Physics and Imaging in Radiation Oncology最新文献

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Comparing different boost concepts and beam configurations for proton therapy of pancreatic cancer 比较用于胰腺癌质子治疗的不同助推概念和射束配置
IF 3.7
Physics and Imaging in Radiation Oncology Pub Date : 2024-04-01 DOI: 10.1016/j.phro.2024.100583
Taiki Takaoka , Takeshi Yanagi , Shinsei Takahashi , Yuta Shibamoto , Yuto Imai , Dai Okazaki , Masanari Niwa , Akira Torii , Nozomi Kita , Seiya Takano , Natsuo Tomita , Akio Hiwatashi
{"title":"Comparing different boost concepts and beam configurations for proton therapy of pancreatic cancer","authors":"Taiki Takaoka ,&nbsp;Takeshi Yanagi ,&nbsp;Shinsei Takahashi ,&nbsp;Yuta Shibamoto ,&nbsp;Yuto Imai ,&nbsp;Dai Okazaki ,&nbsp;Masanari Niwa ,&nbsp;Akira Torii ,&nbsp;Nozomi Kita ,&nbsp;Seiya Takano ,&nbsp;Natsuo Tomita ,&nbsp;Akio Hiwatashi","doi":"10.1016/j.phro.2024.100583","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100583","url":null,"abstract":"<div><h3>Background and Purpose</h3><p>Interfractional geometrical and anatomical variations impact the accuracy of proton therapy for pancreatic cancer. This study investigated field-in-field (FIF) and simultaneous integrated boost (SIB) concepts for scanned proton therapy treatment with different beam configurations.</p></div><div><h3>Materials and Methods</h3><p>Robustly optimized treatment plans for fifteen patients were generated using FIF and SIB techniques with two, three, and four beams. The prescribed dose in 20 fractions was 60 Gy(RBE) for the internal gross tumor volume (IGTV) and 46 Gy(RBE) for the internal clinical target volume. Verification computed tomography (vCT) scans was performed on treatment days 1, 7, and 16. Initial treatment plans were recalculated on the rigidly registered vCTs. V<sub>100%</sub> and D<sub>95%</sub> for targets and D<sub>2cm</sub><sup>3</sup> for the stomach and duodenum were evaluated. Robustness evaluations (range uncertainty of 3.5 %) were performed to evaluate the stomach and duodenum dose-volume parameters.</p></div><div><h3>Results</h3><p>For all techniques, IGTV V<sub>100%</sub> and D<sub>95%</sub> decreased significantly when recalculating the dose on vCTs (p &lt; 0.001). The median IGTV V<sub>100%</sub> and D<sub>95%</sub> over all vCTs ranged from 74.2 % to 90.2 % and 58.8 Gy(RBE) to 59.4 Gy(RBE), respectively. The FIF with two and three beams, and SIB with two beams maintained the highest IGTV V<sub>100%</sub> and D<sub>95%</sub>. In robustness evaluations, the ΔD<sub>2cm</sub><sup>3</sup> of stomach was highest in two beams plans, while the ΔD<sub>2cm</sub><sup>3</sup> of duodenum was highest in four beams plans, for both concepts.</p></div><div><h3>Conclusion</h3><p>Target coverage decreased when recalculating on CTs at different time for both concepts. The FIF with three beams maintained the highest IGTV coverage while sparing normal organs the most.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000538/pdfft?md5=ffe03b8cff6b4534dcb55deeb2392584&pid=1-s2.0-S2405631624000538-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140818431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Relating pre-treatment non-Gaussian intravoxel incoherent motion diffusion-weighted imaging to human papillomavirus status and response in oropharyngeal carcinoma 口咽癌治疗前非高斯体内非相干运动扩散加权成像与人类乳头瘤病毒状态和反应的关系
IF 3.7
Physics and Imaging in Radiation Oncology Pub Date : 2024-04-01 DOI: 10.1016/j.phro.2024.100574
Nienke D. Sijtsema , Iris Lauwers , Gerda M. Verduijn , Mischa S. Hoogeman , Dirk H.J. Poot , Juan A. Hernandez-Tamames , Aad van der Lugt , Marta E. Capala , Steven F. Petit
{"title":"Relating pre-treatment non-Gaussian intravoxel incoherent motion diffusion-weighted imaging to human papillomavirus status and response in oropharyngeal carcinoma","authors":"Nienke D. Sijtsema ,&nbsp;Iris Lauwers ,&nbsp;Gerda M. Verduijn ,&nbsp;Mischa S. Hoogeman ,&nbsp;Dirk H.J. Poot ,&nbsp;Juan A. Hernandez-Tamames ,&nbsp;Aad van der Lugt ,&nbsp;Marta E. Capala ,&nbsp;Steven F. Petit","doi":"10.1016/j.phro.2024.100574","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100574","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Diffusion-weighted imaging (DWI) is a promising technique for response assessment in head-and-neck cancer. Recently, we optimized Non-Gaussian Intravoxel Incoherent Motion Imaging (NG-IVIM), an extension of the conventional apparent diffusion coefficient (<em>ADC</em>) model, for the head and neck. In the current study, we describe the first application in a group of patients with human papillomavirus (HPV)-positive and HPV-negative oropharyngeal squamous cell carcinoma. The aim of this study was to relate <em>ADC</em> and NG-IVIM DWI parameters to HPV status and clinical treatment response.</p></div><div><h3>Materials and methods</h3><p>Thirty-six patients (18 HPV-positive, 18 HPV-negative) were prospectively included. Presence of progressive disease was scored within one year. The mean pre-treatment <em>ADC</em> and NG-IVIM parameters in the gross tumor volume were compared between HPV-positive and HPV-negative patients. In HPV-negative patients, <em>ADC</em> and NG-IVIM parameters were compared between patients with and without progressive disease.</p></div><div><h3>Results</h3><p><em>ADC</em>, the NG-IVIM diffusion coefficient <em>D</em>, and perfusion fraction <em>f</em> were significantly higher, while pseudo-diffusion coefficient <em>D*</em> and kurtosis <em>K</em> were significantly lower in the HPV-negative compared to HPV-positive patients. In the HPV-negative group, a significantly lower <em>D</em> was found for patients with progressive disease compared to complete responders. No relation with <em>ADC</em> was observed.</p></div><div><h3>Conclusion</h3><p>The results of our single-center study suggest that <em>ADC</em> is related to HPV status, but not an independent response predictor. The NG-IVIM parameter <em>D,</em> however, was independently associated to response in the HPV-negative group. Noteworthy in the opposite direction as previously thought based on <em>ADC</em>.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000447/pdfft?md5=21e7ad76975caaf833ab8dd40588c057&pid=1-s2.0-S2405631624000447-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140548132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data 对呼吸信号的周期性进行明确编码,只需最少的训练数据就能在放疗中准确预测呼吸运动
IF 3.7
Physics and Imaging in Radiation Oncology Pub Date : 2024-04-01 DOI: 10.1016/j.phro.2024.100594
Andreas Renner , Ingo Gulyas , Martin Buschmann , Gerd Heilemann , Barbara Knäusl , Martin Heilmann , Joachim Widder , Dietmar Georg , Petra Trnková
{"title":"Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data","authors":"Andreas Renner ,&nbsp;Ingo Gulyas ,&nbsp;Martin Buschmann ,&nbsp;Gerd Heilemann ,&nbsp;Barbara Knäusl ,&nbsp;Martin Heilmann ,&nbsp;Joachim Widder ,&nbsp;Dietmar Georg ,&nbsp;Petra Trnková","doi":"10.1016/j.phro.2024.100594","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100594","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies. However, they require usually a large dataset for training. The objective of this work was to demonstrate that explicitly encoding the cyclic nature of the breathing signal into the training data enables significant reduction of training datasets which can be obtained from healthy volunteers.</p></div><div><h3>Material and methods</h3><p>Seventy surface scanner breathing signals from 25 healthy volunteers in anterior-posterior direction were used for training and validation (ratio 4:1) of long short-term memory models. The model performance was compared to a model using decomposition into phase, amplitude and a time-dependent baseline. Testing of the models was performed on 55 independent breathing signals in anterior-posterior direction from surface scanner (35 lung, 20 liver) of 30 patients with a mean breathing amplitude of (5.9 ± 6.7)<!--> <!-->mm.</p></div><div><h3>Results</h3><p>Using the decomposed breathing signal allowed for a reduction of the absolute root-mean square error (RMSE) from 0.34 mm to 0.12 mm during validation. Testing using patient data yielded an average absolute RMSE of the breathing signal of (0.16 ± 0.11)<!--> <!-->mm with a prediction horizon of 500 ms.</p></div><div><h3>Conclusion</h3><p>It was demonstrated that a motion prediction model can be trained with less than 100 datasets of healthy volunteers if breathing cycle parameters are considered. Applied to 55 patients, the model predicted breathing motion with a high accuracy.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000642/pdfft?md5=11454d87d05403c0a30c0d3df553dcde&pid=1-s2.0-S2405631624000642-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141240197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Effectiveness of multi-criteria optimization in combination with knowledge-based modeling in radiotherapy of left-sided breast including regional nodes 多标准优化与基于知识的建模相结合在包括区域结节在内的左侧乳腺放射治疗中的有效性
IF 3.7
Physics and Imaging in Radiation Oncology Pub Date : 2024-04-01 DOI: 10.1016/j.phro.2024.100595
Sornjarod Oonsiri, Sakda Kingkaew, Mananchaya Vimolnoch, Nichakan Chatchumnan, Nuttha Plangpleng, Puntiwa Oonsiri
{"title":"Effectiveness of multi-criteria optimization in combination with knowledge-based modeling in radiotherapy of left-sided breast including regional nodes","authors":"Sornjarod Oonsiri,&nbsp;Sakda Kingkaew,&nbsp;Mananchaya Vimolnoch,&nbsp;Nichakan Chatchumnan,&nbsp;Nuttha Plangpleng,&nbsp;Puntiwa Oonsiri","doi":"10.1016/j.phro.2024.100595","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100595","url":null,"abstract":"<div><p>Multi-criteria optimization (MCO) is a method that was added to treatment planning to create high-quality treatment plans. This study aimed to investigate the effectiveness of MCO in combination with knowledge-based planning (KBP) in radiotherapy for left-sided breasts, including regional nodes. Dose/volume parameters were evaluated for manual plans (MP), KBP, and KBP + MCO. Planning target volume doses of MP had better coverage while KBP + MCO plans demonstrated the lowest organ at risk doses. KBP and KBP + MCO plans had increasing complexity as expressed in the number of monitor units.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000654/pdfft?md5=aad13395cae5b20bf3da4050293db116&pid=1-s2.0-S2405631624000654-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141240198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Automatic gross tumor volume segmentation with failure detection for safe implementation in locally advanced cervical cancer 通过故障检测自动分割肿瘤总体积,安全实施局部晚期宫颈癌治疗
IF 3.7
Physics and Imaging in Radiation Oncology Pub Date : 2024-04-01 DOI: 10.1016/j.phro.2024.100578
Rahimeh Rouhi , Stéphane Niyoteka , Alexandre Carré , Samir Achkar , Pierre-Antoine Laurent , Mouhamadou Bachir Ba , Cristina Veres , Théophraste Henry , Maria Vakalopoulou , Roger Sun , Sophie Espenel , Linda Mrissa , Adrien Laville , Cyrus Chargari , Eric Deutsch , Charlotte Robert
{"title":"Automatic gross tumor volume segmentation with failure detection for safe implementation in locally advanced cervical cancer","authors":"Rahimeh Rouhi ,&nbsp;Stéphane Niyoteka ,&nbsp;Alexandre Carré ,&nbsp;Samir Achkar ,&nbsp;Pierre-Antoine Laurent ,&nbsp;Mouhamadou Bachir Ba ,&nbsp;Cristina Veres ,&nbsp;Théophraste Henry ,&nbsp;Maria Vakalopoulou ,&nbsp;Roger Sun ,&nbsp;Sophie Espenel ,&nbsp;Linda Mrissa ,&nbsp;Adrien Laville ,&nbsp;Cyrus Chargari ,&nbsp;Eric Deutsch ,&nbsp;Charlotte Robert","doi":"10.1016/j.phro.2024.100578","DOIUrl":"10.1016/j.phro.2024.100578","url":null,"abstract":"<div><h3>Background and Purpose</h3><p>Automatic segmentation methods have greatly changed the RadioTherapy (RT) workflow, but still need to be extended to target volumes. In this paper, Deep Learning (DL) models were compared for Gross Tumor Volume (GTV) segmentation in locally advanced cervical cancer, and a novel investigation into failure detection was introduced by utilizing radiomic features.</p></div><div><h3>Methods and materials</h3><p>We trained eight DL models (UNet, VNet, SegResNet, SegResNetVAE) for 2D and 3D segmentation. Ensembling individually trained models during cross-validation generated the final segmentation. To detect failures, binary classifiers were trained using radiomic features extracted from segmented GTVs as inputs, aiming to classify contours based on whether their Dice Similarity Coefficient <span><math><mrow><mo>(</mo><mi>DSC</mi><mo>)</mo><mo>&lt;</mo><mi>T</mi></mrow></math></span> and <span><math><mrow><mi>DSC</mi><mo>⩾</mo><mi>T</mi></mrow></math></span>. Two distinct cohorts of T2-Weighted (T2W) pre-RT MR images captured in 2D sequences were used: one retrospective cohort consisting of 115 LACC patients from 30 scanners, and the other prospective cohort, comprising 51 patients from 7 scanners, used for testing.</p></div><div><h3>Results</h3><p>Segmentation by 2D-SegResNet achieved the best DSC, Surface DSC (<span><math><mrow><msub><mrow><mi>SDSC</mi></mrow><mrow><mn>3</mn><mi>mm</mi></mrow></msub></mrow></math></span>), and 95th Hausdorff Distance (95HD): DSC = 0.72 ± 0.16, <span><math><mrow><msub><mrow><mi>SDSC</mi></mrow><mrow><mn>3</mn><mi>mm</mi></mrow></msub></mrow></math></span>=0.66 ± 0.17, and 95HD = 14.6 ± 9.0 mm without missing segmentation (<span><math><mrow><mi>M</mi></mrow></math></span>=0) on the test cohort. Failure detection could generate precision (<span><math><mrow><mi>P</mi><mo>=</mo><mn>0.88</mn></mrow></math></span>), recall (<span><math><mrow><mi>R</mi><mo>=</mo><mn>0.75</mn></mrow></math></span>), F1-score (<span><math><mrow><mi>F</mi><mo>=</mo><mn>0.81</mn></mrow></math></span>), and accuracy (<span><math><mrow><mi>A</mi><mo>=</mo><mn>0.86</mn></mrow></math></span>) using Logistic Regression (LR) classifier on the test cohort with a threshold T = 0.67 on DSC values.</p></div><div><h3>Conclusions</h3><p>Our study revealed that segmentation accuracy varies slightly among different DL methods, with 2D networks outperforming 3D networks in 2D MRI sequences. Doctors found the time-saving aspect advantageous. The proposed failure detection could guide doctors in sensitive cases.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000484/pdfft?md5=7b13fafc60565c0e7bc24b1dadfb8ba2&pid=1-s2.0-S2405631624000484-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140793345","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization 脑转移瘤的磁共振成像放射学特征稳定性:图像预处理、图像和特征级协调的影响
IF 3.7
Physics and Imaging in Radiation Oncology Pub Date : 2024-04-01 DOI: 10.1016/j.phro.2024.100585
Zahra Khodabakhshi, Hubert Gabrys, Philipp Wallimann, Matthias Guckenberger, Nicolaus Andratschke, Stephanie Tanadini-Lang
{"title":"Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization","authors":"Zahra Khodabakhshi,&nbsp;Hubert Gabrys,&nbsp;Philipp Wallimann,&nbsp;Matthias Guckenberger,&nbsp;Nicolaus Andratschke,&nbsp;Stephanie Tanadini-Lang","doi":"10.1016/j.phro.2024.100585","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100585","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Magnetic resonance imaging (MRI) scans are highly sensitive to acquisition and reconstruction parameters which affect feature stability and model generalizability in radiomic research. This work aims to investigate the effect of image pre-processing and harmonization methods on the stability of brain MRI radiomic features and the prediction performance of radiomic models in patients with brain metastases (BMs).</p></div><div><h3>Materials and methods</h3><p>Two T1 contrast enhanced brain MRI data-sets were used in this study. The first contained 25 BMs patients with scans at two different time points and was used for features stability analysis. The effect of gray level discretization (GLD), intensity normalization (Z-score, Nyul, WhiteStripe, and in house-developed method named N-Peaks), and ComBat harmonization on features stability was investigated and features with intraclass correlation coefficient &gt;0.8 were considered as stable. The second data-set containing 64 BMs patients was used for a classification task to investigate the informativeness of stable features and the effects of harmonization methods on radiomic model performance.</p></div><div><h3>Results</h3><p>Applying fixed bin number (FBN) GLD, resulted in higher number of stable features compare to fixed bin size (FBS) discretization (10 ± 5.5 % higher). `Harmonization in feature domain improved the stability for non-normalized and normalized images with Z-score and WhiteStripe methods. For the classification task, keeping the stable features resulted in good performance only for normalized images with N-Peaks along with FBS discretization.</p></div><div><h3>Conclusions</h3><p>To develop a robust MRI based radiomic model we recommend using an intensity normalization method based on a reference tissue (e.g N-Peaks) and then using FBS discretization.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000551/pdfft?md5=97d3acbd58ad3959ee1f67cf8dcbaf29&pid=1-s2.0-S2405631624000551-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140951178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Dynamic anthropomorphic thorax phantom for quality assurance of motion management in radiotherapy 用于放射治疗运动管理质量保证的动态拟人胸廓模型
IF 3.7
Physics and Imaging in Radiation Oncology Pub Date : 2024-04-01 DOI: 10.1016/j.phro.2024.100587
Sara Abdollahi , Ali Asghar Mowlavi , Mohammad Hadi Hadizadeh Yazdi , Sofie Ceberg , Marianne Camille Aznar , Fatemeh Varshoee Tabrizi , Roham Salek , Matthias Guckenberger , Stephanie Tanadini-Lang
{"title":"Dynamic anthropomorphic thorax phantom for quality assurance of motion management in radiotherapy","authors":"Sara Abdollahi ,&nbsp;Ali Asghar Mowlavi ,&nbsp;Mohammad Hadi Hadizadeh Yazdi ,&nbsp;Sofie Ceberg ,&nbsp;Marianne Camille Aznar ,&nbsp;Fatemeh Varshoee Tabrizi ,&nbsp;Roham Salek ,&nbsp;Matthias Guckenberger ,&nbsp;Stephanie Tanadini-Lang","doi":"10.1016/j.phro.2024.100587","DOIUrl":"10.1016/j.phro.2024.100587","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Motion management techniques are important to spare the healthy tissue adequately. However, they are complex and need dedicated quality assurance. The aim of this study was to create a dynamic phantom designed for quality assurance and to replicate a patient’s size, anatomy, and tissue density.</p></div><div><h3>Materials and methods</h3><p>A computed tomography (CT) scan of a cancer patient was used to create molds for the lungs, heart, ribs, and vertebral column via additive manufacturing. A pump system and software were developed to simulate respiratory dynamics. The extent of respiratory motion was quantified using a 4DCT scan. End-to-end tests were conducted to evaluate two motion management techniques for lung stereotactic body radiotherapy (SBRT).</p></div><div><h3>Results</h3><p>The chest wall moved between 4 mm and 13 mm anteriorly and 2 mm to 7 mm laterally during the breathing. The diaphragm exhibited superior-inferior movement ranging from 5 mm to 16 mm in the left lung and 10 mm to 36 mm in the right lung. The left lung tumor displaced ± 7 mm superior-inferiorly and anterior-posteriorly. The CT numbers were for lung: −716 ± 108 HU (phantom) and −713 ± 70 HU (patient); bone: 460 ± 20 HU (phantom) and 458 ± 206 HU (patient); soft tissue: 92 ± 9 HU (phantom) and 60 ± 25 HU (patient). The end-to-end testing showed an excellent agreement between the measured and the calculated dose for ion chamber and film dosimetry.</p></div><div><h3>Conclusions</h3><p>The phantom is recommended for quality assurance, evaluating the institution’s specific planning and motion management strategies either through end-to-end testing or as an external audit phantom.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000575/pdfft?md5=47408dbd30f314b2470c02f2550b87bd&pid=1-s2.0-S2405631624000575-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141055946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Feasibility and safety of contrast-enhanced magnetic resonance-guided adaptive radiotherapy for upper abdominal tumors: A preliminary exploration 对比增强磁共振引导的上腹部肿瘤适应性放疗的可行性和安全性:初步探索
IF 3.7
Physics and Imaging in Radiation Oncology Pub Date : 2024-04-01 DOI: 10.1016/j.phro.2024.100582
Wenheng Jiang , Xihua Shi , Xiang Zhang , Zhenjiang Li , Jinbo Yue
{"title":"Feasibility and safety of contrast-enhanced magnetic resonance-guided adaptive radiotherapy for upper abdominal tumors: A preliminary exploration","authors":"Wenheng Jiang ,&nbsp;Xihua Shi ,&nbsp;Xiang Zhang ,&nbsp;Zhenjiang Li ,&nbsp;Jinbo Yue","doi":"10.1016/j.phro.2024.100582","DOIUrl":"10.1016/j.phro.2024.100582","url":null,"abstract":"<div><p>This study investigates the use of contrast-enhanced magnetic resonance (MR) in MR-guided adaptive radiotherapy (MRgART) for upper abdominal tumors. Contrast-enhanced T1-weighted MR (cT1w MR) using half doses of gadoterate was used to guide daily adaptive radiotherapy for tumors poorly visualized without contrast. The use of gadoterate was found to be feasible and safe in 5-fraction MRgART and could improve the contrast-to-noise ratio of MR images. And the use of cT1w MR could reduce the interobserver variation of adaptive tumor delineation compared to plain T1w MR (4.41 vs. 6.58, p &lt; 0.001) and T2w MR (4.41 vs. 7.42, p &lt; 0.001).</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000526/pdfft?md5=d282df7dcb80fe0f3096017be000b790&pid=1-s2.0-S2405631624000526-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140769123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Relative biological effectiveness of oxygen ion beams in the rat spinal cord: Dependence on linear energy transfer and dose and comparison with model predictions 氧离子束在大鼠脊髓中的相对生物有效性:与线性能量传递和剂量的关系以及与模型预测的比较
IF 3.7
Physics and Imaging in Radiation Oncology Pub Date : 2024-04-01 DOI: 10.1016/j.phro.2024.100581
Christin Glowa , Maria Saager , Lisa Hintz , Rosemarie Euler-Lange , Peter Peschke , Stephan Brons , Michael Scholz , Stewart Mein , Andrea Mairani , Christian P. Karger
{"title":"Relative biological effectiveness of oxygen ion beams in the rat spinal cord: Dependence on linear energy transfer and dose and comparison with model predictions","authors":"Christin Glowa ,&nbsp;Maria Saager ,&nbsp;Lisa Hintz ,&nbsp;Rosemarie Euler-Lange ,&nbsp;Peter Peschke ,&nbsp;Stephan Brons ,&nbsp;Michael Scholz ,&nbsp;Stewart Mein ,&nbsp;Andrea Mairani ,&nbsp;Christian P. Karger","doi":"10.1016/j.phro.2024.100581","DOIUrl":"10.1016/j.phro.2024.100581","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Ion beams exhibit an increased relative biological effectiveness (RBE) with respect to photons. This study determined the RBE of oxygen ion beams as a function of linear energy transfer (LET) and dose in the rat spinal cord.</p></div><div><h3>Materials and methods</h3><p>The spinal cord of rats was irradiated at four different positions of a 6 cm spread-out Bragg-peak (LET: 26, 66, 98 and 141 keV/µm) using increasing levels of single and split oxygen ion doses. Dose-response curves were established for the endpoint paresis grade II and based on ED<sub>50</sub> (dose at 50 % effect probability), the RBE was determined and compared to model predictions.</p></div><div><h3>Results</h3><p>When LET increased from 26 to 98 keV/µm, ED<sub>50</sub> decreased from 17.2 ± 0.3 Gy to 13.5 ± 0.4 Gy for single and from 21.7 ± 0.4 Gy to 15.5 ± 0.5 Gy for split doses, however, at 141 keV/µm, ED<sub>50</sub> rose again to 15.8 ± 0.4 Gy and 17.2 ± 0.4 Gy, respectively. As a result, the RBE increased from 1.43 ± 0.05 to 1.82 ± 0.08 (single dose) and from 1.58 ± 0.04 to 2.21 ± 0.08 (split dose), respectively, before declining again to 1.56 ± 0.06 for single and 1.99 ± 0.06 for split doses at the highest LET. Deviations from RBE-predictions were model-dependent.</p></div><div><h3>Conclusion</h3><p>This study established first RBE data for the late reacting central nervous system after single and split doses of oxygen ions. The data was used to validate the RBE-dependence on LET and dose of three RBE-models. This study extends the existing data base for protons, helium and carbon ions and provides important information for future patient treatments with oxygen ions.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000514/pdfft?md5=85eeacebc8a9bc581264509be7601574&pid=1-s2.0-S2405631624000514-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140794471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Large-scale dose evaluation of deep learning organ contours in head-and-neck radiotherapy by leveraging existing plans 利用现有计划对头颈部放疗中深度学习器官轮廓进行大规模剂量评估
IF 3.7
Physics and Imaging in Radiation Oncology Pub Date : 2024-04-01 DOI: 10.1016/j.phro.2024.100572
Prerak Mody , Merle Huiskes , Nicolas F. Chaves-de-Plaza , Alice Onderwater , Rense Lamsma , Klaus Hildebrandt , Nienke Hoekstra , Eleftheria Astreinidou , Marius Staring , Frank Dankers
{"title":"Large-scale dose evaluation of deep learning organ contours in head-and-neck radiotherapy by leveraging existing plans","authors":"Prerak Mody ,&nbsp;Merle Huiskes ,&nbsp;Nicolas F. Chaves-de-Plaza ,&nbsp;Alice Onderwater ,&nbsp;Rense Lamsma ,&nbsp;Klaus Hildebrandt ,&nbsp;Nienke Hoekstra ,&nbsp;Eleftheria Astreinidou ,&nbsp;Marius Staring ,&nbsp;Frank Dankers","doi":"10.1016/j.phro.2024.100572","DOIUrl":"10.1016/j.phro.2024.100572","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Retrospective dose evaluation for organ-at-risk auto-contours has previously used small cohorts due to additional manual effort required for treatment planning on auto-contours. We aimed to do this at large scale, by a) proposing and assessing an automated plan optimization workflow that used existing clinical plan parameters and b) using it for head-and-neck auto-contour dose evaluation.</p></div><div><h3>Materials and methods</h3><p>Our automated workflow emulated our clinic’s treatment planning protocol and reused existing clinical plan optimization parameters. This workflow recreated the original clinical plan (<span><math><mrow><msub><mrow><mi>P</mi></mrow><mrow><mi>OG</mi></mrow></msub></mrow></math></span>) with manual contours (<span><math><mrow><msub><mrow><mi>P</mi></mrow><mrow><mi>MC</mi></mrow></msub></mrow></math></span>) and evaluated the dose effect (<span><math><mrow><msub><mrow><mi>P</mi></mrow><mrow><mi>OG</mi></mrow></msub><mo>-</mo><msub><mrow><mi>P</mi></mrow><mrow><mi>MC</mi></mrow></msub></mrow></math></span>) on 70 photon and 30 proton plans of head-and-neck patients. As a use-case, the same workflow (and parameters) created a plan using auto-contours (<span><math><mrow><msub><mrow><mi>P</mi></mrow><mrow><mi>AC</mi></mrow></msub></mrow></math></span>) of eight head-and-neck organs-at-risk from a commercial tool and evaluated their dose effect (<span><math><mrow><msub><mrow><mi>P</mi></mrow><mrow><mi>MC</mi></mrow></msub><mo>-</mo><msub><mrow><mi>P</mi></mrow><mrow><mi>AC</mi></mrow></msub></mrow></math></span>).</p></div><div><h3>Results</h3><p>For plan recreation (<span><math><mrow><msub><mrow><mi>P</mi></mrow><mrow><mi>OG</mi></mrow></msub><mo>-</mo><msub><mrow><mi>P</mi></mrow><mrow><mi>MC</mi></mrow></msub></mrow></math></span>), our workflow had a median impact of 1.0% and 1.5% across dose metrics of auto-contours, for photon and proton respectively. Computer time of automated planning was 25% (photon) and 42% (proton) of manual planning time. For auto-contour evaluation (<span><math><mrow><msub><mrow><mi>P</mi></mrow><mrow><mi>MC</mi></mrow></msub><mo>-</mo><msub><mrow><mi>P</mi></mrow><mrow><mi>AC</mi></mrow></msub></mrow></math></span>), we noticed an impact of 2.0% and 2.6% for photon and proton radiotherapy. All evaluations had a median <span><math><mrow><mi>Δ</mi></mrow></math></span>NTCP (Normal Tissue Complication Probability) less than 0.3%.</p></div><div><h3>Conclusions</h3><p>The plan replication capability of our automated program provides a blueprint for other clinics to perform auto-contour dose evaluation with large patient cohorts. Finally, despite geometric differences, auto-contours had a minimal median dose impact, hence inspiring confidence in their utility and facilitating their clinical adoption.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000423/pdfft?md5=f7fecc1633d5c42a78b43fb87cb878ea&pid=1-s2.0-S2405631624000423-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140406298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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