic computed tomography (sCT) conversion have shown that treatment planning is possible without an initial planning CT. Promising conversion results have been demonstrated recently using conditional generative adversarial networks (cGANs). However, the performance is generally only tested on images from one MR scanner, which neglects the potential of neural networks to find general high-level abstract features.

Basic Properties of a New Polymer Gel for 3D-Dosimetry at High Dose-Rates Typical for FFF Irradiation Based on Dithiothreitol and Methacrylic Acid (MAGADIT): Sensitivity, Range, Reproducibility, Accuracy, Dose Rate Effect and Impact of Oxygen Scavenger.

The photon induced radical-initiated polymerization in polymer gels can be used for high-resolution tissue equivalent dosimeters in quality control of radiation therapy. The dose (D) distribution in radiation therapy can be measured as a change of the physical measurement parameter T2 using T2-weighted magnetic resonance imaging. The detection by T2 is relying on the local change of the molecular mobility due to local polymerization initiated by radicals generated by the ionizing radiation.

The impact of the oxygen scavenger on the dose-rate dependence and dose sensitivity of MAGIC type polymer gels.

Recent developments in radiation therapy aimed at more precise dose delivery along with higher dose gradients (dose painting) and more efficient dose delivery with higher dose rates e.g. flattening filter free (FFF) irradiation.

Characteristic of EBT-XD and EBT3 radiochromic film dosimetry for photon and proton beams.

Recently, a new type of radiochromic film, the EBT-XD film, has been introduced for high dose radiotherapy. The EBT-XD film contains the same structure as the EBT3 film but has a slightly different composition and a thinner active layer. This study benchmarks the EBT-XD against EBT3 film for 6 MV and 10 MV photon beams, as well as for 97.