Physics and Imaging in Radiation Oncology最新文献

英文中文

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2026-01-01

引用次数: 0

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2026-01-01

引用次数: 0

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2026-01-01

引用次数: 0

Quality assurance of online adaptive radiotherapy workflows using film dosimetry in a 3D printed thorax anthropomorphic phantom 在3D打印胸腔拟人模型中使用胶片剂量法在线自适应放疗工作流程的质量保证

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2026-01-01 DOI: 10.1016/j.phro.2026.100909

Daan Hoffmans , Koen Nelissen , Eva Versteijne , Wilko Verbakel

Background and purpose

Quality Assurance for online adaptive radiotherapy (oART) can be challenging. Several tests can demonstrate the dosimetric and position accuracy, but commercial phantoms are often not anatomically representative. The aim of this study was to investigate the accuracy of cone-beam computed tomography guided oART palliative and breast cancer trials by using a 3D printed thorax anthropomorphic phantom.

Materials and methods

An anthropomorphic phantom was 3D printed for this study which accommodates film through the spine, breast, heart, and lungs. Dose was measured for spine and breast treatment plans, whilst variations were simulated which can occur during treatment. Measurements were compared to calculated dose on the planning (pCT) and synthetic computed tomography (sCT) using gamma pass rate criteria of minimal 95 % (for gamma of 4 %/2 mm). Differences between the mean gamma were tested for significance.

Results

Measurements done with positional and target volume changes showed no significant difference between the gamma analyses for the pCT and sCT (p = 0.15), indicating a robust and safe workflow. For extreme variations, difference was found between gamma analyses for the pCT and sCT (p = 0.051). Pass rates were all >95 %, except for three measurements in which the sCT showed density errors up to 1000 Hounsfield Units.

Conclusions

This QA approach for oART, which used film measurements in a custom 3D-printed anthropomorphic phantom was able to validate the accuracy of the oART workflow when anatomical deviations arise and could be suitable as end-to-end test in the future.

背景和目的在线适应性放疗（oART）的质量保证具有挑战性。几个测试可以证明剂量学和位置的准确性，但商业模型往往不具有解剖学代表性。本研究的目的是通过使用3D打印的胸腔拟人化幻影来研究锥束计算机断层扫描引导的oART姑息治疗和乳腺癌试验的准确性。材料和方法本研究使用3D打印的拟人化假体，该假体可通过脊柱、乳房、心脏和肺部容纳薄膜。测量了脊柱和乳房治疗方案的剂量，同时模拟了治疗过程中可能发生的变化。测量结果与计划（pCT）和合成计算机断层扫描（sCT）上的计算剂量进行比较，使用至少95%的伽马通过率标准（伽马为4% / 2mm）。对平均值之间的差异进行显著性检验。结果位置和靶体积变化的测量结果显示pCT和sCT的伽马分析之间没有显著差异（p = 0.15），表明工作流程稳健且安全。对于极端的变异，pCT和sCT的伽马分析之间存在差异（p = 0.051）。通过率均为95%，除了三个测量中sCT显示密度误差高达1000霍斯菲尔德单位。oART的这种QA方法，在定制的3d打印拟人化幻影中使用薄膜测量，能够在解剖偏差出现时验证oART工作流程的准确性，并且可以适用于未来的端到端测试。

{"title":"Quality assurance of online adaptive radiotherapy workflows using film dosimetry in a 3D printed thorax anthropomorphic phantom","authors":"Daan Hoffmans , Koen Nelissen , Eva Versteijne , Wilko Verbakel","doi":"10.1016/j.phro.2026.100909","DOIUrl":"10.1016/j.phro.2026.100909","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Quality Assurance for online adaptive radiotherapy (oART) can be challenging. Several tests can demonstrate the dosimetric and position accuracy, but commercial phantoms are often not anatomically representative. The aim of this study was to investigate the accuracy of cone-beam computed tomography guided oART palliative and breast cancer trials by using a 3Dprinted thorax anthropomorphic phantom.</div></div><div><h3>Materials and methods</h3><div>An anthropomorphic phantom was 3Dprinted for this study which accommodates film through the spine, breast, heart, and lungs. Dose was measured for spine and breast treatment plans, whilst variations were simulated which can occur during treatment. Measurements were compared to calculated dose on the planning (pCT) and synthetic computed tomography (sCT) using gamma pass rate criteria of minimal 95 % (for gamma of 4 %/2mm). Differences between the mean gamma were tested for significance.</div></div><div><h3>Results</h3><div>Measurements done with positional and target volume changes showed no significant difference between the gamma analyses for the pCT and sCT (p = 0.15), indicating a robust and safe workflow. For extreme variations, difference was found between gamma analyses for the pCT and sCT (p = 0.051). Pass rates were all >95 %, except for three measurements in which the sCT showed density errors up to 1000 HounsfieldUnits.</div></div><div><h3>Conclusions</h3><div>This QA approach for oART, which used film measurements in a custom 3D-printed anthropomorphic phantom was able to validate the accuracy of the oART workflow when anatomical deviations arise and could be suitable as end-to-end test in the future.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100909"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146078123","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

Deep learning and dual-radiomics model incorporating brachytherapy applicator type to predict radiation-induced acute rectal injury in cervical cancer patients 深度学习和结合近距离治疗应用器类型的双放射组学模型预测宫颈癌患者放射性急性直肠损伤

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2026-01-01 DOI: 10.1016/j.phro.2026.100908

Boda Ning , Zhengxian Li , Deyang Yu , Chenyu Li , Qi Liu , Yanling Bai

Background and purpose

Radiation-induced acute rectal injury (RARI) is a common early toxicity after radiotherapy for cervical cancer (CC) and remains difficult to predict before treatment, which can adversely affect life quality of patients. We aimed to develop a combined dual-radiomics and deep learning (DL) model to improve the prediction of RARI in CC patients treated with radiotherapy.

Materials and methods

This retrospective study included 200 CC patients from one hospital, randomly divided into training (n = 160), internal validation (n = 40) cohorts and external validation (n = 40) from another hospital. Patients were classified as RARI (CTCAE v5.0 grade ≥ 2) or Non-RARI (grade < 2). Radiomic and dosiomic features were extracted from CT images and dose distributions, and DL features were learned using 3D CNNs. The performance of radiomics, dosiomics, DL and hybrid features models for RARI prediction was compared using the receiver operating characteristic (ROC) curve with measurement of the area under the curve (AUC).

Results

For radiomics combining dosiomics, XGBoost achieved the best performance with AUCs of 0.786 and 0.755 in internal and external validation cohorts, respectively. For DL, Resnet_with_CBAM achieved the best performance in the input of combining CT and dose distribution with AUCs of 0.786 and 0.773 in internal and external validation cohorts, respectively. Nomogram integrating radiomics, dosiomics, DL features, and clinical factor improved the AUC to 0.810, 0.803 in internal and external validation cohorts, respectively.

Conclusion

The nomogram integrating radiomics, dosiomics, DL, and clinical factors can improve the predictive performance for RARI in CC patients followed by radiotherapy.

背景与目的放射引起的急性直肠损伤（RARI）是宫颈癌（CC）放疗后常见的早期毒性，治疗前难以预测，影响患者的生活质量。我们的目标是建立一个联合的双放射组学和深度学习（DL）模型，以提高对放射治疗的CC患者RARI的预测。材料与方法本回顾性研究纳入一家医院的200例CC患者，随机分为训练组（n = 160）、内部验证组（n = 40）和另一家医院的外部验证组（n = 40）。患者分为RARI （CTCAE v5.0分级≥2级）和非RARI（分级<； 2）。从CT图像和剂量分布中提取放射组学和剂量组学特征，并使用3D cnn学习DL特征。采用受试者工作特征（ROC）曲线和曲线下面积（AUC）测量，比较放射组学、剂量组学、DL和混合特征模型预测RARI的性能。结果对于放射组学联合剂量组学，XGBoost在内部和外部验证队列中的auc分别为0.786和0.755，表现最佳。对于DL， Resnet_with_CBAM在CT与剂量分布相结合的输入中表现最佳，在内部验证队列和外部验证队列中的auc分别为0.786和0.773。整合放射组学、剂量组学、DL特征和临床因素的Nomogram将内部验证队列和外部验证队列的AUC分别提高至0.810和0.803。结论综合放射组学、剂量组学、DL和临床因素的nomogram放射组学可提高对CC患者放疗后RARI的预测能力。

{"title":"Deep learning and dual-radiomics model incorporating brachytherapy applicator type to predict radiation-induced acute rectal injury in cervical cancer patients","authors":"Boda Ning , Zhengxian Li , Deyang Yu , Chenyu Li , Qi Liu , Yanling Bai","doi":"10.1016/j.phro.2026.100908","DOIUrl":"10.1016/j.phro.2026.100908","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Radiation-induced acute rectal injury (RARI) is a common early toxicity after radiotherapy for cervical cancer (CC) and remains difficult to predict before treatment, which can adversely affect life quality of patients. We aimed to develop a combined dual-radiomics and deep learning (DL) model to improve the prediction of RARI in CC patients treated with radiotherapy.</div></div><div><h3>Materials and methods</h3><div>This retrospective study included 200 CC patients from one hospital, randomly divided into training (n = 160), internal validation (n = 40) cohorts and external validation (n = 40) from another hospital. Patients were classified as RARI (CTCAE v5.0 grade ≥ 2) or Non-RARI (grade < 2). Radiomic and dosiomic features were extracted from CT images and dose distributions, and DL features were learned using 3D CNNs. The performance of radiomics, dosiomics, DL and hybrid features models for RARI prediction was compared using the receiver operating characteristic (ROC) curve with measurement of the area under the curve (AUC).</div></div><div><h3>Results</h3><div>For radiomics combining dosiomics, XGBoost achieved the best performance with AUCs of 0.786 and 0.755 in internal and external validation cohorts, respectively. For DL, Resnet_with_CBAM achieved the best performance in the input of combining CT and dose distribution with AUCs of 0.786 and 0.773 in internal and external validation cohorts, respectively. Nomogram integrating radiomics, dosiomics, DL features, and clinical factor improved the AUC to 0.810, 0.803 in internal and external validation cohorts, respectively.</div></div><div><h3>Conclusion</h3><div>The nomogram integrating radiomics, dosiomics, DL, and clinical factors can improve the predictive performance for RARI in CC patients followed by radiotherapy.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"37 ","pages":"Article 100908"},"PeriodicalIF":3.3,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146038122","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

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2026-01-01

引用次数: 0

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2026-01-01

引用次数: 0

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2026-01-01

引用次数: 0

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2026-01-01

引用次数: 0

IF 3.3 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2026-01-01

引用次数: 0

首页上一页

下一页尾页

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

Physics and Imaging in Radiation Oncology

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀