Physics and Imaging in Radiation Oncology最新文献

英文中文

Electrocardiogram-gated cardiac computed tomography-based patient- and segment-specific cardiac motion estimation method in stereotactic arrhythmia radioablation for ventricular tachycardia

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-01-01 DOI: 10.1016/j.phro.2025.100700

Jingyang Xie , Alicia S. Bicu , Melanie Grehn , Mustafa Kuru , Adrian Zaman , Xinyu Lu , Christian Janorschke , Luuk H.G. van der Pol , Martin F. Fast , Jens Fleckenstein , Marcus Both , Stephan Hohmann , Egor Borzov , Peter Winkler , Roland R. Tilz , Dirk Rades , Frank A. Giordano , Daniel Buergy , Boris Rudic , David Duncker , Lena Kaestner

Background and purpose

Motion management strategies such as gating under breath-hold can reduce breathing-induced motion during stereotactic arrhythmia radioablation (STAR) for refractory ventricular tachycardia. However, heartbeat-induced motion is essential to define an appropriate cardiac internal target volume (ITV) margin. In this study, we introduce a patient- and segment-specific cardiac motion estimation method and cardiac motion data of the clinical target volume (CTV), ICD lead tips and left ventricle (LV) segments.

Materials and methods

Data from 10 STAR-treated patients were retrospectively analyzed. The LV was semi-automatically segmented according to the 17-segment model. Electrocardiogram-gated contrast-enhanced breath-hold cardiac CTs were automatically non-rigidly registered for motion estimation. The correlation and significant differences between ICD tip motion and CTV motion were assessed using the Pearson correlation coefficient (PCC) and Wilcoxon signed-rank test, while spatial discrepancies with both CTV and segment motion were quantified using the Euclidean distance.

Results

The CTVs (center of mass) moved 3.4 ± 1.4 mm and the ICD lead tips moved 4.9 ± 2.2 mm. The maximum motion per patient was observed in basal and mid-cavity LV segments in 3D. The PCC showed a strong positive motion correlation between the ICD tip and CTV in 3D (0.84), while the p-values indicated statistically significant differences in the right-left, anterior-posterior and 3D directions.

Conclusion

The proposed methods enable patient- and segment-specific cardiac ITV margin estimation. The motion in most LV segments was limited, however, cardiac ITV margins may need adjustment in individual cases. The impact of cardiac motion on the dosimetry needs further investigation.

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引用次数: 0

Normal tissue complication probability model for severe radiation-induced lymphopenia in patients with pancreatic cancer treated with concurrent chemoradiotherapy 同步放化疗胰腺癌患者严重放化疗淋巴细胞减少的正常组织并发症概率模型。

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-01-01 DOI: 10.1016/j.phro.2024.100690

Fuki Koizumi , Norio Katoh , Takahiro Kanehira , Yasuyuki Kawamoto , Toru Nakamura , Tatsuhiko Kakisaka , Miyako Myojin , Noriaki Nishiyama , Akio Yonesaka , Manami Otsuka , Rikiya Takashina , Hideki Minatogawa , Hajime Higaki , Yusuke Uchinami , Hiroshi Taguchi , Kentaro Nishioka , Koichi Yasuda , Naoki Miyamoto , Isao Yokota , Keiji Kobashi , Hidefumi Aoyama

Background and purpose

Radiation-induced lymphopenia (RIL) may be associated with a worse prognosis in pancreatic cancer. This study aimed to develop a normal tissue complication probability (NTCP) model to predict severe RIL in patients with pancreatic cancer undergoing concurrent chemoradiotherapy (CCRT).

Materials and methods

We reviewed pancreatic cancer patients treated at our facility for model training and internal validation. Subsequently, we reviewed data from three other facilities to validate model fit externally. An absolute lymphocyte count (ALC) of ＜0.5 × 10³/μL during CCRT was defined as severe RIL. An NTCP model was trained using a least absolute shrinkage and selection operator (LASSO)-based logistic model. The model’s predictive performance was evaluated using the receiver operating characteristic area under the curve (AUC), scaled Brier score, and calibration plots.

Results

Among the 114 patients in the training set, 78 had severe RIL. LASSO showed that low baseline ALC, large planning target volume, and high percentage of bilateral kidneys receiving ≥ 5Gy were selected as parameters of the NTCP model for severe RIL. The AUC and scaled Brier score were 0.91 and 0.49, respectively. Internal validation yielded an average AUC of 0.92. In the external validation with 68 patients, the AUC and scaled Brier score was 0.83 and 0.30, respectively. Calibration plots showed good conformity.

Conclusions

The NTCP model for severe RIL during CCRT for pancreatic cancer, developed and validated in this study, demonstrated good predictive performance. This model can be used to evaluate and compare the risk of RIL.

背景与目的：胰腺癌放射性淋巴细胞减少症（RIL）可能与较差的预后有关。本研究旨在建立一个正常组织并发症概率（NTCP）模型来预测胰腺癌同步放化疗（CCRT）患者的严重RIL。材料和方法：我们回顾了在本院治疗的胰腺癌患者，进行模型训练和内部验证。随后，我们审查了来自其他三个设施的数据，以验证模型是否适合外部。CCRT期间淋巴细胞绝对计数(ALC) <0.5 × 103/μL为严重RIL。使用最小绝对收缩和选择算子（LASSO）为基础的逻辑模型训练NTCP模型。模型的预测性能通过受试者工作特征曲线下面积（AUC）、缩放后的Brier评分和校准图进行评估。结果：114例患者中，重度RIL患者78例。LASSO结果显示，较低的基线ALC、较大的规划靶体积和接受≥5Gy的双侧肾脏比例较高被选为重度RIL的NTCP模型参数。AUC和Brier评分分别为0.91和0.49。内部验证的平均AUC为0.92。在68例患者的外部验证中，AUC和尺度Brier评分分别为0.83和0.30。标定图一致性较好。结论：本研究开发并验证的胰腺癌CCRT期间严重RIL的NTCP模型具有良好的预测性能。该模型可用于评估和比较RIL的风险。

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引用次数: 0

Dose-volume parameter evaluation of a sub-fractionation workflow for adaptive radiotherapy of prostate cancer patients on a 1.5 T magnetic resonance imaging radiotherapy system

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-01-01 DOI: 10.1016/j.phro.2025.100706

Georgios Tsekas, Cornel Zachiu, Gijsbert H. Bol, Jochem R.N. van der Voort van Zyp, Sandrine M.G. van de Pol, Johannes C.J. de Boer, Bas W. Raaymakers

Background and purpose:

This study focuses on evaluating a sub-fractionation workflow for intrafraction motion mitigation of prostate cancer patients on a 1.5 T magnetic resonance imaging radiotherapy system.

Materials and methods:

The investigated workflow consisted of two sub-fractions where intrafraction drift correction steps were applied based on a daily reference plan. However, the daily contours were only rigidly shifted to match the intrafraction anatomies and therefore the clinical dosimetric constraints might be violated. In this work, daily contours were deformed to match the intrafraction anatomies and the online plans were re-calculated for a total of 15 patients. The deformed prostate contours were inspected by radiation oncologists and corrections were performed when necessary. Finally, a dose-volume parameter evaluation was performed on a sub-fraction level using the clinical plan parameters.

Results:

Clinically acceptable coverage was reported for the target structures resulting in mean V_95% of 99.7 % and 97.8 % for the clinical target volume (CTV) and planning target volume (PTV) respectively. Sub-fractions with insufficient CTV dose can be explained by the presence of intrafraction rotations and deformations that were not taken into account during intrafraction corrections. Additionally, for no sub-fraction the dose to the organs-at-risk exceeded the clinical constraints.

Conclusion:

Given our results on the CTV coverage we can conclude that the sub-fractionation workflow met the dosimetric constraints for the hypofractionated treatment of the analyzed group of prostate cancer patients. A future dose accumulation study can provide further insights into the suitability of the clinical margins.

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引用次数: 0

Deep learning combining imaging, dose and clinical data for predicting bowel toxicity after pelvic radiotherapy

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-01-01 DOI: 10.1016/j.phro.2025.100710

Behnaz Elhaminia , Alexandra Gilbert , Andrew Scarsbrook , John Lilley , Ane Appelt , Ali Gooya

Background and Purpose:

A comprehensive understanding of radiotherapy toxicity requires analysis of multimodal data. However, it is challenging to develop a model that can analyse both 3D imaging and clinical data simultaneously. In this study, a deep learning model is proposed for simultaneously analysing computed tomography scans, dose distributions, and clinical metadata to predict toxicity, and identify the impact of clinical risk factors and anatomical regions.

Materials and methods

: A deep model based on multiple instance learning with feature-level fusion and attention was developed. The study used a dataset of 313 patients treated with 3D conformal radiation therapy and volumetric modulated arc therapy, with heterogeneous cohorts varying in dose, volume, fractionation, concomitant therapies, and follow-up periods. The dataset included 3D computed tomography scans, planned dose distributions to the bowel cavity, and patient clinical data. The model was trained on patient-reported data on late bowel toxicity.

Results:

Results showed that the network can identify potential risk factors and critical anatomical regions. Analysis of clinical data jointly with imaging and dose for bowel urgency and faecal incontinence improved performance (area under receiver operating characteristic curve [AUC] of 88% and 78%, respectively) while best performance for diarrhoea was when analysing clinical features alone (68% AUC).

Conclusions:

Results demonstrated that feature-level fusion along with attention enables the network to analyse multimodal data. This method also provides explanations for each input’s contribution to the final result and detects spatial associations of toxicity.

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引用次数: 0

Demonstration of motion-compensated volumetric modulated arc radiotherapy on an MR-linac

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-01-01 DOI: 10.1016/j.phro.2025.100729

Pim T.S. Borman , Prescilla Uijtewaal , Jeffrey Snyder , Bryan Allen , Caiden K. Atienza , Peter Woodhead , Daniel E. Hyer , Bas W. Raaymakers , Martin F. Fast

Intensity-modulated radiotherapy (IMRT) in combination with magnetic resonance imaging (MRI)-guided gated delivery represents the latest development in the treatment of abdominothoracic tumours on MR-linac. In contrast, volumetric-modulated arc therapy (VMAT) is typically used on conventional linacs due to its superior delivery efficiency and speed. Non-inferior VMAT plans were created in a research treatment planning system for eight lung cancer patients previously treated on an MR-linac. VMAT plans were delivered on a moving dosimeter using respiratory multi-leaf collimator (MLC) tracking. VMAT with MLC tracking achieved an average 2%/2 mm local gamma pass rate of 93% relative to planned dose with a delivery efficiency of 83%.

引用次数: 0

Added value of non-rigid image registration for intrafraction dose accumulation in magnetic resonance imaging-guided prostate radiotherapy

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-01-01 DOI: 10.1016/j.phro.2025.100711

Georgios Tsekas, Cornel Zachiu, Gijsbert H. Bol, Johannes C.J. de Boer, Bas W. Raaymakers

This work investigates potential advantages of non-rigid versus rigid image registration for intrafraction dose reconstruction in hypofractionated prostate radiotherapy. The data of 15 patients were analyzed using 3D cine magnetic resonance imaging (MRI) in combination with machine log files and the accumulated dose distributions were compared to the planned ones. Both image registration methods resulted in comparable results for the majority (

\sim

95%) of patient fractions. However, better image alignment was reported for the non-rigid method compared to rigid in cases of transient gas pockets, indicating better image registration quality in the presence of large intrafraction deformations.

引用次数: 0

Uncertainty estimation in female pelvic synthetic computed tomography generated from iterative reconstructed cone-beam computed tomography

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-01-01 DOI: 10.1016/j.phro.2025.100743

Yvonne J.M. de Hond, Paul M.A. van Haaren, Rob H.N. Tijssen, Coen W. Hurkmans

Background and Purpose

Iterative reconstruction (IR) can be used to improve cone-beam computed tomography (CBCT) image quality and from such iterative reconstructed (iCBCT) images, synthetic CT (sCT) images can be generated to enable accurate dose calculations. The aim of this study was to evaluate the uncertainty in generating sCT from iCBCT using vendor-supplied software for online adaptive radiotherapy.

Materials and Methods

Projection data from 20 female pelvic CBCTs were used to reconstruct iCBCT images. The process was repeated with 128 different IR parameter combinations. From these iCBCTs, sCTs were generated. Voxel value variation in the 128 iCBCT and 128 sCT images per patient was quantified by the standard deviation (STD). Additional sub-analysis was performed per parameter category.

Results

Generated sCTs had significantly higher maximum STD-values, median of 438 HU, compared to input iCBCT, median of 198 HU, indicating limited robustness to parameter changes. The highest STD-values of sCTs were within bone and soft-tissue compared to air. Variations in sCT numbers were parameter dependent. Scatter correction produced the highest variance in sCTs (median: 358 HU) despite no visible changes in iCBCTs, whereas total variation regularization resulted in the lowest variance in sCTs (median: 233 HU) despite increased iCBCT blurriness.

Conclusions

Variations in iCBCT reconstruction parameters affected the CT number representation in the sCT. The sCT variance depended on the parameter category, with subtle iCBCT changes leading to significant density alterations in sCT. Therefore, it is recommended to evaluate both iCBCT and sCT generation, especially when updating software or settings.

{"title":"Uncertainty estimation in female pelvic synthetic computed tomography generated from iterative reconstructed cone-beam computed tomography","authors":"Yvonne J.M. de Hond, Paul M.A. van Haaren, Rob H.N. Tijssen, Coen W. Hurkmans","doi":"10.1016/j.phro.2025.100743","DOIUrl":"10.1016/j.phro.2025.100743","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Iterative reconstruction (IR) can be used to improve cone-beam computed tomography (CBCT) image quality and from such iterative reconstructed (iCBCT) images, synthetic CT (sCT) images can be generated to enable accurate dose calculations. The aim of this study was to evaluate the uncertainty in generating sCT from iCBCT using vendor-supplied software for online adaptive radiotherapy.</div></div><div><h3>Materials and Methods</h3><div>Projection data from 20 female pelvic CBCTs were used to reconstruct iCBCT images. The process was repeated with 128 different IR parameter combinations. From these iCBCTs, sCTs were generated. Voxel value variation in the 128 iCBCT and 128 sCT images per patient was quantified by the standard deviation (STD). Additional sub-analysis was performed per parameter category.</div></div><div><h3>Results</h3><div>Generated sCTs had significantly higher maximum STD-values, median of 438 HU, compared to input iCBCT, median of 198 HU, indicating limited robustness to parameter changes. The highest STD-values of sCTs were within bone and soft-tissue compared to air. Variations in sCT numbers were parameter dependent. Scatter correction produced the highest variance in sCTs (median: 358 HU) despite no visible changes in iCBCTs, whereas total variation regularization resulted in the lowest variance in sCTs (median: 233 HU) despite increased iCBCT blurriness.</div></div><div><h3>Conclusions</h3><div>Variations in iCBCT reconstruction parameters affected the CT number representation in the sCT. The sCT variance depended on the parameter category, with subtle iCBCT changes leading to significant density alterations in sCT. Therefore, it is recommended to evaluate both iCBCT and sCT generation, especially when updating software or settings.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100743"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143561888","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

Gross tumor volume confidence maps prediction for soft tissue sarcomas from multi-modality medical images using a diffusion model

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-01-01 DOI: 10.1016/j.phro.2025.100734

Yafei Dong , Thibault Marin , Yue Zhuo , Elie Najem , Maryam Moteabbed , Fangxu Xing , Arnaud Beddok , Rita Maria Lahoud , Laura Rozenblum , Zhiyuan Ding , Xiaofeng Liu , Kira Grogg , Jonghye Woo , Yen-Lin E. Chen , Ruth Lim , Chao Ma , Georges El Fakhri

Background and purpose:

Accurate delineation of the gross tumor volume (GTV) is essential for radiotherapy of soft tissue sarcomas. However, manual GTV delineation from multi-modality images is time-consuming. Furthermore, GTV delineation is subject to inter- and intra-reader variability, which reduces the reproducibility of treatment planning. To address these issues, this work aims to develop a highly accurate automatic delineation technique modeling reader variability for soft tissue sarcomas using deep learning.

Materials and methods:

We employed a publicly available soft tissue sarcoma dataset consisting of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET), X-ray Computed Tomography (CT), and pre-contrast T1-weighted Magnetic Resonance Imaging (MRI) scans for 51 patients, of which 49 were selected for analysis. The GTVs were delineated by six experienced readers, each reader performing GTV contouring multiple times for every patient. The confidence maps were calculated by averaging the labels provided by all readers, resulting in values ranging from 0 to 1. We developed and trained a diffusion model-based neural network to predict confidence maps of GTV for soft tissue sarcomas from multi-modality medical images.

Results:

Quantitative analysis showed that the proposed diffusion model performed competitively with U-Net-based models, frequently ranking first or second across five evaluation metrics: Dice Index, Hausdorff Distance, Recall, Precision, and Brier Score. Additionally, experiments evaluating the impact of different imaging modalities demonstrated that incorporating multi-modality image inputs provided improved performance compared to single-modality and dual-modality inputs.

Conclusion:

The proposed diffusion model is capable of predicting accurate confidence maps of GTV for soft tissue sarcomas from multi-modality inputs.

{"title":"Gross tumor volume confidence maps prediction for soft tissue sarcomas from multi-modality medical images using a diffusion model","authors":"Yafei Dong , Thibault Marin , Yue Zhuo , Elie Najem , Maryam Moteabbed , Fangxu Xing , Arnaud Beddok , Rita Maria Lahoud , Laura Rozenblum , Zhiyuan Ding , Xiaofeng Liu , Kira Grogg , Jonghye Woo , Yen-Lin E. Chen , Ruth Lim , Chao Ma , Georges El Fakhri","doi":"10.1016/j.phro.2025.100734","DOIUrl":"10.1016/j.phro.2025.100734","url":null,"abstract":"<div><h3>Background and purpose:</h3><div>Accurate delineation of the gross tumor volume (GTV) is essential for radiotherapy of soft tissue sarcomas. However, manual GTV delineation from multi-modality images is time-consuming. Furthermore, GTV delineation is subject to inter- and intra-reader variability, which reduces the reproducibility of treatment planning. To address these issues, this work aims to develop a highly accurate automatic delineation technique modeling reader variability for soft tissue sarcomas using deep learning.</div></div><div><h3>Materials and methods:</h3><div>We employed a publicly available soft tissue sarcoma dataset consisting of Fluorodeoxyglucose Positron Emission Tomography (FDG-PET), X-ray Computed Tomography (CT), and pre-contrast T1-weighted Magnetic Resonance Imaging (MRI) scans for 51 patients, of which 49 were selected for analysis. The GTVs were delineated by six experienced readers, each reader performing GTV contouring multiple times for every patient. The confidence maps were calculated by averaging the labels provided by all readers, resulting in values ranging from 0 to 1. We developed and trained a diffusion model-based neural network to predict confidence maps of GTV for soft tissue sarcomas from multi-modality medical images.</div></div><div><h3>Results:</h3><div>Quantitative analysis showed that the proposed diffusion model performed competitively with U-Net-based models, frequently ranking first or second across five evaluation metrics: Dice Index, Hausdorff Distance, Recall, Precision, and Brier Score. Additionally, experiments evaluating the impact of different imaging modalities demonstrated that incorporating multi-modality image inputs provided improved performance compared to single-modality and dual-modality inputs.</div></div><div><h3>Conclusion:</h3><div>The proposed diffusion model is capable of predicting accurate confidence maps of GTV for soft tissue sarcomas from multi-modality inputs.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100734"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547973","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

Towards faster plan adaptation for proton arc therapy using initial treatment plan information

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-01-01 DOI: 10.1016/j.phro.2025.100705

Benjamin Roberfroid , Margerie Huet-Dastarac , Elena Borderías-Villarroel , Rodin Koffeing , John A. Lee , Ana M. Barragán-Montero , Edmond Sterpin

Background and Purpose

Proton arc therapy (PAT) is an emerging modality delivering continuously rotating proton beams. Current PAT planning approaches are time-consuming, making them unsuitable for online adaptation. This study proposes an accelerated workflow for adapting PAT plans.

Materials and Methods

The proposed workflow transfers spots from initial computed tomography (CT) to the CT of the day, updates energy layers considering the initial pattern, and re-optimizes selected transferred spots based on their initial weights and impact on the objective function.

A retrospective study was conducted on five head and neck patients who underwent plan adaptation on a repeated CT. PAT plans were generated with two different methods on the repeated CT: reference, created de novo, and smart-adapted, generated with the proposed adaptive workflow. Robust optimization was performed for all plans.

Results

Smart-adapted plans achieved similar mean dose to organs at risk as the reference: the largest median increase of mean dose was 1.9 Gy to the mandible; the median of maximum dose to spinal cord was 0.5 Gy lower for the smart-adapted plans. The median target coverage, i.e. D₉₈, to primary tumor and nodes of smart-adapted plans decreased by 0.2 and 0.4 Gy for the nominal case, and 0.4 and 0.6 Gy for the worst-case scenario; all smart-adapted plans met clinical objectives. The smart-adaptation method reduced average planning time from 19184 s to 5626 s, a 3.4-fold improvement.

Conclusions

Smart-adapted plans achieve similar plan quality to the reference method, while significantly reducing plan generation time for new patient anatomy.

{"title":"Towards faster plan adaptation for proton arc therapy using initial treatment plan information","authors":"Benjamin Roberfroid , Margerie Huet-Dastarac , Elena Borderías-Villarroel , Rodin Koffeing , John A. Lee , Ana M. Barragán-Montero , Edmond Sterpin","doi":"10.1016/j.phro.2025.100705","DOIUrl":"10.1016/j.phro.2025.100705","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Proton arc therapy (PAT) is an emerging modality delivering continuously rotating proton beams. Current PAT planning approaches are time-consuming, making them unsuitable for online adaptation. This study proposes an accelerated workflow for adapting PAT plans.</div></div><div><h3>Materials and Methods</h3><div>The proposed workflow transfers spots from initial computed tomography (CT) to the CT of the day, updates energy layers considering the initial pattern, and re-optimizes selected transferred spots based on their initial weights and impact on the objective function.</div><div>A retrospective study was conducted on five head and neck patients who underwent plan adaptation on a repeated CT. PAT plans were generated with two different methods on the repeated CT: <em>reference</em>, created de novo, and <em>smart-adapted</em>, generated with the proposed adaptive workflow. Robust optimization was performed for all plans.</div></div><div><h3>Results</h3><div><em>Smart-adapted</em> plans achieved similar mean dose to organs at risk as the <em>reference</em>: the largest median increase of mean dose was 1.9 Gy to the mandible; the median of maximum dose to spinal cord was 0.5 Gy lower for the <em>smart-adapted</em> plans. The median target coverage, i.e. D<sub>98</sub>, to primary tumor and nodes of <em>smart-adapted</em> plans decreased by 0.2 and 0.4 Gy for the nominal case, and 0.4 and 0.6 Gy for the worst-case scenario; all <em>smart-adapted</em> plans met clinical objectives. The smart-adaptation method reduced average planning time from 19184 s to 5626 s, a 3.4-fold improvement.</div></div><div><h3>Conclusions</h3><div><em>Smart-adapted</em> plans achieve similar plan quality to the reference method, while significantly reducing plan generation time for new patient anatomy.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100705"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143270412","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

New guidelines and recommendations to advance treatment planning in proton therapy

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology

Pub Date : 2025-01-01 DOI: 10.1016/j.phro.2024.100695

Barbara Knäusl, Anne Vestergaard, Marco Schwarz, Ludvig P. Muren

引用次数: 0

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