Physics and Imaging in Radiation Oncology最新文献_第9页

Isotoxic stereotactic reirradiation for recurrent pelvic cancers 等毒立体定向再照射治疗复发性盆腔癌

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-12-11 DOI: 10.1016/j.phro.2025.100889

Christopher J.H. Pagett , John Lilley , Christopher O’Hara , Ane Appelt , Louise Murray , Rasmus Bokrantz , Jakob Ödén , Stina Svensson , Mark Harrison , Philip Camilleri , Rebecca Muirhead , Maxwell Robinson , Christopher Thompson

Background and purpose

Reirradiation is clinically challenging, requiring a balance between delivery of dose to tumour while respecting cumulative organ at risk (OAR) dose constraints. Standard prescriptions are often conservative, ignoring patient variability in achievable OAR doses. Isotoxic radiotherapy individualises treatment by delivering the highest equieffective dose in 2 Gy per fraction (EQD2Gy) while meeting OAR constraints. This technical feasibility study assessed isotoxic pelvic reirradiation using cumulative OAR constraints, the original dose distribution as background, and voxel-by-voxel EQD2Gy optimisation.

Materials and methods

Data from 30 patients previously treated with pelvic stereotactic body radiotherapy (SBRT) at three UK centres were included. OARs were delineated on both previous and reirradiation image sets and deformably registered. Previous dose was mapped to the current image set and used as background dose for SBRT planning, following published methods. Initial 25 Gy in five fractions (25 Gy/5#) plans were generated for all patients, with further isotoxic dose escalation conducted up to a maximum of 50 Gy (fraction number fixed) until cumulative EQD2Gy constraints were reached.

Results

For 25 of 30 patients, clinically acceptable isotoxic plans were obtained, with 23 exceeding the standard UK reirradiation prescription dose of 30 Gy/5#. The median isotoxic prescription was 42 Gy/5#, with four patient plans reaching the upper evaluated limit of 50 Gy. Vessels and the sacral plexus were most frequently dose limiting.

Conclusion

This study highlighted the feasibility of isotoxic pelvic reirradiation and supports further investigation into automation and prediction models to streamline implementation in clinical practice.

背景和目的放射治疗在临床上具有挑战性，需要在向肿瘤输送剂量和尊重累积危险器官（OAR）剂量限制之间取得平衡。标准处方通常是保守的，忽略了患者在可达到的桨叶剂量上的可变性。等毒放射治疗通过在满足OAR限制的情况下提供每部分2gy的最高等有效剂量（EQD2Gy）来实现个体化治疗。这项技术可行性研究使用累积OAR约束、原始剂量分布作为背景和逐体素EQD2Gy优化来评估骨盆再照射的等毒性。材料和方法本研究纳入了来自英国三个中心的30例既往接受盆腔立体定向放射治疗（SBRT）的患者的数据。在先前和再照射图像集上圈定桨形，并进行形变配准。按照已发表的方法，将先前剂量映射到当前图像集，并用作SBRT计划的背景剂量。为所有患者制定了五个部分（25 Gy/5#）的初始25 Gy计划，进一步进行同毒剂量递增，直至最大50 Gy（分数固定），直到达到累积EQD2Gy限制。结果30例患者中有25例获得了临床可接受的同毒方案，其中23例超过了英国标准再照射处方剂量30 Gy/5#。中位同毒处方为42 Gy/5，有4个患者方案达到了50 Gy的评估上限。血管和骶神经丛是最常见的剂量限制。结论本研究强调了骨盆再照射的可行性，并支持进一步研究自动化和预测模型，以简化临床实践中的实施。

{"title":"Isotoxic stereotactic reirradiation for recurrent pelvic cancers","authors":"Christopher J.H. Pagett , John Lilley , Christopher O’Hara , Ane Appelt , Louise Murray , Rasmus Bokrantz , Jakob Ödén , Stina Svensson , Mark Harrison , Philip Camilleri , Rebecca Muirhead , Maxwell Robinson , Christopher Thompson","doi":"10.1016/j.phro.2025.100889","DOIUrl":"10.1016/j.phro.2025.100889","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Reirradiation is clinically challenging, requiring a balance between delivery of dose to tumour while respecting cumulative organ at risk (OAR) dose constraints. Standard prescriptions are often conservative, ignoring patient variability in achievable OAR doses. Isotoxic radiotherapy individualises treatment by delivering the highest equieffective dose in 2 Gy per fraction (EQD2Gy) while meeting OAR constraints. This technical feasibility study assessed isotoxic pelvic reirradiation using cumulative OAR constraints, the original dose distribution as background, and voxel-by-voxel EQD2Gy optimisation.</div></div><div><h3>Materials and methods</h3><div>Data from 30 patients previously treated with pelvic stereotactic body radiotherapy (SBRT) at three UK centres were included. OARs were delineated on both previous and reirradiation image sets and deformably registered. Previous dose was mapped to the current image set and used as background dose for SBRT planning, following published methods. Initial 25 Gy in five fractions (25 Gy/5#) plans were generated for all patients, with further isotoxic dose escalation conducted up to a maximum of 50 Gy (fraction number fixed) until cumulative EQD2Gy constraints were reached.</div></div><div><h3>Results</h3><div>For 25 of 30 patients, clinically acceptable isotoxic plans were obtained, with 23 exceeding the standard UK reirradiation prescription dose of 30 Gy/5#. The median isotoxic prescription was 42 Gy/5#, with four patient plans reaching the upper evaluated limit of 50 Gy. Vessels and the sacral plexus were most frequently dose limiting.</div></div><div><h3>Conclusion</h3><div>This study highlighted the feasibility of isotoxic pelvic reirradiation and supports further investigation into automation and prediction models to streamline implementation in clinical practice.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100889"},"PeriodicalIF":3.3,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145799271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning approach for predicting linear accelerator output settings in automated radiotherapy planning of oligometastatic cancer 用于预测线性加速器输出设置的深度学习方法在低转移性癌症的自动放疗计划中

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-12-11 DOI: 10.1016/j.phro.2025.100890

Mathieu Gaudreault , Lachlan McIntosh , Katrina Woodford , Jason Li , Susan Harden , Sandro Porceddu , Nicholas Hardcastle , Vanessa Panettieri

Background and purpose

The monitor units (MU) per control point (CP) control the necessary fine-tuned ablative dose for hypofractionated radiotherapy of oligometastatic cancer. We aimed to introduce strategies maximising the sample size to accurately predict the MU per CP with artificial intelligence (AI).

Materials and methods

The 40/68/88 treatment plans of consecutive patients treated between 01/2019 and 06/2024 at our institution for metastatic cancer to the liver/bone/lung were included. Two approaches were considered to maximise the sample size. In one approach, the samples of each anatomical site were extensively augmented to predict the MU per CP from the dose distribution per CP, providing the MU per beam and meterset weight per CP. In the other approach, all samples from all anatomical sites were combined for training. The number of achieved clinical goals based on dose-volume calculation metrics in AI radiotherapy plans (AI-RTPlan) was compared with the number of achieved clinical goals in the clinical plans.

Results

The mean absolute percentage error between predicted and clinical MU per beam/meterset weight per CP was less than 6.2%. All AI-RTPlans were generated in less than 5 s. At least 90%/5% of patients had the same, or more, achieved clinical goals with AI-RTPlans. Target coverage and dose to organs at risk metrics were within ± 2% and ± 2.3 Gy of the clinical value in all patients, respectively.

Conclusions

Augmenting data extensively and combining anatomical sites were equivalent and proficient strategies to predict machine settings for radiotherapy planning of oligometastatic cancer.

背景与目的利用每控制点监测单位（MU）控制低转移性肿瘤低分割放疗所需的微调消融剂量。我们的目标是引入最大化样本量的策略，以利用人工智能（AI）准确预测每个CP的MU。材料与方法纳入2019年1月至2024年6月在我院连续治疗的肝/骨/肺转移性癌症患者的40/68/88个治疗方案。考虑了两种方法来最大化样本量。在一种方法中，每个解剖部位的样本被广泛增加，以从每CP的剂量分布预测每CP的MU，提供每束的MU和每CP的计量重量。在另一种方法中，来自所有解剖部位的所有样本被组合起来进行训练。将人工智能放疗计划（AI- rtplan）中基于剂量-体积计算指标的临床目标实现数与临床计划中临床目标实现数进行比较。结果预测结果与临床结果的平均绝对百分比误差小于6.2%。所有ai - rtplan都在不到5秒的时间内生成。至少90%/5%的患者通过AI-RTPlans达到了相同或更多的临床目标。在所有患者中，靶覆盖率和器官危险指标剂量分别在临床值的±2%和±2.3 Gy范围内。结论广泛增强数据和结合解剖部位是预测少转移癌放疗计划机器设置的等效和熟练策略。

{"title":"A deep learning approach for predicting linear accelerator output settings in automated radiotherapy planning of oligometastatic cancer","authors":"Mathieu Gaudreault , Lachlan McIntosh , Katrina Woodford , Jason Li , Susan Harden , Sandro Porceddu , Nicholas Hardcastle , Vanessa Panettieri","doi":"10.1016/j.phro.2025.100890","DOIUrl":"10.1016/j.phro.2025.100890","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The monitor units (MU) per control point (CP) control the necessary fine-tuned ablative dose for hypofractionated radiotherapy of oligometastatic cancer. We aimed to introduce strategies maximising the sample size to accurately predict the MU per CP with artificial intelligence (AI).</div></div><div><h3>Materials and methods</h3><div>The 40/68/88 treatment plans of consecutive patients treated between 01/2019 and 06/2024 at our institution for metastatic cancer to the liver/bone/lung were included. Two approaches were considered to maximise the sample size. In one approach, the samples of each anatomical site were extensively augmented to predict the MU per CP from the dose distribution per CP, providing the MU per beam and meterset weight per CP. In the other approach, all samples from all anatomical sites were combined for training. The number of achieved clinical goals based on dose-volume calculation metrics in AI radiotherapy plans (AI-RTPlan) was compared with the number of achieved clinical goals in the clinical plans.</div></div><div><h3>Results</h3><div>The mean absolute percentage error between predicted and clinical MU per beam/meterset weight per CP was less than 6.2%. All AI-RTPlans were generated in less than 5 s. At least 90%/5% of patients had the same, or more, achieved clinical goals with AI-RTPlans. Target coverage and dose to organs at risk metrics were within ± 2% and ± 2.3 Gy of the clinical value in all patients, respectively.</div></div><div><h3>Conclusions</h3><div>Augmenting data extensively and combining anatomical sites were equivalent and proficient strategies to predict machine settings for radiotherapy planning of oligometastatic cancer.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100890"},"PeriodicalIF":3.3,"publicationDate":"2025-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145760658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Apparent diffusion coefficient increases during short course radiotherapy in rectal tumours: Results from a multicentre longitudinal trial 在直肠肿瘤的短期放疗中，表观扩散系数增加：来自一项多中心纵向试验的结果

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-10-01 DOI: 10.1016/j.phro.2025.100880

Anne L.H. Bisgaard , Chavelli M. Kensen , Marielle E.P. Philippens , Martijn P.W. Intven , Gert J. Meijer , Alice M. Couwenberg , Doenja M.J. Lambregts , Uulke A. van der Heide , Erik van der Bijl , Pètra M. Braam , Faisal Mahmood , Petra J. van Houdt

Background and purpose

The apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI), a form of magnetic resonance imaging (MRI), has shown promise for predicting response to long course neoadjuvant chemoradiotherapy in rectal cancer. This study investigated whether ADC changes are detectable during short course radiotherapy in patients with rectal cancer.

Materials and methods

Across 3 centres, this study included 138 patients with primary tumours, who received neoadjuvant short course radiotherapy (5 fractions of 5 Gy) on a 1.5 T MRI linear accelerator (MRI-linac), without any prior oncological treatments. DWI was acquired at each fraction prior to beam-on. ADC maps were calculated centrally using a mono-exponential model using b-values between 150–800 s/mm². Median scaling of ADC voxel values was performed between two identified groups of DWI sequences. Tumours were semi-automatically delineated on DWI, and median ADCs were extracted per fraction. ADC time-trends over the course of radiotherapy were extracted using linear fitting, with 95% confidence intervals (CI) estimated using bootstrapping.

Results

A scaling factor of 0.93 was used to account for ADC variation between the DWI sequence groups. The median (range) slope of the ADC time-trends was 0.05 (−0.18, 0.42) 10⁻³mm²/s/fraction. In 77 patients (56%), the 95% CI of the slope did not include zero.

Conclusions

ADC changes during short course radiotherapy were detectable in 56% of the patients. Furthermore, the limited ADC variation across DWI sequences supports feasibility of multicentre investigations of MRI-linac based DWI. These findings encourage future research linking ADC to clinical outcomes in rectal cancer for potential treatment personalization.

背景与目的磁共振成像（MRI）的一种形式——弥散加权成像（DWI）得出的表观扩散系数（ADC）有望预测直肠癌患者对长期新辅助放化疗的反应。本研究探讨了在直肠癌患者的短期放疗中是否可以检测到ADC的变化。材料和方法本研究纳入了3个中心的138例原发肿瘤患者，这些患者在1.5 T MRI直线加速器（MRI-linac）上接受了新辅助短期放疗（5 Gy的5个部分），之前没有任何肿瘤治疗。在光束照射前，在每个分数处获取DWI。ADC图使用单指数模型集中计算，b值在150-800 s/mm2之间。在确定的两组DWI序列之间进行ADC体素值的中位数缩放。在DWI上半自动划定肿瘤，并提取每个分数的中位adc。放疗过程中的ADC时间趋势采用线性拟合提取，95%置信区间（CI）采用自举法估计。结果DWI序列组间ADC差异的比例因子为0.93。ADC时间趋势的中位（范围）斜率为0.05 (- 0.18,0.42)10 - 3mm2/s/fraction。在77例（56%）患者中，斜率的95% CI不为零。结论56%的患者在短期放疗中可检测到sadc的改变。此外，DWI序列之间有限的ADC变化支持了基于MRI-linac的DWI多中心研究的可行性。这些发现鼓励未来的研究将ADC与直肠癌的临床结果联系起来，以实现潜在的个性化治疗。

{"title":"Apparent diffusion coefficient increases during short course radiotherapy in rectal tumours: Results from a multicentre longitudinal trial","authors":"Anne L.H. Bisgaard , Chavelli M. Kensen , Marielle E.P. Philippens , Martijn P.W. Intven , Gert J. Meijer , Alice M. Couwenberg , Doenja M.J. Lambregts , Uulke A. van der Heide , Erik van der Bijl , Pètra M. Braam , Faisal Mahmood , Petra J. van Houdt","doi":"10.1016/j.phro.2025.100880","DOIUrl":"10.1016/j.phro.2025.100880","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The apparent diffusion coefficient (ADC) derived from diffusion-weighted imaging (DWI), a form of magnetic resonance imaging (MRI), has shown promise for predicting response to long course neoadjuvant chemoradiotherapy in rectal cancer. This study investigated whether ADC changes are detectable during short course radiotherapy in patients with rectal cancer.</div></div><div><h3>Materials and methods</h3><div>Across 3 centres, this study included 138 patients with primary tumours, who received neoadjuvant short course radiotherapy (5 fractions of 5 Gy) on a 1.5 T MRI linear accelerator (MRI-linac), without any prior oncological treatments. DWI was acquired at each fraction prior to beam-on. ADC maps were calculated centrally using a mono-exponential model using b-values between 150–800 s/mm<sup>2</sup>. Median scaling of ADC voxel values was performed between two identified groups of DWI sequences. Tumours were semi-automatically delineated on DWI, and median ADCs were extracted per fraction. ADC time-trends over the course of radiotherapy were extracted using linear fitting, with 95% confidence intervals (CI) estimated using bootstrapping.</div></div><div><h3>Results</h3><div>A scaling factor of 0.93 was used to account for ADC variation between the DWI sequence groups. The median (range) slope of the ADC time-trends was 0.05 (−0.18, 0.42) 10<sup>−3</sup>mm<sup>2</sup>/s/fraction. In 77 patients (56%), the 95% CI of the slope did not include zero.</div></div><div><h3>Conclusions</h3><div>ADC changes during short course radiotherapy were detectable in 56% of the patients. Furthermore, the limited ADC variation across DWI sequences supports feasibility of multicentre investigations of MRI-linac based DWI. These findings encourage future research linking ADC to clinical outcomes in rectal cancer for potential treatment personalization.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"36 ","pages":"Article 100880"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A proof of concept for improving comparability of dosimetry audits through centralised planning 通过集中规划提高剂量学审计可比性的概念证明

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-10-01 DOI: 10.1016/j.phro.2025.100879

José Antonio Baeza-Ortega , Lauren May , Mohammad Hussein , Sarah Porter , Alisha Moore , Peter B. Greer , Catharine H. Clark , Joerg Lehmann

Background and purpose

The role of dosimetry audits is well established in the development and verification of radiotherapy safety. Differences in planning and beam modelling make inter-centre comparisons challenging, which can be addressed through distribution of centrally created plans. This study developed a centralised planning approach applicable to multiple audit methodologies, using an example of remote patient specific quality assurance assessment, increasing the interpretability of results and facilitating automation and scalability.

Material and methods

Starting with an established plan which met all clinical goals, a commercial dose mimicking algorithm was used to replicate this plan to be suitable for multiple treatment machines. Beam and machine limitation data were collected from participating centres to develop universally acceptable beam models. The influence of variation in beam modelling parameters among centres was assessed by creating additional models using the 2.5th, 25th, 75th and 97.5th percentiles of previously reported data. Multi-leaf collimator angle and leaf position, gantry angle and output deviations were then introduced into copies of these plans.

Results

Introduced delivery errors caused consistent change in dose metrics across machine models (excluding outliers) with a median (range) standard deviation of 1.0 % (from 0.1 % to 1.7 %) demonstrating similar robustness. Beam model variation did not change whether simulated delivery errors were clinically impactful or not for 95 % of tested plans.

Conclusion

This study lays the foundation for future standardised methodology for dosimetry audits by providing a centralised planning approach that allows a more consistent assessment of centres.

背景和目的剂量学审核在放射治疗安全性的制定和验证中的作用已得到充分确立。规划和光束建模的差异使得中心间比较具有挑战性，这可以通过中央创建的计划的分布来解决。本研究开发了一种适用于多种审计方法的集中规划方法，以远程患者特定质量保证评估为例，提高了结果的可解释性，促进了自动化和可扩展性。材料和方法从满足所有临床目标的既定计划开始，使用商业剂量模拟算法来复制该计划，以适用于多个治疗机器。从参与中心收集光束和机器限制数据，以开发普遍接受的光束模型。通过使用先前报告数据的第2.5、第25、第75和第975百分位创建额外的模型，评估了中心之间光束建模参数变化的影响。然后将多叶准直器角度和叶片位置、龙门角度和输出偏差引入到这些平面图的副本中。结果引入的给药错误导致不同机器模型（不包括异常值）剂量计量的一致变化，中位（范围）标准差为1.0%（从0.1%到1.7%），显示出类似的稳健性。对于95%的测试计划，光束模型的变化并没有改变模拟分娩错误是否对临床有影响。本研究通过提供一种允许对中心进行更一致评估的集中规划方法，为未来剂量学审计的标准化方法奠定了基础。

{"title":"A proof of concept for improving comparability of dosimetry audits through centralised planning","authors":"José Antonio Baeza-Ortega , Lauren May , Mohammad Hussein , Sarah Porter , Alisha Moore , Peter B. Greer , Catharine H. Clark , Joerg Lehmann","doi":"10.1016/j.phro.2025.100879","DOIUrl":"10.1016/j.phro.2025.100879","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The role of dosimetry audits is well established in the development and verification of radiotherapy safety. Differences in planning and beam modelling make inter-centre comparisons challenging, which can be addressed through distribution of centrally created plans. This study developed a centralised planning approach applicable to multiple audit methodologies, using an example of remote patient specific quality assurance assessment, increasing the interpretability of results and facilitating automation and scalability.</div></div><div><h3>Material and methods</h3><div>Starting with an established plan which met all clinical goals, a commercial dose mimicking algorithm was used to replicate this plan to be suitable for multiple treatment machines. Beam and machine limitation data were collected from participating centres to develop universally acceptable beam models. The influence of variation in beam modelling parameters among centres was assessed by creating additional models using the 2.5th, 25th, 75th and 97.5th percentiles of previously reported data. Multi-leaf collimator angle and leaf position, gantry angle and output deviations were then introduced into copies of these plans.</div></div><div><h3>Results</h3><div>Introduced delivery errors caused consistent change in dose metrics across machine models (excluding outliers) with a median (range) standard deviation of 1.0 % (from 0.1 % to 1.7 %) demonstrating similar robustness. Beam model variation did not change whether simulated delivery errors were clinically impactful or not for 95 % of tested plans.</div></div><div><h3>Conclusion</h3><div>This study lays the foundation for future standardised methodology for dosimetry audits by providing a centralised planning approach that allows a more consistent assessment of centres.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"36 ","pages":"Article 100879"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Positioning uncertainties in single-target longitudinal segmentation for hippocampal-avoidance whole brain radiotherapy using volumetric modulated arc therapy 体积调制弧线治疗海马回避全脑放疗的单目标纵向分割定位不确定性

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-10-01 DOI: 10.1016/j.phro.2025.100862

Chunbo Tang , Houjin Zhang , Longqiu Wu , Minfeng Huang , Pengfei Wang , Jun Yuan , Junjie Zhang , Biaoshui Liu , Ji He

Hippocampal avoidance whole-brain radiotherapy (HA-WBRT) aims to preserve cognitive function during treatment for brain metastases. This study investigated the potential of Single-Target Longitudinal Segmentation Volumetric Modulated Arc Therapy (VMAT) in HA-WBRT, which segments the planning target volume (PTV) into sub-PTVs, using single or dual arcs to generate s-VMAT and d-VMAT strategies. For 20 patients, s-VMAT and d-VMAT achieved lower median Dmean values of 8.3 Gy and 8.1 Gy, and reduced the median Dmax to 13.5 Gy and 12.8 Gy, compared to traditional coplanar/non-coplanar VMAT plans. These strategies showed enhanced robustness but required more monitor units and greater delivery complexity.

海马回避全脑放疗（HA-WBRT）旨在在脑转移治疗期间保持认知功能。本研究探讨了单目标纵向分割体积调制弧线疗法（VMAT）在HA-WBRT中的潜力，该疗法将规划目标体积（PTV）分割成子PTV，使用单或双弧线生成s-VMAT和d-VMAT策略。与传统的共面/非共面VMAT方案相比，20例患者的s-VMAT和d-VMAT方案的中位d均值较低，分别为8.3 Gy和8.1 Gy，中位Dmax降至13.5 Gy和12.8 Gy。这些策略显示出增强的健壮性，但需要更多的监测单元和更大的交付复杂性。

引用次数: 0

Establishing prospective performance monitoring for real-world implementation of deep learning-based auto-segmentation in prostate cancer radiotherapy 为前列腺癌放疗中基于深度学习的自动分割的实际实施建立前瞻性性能监测

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-10-01 DOI: 10.1016/j.phro.2025.100886

Libing Zhu , Yi Rong , Nathan Y. Yu , Jason M. Holmes , Carlos E. Vargas , Sarah E. James , Lu Shang , Jean-Claude M. Rwigema , Quan Chen

Background and purpose

Deep-learning auto-segmentation (DLAS) performance in radiotherapy may change over time due to data shift/drift or practice changes, yet guidance for quality assurance is lacking. This study developed a practical framework for prospective performance monitoring using retrospective data.

Methods

A total of 464 prostate cases over 20 months were retrospectively collected. Two commercial DLAS models were clinically used: model A (2D U-Net, January 2022–January 2023) and model B (3D U-Net, February–August 2023). The agreement between DLAS and clinical contours was assessed using Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95), and Surface DSC with a 2 mm tolerance (SDSC). Statistical process control charts were created to monitor performance drift and model switching. The first 150 cases were used to define organ-specific control limits with two and three standard deviations of monthly mean values,

σ_{\bar{x}}

.

Results

2

σ_{\bar{x}}

and 3

σ_{\bar{x}}

-based control limits were established for the monthly average charts, ranging from DSC 0.82–0.97, HD95 1.4–10.5 mm, and SDSC 0.45–0.91 across organs. Model A showed stable performance, with 9–13 months per organ remaining within the 3

σ_{\bar{x}}

thresholds. In contrast, model B demonstrated a marked performance shift (p < 0.001), with all five organs exceeding both thresholds across all 7 months. The 2

σ_{\bar{x}}

thresholds were more sensitive in detecting mild deviations for model A, while both limits effectively identified the substantial drift of model B.

Conclusion

The monitoring system effectively detected out-of-distribution outliers and clinical practice changes, providing a reliable framework for early detection of monthly performance degradation.

背景和目的放疗中的深度学习自动分割（DLAS）性能可能会随着时间的推移而变化，因为数据移位/漂移或实践变化，但缺乏质量保证的指导。本研究开发了一个使用回顾性数据进行前瞻性绩效监测的实用框架。方法回顾性收集近20个月464例前列腺癌患者的资料。临床使用两种商用DLAS模型：A模型（2D U-Net， 2022年1月- 2023年1月）和B模型（3D U-Net， 2023年2月- 8月）。采用Dice Similarity Coefficient （DSC）、第95百分位Hausdorff Distance （HD95）和Surface DSC与2mm容差（SDSC）来评估DLAS与临床轮廓的一致性。创建了统计过程控制图来监视性能漂移和模型切换。用前150例的月平均值σx¯的2和3个标准差来定义器官特异性控制极限。结果建立了2σx¯和3σx¯的月平均图控制限，各器官间DSC为0.82 ~ 0.97，HD95为1.4 ~ 10.5 mm， SDSC为0.45 ~ 0.91。模型A表现出稳定的性能，每个器官9-13个月保持在3σx¯阈值内。相比之下，模型B表现出明显的性能变化（p < 0.001），所有五个器官在所有7个月内都超过了两个阈值。2σx¯阈值在检测模型A的轻微偏差时更为敏感，而两个阈值都能有效识别模型b的重大偏差。结论监测系统能有效检测出分布外异常值和临床实践变化，为早期检测月度性能下降提供了可靠的框架。

{"title":"Establishing prospective performance monitoring for real-world implementation of deep learning-based auto-segmentation in prostate cancer radiotherapy","authors":"Libing Zhu , Yi Rong , Nathan Y. Yu , Jason M. Holmes , Carlos E. Vargas , Sarah E. James , Lu Shang , Jean-Claude M. Rwigema , Quan Chen","doi":"10.1016/j.phro.2025.100886","DOIUrl":"10.1016/j.phro.2025.100886","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Deep-learning auto-segmentation (DLAS) performance in radiotherapy may change over time due to data shift/drift or practice changes, yet guidance for quality assurance is lacking. This study developed a practical framework for prospective performance monitoring using retrospective data.</div></div><div><h3>Methods</h3><div>A total of 464 prostate cases over 20 months were retrospectively collected. Two commercial DLAS models were clinically used: model A (2D U-Net, January 2022–January 2023) and model B (3D U-Net, February–August 2023). The agreement between DLAS and clinical contours was assessed using Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95), and Surface DSC with a 2 mm tolerance (SDSC). Statistical process control charts were created to monitor performance drift and model switching. The first 150 cases were used to define organ-specific control limits with two and three standard deviations of monthly mean values, <span><math><mrow><msub><mi>σ</mi><mover><mrow><mi>x</mi></mrow><mrow><mo>¯</mo></mrow></mover></msub></mrow></math></span>.</div></div><div><h3>Results</h3><div>2<span><math><mrow><msub><mi>σ</mi><mover><mrow><mi>x</mi></mrow><mrow><mo>¯</mo></mrow></mover></msub></mrow></math></span> and 3<span><math><mrow><msub><mi>σ</mi><mover><mrow><mi>x</mi></mrow><mrow><mo>¯</mo></mrow></mover></msub></mrow></math></span>-based control limits were established for the monthly average charts, ranging from DSC 0.82–0.97, HD95 1.4–10.5 mm, and SDSC 0.45–0.91 across organs. Model A showed stable performance, with 9–13 months per organ remaining within the 3<span><math><mrow><msub><mi>σ</mi><mover><mrow><mi>x</mi></mrow><mrow><mo>¯</mo></mrow></mover></msub></mrow></math></span> thresholds. In contrast, model B demonstrated a marked performance shift (p < 0.001), with all five organs exceeding both thresholds across all 7 months. The 2<span><math><mrow><msub><mi>σ</mi><mover><mrow><mi>x</mi></mrow><mrow><mo>¯</mo></mrow></mover></msub></mrow></math></span> thresholds were more sensitive in detecting mild deviations for model A, while both limits effectively identified the substantial drift of model B.</div></div><div><h3>Conclusion</h3><div>The monitoring system effectively detected out-of-distribution outliers and clinical practice changes, providing a reliable framework for early detection of monthly performance degradation.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"36 ","pages":"Article 100886"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145736394","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multi-institutional dummy run on segmentation variability and plan quality of stereotactic body radiotherapy for oligometastatic disease 对低转移性疾病立体定向放射治疗的分割可变性和计划质量的多机构模拟试验

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-10-01 DOI: 10.1016/j.phro.2025.100857

Hideaki Hirashima , Yukinori Matsuo , Satoshi Ishikura , Mitsuhiro Nakamura , Ikuno Nishibuchi , Daisuke Kawahara , Yoshihisa Shimada , Yoshiro Nakahara , Teiji Nishio , Naoto Shikama , Shun-ichi Watanabe , Isamu Okamoto , Toshiyuki Ishiba , Fumikata Hara , Tadahiko Shien , Takashi Mizowaki

Background and purpose

Oligometastatic disease represents limited metastatic burden, and local ablative therapies such as stereotactic body radiotherapy (SBRT) may improve survival. However, inter-institutional variability in target segmentation and treatment planning can compromise treatment quality. This study aimed to evaluate the segmentation variability and dose distribution quality of SBRT in oligometastatic settings using a multi-institutional dummy run approach.

Methods and materials

Sixty-nine institutions were provided with two anonymized cases of adrenal and spine metastases to delineate targets and organs at risk (OARs) and create intensity-modulated radiotherapy plans following a protocol. Variability was quantified using the Dice similarity coefficient (DSC), Hausdorff distance, and mean distance to agreement. Plan qualities were assessed using the Paddick conformity index, modified gradient index, and a new three-dimensional conformity–gradient index (3D-CGI). Knowledge-based planning (KBP) was applied to explore potential improvements in OAR sparing.

Results

All submitted plans met protocol dose constraints. However, substantial segmentation variability was observed, particularly for the spine case. Among 136 plans, 79% demonstrated acceptable conformity and dose gradients, with 3D-CGI < 6 correlating with favorable distributions. Mean DSC was 0.93 for the clinical target volume and 0.76 for the cauda equina, which showed the highest variability. KBP reduced OAR doses for the adrenal case but showed limited impact for the spine case.

Conclusions

Although dose constraints were achieved, segmentation variability remained substantial, particularly for the cauda equina in the spine case. These findings emphasize inter-institutional differences and the need for standardization and tools to improve SBRT consistency.

背景和目的低转移性疾病代表有限的转移负担，局部消融治疗如立体定向全身放疗（SBRT）可能提高生存率。然而，机构间在目标分割和治疗计划方面的差异会影响治疗质量。本研究旨在通过多机构虚拟试验方法评估SBRT在低转移环境中的分割可变性和剂量分布质量。方法和材料69家机构提供了2例匿名的肾上腺和脊柱转移病例，以划定靶和危险器官（OARs），并根据协议制定调强放疗计划。使用Dice相似系数（DSC）、Hausdorff距离和平均一致距离来量化变异。采用Paddick整合指数、改良的梯度指数和一种新的三维整合梯度指数（3D-CGI）来评估计划质量。应用基于知识的计划（KBP）来探索OAR节约的潜在改进。结果所有提交的方案均满足方案剂量限制。然而，观察到大量的分割变异性，特别是脊柱病例。在136个方案中，79%表现出可接受的符合性和剂量梯度，3D-CGI <； 6与良好的分布相关。临床靶体积的平均DSC为0.93，马尾的平均DSC为0.76，表现出最高的变异性。KBP减少了肾上腺病例的OAR剂量，但对脊柱病例的影响有限。结论虽然达到了剂量限制，但分割的可变性仍然很大，特别是对于脊柱病例的马尾。这些发现强调了机构间的差异以及提高SBRT一致性的标准化和工具的必要性。

{"title":"A multi-institutional dummy run on segmentation variability and plan quality of stereotactic body radiotherapy for oligometastatic disease","authors":"Hideaki Hirashima , Yukinori Matsuo , Satoshi Ishikura , Mitsuhiro Nakamura , Ikuno Nishibuchi , Daisuke Kawahara , Yoshihisa Shimada , Yoshiro Nakahara , Teiji Nishio , Naoto Shikama , Shun-ichi Watanabe , Isamu Okamoto , Toshiyuki Ishiba , Fumikata Hara , Tadahiko Shien , Takashi Mizowaki","doi":"10.1016/j.phro.2025.100857","DOIUrl":"10.1016/j.phro.2025.100857","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Oligometastatic disease represents limited metastatic burden, and local ablative therapies such as stereotactic body radiotherapy (SBRT) may improve survival. However, inter-institutional variability in target segmentation and treatment planning can compromise treatment quality. This study aimed to evaluate the segmentation variability and dose distribution quality of SBRT in oligometastatic settings using a multi-institutional dummy run approach.</div></div><div><h3>Methods and materials</h3><div>Sixty-nine institutions were provided with two anonymized cases of adrenal and spine metastases to delineate targets and organs at risk (OARs) and create intensity-modulated radiotherapy plans following a protocol. Variability was quantified using the Dice similarity coefficient (DSC), Hausdorff distance, and mean distance to agreement. Plan qualities were assessed using the Paddick conformity index, modified gradient index, and a new three-dimensional conformity–gradient index (3D-CGI). Knowledge-based planning (KBP) was applied to explore potential improvements in OAR sparing.</div></div><div><h3>Results</h3><div>All submitted plans met protocol dose constraints. However, substantial segmentation variability was observed, particularly for the spine case. Among 136 plans, 79% demonstrated acceptable conformity and dose gradients, with 3D-CGI < 6 correlating with favorable distributions. Mean DSC was 0.93 for the clinical target volume and 0.76 for the cauda equina, which showed the highest variability. KBP reduced OAR doses for the adrenal case but showed limited impact for the spine case.</div></div><div><h3>Conclusions</h3><div>Although dose constraints were achieved, segmentation variability remained substantial, particularly for the cauda equina in the spine case. These findings emphasize inter-institutional differences and the need for standardization and tools to improve SBRT consistency.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"36 ","pages":"Article 100857"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145520095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A machine learning approach for radiation pneumonitis prediction in elderly esophageal cancer patients by integrating baseline computed tomography radiomics, dosiomics, and clinical characteristics 结合基线计算机断层放射组学、剂量组学和临床特征预测老年食管癌患者放射性肺炎的机器学习方法

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-10-01 DOI: 10.1016/j.phro.2025.100863

Zhunhao Zheng , Junqiang Chen , Xiaolin Ge , Meng Yan , Ling Li , Qifeng Wang , Xiaomin Wang , Xin Wang , Wenyang Liu , Yonggang Shi , Xiaofeng Wang , Hongyun Shi , Zhilong Yu , Qingsong Pang , Zefen Xiao , Wencheng Zhang

Background and purpose

To integrate clinical characteristics, radiomics, and dosiomics to provide accurate and individualized prediction of radiation pneumonitis (RP) in elderly patients aged 70 and over with esophageal cancer receiving radiotherapy.

Materials and methods

Based on a phase III clinical study (NCT02979691) that included elderly patients with esophageal squamous cell carcinoma (ESCC) who received definitive radiotherapy, we selected a total of 229 patients with available computed tomography (CT) and dose images. Radiomic and dosiomics features were extracted from both lungs. The patients were randomly assigned to either the training group (N = 161) or the test group (N = 68) in a 7:3 ratio. In the training set, logistic regression (LR) was applied to calculate the radiomic score (R score) and dosiomic score (D score). The constructed multivariate LR and ridge regression prediction models were evaluated using the test set. The endpoint of the predictive model is defined as a grade ≥ 2 RP. Discrimination and prediction were assessed by calculating the area under curve (AUC) of the receiver operating characteristic curve and plotting calibration and decision curve analyses (DCA).

Results

The hybrid LR model integrating R score, D score and clinical characteristics had the best clinical applicability. The hybrid model demonstrated superior predictive performance on the test set, achieving an area under the curve (AUC) of 0.76, while the combined clinical and DVH model achieved an AUC of 0.70.

Conclusions

A hybrid model combining radiomics and dosiomics with clinical characteristics showed the best performance for predicting RP.

背景与目的将临床特征、放射组学和剂量组学相结合，为70岁及以上高龄食管癌放疗患者放射性肺炎（RP）的准确、个体化预测提供依据。材料和方法基于一项III期临床研究（NCT02979691），该研究纳入了接受最终放疗的老年食管鳞状细胞癌（ESCC）患者，我们共选择了229例具有可用计算机断层扫描（CT）和剂量图像的患者。从两肺提取放射组学和剂量组学特征。患者按7:3的比例随机分为训练组（N = 161）和试验组（N = 68）。在训练集中，应用logistic回归（LR）计算放射组学评分（R评分）和剂量组学评分（D评分）。使用测试集对构建的多元LR和岭回归预测模型进行评估。预测模型的终点定义为RP≥2级。通过计算受试者工作特性曲线下面积（AUC）和绘制校准和决策曲线分析（DCA）来评估鉴别和预测能力。结果综合R评分、D评分和临床特征的混合型LR模型具有最佳的临床适用性。混合模型在测试集上表现出更好的预测性能，曲线下面积（AUC）为0.76，而临床和DVH联合模型的AUC为0.70。结论放射组学和剂量组学结合临床特征的混合模型预测RP的效果最好。

{"title":"A machine learning approach for radiation pneumonitis prediction in elderly esophageal cancer patients by integrating baseline computed tomography radiomics, dosiomics, and clinical characteristics","authors":"Zhunhao Zheng , Junqiang Chen , Xiaolin Ge , Meng Yan , Ling Li , Qifeng Wang , Xiaomin Wang , Xin Wang , Wenyang Liu , Yonggang Shi , Xiaofeng Wang , Hongyun Shi , Zhilong Yu , Qingsong Pang , Zefen Xiao , Wencheng Zhang","doi":"10.1016/j.phro.2025.100863","DOIUrl":"10.1016/j.phro.2025.100863","url":null,"abstract":"<div><h3>Background and purpose</h3><div>To integrate clinical characteristics, radiomics, and dosiomics to provide accurate and individualized prediction of radiation pneumonitis (RP) in elderly patients aged 70 and over with esophageal cancer receiving radiotherapy.</div></div><div><h3>Materials and methods</h3><div>Based on a phase III clinical study (NCT02979691) that included elderly patients with esophageal squamous cell carcinoma (ESCC) who received definitive radiotherapy, we selected a total of 229 patients with available computed tomography (CT) and dose images. Radiomic and dosiomics features were extracted from both lungs. The patients were randomly assigned to either the training group (N = 161) or the test group (N = 68) in a 7:3 ratio. In the training set, logistic regression (LR) was applied to calculate the radiomic score (R score) and dosiomic score (D score). The constructed multivariate LR and ridge regression prediction models were evaluated using the test set. The endpoint of the predictive model is defined as a grade ≥ 2 RP. Discrimination and prediction were assessed by calculating the area under curve (AUC) of the receiver operating characteristic curve and plotting calibration and decision curve analyses (DCA).</div></div><div><h3>Results</h3><div>The hybrid LR model integrating R score, D score and clinical characteristics had the best clinical applicability. The hybrid model demonstrated superior predictive performance on the test set, achieving an area under the curve (AUC) of 0.76, while the combined clinical and DVH model achieved an AUC of 0.70.</div></div><div><h3>Conclusions</h3><div>A hybrid model combining radiomics and dosiomics with clinical characteristics showed the best performance for predicting RP.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"36 ","pages":"Article 100863"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145576373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Workflow evaluation of surface-guided initial patient set-up in radiotherapy 放射治疗中表面引导初始病人设置的工作流程评估

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-10-01 DOI: 10.1016/j.phro.2025.100877

Mikkel Skaarup, Nikolaj Kylling Gyldenløve Jensen

Background and purpose

The desire to avoid tattooing radiotherapy patients lead us to implement surface-guided initial patient set-up (SGRT). To validate SGRT we investigated setup precision, user attitude, and impact on radiotherapy technician (RTT) workflow.

Materials and methods

During a six-month period, initial setup was investigated on six linear accelerators (Truebeam, Varian), each equipped with a thermo-optical surface camera (ExacTrac Dynamic, Brainlab). Precision was assessed by comparing couch shifts based on x-ray imaging acquired after initial setup and number of x-ray imaging procedures for each fraction to data from the prior year, using a tattoo-based setup. The data was split into subgroups (thoracic, abdominal/pelvic, palliative and miscellaneous (gastrointestinal, head and neck, cranial and extremities)). User attitude and impact on RTT workflow was assessed qualitatively by questionnaire. RTTs were asked to rate how SGRT compared to tattoo-based setup and record the need for manual adjustments of the patient (e.g. pushing, lifting or pulling). Questionnaires were repeated 1.5 years after implementation.

Results

We included 460 patients setup with SGRT, and 468 patients with tattoo-based methods. Median couch shifts and repeated imaging were comparable overall (0.6 cm and 9 % respectively for both setup methods), SGRT performed better for the thoracic and miscellaneous sites subgroups. RTTs preferred SGRT to laser and tattoo initial setup for >90 % of fractions. Manual adjustments were reduced with SGRT (15 % of fractions) compared to tattoo-based (60 % of fractions).

Conclusions

SGRT achieved the same or better precision as tattoo-based initial setup while providing a better workflow and reduced physical adjustments performed by the RTTs by 75 %.

背景和目的为了避免放射治疗患者纹身，我们实施了表面引导初始患者设置（SGRT）。为了验证SGRT，我们调查了设置精度、用户态度和对放疗技术员（RTT）工作流程的影响。材料和方法在六个月的时间里，研究了六个线性加速器（Truebeam, Varian）的初始设置，每个加速器都配备了一个热光学表面摄像机（ExacTrac Dynamic, Brainlab）。通过比较初始设置后获得的x射线成像和每个部分的x射线成像程序数与上一年的数据，使用基于纹身的设置，来评估精度。数据被分成亚组（胸部、腹部/骨盆、姑息治疗和其他（胃肠道、头颈部、颅骨和四肢））。采用问卷法定性评价用户态度及其对RTT工作流程的影响。rtt被要求评价SGRT与基于纹身的设置相比如何，并记录患者手动调整的需要（例如推，举或拉）。调查问卷在实施后1.5年重复进行。结果460例患者采用SGRT， 468例患者采用文身法。中位卧移和重复成像总体上具有可比性（两种设置方法分别为0.6厘米和9%），SGRT在胸部和其他部位亚组中表现更好。对于90%的分数，rtt首选SGRT而不是激光和纹身初始设置。与基于纹身的（60%）相比，SGRT减少了手动调整（15%的分数）。结论ssgrt达到了与基于纹身的初始设置相同或更好的精度，同时提供了更好的工作流程，并将rtt进行的物理调整减少了75%。

{"title":"Workflow evaluation of surface-guided initial patient set-up in radiotherapy","authors":"Mikkel Skaarup, Nikolaj Kylling Gyldenløve Jensen","doi":"10.1016/j.phro.2025.100877","DOIUrl":"10.1016/j.phro.2025.100877","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The desire to avoid tattooing radiotherapy patients lead us to implement surface-guided initial patient set-up (SGRT). To validate SGRT we investigated setup precision, user attitude, and impact on radiotherapy technician (RTT) workflow.</div></div><div><h3>Materials and methods</h3><div>During a six-month period, initial setup was investigated on six linear accelerators (Truebeam, Varian), each equipped with a thermo-optical surface camera (ExacTrac Dynamic, Brainlab). Precision was assessed by comparing couch shifts based on x-ray imaging acquired after initial setup and number of x-ray imaging procedures for each fraction to data from the prior year, using a tattoo-based setup. The data was split into subgroups (thoracic, abdominal/pelvic, palliative and miscellaneous (gastrointestinal, head and neck, cranial and extremities)). User attitude and impact on RTT workflow was assessed qualitatively by questionnaire. RTTs were asked to rate how SGRT compared to tattoo-based setup and record the need for manual adjustments of the patient (e.g. pushing, lifting or pulling). Questionnaires were repeated 1.5 years after implementation.</div></div><div><h3>Results</h3><div>We included 460 patients setup with SGRT, and 468 patients with tattoo-based methods. Median couch shifts and repeated imaging were comparable overall (0.6 cm and 9 % respectively for both setup methods), SGRT performed better for the thoracic and miscellaneous sites subgroups. RTTs preferred SGRT to laser and tattoo initial setup for >90 % of fractions. Manual adjustments were reduced with SGRT (15 % of fractions) compared to tattoo-based (60 % of fractions).</div></div><div><h3>Conclusions</h3><div>SGRT achieved the same or better precision as tattoo-based initial setup while providing a better workflow and reduced physical adjustments performed by the RTTs by 75 %.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"36 ","pages":"Article 100877"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Evaluating the quality of multiple automatically produced segmentation variants of the prostate on Magnetic Resonance Imaging scans for brachytherapy 评估近距离放射治疗的磁共振成像扫描中自动生成的多个前列腺分割变体的质量

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-10-01 DOI: 10.1016/j.phro.2025.100852

Arkadiy Dushatskiy , Peter A.N. Bosman , Karel A. Hinnen , Jan Wiersma , Henrike Westerveld , Bradley R. Pieters , Tanja Alderliesten

Background and Purpose

Recently, we introduced a novel Deep Learning (DL) based (semi-)automatic method for medical image segmentation. Unlike classical DL segmentation methods, it produces multiple segmentation variants (reflecting the variation of manual segmentations) instead of just one. Potentially, with this approach, there is a higher chance that a clinician prefers one of automatically produced segmentation variants. This work focuses on evaluating this method on prostate segmentation in MRI scans used for brachytherapy and investigating its potential clinical usefulness.

Materials and Methods

Three experienced radiation oncologists graded (per-slice and per-scan) segmentations produced by our method, reference segmentations (manually created and used for brachytherapy treatment planning) and segmentations produced by a classical DL method. The study was retrospective and the way the segmentation was generated (our method, classical DL method, or manually) was blinded for the clinicians. The grades reflect the amount of manual correction required. Additionally, the clinicians were asked to rank segmentations to evaluate which one is preferred for each scan. The study was performed on 13 prostate cancer patients.

Results

Segmentations produced by our method are graded as requiring no manual correction in 292/576 (51 %) slices compared to 240/576 (42 %) slices in the case of the segmentations produced by a classical DL method. Furthermore, in fewer slices, 38 (6.6 %) vs. 48 (8.3 %), segmentations by our method were graded as unacceptable.

Conclusion

Our study has demonstrated that deep-learning-based segmentation methods can produce high-quality segmentations. Our method was evaluated better than a classical DL method, indicating the potential for integration into clinical practice.

背景与目的最近，我们提出了一种新的基于深度学习的（半）自动医学图像分割方法。与经典的深度学习分割方法不同，它产生多个分割变量（反映手动分割的变化），而不仅仅是一个。潜在地，使用这种方法，临床医生更倾向于自动生成的分割变体之一的可能性更高。这项工作的重点是评估这种方法在前列腺分割的MRI扫描用于近距离治疗和研究其潜在的临床用途。材料和方法三位经验丰富的放射肿瘤学家对我们的方法生成的分割（每片和每扫描），参考分割（手动创建并用于近距离治疗计划）和经典DL方法生成的分割进行了分级。该研究是回顾性的，分割产生的方式（我们的方法，经典DL方法，或手动）对临床医生是盲目的。这些成绩反映了需要手工改正的数量。此外，临床医生被要求对分割进行排序，以评估哪一个是每次扫描的首选。这项研究是在13名前列腺癌患者身上进行的。结果与经典DL方法产生的分割结果相比，我们的方法产生的分割结果在292/576（51%）片中不需要人工校正，而在240/576（42%）片中不需要人工校正。此外，在较少的切片中，38（6.6%）对48(8.3%)，我们的方法分割被评为不可接受。结论基于深度学习的分割方法可以产生高质量的分割结果。我们的方法被评估为比经典的DL方法更好，表明整合到临床实践的潜力。

{"title":"Evaluating the quality of multiple automatically produced segmentation variants of the prostate on Magnetic Resonance Imaging scans for brachytherapy","authors":"Arkadiy Dushatskiy , Peter A.N. Bosman , Karel A. Hinnen , Jan Wiersma , Henrike Westerveld , Bradley R. Pieters , Tanja Alderliesten","doi":"10.1016/j.phro.2025.100852","DOIUrl":"10.1016/j.phro.2025.100852","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Recently, we introduced a novel Deep Learning (DL) based (semi-)automatic method for medical image segmentation. Unlike classical DL segmentation methods, it produces multiple segmentation variants (reflecting the variation of manual segmentations) instead of just one. Potentially, with this approach, there is a higher chance that a clinician prefers one of automatically produced segmentation variants. This work focuses on evaluating this method on prostate segmentation in MRI scans used for brachytherapy and investigating its potential clinical usefulness.</div></div><div><h3>Materials and Methods</h3><div>Three experienced radiation oncologists graded (per-slice and per-scan) segmentations produced by our method, reference segmentations (manually created and used for brachytherapy treatment planning) and segmentations produced by a classical DL method. The study was retrospective and the way the segmentation was generated (our method, classical DL method, or manually) was blinded for the clinicians. The grades reflect the amount of manual correction required. Additionally, the clinicians were asked to rank segmentations to evaluate which one is preferred for each scan. The study was performed on 13 prostate cancer patients.</div></div><div><h3>Results</h3><div>Segmentations produced by our method are graded as requiring no manual correction in 292/576 (51 %) slices compared to 240/576 (42 %) slices in the case of the segmentations produced by a classical DL method. Furthermore, in fewer slices, 38 (6.6 %) vs. 48 (8.3 %), segmentations by our method were graded as unacceptable.</div></div><div><h3>Conclusion</h3><div>Our study has demonstrated that deep-learning-based segmentation methods can produce high-quality segmentations. Our method was evaluated better than a classical DL method, indicating the potential for integration into clinical practice.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"36 ","pages":"Article 100852"},"PeriodicalIF":3.3,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0