LUND-PROBE -- LUND Prostate Radiotherapy Open Benchmarking and Evaluation dataset
Viktor Rogowski, Lars E Olsson, Jonas Scherman, Emilia Persson, Mustafa Kadhim, Sacha af Wetterstedt, Adalsteinn Gunnlaugsson, Martin P. Nilsson, Nandor Vass, M… 展开作者
Viktor Rogowski, Lars E Olsson, Jonas Scherman, Emilia Persson, Mustafa Kadhim, Sacha af Wetterstedt, Adalsteinn Gunnlaugsson, Martin P. Nilsson, Nandor Vass, Mathieu Moreau, Maria Gebre Medhin, Sven Bäck, Per Munck af Rosenschöld, Silke Engelholm, Christian Jamtheim Gustafsson
Radiotherapy treatment for prostate cancer relies on computed tomography (CT) and/or magnetic resonance imaging (MRI) for segmentation of target volumes and organs at risk (OARs). Manual segmentation of these volumes is regarded as the gold standard for ground truth in machine learning applications but to acquire such data is tedious and time-consuming. A publicly available clinical dataset is presented, comprising MRI- and synthetic CT (sCT) images, target and OARs segmentations, and radiotherapy dose distributions for 432 prostate cancer patients treated with MRI-guided radiotherapy. An extended dataset with 35 patients is also included, with the addition of deep learning (DL)-generated segmentations, DL segmentation uncertainty maps, and DL segmentations manually adjusted by four radiation oncologists. The publication of these resources aims to aid research within the fields of automated radiotherapy treatment planning, segmentation, inter-observer analyses, and DL model uncertainty investigation. The dataset is hosted on the AIDA Data Hub and offers a free-to-use resource for the scientific community, valuable for the advancement of medical imaging and prostate cancer radiotherapy research.
A deep learning-based framework for segmenting invisible clinical target volumes with estimated uncertainties for post-operative prostate cancer radiotherapy
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Assessing Bilateral Neurovascular Bundles Function with Pulsed Wave Doppler Ultrasound: Implications for Reducing Erectile Dysfunction Following Prostate Radiotherapy
Deep Learning (DL)-based Automatic Segmentation of the Internal Pudendal Artery (IPA) for Reduction of Erectile Dysfunction in Definitive Radiotherapy of Localized Prostate Cancer
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Voxel-Based Conversion of Hypofractionated Radiotherapy Dose Distributions to 2 Gy-Equivalent OAR Constraints: Proof-of-Concept Demonstrating the Radiobiological Benefits of Hypofractionation in a Prostate Radiotherapy Case
Healthy Innovations；Holy Family University（圣家大学）；Tripoli Governmental Hospital and Doctors Center（的黎波里政府医院和医生中心）
A Machine-to-Machine Knowledge-Guided LLM Agent for Generalizable Radiotherapy Treatment Planning
National Institutes of Health (NIH)（国家卫生研究院）；Department of Physics, The University of Texas at Arlington（德克萨斯理工大学阿灵顿分校物理系）；Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University（约翰霍普金斯大学放射肿瘤学与分子放射科学系）
Deep learning-based self-supervised detection and localization of magnetic resonance imaging anomalies in artificial intelligence-supported radiotherapy workflows
Physics and Imaging in Radiation Oncology (phiRO)（物理与放射治疗成像（phiRO））
Improving Efficiency of Training a Virtual Treatment Planner Network via Knowledge-guided Deep Reinforcement Learning for Intelligent Automatic Treatment Planning of Radiotherapy
Voxel-Based Dose Prediction with Multi-Patient Atlas Selection for Automated Radiotherapy Treatment Planning
放射治疗计划过程的自动化是一个技术上具有挑战性的问题。大多数自动化方法集中于定制和推断剂量体积目标以用于计划优化。在这项工作中，我们概述了一个多病人的图集为基础的剂量预测方法，学习预测剂量每体素的一个新的病人直接从计算机断层成像（CT）计划扫描，而不需要指定任何目标。我们的方法学会自动为新患者选择最有效的图谱，然后将这些图谱中的剂量映射到新患者身上。我们扩展我们以前的工作，包括一个有条件的随机场的联合分布之前，匹配的互补目标的准确的空间分布的剂量分布，同时仍然坚持所需的剂量体积直方图的优化。然后，所得到的分布可以用于具有新的空间剂量目标的逆规划，或者用于为规范优化管道创建典型的剂量体积目标。我们调查了6个治疗部位（633例患者用于培训，113例患者用于测试），并评估了所有DVH的临床和预测剂量分布的平均绝对差（MAD）。与我们以前的方法相比，平均结果是有利的（1.91 vs 2.57）。将我们的方法与有和没有图谱选择进行比较，进一步验证了图谱选择平均改善了全乳腺中的剂量预测。（0.64 vs 1.59），前列腺（2.13对4.07），和直肠（1.46 vs 3.29），而在乳腺腔中不太重要（0.79 vs 0.92）和肺（1.33 vs 1.27），具有高度一致性和最小剂量成形。在CNS脑中，图谱选择具有潜在的影响力（3.65 vs 5.09），但选择理想的图谱是最具挑战性的。
New Insights into Automatic Treatment Planning for Cancer Radiotherapy Using Explainable Artificial Intelligence
Department of Physics, The University of Texas at Arlington（德克萨斯大学阿灵顿分校物理系）；Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University（约翰霍普金斯大学放射肿瘤学与分子放射科学系）
