目的：评估头颈癌（HNC）患者自适应放射治疗（ART）自动决策支持平台的临床需求。方法：我们测试RTapp（SegAna），一种新的ART决策支持软件，回顾性调查22例HNC患者的数据。对于每一部分，RTapp从每日3D成像实时估计目标和OAR接收的每日和累积剂量。RTapp还包括一个预测算法，该算法分析剂量测定参数（DP）与剂量测定终点（DE）的趋势，以提前4个分数触发适应。对PTV设置警告（V95<95%）和自适应（V95<93%）DE。所有Dmax和Dmean DP的OAR自适应DE均设置为+10%（DE 10）。治疗结束（EOT）时的任何DE违背均触发DP趋势审查，以确定DE交叉分数Fx并评价预测模型准确度（计算和预测DP值之间的差异及95%置信区间）。结果：RTapp能够满足治疗适应的需求。15/22项研究（68%）在EOT时违反PTV覆盖范围或腮腺Dmean。9个PTV的V95<95%（平均覆盖率降低-7.7+/-3.3%），包括4个标记为适应的中值Fx=11.5（范围：6-18）。15个腮腺被标记为超过Dmean限制，EOT时中位数增加+3.18 Gy（范围：0.18-6.31 Gy），包括8个DP> DE 10。PTV V95和腮腺Dmean的预测值与计算值之间的差异分别高达7.6%（平均值：-2.9+/-4.6%）和5Gy（平均值：0.2+/-1.6Gy）。最准确的预测是在最接近Fx的情况下获得的。对于腮腺，Fx的范围在第1至23部分之间，缺乏特定趋势表明需要验证每个部分的治疗适应性。结论：RTapp集成在ART临床工作流程中，可以预测特定治疗是否需要提前4个部分进行调整。
英文摘要：
Purpose: To evaluate the clinical need for automated decision-support platforms for Adaptive Radiotherapy Therapy (ART) of Head & Neck cancer (HNC) patients. Methods: We tested RTapp (SegAna), a new decision-support software for ART, to investigate 22 HNC patients data retrospectively. For each fraction, RTapp estimated the daily and cumulative doses received by targets and OARs from daily 3D imaging in real-time. RTapp also included a prediction algorithm that analyzed dosimetric parameters (DP) trends against dosimetric endpoints (DE) to trigger adaptation up to 4 fractions ahead. Warning (V95<95%) and adaptation (V95<93%) DEs were set for PTVs. OAR adaptation DEs of +10% (DE10) were set for all Dmax and Dmean DPs. Any DE violation at end of treatment (EOT) triggered a DP trends review to determine the DE-crossing fraction Fx and evaluate the prediction model accuracy (difference between calculated and predicted DP values with 95% confidence intervals). Results: RTapp was able to address the needs of treatment adaptation. 15/22 studies (68%) violated PTV coverage or parotids Dmean at EOT. 9 PTVs had V95<95% (mean coverage decrease of -7.7+/-3.3 %) including 4 flagged for adaptation at median Fx=11.5 (range: 6-18). 15 parotids were flagged for exceeding Dmean constraints with median increase of +3.18 Gy (range: 0.18-6.31 Gy) at EOT, including 8 with DP>DE10. The differences between predicted and calculated PTV V95 and parotids Dmean was up to 7.6% (mean: -2.9+/-4.6 %) and 5 Gy (mean: 0.2+/-1.6 Gy), respectively. The most accurate predictions were obtained closest to Fx. For parotids, Fx ranged between fractions 1 to 23, the lack of specific trend demonstrated the need to verify treatment adaptation for every fraction. Conclusion: Integrated in an ART clinical workflow, RTapp can predict whether specific treatment would require adaptation up to 4 fractions ahead of time.
Fully Automated Treatment Planning for Head and Neck Radiotherapy using a Voxel-Based Dose Prediction and Dose Mimicking Method
最近在自动放射治疗计划中的工作已经使用基于历史治疗计划的机器学习来直接从计划图像推断新患者的空间剂量分布。我们提出了一种基于图谱的方法，该方法在训练数据库中学习每个患者的剂量预测模型（图谱），然后学习将新患者与最相关的图谱相匹配。该方法创建空间剂量目标，其指定期望的每体素剂量，并且因此取代指定剂量体积目标的任何要求，以传达治疗计划的目标。概率剂量分布从最相关的图谱中推断，并使用条件随机场进行定标，以确定最可能的剂量空间分布，从而为相关的感兴趣区域产生特定的剂量先验（直方图）。然后，基于体素的剂量模拟将预测的剂量分布转换为可递送的治疗计划剂量分布。在这项研究中，我们研究了接受IMRT和VMAT治疗的右侧orbiliaynx头颈部患者的自动计划。我们使用54个训练和12个独立测试患者的数据库比较了我们的剂量预测管道的四个版本。我们的初步结果是有希望的;与临床相比，自动计划在7例患者中实现了更多的剂量评价标准，在4例患者中实现了相同数量的剂量评价标准。总体而言，自动化计划达到的标准相对数量高于临床（17 vs 8），自动化计划显示出对风险器官的保留增加（52 vs 44）和更好的靶覆盖/均匀性（41 vs 31）。
Divergent Clinical Equivalence Findings from DVH and NTCP Metrics for Alternative OAR Delineations with Increasing Setup Variability in Head-and-Neck Radiotherapy
Causal Machine Learning Analysis of Empirical Relative Biological Effectiveness (RBE) for Mandible Osteoradionecrosis in Head and Neck Cancer Radiotherapy
Quantification of head and neck cancer patients' anatomical changes during radiotherapy: prediction of replanning need
McGill University（麦吉尔大学）；Research Institute of the McGill University Health Centre, McGill University（麦吉尔大学健康中心研究所，麦吉尔大学）；Department of Radiation Oncology, Hôpital Maisonneuve-Rosemont（Maisonneuve-Rosemont医院放射肿瘤科）；Department of Radiation Oncology, McGill University（麦吉尔大学放射肿瘤科）
TransAnaNet: Transformer-based Anatomy Change Prediction Network for Head and Neck Cancer Patient Radiotherapy
UT Southwestern Medical Center（德克萨斯大学西南医学中心）；University of Maryland Medical Center（马里兰大学医学中心）；Central Arkansas Radiation Therapy Institute（中央阿肯色放射治疗研究所）
Deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy
University College London Hospitals NHS Foundation Trust（伦敦大学学院医院国民保健服务基金会信托）；Google Health（谷歌健康）；DeepMind（深度思维）；Clatterbridge Cancer Centre NHS Foundation Trust（克拉特布里奇癌症中心国民保健服务基金会信托）；University College London（伦敦大学学院）
Three-Dimensional Radiotherapy Dose Prediction on Head and Neck Cancer Patients with a Hierarchically Densely Connected U-net Deep Learning Architecture
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
A Contour-Guided Deformable Image Registration Algorithm for Adaptive Radiotherapy
在自适应放射治疗中，通常在计划CT和治疗CT（或锥形束CT）之间进行可变形图像配准，以生成用于剂量累积和轮廓传播的变形向量场（DVF）。治疗CT上的自动传播轮廓可能包含相对较大的误差，特别是在低对比度区域。经常需要临床医生检查和编辑传播的轮廓。编辑后的轮廓能够满足适应性治疗的临床要求;但DVF仍然不准确且与编辑后的轮廓不一致。本工作的目的是开发一种轮廓引导的可变形图像配准（CG-100）算法，以提高自适应放射治疗的DVF的准确性和一致性。将编辑的轮廓并入配准算法是通过用描绘的结构集对之间的强度匹配项正则化原始demons算法的目标函数来实现的。通过遵循原始的基于GPU的demons算法计算框架[Gu等人，Phys Med Biol.55（1）：207-219，2010]，在计算机图形处理单元（GPU）上实现CG-DES算法。CG-ESTA的性能进行了评估的五个临床头颈部和盆腔癌患者的数据。结果表明，与原Demons算法相比，CG-DVF算法在保持原Demons算法较高计算效率的同时，提高了DVF算法的精度和一致性.
Magnetic resonance imaging assessment of the suitability and consistency of radiotherapy treatment positioning achieved using intra-oral stents
Royal Brisbane and Women's Hospital（皇家布里斯班和妇女医院）；University of Queensland（昆士兰大学）；Queensland University of Technology（昆士兰科技大学）；Herston Biofabrication Institute, Metro North Hospital and Health Service（赫斯顿生物制造研究所，北都会区医院与健康服务）
Comprehensive and Clinically Accurate Head and Neck Organs at Risk Delineation via Stratified Deep Learning: A Large-scale Multi-Institutional Study
PAII Inc.（PAII公司）；The First Affiliated Hospital Zhejiang University（浙江大学附属第一医院）；Ping An Technology（平安科技）；Chang Gung Memorial Hospital（长庚纪念医院）；Johns Hopkins University（约翰斯·霍普金斯大学）；Huadong Hospital Affiliated to Fudan University（复旦大学附属华东医院）；First Affiliated Hospital of Xi'an Jiaotong University（西安交通大学第一附属医院）；People's Hospital of Shanxi Province（山西省人民医院）；Nanfang Hospital, Southern Medical University（南方医科大学南方医院）；The First Hospital of Lanzhou University（兰州大学第一医院）；Chang Gung University（长庚大学）
Multiomics-based Outcome Prediction for the Treatment of Brain Metastases with Personalized Ultra-fractionated Stereotactic Adaptive Radiotherapy (PULSAR)
Bi-level Multi-criteria Optimization for Risk-informed Radiotherapy
Fraunhofer Institute for Industrial Mathematics (ITWM)（弗劳恩霍夫工业数学研究所）；Department of Radiation Oncology, Massachusetts General Hospital & Harvard Medical School（马萨诸塞州总医院及哈佛医学院放射肿瘤学系）
Predicting respiratory motion for real-time tumour tracking in radiotherapy
Baltic Institute of Advanced Technology（波罗的海高级技术研究所）；Vytautas Magnus University（维陶塔斯·马格努斯大学）；Aalto University（阿尔托大学）；Rubedo Systems, UAB（Rubedo系统有限责任公司）；Hospital of Lithuanian University of Health Sciences Kauno Klinikos（立陶宛健康科学大学考纳斯诊所医院）
Magnetic resonance delta radiomics to track radiation response in lung tumors receiving stereotactic MRI-guided radiotherapy
Mass General Brigham（麻省总医院布里格姆医疗系统）；Harvard Medical School（哈佛医学院）；Dana-Farber Cancer Institute（丹娜-法伯癌症研究所）；Harvard T.H. Chan School of Public Health（哈佛大学陈曾熙公共卫生学院）；Maastricht University（马斯特里赫特大学）
The Role of Free-breathing GRASP MRI in Accurate Phase Matching with 4D-CT for Motion Representation in Liver Cancer Radiotherapy
Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology（华中科技大学同济医学院同济医院）；Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College（中国医学科学院北京协和医学院肿瘤医院/国家癌症中心/国家临床医学研究中心放射肿瘤科）；School of Electronic Engineering, Beijing University of Posts and Telecommunications（北京邮电大学电子工程学院）
Prediction of Local Failure after Stereotactic Radiotherapy in Melanoma Brain Metastases Using Ensemble Learning on Clinical, Dosimetric, and Radiomic Data
Medical Center University of Freiburg（弗莱堡大学医学中心）；University Medical Center Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医学中心）；Technical University of Munich (TUM)（慕尼黑工业大学）；German Cancer Consortium (DKTK)（德国癌症联盟）
A Dual Path Framework with Hotspot Guided Fusion for Three Dimensional CT to PET Synthesis in Head and Neck Cancer
Advanced Molecular Imaging in Radiotherapy (AdMIRe) Research Laboratory, School of Health Sciences, Purdue University, West Lafayette, IN, 47907, USA（高级分子影像在放射治疗（AdMIRe）研究实验室，健康科学学院，普渡大学，西拉法叶，IN，47907，美国）；Department of Radiation Oncology, Indiana University School of Medicine, Indianapolis, IN 46202, USA（放射肿瘤学部，印第安纳大学医学院，印第安纳波利斯，IN，46202，美国）
