Physics and Imaging in Radiation Oncology最新文献_第6页

Treatment planning for very high energy electrons: Studies that indicate the potential of the modality 高能电子治疗规划：显示该模式潜力的研究

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2024-10-01 DOI: 10.1016/j.phro.2024.100670

James L. Bedford, Uwe Oelfke

Background and purpose

Radiotherapy using Very High Energy Electrons (VHEE) has the potential to reduce dose to organs at risk compared to photons. This article therefore reviews treatment planning for VHEE, to clarify the potential benefit of the modality.

Materials and methods

Articles on VHEE were identified and those which focused on treatment planning were manually selected, particularly those which contained results on patient datasets. Benefits in absorbed dose to organs at risk were converted to percentages of prescription dose so as to provide uniform, clinically relevant reporting.

Results

Increased beam energy was found to reduce electron scatter and give rise to a narrower penumbra but lead to a rather constant depth dose curve, which was not as useful for sparing normal tissues as that of protons. The sharp penumbra of VHEE was of benefit in treatment planning for producing treatment plans with conformal dose shaping, with improved dose to critical structures being demonstrated for several treatment sites. Mean dose to critical structures, relative to the prescribed dose, was in the order of 0–10% lower than photons and 0–10% higher than protons. The delivery technology and dose distributions were also promising for radiotherapy with ultra-high dose rate (FLASH).

Conclusion

At present, the potential clinical benefit of VHEE relative to photons or protons is small. Further studies are needed to more precisely quantify the relative performance of broad beams versus pencil beam scanning and to investigate treatment sites that might benefit maximally from the use of VHEE beams.

背景和目的与光子相比，使用甚高能电子（VHEE）进行放疗有可能减少危险器官的剂量。因此，本文对 VHEE 的治疗计划进行了回顾，以明确该模式的潜在益处。材料与方法对有关 VHEE 的文章进行了识别，并人工筛选出那些关注治疗计划的文章，尤其是那些包含患者数据集结果的文章。结果发现增加射束能量可减少电子散射，使半影更窄，但导致深度剂量曲线相当恒定，不像质子那样有助于保护正常组织。超高频电子显微镜的尖锐半影有利于治疗计划的制定，从而产生适形剂量整形的治疗计划，多个治疗部位的关键结构的剂量都得到了改善。与规定剂量相比，关键结构的平均剂量比光子低 0-10%，比质子高 0-10%。结论目前，相对于光子或质子，VHEE 的潜在临床效益还很小。还需要进一步研究，以更精确地量化宽光束与铅笔束扫描的相对性能，并调查可能从使用 VHEE 光束中获益最大的治疗部位。

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引用次数: 0

Automatic segmentation for magnetic resonance imaging guided individual elective lymph node irradiation in head and neck cancer patients 磁共振成像引导头颈部癌症患者进行个体选择性淋巴结照射的自动分割技术

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2024-10-01 DOI: 10.1016/j.phro.2024.100655

Floris C.J. Reinders , Mark H.F. Savenije , Mischa de Ridder , Matteo Maspero , Patricia A.H. Doornaert , Chris H.J. Terhaard , Cornelis P.J. Raaijmakers , Kaveh Zakeri , Nancy Y. Lee , Eric Aliotta , Aneesh Rangnekar , Harini Veeraraghavan , Marielle E.P. Philippens

Background and purpose

In head and neck squamous cell carcinoma (HNSCC) patients, the radiation dose to nearby organs at risk can be reduced by restricting elective neck irradiation from lymph node levels to individual lymph nodes. However, manual delineation of every individual lymph node is time-consuming and error prone. Therefore, automatic magnetic resonance imaging (MRI) segmentation of individual lymph nodes was developed and tested using a convolutional neural network (CNN).

Materials and methods

In 50 HNSCC patients (UMC-Utrecht), individual lymph nodes located in lymph node levels Ib-II-III-IV-V were manually segmented on MRI by consensus of two experts, obtaining ground truth segmentations. A 3D CNN (nnU-Net) was trained on 40 patients and tested on 10. Evaluation metrics were Dice Similarity Coefficient (DSC), recall, precision, and F1-score. The segmentations of the CNN was compared to segmentations of two observers. Transfer learning was used with 20 additional patients to re-train and test the CNN in another medical center.

Results

nnU-Net produced automatic segmentations of elective lymph nodes with median DSC: 0.72, recall: 0.76, precision: 0.78, and F1-score: 0.78. The CNN had higher recall compared to both observers (p = 0.002). No difference in evaluation scores of the networks in both medical centers was found after re-training with 5 or 10 patients.

Conclusion

nnU-Net was able to automatically segment individual lymph nodes on MRI. The detection rate of lymph nodes using nnU-Net was higher than manual segmentations. Re-training nnU-Net was required to successfully transfer the network to the other medical center.

背景和目的在头颈部鳞状细胞癌（HNSCC）患者中，通过将选择性颈部照射从淋巴结水平限制到单个淋巴结，可以减少对附近危险器官的辐射剂量。然而，人工划定每个淋巴结既费时又容易出错。因此，我们使用卷积神经网络（CNN）开发并测试了单个淋巴结的自动磁共振成像（MRI）分割。材料与方法在 50 名 HNSCC 患者（UMC-Utrecht）中，通过两名专家的共识，对位于淋巴结 Ib-II-III-IV-V 层的单个淋巴结进行了 MRI 人工分割，获得了基本真实分割结果。在 40 名患者身上训练了 3D CNN（nnU-Net），并在 10 名患者身上进行了测试。评估指标包括骰子相似系数（DSC）、召回率、精确度和 F1-分数。CNN 的分割结果与两名观察者的分割结果进行了比较。结果nnU-Net对选择性淋巴结进行了自动分割，DSC中位数为0.72，召回率为0.76，精确度为0.1：0.76，精确度：0.78，F1-分数：0.78。与两位观察者相比，CNN 的召回率更高（p = 0.002）。结论 nnU-Net 能够自动分割 MRI 上的单个淋巴结。使用 nnU-Net 的淋巴结检测率高于人工分割。需要重新训练 nnU-Net 才能将网络成功转移到另一家医疗中心。

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引用次数: 0

Evaluation of deep learning-based target auto-segmentation for Magnetic Resonance Imaging-guided cervix brachytherapy 评估基于深度学习的目标自动分割技术在磁共振成像引导下的宫颈近距离治疗中的应用

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2024-10-01 DOI: 10.1016/j.phro.2024.100669

Rita Simões, Eva C. Rijkmans, Eva E. Schaake, Marlies E. Nowee, Sandra van der Velden, Tomas Janssen

Background and purpose

The target structures for cervix brachytherapy are segmented by radiation oncologists using imaging and clinical information. At the first fraction, this is performed manually from scratch. For subsequent fractions the first fraction segmentations are rigidly propagated and edited manually. This process is time-consuming while patients wait immobilized. In this work, we evaluate the potential clinical impact of using population-based and patient-specific auto-segmentations as a starting point for target segmentation of the second fraction.

Materials and method

For twenty-eight patients with locally advanced cervical cancer, treated with MRI-guided brachytherapy, auto-segmentations were retrospectively generated for the second fraction image using two approaches: 1) population-based model, 2) patient-specific models fine-tuned on first fraction information. A radiation oncologist manually edited the auto-segmentations to assess model-induced bias. Pairwise geometric and dosimetric comparisons were performed for the automatic, edited and clinical structures. The time spent editing the auto-segmentations was compared to the current clinical workflow.

Results

The edited structures were more similar to the automatic than to the clinical structures. The geometric and dosimetric differences between the edited and the clinical structures were comparable to the inter-observer variability investigated in literature. Editing the auto-segmentations was faster than the manual segmentation performed during our clinical workflow. Patient-specific auto-segmentations required less edits than population-based structures.

Conclusions

Auto-segmentation introduces a bias in the manual delineations but this bias is clinically irrelevant. Auto-segmentation, particularly patient-specific fine-tuning, is a time-saving tool that can improve treatment logistics and therefore reduce patient burden during the second fraction of cervix brachytherapy.

背景和目的宫颈近距离放射治疗的目标结构是由放射肿瘤专家利用成像和临床信息进行分割的。第一次分割时，需要从头开始手工操作。在随后的分次治疗中，第一次分次分割的结果会被硬性传播并进行人工编辑。这一过程非常耗时，而患者只能静静地等待。在这项工作中，我们评估了使用基于人群和患者特异性的自动分割作为第二部分目标分割起点的潜在临床影响。材料和方法对于 28 位接受 MRI 引导近距离放射治疗的局部晚期宫颈癌患者，我们使用两种方法对第二部分图像进行了自动分割：1）基于人群的模型；2）根据第一部分信息微调的患者特定模型。放射肿瘤学家手动编辑自动分割，以评估模型引起的偏差。对自动结构、编辑结构和临床结构进行成对几何和剂量比较。结果编辑后的结构与自动结构的相似程度高于临床结构。编辑结构与临床结构之间的几何和剂量测定差异与文献中研究的观察者间差异相当。与临床工作流程中的手动分割相比，编辑自动分割的速度更快。结论：自动分割会给手动划分带来偏差，但这种偏差与临床无关。自动分区，尤其是针对患者的微调，是一种节省时间的工具，可以改善治疗流程，从而减轻宫颈近距离治疗第二部分的患者负担。

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引用次数: 0

Monte Carlo simulated correction factors for high dose rate brachytherapy postal dosimetry audit methodology 蒙特卡罗模拟高剂量率近距离放射邮政剂量测定审计方法的校正系数

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2024-10-01 DOI: 10.1016/j.phro.2024.100657

Krzysztof Chelminski , Alexis Dimitriadis , Roua Abdulrahim , Pavel Kazantsev , Evelyn Granizo-Roman , Jonathan Kalinowski , Shirin Abbasi Enger , Godfrey Azangwe , Mauro Carrara , Jamema Swamidas

Background and Purpose

Full-scatter conditions in water are impractical for postal dosimetry audits in brachytherapy. This work presents a method to obtain correction factors that account for deviations from full-scatter water-equivalent conditions for a small plastic phantom.

Material and Methods

A 16 × 8 × 3 cm phantom (PMMA) with a radiophotoluminescent dosimeter (RPLD) at the centre and two catheters on either side was simulated using Monte Carlo (MC) to calculate correction factors accounting for the lack of scatter, non-water equivalence of the RPLD and phantom, source model and backscatter for HDR ⁶⁰Co and ¹⁹²Ir sources.

Results

The correction factors for non-water equivalence, lack of full scatter, and the use of PMMA were 1.062 ± 0.013, 1.059 ± 0.008 and 0.993 ± 0.009 for ¹⁹²Ir and 1.129 ± 0.005, 1.009 ± 0.005 and 1.005 ± 0.005 for ⁶⁰Co respectively. Water-equivalent backscatter thickness of 5 cm was found to be adequate and increasing thickness of backscatter did not have an influence on the RPLD dose. The mean photon energy in the RPLD for four HDR ¹⁹²Ir sources was 279 ± 2 keV in full scatter conditions and 295 ± 1 keV in the audit conditions. For ⁶⁰Co source the corresponding mean energies were 989 ± 1 keV and 1022 ± 1 keV respectively.

Conclusions

Correction factors were obtained through the MC simulations for conditions deviating from TG-43, including the amount of back scatter, and the optimum audit set up. Additionally, the influence of different source models on the correction factors was negligible and demonstrates their generic applicability.

背景和目的水中的全散射条件对于近距离放射治疗中的邮政剂量测定审核来说是不切实际的。材料和方法使用蒙特卡洛（Monte Carlo，MC）模拟了一个 16 × 8 × 3 厘米的模型（PMMA），模型中心有一个放射性光致发光剂量计（RPLD），两侧有两根导管，计算出校正系数，校正系数考虑了散射不足、RPLD 和模型的非水等效性、放射源模型以及 HDR 60Co 和 192Ir 放射源的反向散射。结果对于 192Ir 和 60Co，非水等效、缺乏完全散射和使用 PMMA 的校正系数分别为 1.062 ± 0.013、1.059 ± 0.008 和 0.993 ± 0.009，对于 192Ir 和 60Co，分别为 1.129 ± 0.005、1.009 ± 0.005 和 1.005 ± 0.005。研究发现，5 厘米的水等效后向散射厚度是足够的，增加后向散射厚度对 RPLD 剂量没有影响。在全散射条件下，4 个 HDR 192Ir 源在 RPLD 中的平均光子能量为 279 ± 2 keV，在审计条件下为 295 ± 1 keV。对于 60Co 光源，相应的平均能量分别为 989 ± 1 keV 和 1022 ± 1 keV。结论通过 MC 模拟获得了偏离 TG-43 条件的校正因子，包括后向散射量和最佳审核设置。此外，不同源模型对校正因子的影响可以忽略不计，这也证明了校正因子的通用性。

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引用次数: 0

Feasibility and potential clinical benefit of dose de-escalation in stereotactic ablative radiotherapy for lung cancer lesions with ground glass opacities 立体定向消融放疗剂量递减治疗肺癌磨玻璃混浊的可行性及潜在临床效益。

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2024-10-01 DOI: 10.1016/j.phro.2024.100681

Carla Cases , Meritxell Mollà , Marcelo Sánchez , Mariana Benegas , Marc Ballestero , Sergi Serrano-Rueda , Gabriela Antelo , Carles Gomà

Introduction

Treatment of neoplasic lung nodules with ground glass opacities (GGO) faces two primary challenges. First, the standard practice of treating GGOs as solid nodules, which effectively controls the tumor locally, but might increase associated toxicities. The second is the potential for dose calculation errors related to increased heterogeneity. This study addresses the optimization of a dose de-escalation regime for stereotactic ablative radiotherapy (SABR) for GGO lesions.

Materials and Methods

We used the CT scans of 35 patients (40 lesions) with some degree of GGO component treated at our institution between 2017 and 2021. We first assessed the dose calculation accuracy as a function of the GGO component of the lesion. We then analysed the advantages of a dose de-escalation regime in terms of lung dose reduction (Dmean, V20Gy and V300GyBED3) and plan robustness.

Results

We found a positive correlation between the presence of GGO and the dose calculation errors in a phantom scenario. These differences are reduced for patient data and in the presence of breathing motion. When using a de-escalation regime, significant reductions were achieved in mean lung dose, V20Gy and V300GyBED3. This study also revealed that lower doses in GGO areas lead to more stable fluence patterns, increasing treatment robustness.

Conclusions

The study lays the foundation for an eventual use of dose de-escalation in SABR for treating lung lesions with GGO, potentially leading to equivalent local control while reducing associated toxicities. These findings lay the groundwork for future clinical trials.

治疗伴有磨玻璃混浊（GGO）的肿瘤性肺结节面临两个主要挑战。首先，将ggo作为实性结节治疗的标准做法，可以有效地局部控制肿瘤，但可能增加相关的毒性。第二是与异质性增加有关的剂量计算误差的可能性。本研究旨在优化立体定向消融放疗（SABR）治疗GGO病变的剂量递减方案。材料和方法：我们使用了2017年至2021年间在我院治疗的35例不同程度GGO成分患者（40个病变）的CT扫描。我们首先评估了剂量计算准确性作为病变GGO成分的函数。然后，我们分析了剂量递减方案在肺剂量减少（Dmean， V20Gy和v300r00bed3）和计划稳健性方面的优势。结果：我们发现幻像情景中GGO的存在与剂量计算误差呈正相关。这些差异在患者数据和呼吸运动的存在下减少。当使用降级方案时，平均肺剂量、V20Gy和v300r00bed3显著降低。该研究还表明，GGO区域的较低剂量导致更稳定的影响模式，增加了治疗的稳健性。结论：该研究为最终使用剂量递减法在SABR中使用GGO治疗肺部病变奠定了基础，可能导致等效的局部控制，同时减少相关毒性。这些发现为未来的临床试验奠定了基础。

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引用次数: 0

Feasibility of quantitative relaxometry for prostate target localization and response assessment in magnetic resonance-guided online adaptive stereotactic body radiotherapy 磁共振引导在线自适应立体定向放射治疗中前列腺靶标定位和反应评估的定量松弛法的可行性。

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2024-10-01 DOI: 10.1016/j.phro.2024.100678

Ergys Subashi , Eve LoCastro , Sarah Burleson , Aditya Apte , Michael Zelefsky , Neelam Tyagi

Purpose

Multiparametric magnetic resonance imaging (MRI) is known to provide predictors for malignancy and treatment outcome. The inclusion of these datasets in workflows for online adaptive planning remains under investigation. We demonstrate the feasibility of longitudinal relaxometry in online MR-guided adaptive stereotactic body radiotherapy (SBRT) to the prostate and dominant intra-prostatic lesion (DIL).

Methods

Fifty patients with intermediate-risk prostate cancer were included in the study. The clinical target volume (CTV) was defined as the prostate gland plus 1 cm of seminal vesicles. The gross tumor volume (GTV) was defined as the DIL identified on multiparametric MRI. Online adaptive radiotherapy was delivered in a 1.5 T MR-Linac using a prescription of 800 cGy/900 cGy × 5 fractions to the CTV + 3 mm/GTV + 2 mm. Relaxometry and diffusion-weighted imaging were implemented using clinically available sequences. Test-retest measurements were performed in eight patients, at each treatment fraction. Bias and uncertainty in relaxometry measurements were also assessed using a reference phantom.

Results

The bias in longitudinal/transverse relaxation times was negligible while uncertainty was within 3 %. Test-retest measurements demonstrate that bias/uncertainty in patient T1 and T2 were comparable to bias/uncertainty estimated in the phantom. Mean T1 and T2 relaxation were significantly different between the prostate and DIL. The correlation between T1, T2, and diffusion was significant in the DIL, but not in the prostate. During treatment, mean T1 in the DIL approaches mean T1 in the prostate.

Conclusions

Longitudinal relaxometry for online MR-guided adaptive SBRT is feasible in a high-field MR-Linac and may provide complementary information for target delineation and response assessment.

目的：已知多参数磁共振成像（MRI）可提供恶性肿瘤和治疗结果的预测因子。将这些数据集纳入在线适应性规划的工作流程仍在调查中。我们证明纵向松弛测量在在线磁共振引导的自适应立体定向放射治疗（SBRT）中对前列腺和显性前列腺内病变（DIL）的可行性。方法：选取50例中危前列腺癌患者作为研究对象。临床靶体积（CTV）定义为前列腺加1cm精囊。总肿瘤体积（GTV）定义为在多参数MRI上识别的DIL。在线自适应放疗在1.5 T MR-Linac中进行，处方为800 cGy/900 cGy × 5分数至CTV + 3mm /GTV + 2mm。使用临床可用的序列进行松弛测量和弥散加权成像。在每个治疗阶段，对8名患者进行了测试-重测试测量。使用参考模体评估松弛测量的偏差和不确定度。结果：纵向/横向弛豫时间偏差可忽略不计，不确定度在3%以内。测试-再测试测量表明，患者T1和T2的偏倚/不确定性与幻影中估计的偏倚/不确定性相当。平均T1和T2松弛在前列腺和DIL之间有显著差异。T1、T2和弥散在DIL中有显著相关性，而在前列腺中无显著相关性。在治疗期间，DIL的平均T1接近前列腺的平均T1。结论：纵向松弛测量在高场MR-Linac中是可行的，可以为靶点描绘和反应评估提供补充信息。

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引用次数: 0

Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc Therapy 内部开发的基于深度学习的乳房体积调制弧线治疗自动规划工具的主动风险分析。

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2024-10-01 DOI: 10.1016/j.phro.2024.100677

Liesbeth Vandewinckele , Chahrazad Benazzouz , Laurence Delombaerde , Laure Pape , Truus Reynders , Aline Van der Vorst , Dylan Callens , Jan Verstraete , Adinda Baeten , Caroline Weltens , Wouter Crijns

Background and Purpose:

With the increasing amount of in-house created deep learning models in radiotherapy, it is important to know how to minimise the risks associated with the local clinical implementation prior to clinical use. The goal of this study is to give an example of how to identify the risks and find mitigation strategies to reduce these risks in an implemented workflow containing a deep learning based planning tool for breast Volumetric Modulated Arc Therapy.

Materials and Methods:

The deep learning model ran on a private Google Cloud environment for adequate computational capacity and was integrated into a workflow that could be initiated within the clinical Treatment Planning System (TPS). A proactive Failure Mode and Effect Analysis (FMEA) was conducted by a multidisciplinary team, including physicians, physicists, dosimetrists, technologists, quality managers, and the research and development team. Failure modes categorised as ‘Not acceptable’ and ‘Tolerable’ on the risk matrix were further examined to find mitigation strategies.

Results:

In total, 39 failure modes were defined for the total workflow, divided over four steps. Of these, 33 were deemed ‘Acceptable’, five ‘Tolerable’, and one ‘Not acceptable’. Mitigation strategies, such as a case-specific Quality Assurance report, additional scripted checks and properties, a pop-up window, and time stamp analysis, reduced the failure modes to two ‘Tolerable’ and none in the ‘Not acceptable’ region.

Conclusions:

The pro-active risk analysis revealed possible risks in the implemented workflow and led to the implementation of mitigation strategies that decreased the risk scores for safer clinical use.

背景和目的：随着放射治疗领域内部创建的深度学习模型数量的增加，在临床使用之前，了解如何将与当地临床实施相关的风险降至最低是很重要的。本研究的目的是给出一个示例，说明如何在包含基于深度学习的乳房体积调制弧线疗法规划工具的实施工作流中识别风险并找到缓解策略，以降低这些风险。材料和方法：深度学习模型在私有谷歌云环境上运行，以获得足够的计算能力，并集成到可以在临床治疗计划系统（TPS）中启动的工作流中。一个多学科团队进行了主动失效模式和影响分析（FMEA），包括医生、物理学家、剂量师、技术人员、质量管理人员和研发团队。进一步检查了风险矩阵中被分类为“不可接受”和“可容忍”的失效模式，以找到缓解策略。结果：总共为整个工作流程定义了39种失效模式，分为四个步骤。其中33项为“可接受”，5项为“可接受”，1项为“不可接受”。缓解策略，例如特定案例的质量保证报告、附加脚本化检查和属性、弹出窗口和时间戳分析，将故障模式减少到两个“可容忍”区域，而在“不可接受”区域中没有故障模式。结论：前瞻性风险分析揭示了实施的工作流程中可能存在的风险，并导致实施降低风险评分的缓解策略，以提高临床使用的安全性。

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引用次数: 0

External validation of a multimodality deep-learning normal tissue complication probability model for mandibular osteoradionecrosis trained on 3D radiation distribution maps and clinical variables 根据三维辐射分布图和临床变量训练的下颌骨骨坏死多模态深度学习正常组织并发症概率模型的外部验证

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2024-10-01 DOI: 10.1016/j.phro.2024.100668

Laia Humbert-Vidan , Christian R. Hansen , Vinod Patel , Jørgen Johansen , Andrew P. King , Teresa Guerrero Urbano

Background and purpose

While the inclusion of spatial dose information in deep learning (DL)-based normal-tissue complication probability (NTCP) models has been the focus of recent research studies, external validation is still lacking. This study aimed to externally validate a DL-based NTCP model for mandibular osteoradionecrosis (ORN) trained on 3D radiation dose distribution maps and clinical variables.

Methods and materials

A 3D DenseNet-40 convolutional neural network (3D-mDN40) was trained on clinical and radiation dose distribution maps on a retrospective class-balanced matched cohort of 184 subjects. A second model (3D-DN40) was trained on dose maps only and both DL models were compared to a logistic regression (LR) model trained on DVH metrics and clinical variables. All models were externally validated by means of their discriminative ability and calibration on an independent dataset of 82 subjects.

Results

No significant difference in performance was observed between models. In internal validation, these exhibited similar Brier scores around 0.2, Log Loss values of 0.6–0.7 and ROC AUC values around 0.7 (internal) and 0.6 (external). Differences in clinical variable distributions and their effect sizes were observed between internal and external cohorts, such as smoking status (0.6 vs. 0.1) and chemotherapy (0.1 vs. −0.5), respectively.

Conclusion

To our knowledge, this is the first study to externally validate a multimodality DL-based ORN NTCP model. Utilising mandible dose distribution maps, these models show promise for enhancing spatial risk assessment and guiding dental and oncological decision-making, though further research is essential to address overfitting and domain shift for reliable clinical use.

背景和目的虽然将空间剂量信息纳入基于深度学习（DL）的正常组织并发症概率（NTCP）模型是近期研究的重点，但仍缺乏外部验证。本研究旨在从外部验证基于三维辐射剂量分布图和临床变量训练的下颌骨骨坏死（ORN）深度学习并发症概率（NTCP）模型。方法和材料在184名受试者的回顾性类平衡匹配队列中，根据临床和辐射剂量分布图训练了一个三维DenseNet-40卷积神经网络（3D-mDN40）。第二个模型（3D-DN40）仅在剂量分布图上进行了训练，两个 DL 模型都与在 DVH 指标和临床变量上训练的逻辑回归 (LR) 模型进行了比较。所有模型都在一个由 82 名受试者组成的独立数据集上通过判别能力和校准进行了外部验证。在内部验证中，这些模型表现出相似的 Brier 分数（0.2 左右）、Log Loss 值（0.6-0.7）和 ROC AUC 值（0.7（内部）和 0.6（外部））。内部和外部队列之间的临床变量分布及其效应大小存在差异，如吸烟状态（0.6 vs. 0.1）和化疗（0.1 vs. -0.5）。利用下颌骨剂量分布图，这些模型有望加强空间风险评估，并指导牙科和肿瘤决策，但为了可靠地应用于临床，解决过拟合和域偏移问题还需要进一步的研究。

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引用次数: 0

Accuracy, repeatability, and reproducibility of water-fat magnetic resonance imaging in a phantom and healthy volunteer 模型和健康志愿者的水脂磁共振成像的准确性、可重复性和再现性

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2024-10-01 DOI: 10.1016/j.phro.2024.100651

Anouk Corbeau , Pien van Gastel , Piotr A. Wielopolski , Nick de Jong , Carien L. Creutzberg , Uulke A. van der Heide , Stephanie M. de Boer , Eleftheria Astreinidou

Bone marrow (BM) damage due to chemoradiotherapy can increase BM fat in cervical cancer patients. Water-fat magnetic resonance (MR) scans were performed on a phantom and a healthy female volunteer to validate proton density fat fraction accuracy, reproducibility, and repeatability across different vendors, field strengths, and protocols. Phantom measurements showed a high accuracy, high repeatability, and excellent reproducibility. Volunteer measurements had an excellent intra- and interreader reliability, good repeatability, and moderate to good reproducibility. Water-fat MRI show potential for quantification of longitudinal vertebral BM fat changes. Further studies are needed to validate and extend these findings for broader clinical applicability.

化放疗导致的骨髓（BM）损伤会增加宫颈癌患者的骨髓脂肪。为了验证质子密度脂肪分数的准确性、可重复性以及在不同供应商、场强和方案下的可重复性，我们在一个模型和一名健康女性志愿者身上进行了水脂肪磁共振（MR）扫描。模型测量结果显示了高准确性、高重复性和出色的再现性。志愿者的测量结果在读取器内部和读取器之间具有极佳的可靠性、良好的可重复性和中等至良好的再现性。水脂磁共振成像显示出量化椎体基质脂肪纵向变化的潜力。还需要进一步的研究来验证和扩展这些发现的临床适用性。

引用次数: 0

Comparing robust proton versus online adaptive photon radiotherapy for short-course treatment of rectal cancer 比较用于直肠癌短程治疗的强质子放疗和在线自适应光子放疗

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2024-10-01 DOI: 10.1016/j.phro.2024.100663

Johanna A. Hundvin , Unn Hege Lilleøren , Alexander Valdman , Bruno Sorcini , John Alfred Brennsæter , Camilla G. Boer , Helge E.S. Pettersen , Kathrine R. Redalen , Inger Marie Løes , Sara Pilskog

Background and purpose

Image-guided proton beam therapy (IG-PBT) and cone-beam CT (CBCT)-based online adaptive photon radiotherapy (oART) have potentials to restrict radiation toxicity. They are both hypothesised to reduce therapy limiting bowel toxicity in the multimodality treatment of locally advanced rectal cancer (LARC). This study aimed to quantify the difference in relevant dose-volume metrics for these modalities.

Material and Methods

Six-degrees-of-freedom IG-PBT and oART short-course radiotherapy (SCRT) were simulated for 18 LARC patients. Relative biological effectiveness (RBE) was 1.1 for IG-PBT. Delivered dose was evaluated using post-CBCTs. Target coverage was considered robust if average dose to 99% of the clinical target volume was

\geq

95% of the prescription. Organ at risk (OAR) doses were compared using dose-volume histograms and severe bowel toxicity estimated using dose–response modelling.

Results

Target coverage was robust in all patients for oART and all but one patient for IG-PBT. For the main OARs, IG-PBT increased the volume exposed to

\geq

15 Gy (RBE), but reduced volumes exposed to lower doses. Both low- and high-dose exposure to bowel loops were significantly different between the modalities (median (interquartile range) IG-PBT-V_8.9Gy(RBE) = 92 (51–156) cm³, oART-V_8.9Gy(RBE) = 166 (107–234) cm³, p < 0.001; IG-PBT-V_23Gy(RBE) = 62 (25–106) cm³, oART-V_23Gy(RBE) = 38 (18–75) cm³, p < 0.001), translating into similar total grade ≥ 3 bowel toxicity risk.

Conclusion

IG-PBT and oART delivered comparable and satisfying target coverage in SCRT for LARC with similar estimated risk of severe bowel toxicity. Volumes of OAR exposed to 15 Gy (RBE) or more were reduced by oART, while IG-PBT reduced the volumes receiving doses below this level.

背景和目的图像引导质子束疗法（IG-PBT）和基于锥束 CT（CBCT）的在线自适应光子放疗（oART）具有限制放射毒性的潜力。在局部晚期直肠癌（LARC）的多模态治疗中，这两种疗法都被假定能减少限制性肠毒性。本研究旨在量化这些模式的相关剂量-体积指标的差异。材料与方法模拟 18 名 LARC 患者的六自由度 IG-PBT 和 oART 短程放疗（SCRT）。IG-PBT 的相对生物有效性 (RBE) 为 1.1。使用后 CBCT 对投放剂量进行评估。如果 99% 临床靶体积的平均剂量≥处方剂量的 95%，则认为目标覆盖稳健。使用剂量-体积直方图比较风险器官（OAR）剂量，并使用剂量-反应模型估计严重肠毒性。对于主要的 OARs，IG-PBT 增加了≥ 15 Gy（RBE）的暴露量，但减少了较低剂量的暴露量。低剂量和高剂量暴露于肠道襻的情况在不同模式之间有显著差异（中位数（四分位数间距）IG-PBT-V8.9Gy(RBE) = 92 (51-156) cm3，oART-V8.9Gy(RBE) = 166 (107-234) cm3，p < 0.001；IG-PBT-V23Gy(RBE) = 62 (25-106) cm3，oART-V23Gy(RBE) = 38 (18-75) cm3，p < 0.001），转化为相似的总≥3级肠毒性风险。oART 减少了暴露于 15 Gy（RBE）或更高剂量的 OAR 的体积，而 IG-PBT 则减少了低于此剂量的体积。

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