Physics and Imaging in Radiation Oncology最新文献

{"title":"The impact of plan complexity on calculation and measurement-based pre-treatment verifications for sliding-window intensity-modulated radiotherapy","authors":"Shi Li ,&nbsp;Huanli Luo ,&nbsp;Xia Tan,&nbsp;Tao Qiu,&nbsp;Xin Yang,&nbsp;Bin Feng,&nbsp;Liyuan Chen,&nbsp;Ying Wang,&nbsp;Fu Jin","doi":"10.1016/j.phro.2024.100622","DOIUrl":"10.1016/j.phro.2024.100622","url":null,"abstract":"<div><h3>Background and purpose</h3><p>In sliding-window intensity-modulated radiotherapy, increased plan modulation often leads to increased plan complexities and dose uncertainties. Dose calculation and/or measurement checks are usually adopted for pre-treatment verification. This study aims to evaluate the relationship among plan complexities, calculated doses and measured doses.</p></div><div><h3>Materials and methods</h3><p>A total of 53 plan complexity metrics (PCMs) were selected, emphasizing small field characteristics and leaf speed/acceleration. Doses were retrieved from two beam-matched treatment devices. The intended dose was computed employing the Anisotropic Analytical Algorithm and validated through Monte Carlo (MC) and Collapsed Cone Convolution (CCC) algorithms. To measure the delivered dose, 3D diode arrays of various geometries, encompassing helical, cross, and oblique cross shapes, were utilized. Their interrelation was assessed via Spearman correlation analysis and principal component linear regression (PCR).</p></div><div><h3>Results</h3><p>The correlation coefficients between calculation-based (CQA) and measurement-based verification quality assurance (MQA) were below 0.53. Most PCMs showed higher correlation <em>r_pcm-QA</em> with CQA (max: 0.84) than MQA (max: 0.65). The proportion of <em>r_pcm-QA</em> ≥ 0.5 was the largest in the pelvis compared to head-and-neck and chest-and-abdomen, and the highest <em>r_pcm-QA</em> occurred at 1 %/1mm. Some modulation indices for the MLC speed and acceleration were significantly correlated with CQA and MQA. PCR’s determination coefficients (<em>R²</em>) indicated PCMs had higher accuracy in predicting CQA (max: 0.75) than MQA (max: 0.42).</p></div><div><h3>Conclusions</h3><p>CQA and MQA demonstrated a weak correlation. Compared to MQA, CQA exhibited a stronger correlation with PCMs. Certain PCMs related to MLC movement effectively indicated variations in both quality assurances.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000927/pdfft?md5=054c998db82efb2545d759cf52f9e86a&pid=1-s2.0-S2405631624000927-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953352","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}

{"title":"Physical and clinical results of a radiation bra in patients treated with total skin electron beam therapy","authors":"Isabel Falke ,&nbsp;Khaled Elsayad ,&nbsp;Mohammed Channaoui ,&nbsp;Christian Kandler ,&nbsp;Christos Moustakis ,&nbsp;Hans Theodor Eich","doi":"10.1016/j.phro.2024.100628","DOIUrl":"10.1016/j.phro.2024.100628","url":null,"abstract":"<div><p>Total skin electron beam therapy (TSEBT) in female patients with large or pendulous breasts is usually associated with shaded inframammary folds. In this analysis, 18 patients with cutaneous malignancy and pendulous breasts were irradiated with a radiation bra and five patients received TSEBT without bra. All patients had moderate or severe sagging of the breasts. The median inframammary dose in the radiation bra group was 89% of the prescription dose versus 4% in the group without bra. The usage of the radiation bra enables an adequate radiation dose for the inframammary folds during TSEBT with no additional local irradiation.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000988/pdfft?md5=9f2d22d1cf7f26875924e46679659851&pid=1-s2.0-S2405631624000988-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142021038","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}

{"title":"3D Unsupervised deep learning method for magnetic resonance imaging-to-computed tomography synthesis in prostate radiotherapy","authors":"Blanche Texier ,&nbsp;Cédric Hémon ,&nbsp;Adélie Queffélec ,&nbsp;Jason Dowling ,&nbsp;Igor Bessieres ,&nbsp;Peter Greer ,&nbsp;Oscar Acosta ,&nbsp;Adrien Boue-Rafle ,&nbsp;Renaud de Crevoisier ,&nbsp;Caroline Lafond ,&nbsp;Joël Castelli ,&nbsp;Anaïs Barateau ,&nbsp;Jean-Claude Nunes","doi":"10.1016/j.phro.2024.100612","DOIUrl":"10.1016/j.phro.2024.100612","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis is essential in MRI-only radiotherapy workflows, particularly through deep learning techniques known for their accuracy. However, current supervised methods are limited to specific center’s learnings and depend on registration precision. The aim of this study was to evaluate the accuracy of unsupervised and supervised approaches in the context of prostate MRI-to-CT generation for radiotherapy dose calculation.</p></div><div><h3>Methods</h3><p>CT/MRI image pairs from 99 prostate cancer patients across three different centers were used. A comparison between supervised and unsupervised conditional Generative Adversarial Networks (cGAN) was conducted. Unsupervised training incorporates a style transfer method with. Content and Style Representation for Enhanced Perceptual synthesis (CREPs) loss. For dose evaluation, the photon prescription dose was 60 Gy delivered in volumetric modulated arc therapy (VMAT). Imaging endpoint for sCT evaluation was Mean Absolute Error (MAE). Dosimetric endpoints included absolute dose differences and gamma analysis between CT and sCT dose calculations.</p></div><div><h3>Results</h3><p>The unsupervised paired network exhibited the highest accuracy for the body with a MAE at 33.6 HU, the highest MAE was 45.5 HU obtained with unsupervised unpaired learning. All architectures provided clinically acceptable results for dose calculation with gamma pass rates above 94 % (1 % 1 mm 10 %).</p></div><div><h3>Conclusions</h3><p>This study shows that multicenter data can produce accurate sCTs via unsupervised learning, eliminating CT-MRI registration. The sCTs not only matched HU values but also enabled precise dose calculations, suggesting their potential for wider use in MRI-only radiotherapy workflows.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000824/pdfft?md5=452e70a66f63e6bbc801a2ec1489bae9&pid=1-s2.0-S2405631624000824-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141839352","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}

{"title":"Autocontouring of primary lung lesions and nodal disease for radiotherapy based only on computed tomography images","authors":"Stephen Skett ,&nbsp;Tina Patel ,&nbsp;Didier Duprez ,&nbsp;Sunnia Gupta ,&nbsp;Tucker Netherton ,&nbsp;Christoph Trauernicht ,&nbsp;Sarah Aldridge ,&nbsp;David Eaton ,&nbsp;Carlos Cardenas ,&nbsp;Laurence E. Court ,&nbsp;Daniel Smith ,&nbsp;Ajay Aggarwal","doi":"10.1016/j.phro.2024.100637","DOIUrl":"10.1016/j.phro.2024.100637","url":null,"abstract":"<div><h3>Background and purpose</h3><p>In many clinics, positron-emission tomography is unavailable and clinician time extremely limited. Here we describe a deep-learning model for autocontouring gross disease for patients undergoing palliative radiotherapy for primary lung lesions and/or hilar/mediastinal nodal disease, based only on computed tomography (CT) images.</p></div><div><h3>Materials and methods</h3><p>An autocontouring model (nnU-Net) was trained to contour gross disease in 379 cases (352 training, 27 test); 11 further test cases from an external centre were also included. Anchor-point-based post-processing was applied to remove extraneous autocontoured regions. The autocontours were evaluated quantitatively in terms of volume similarity (Dice similarity coefficient [DSC], surface Dice coefficient, 95^th percentile Hausdorff distance [HD95], and mean surface distance), and scored for usability by two consultant oncologists. The magnitude of treatment margin needed to account for geometric discrepancies was also assessed.</p></div><div><h3>Results</h3><p>The anchor point process successfully removed all erroneous regions from the autocontoured disease, and identified two cases to be excluded from further analysis due to ‘missed’ disease. The average DSC and HD95 were 0.8 ± 0.1 and 10.5 ± 7.3 mm, respectively. A 10-mm uniform margin-distance applied to the autocontoured region was found to yield “full coverage” (sensitivity &gt; 0.99) of the clinical contour for 64 % of cases. Ninety-seven percent of evaluated autocontours were scored by both clinicians as requiring no or minor edits.</p></div><div><h3>Conclusions</h3><p>Our autocontouring model was shown to produce clinically usable disease outlines, based on CT alone, for approximately two-thirds of patients undergoing lung radiotherapy. Further work is necessary to improve this before clinical implementation.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624001076/pdfft?md5=66dee5a039c29412f0b49ae3be06dd45&pid=1-s2.0-S2405631624001076-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151125","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}

{"title":"Prediction of radiologic outcome-optimized dose plans and post-treatment magnetic resonance images: A proof-of-concept study in breast cancer brain metastases treated with stereotactic radiosurgery","authors":"Shraddha Pandey ,&nbsp;Tugce Kutuk ,&nbsp;Mahmoud A. Abdalah ,&nbsp;Olya Stringfield ,&nbsp;Harshan Ravi ,&nbsp;Matthew N. Mills ,&nbsp;Jasmine A. Graham ,&nbsp;Kujtim Latifi ,&nbsp;Wilfrido A. Moreno ,&nbsp;Kamran A. Ahmed ,&nbsp;Natarajan Raghunand","doi":"10.1016/j.phro.2024.100602","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100602","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Information in multiparametric Magnetic Resonance (mpMR) images is relatable to voxel-level tumor response to Radiation Treatment (RT). We have investigated a deep learning framework to predict (i) post-treatment mpMR images from pre-treatment mpMR images and the dose map (“forward models”), and, (ii) the RT dose map that will produce prescribed changes within the Gross Tumor Volume (GTV) on post-treatment mpMR images (“inverse model”), in Breast Cancer Metastases to the Brain (BCMB) treated with Stereotactic Radiosurgery (SRS).</p></div><div><h3>Materials and methods</h3><p>Local outcomes, planning computed tomography (CT) images, dose maps, and pre-treatment and post-treatment Apparent Diffusion Coefficient of water (ADC) maps, T1-weighted unenhanced (T1w) and contrast-enhanced (T1wCE), T2-weighted (T2w) and Fluid-Attenuated Inversion Recovery (FLAIR) mpMR images were curated from 39 BCMB patients. mpMR images were co-registered to the planning CT and intensity-calibrated. A 2D pix2pix architecture was used to train 5 forward models (ADC, T2w, FLAIR, T1w, T1wCE) and 1 inverse model on 1940 slices from 18 BCMB patients, and tested on 437 slices from another 9 BCMB patients.</p></div><div><h3>Results</h3><p>Root Mean Square Percent Error (RMSPE) within the GTV between predicted and ground-truth post-RT images for the 5 forward models, in 136 test slices containing GTV, were (mean ± SD) 0.12 ± 0.044 (ADC), 0.14 ± 0.066 (T2w), 0.08 ± 0.038 (T1w), 0.13 ± 0.058 (T1wCE), and 0.09 ± 0.056 (FLAIR). RMSPE within the GTV on the same 136 test slices, between the predicted and ground-truth dose maps, was 0.37 ± 0.20 for the inverse model.</p></div><div><h3>Conclusions</h3><p>A deep learning-based approach for radiologic outcome-optimized dose planning in SRS of BCMB has been demonstrated.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000721/pdfft?md5=efffb06cbc1579bd9ae00f80c49fcd84&pid=1-s2.0-S2405631624000721-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479447","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}

{"title":"Correlation between local instantaneous dose rate and oxygen pressure reduction during proton pencil beam scanning irradiation","authors":"Eleni Kanouta ,&nbsp;Jacob Graversen Johansen ,&nbsp;Sara Poulsen ,&nbsp;Line Kristensen ,&nbsp;Brita Singers Sørensen ,&nbsp;Cai Grau ,&nbsp;Morten Busk ,&nbsp;Per Rugaard Poulsen","doi":"10.1016/j.phro.2024.100614","DOIUrl":"10.1016/j.phro.2024.100614","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Oxygen dynamics may be important for the tissue-sparing effect observed at ultra-high dose rates (FLASH sparing effect). This study investigated the correlation between local instantaneous dose rate and radiation-induced oxygen pressure reduction during proton pencil beam scanning (PBS) irradiations of a sample and quantified the oxygen consumption g-value.</p></div><div><h3>Materials and methods</h3><p>A 0.2 ml phosphorescent sample (1 μM PtG4 Oxyphor probe in saline) was irradiated with a 244 MeV proton PBS beam. Four irradiations were performed with variations of a PBS spot pattern with 5 × 7 spots. During irradiation, the partial oxygen pressure (pO₂) was measured with 4.5 Hz temporal resolution with a phosphorometer (Oxyled) that optically excited the probe and recorded the subsequently emitted light. A calibration was performed to calculate the pO₂ level from the measured phosphorescence lifetime. A fiber-coupled scintillator simultaneously measured the instantaneous dose rate in the sample with 50 kHz sampling rate. The oxygen consumption g-value was determined on a spot-by-spot level and using the total pO₂ change for full spot pattern irradiation.</p></div><div><h3>Results</h3><p>A high correlation was found between the local instantaneous dose rate and pO₂ reduction rate, with a correlation coefficient of 0.96–0.99. The g-vales were 0.18 ± 0.01 mmHg/Gy on a spot-by-spot level and 0.17 ± 0.01 mmHg/Gy for full spot pattern irradiation.</p></div><div><h3>Conclusions</h3><p>The pO₂ reduction rate was directly related to the local instantaneous dose rate per delivered spot in PBS deliveries. The methodology presented here can be applied to irradiation at ultra-high dose rates with modifications in the experimental setup.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000848/pdfft?md5=208a49b4c62262ac9e7dcbf5e7a6ba13&pid=1-s2.0-S2405631624000848-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141853451","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}

{"title":"Delta radiomics to track radiation response in lung tumors receiving stereotactic magnetic resonance-guided radiotherapy","authors":"Yining Zha ,&nbsp;Zezhong Ye ,&nbsp;Anna Zapaishchykova ,&nbsp;John He ,&nbsp;Shu-Hui Hsu ,&nbsp;Jonathan E. Leeman ,&nbsp;Kelly J. Fitzgerald ,&nbsp;David E. Kozono ,&nbsp;Raymond H. Mak ,&nbsp;Hugo J.W.L. Aerts ,&nbsp;Benjamin H. Kann","doi":"10.1016/j.phro.2024.100626","DOIUrl":"10.1016/j.phro.2024.100626","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Lung cancer is a leading cause of cancer-related mortality, and stereotactic body radiotherapy (SBRT) has become a standard treatment for early-stage lung cancer. However, the heterogeneous response to radiation at the tumor level poses challenges. Currently, standardized dosage regimens lack adaptation based on individual patient or tumor characteristics. Thus, we explore the potential of delta radiomics from on-treatment magnetic resonance (MR) imaging to track radiation dose response, inform personalized radiotherapy dosing, and predict outcomes.</p></div><div><h3>Materials and methods</h3><p>A retrospective study of 47 MR-guided lung SBRT treatments for 39 patients was conducted. Radiomic features were extracted using Pyradiomics, and stability was evaluated temporally and spatially. Delta radiomics were correlated with radiation dose delivery and assessed for associations with tumor control and survival with Cox regressions.</p></div><div><h3>Results</h3><p>Among 107 features, 49 demonstrated temporal stability, and 57 showed spatial stability. Fifteen stable and non-collinear features were analyzed. Median Skewness and surface to volume ratio decreased with radiation dose fraction delivery, while coarseness and 90th percentile values increased. Skewness had the largest relative median absolute changes (22 %–45 %) per fraction from baseline and was associated with locoregional failure (p = 0.012) by analysis of covariance. Skewness, Elongation, and Flatness were significantly associated with local recurrence-free survival, while tumor diameter and volume were not.</p></div><div><h3>Conclusions</h3><p>Our study establishes the feasibility and stability of delta radiomics analysis for MR-guided lung SBRT. Findings suggest that MR delta radiomics can capture short-term radiographic manifestations of the intra-tumoral radiation effect.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000964/pdfft?md5=1de0b8382c5e6c038f7c8805ee279158&pid=1-s2.0-S2405631624000964-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012812","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}

{"title":"A treatment-site-specific evaluation of commercial synthetic computed tomography solutions for proton therapy","authors":"Ping Lin Yeap ,&nbsp;Yun Ming Wong ,&nbsp;Kang Hao Lee ,&nbsp;Calvin Wei Yang Koh ,&nbsp;Kah Seng Lew ,&nbsp;Clifford Ghee Ann Chua ,&nbsp;Andrew Wibawa ,&nbsp;Zubin Master ,&nbsp;James Cheow Lei Lee ,&nbsp;Sung Yong Park ,&nbsp;Hong Qi Tan","doi":"10.1016/j.phro.2024.100639","DOIUrl":"10.1016/j.phro.2024.100639","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Despite the superior dose conformity of proton therapy, the dose distribution is sensitive to daily anatomical changes, which can affect treatment accuracy. This study evaluated the dose recalculation accuracy of two synthetic computed tomography (sCT) generation algorithms in a commercial treatment planning system.</p></div><div><h3>Materials and methods</h3><p>The evaluation was conducted for head-and-neck, thorax-and-abdomen, and pelvis sites treated with proton therapy. Thirty patients with two cone-beam computed tomography (CBCT) scans each were selected. The sCT images were generated from CBCT scans using two algorithms, Corrected CBCT (corrCBCT) and Virtual CT (vCT). Dose recalculations were performed based on these images for comparison with “ground truth” deformed CTs.</p></div><div><h3>Results</h3><p>The choice of algorithm influenced dose recalculation accuracy, particularly in high dose regions. For head-and-neck cases, the corrCBCT method showed closer agreement with the “ground truth”, while for thorax-and-abdomen and pelvis cases, the vCT algorithm yielded better results (mean percentage dose discrepancy of 0.6 %, 1.3 % and 0.5 % for the three sites, respectively, in the high dose region). Head-and-neck and pelvis cases exhibited excellent agreement in high dose regions (2 %/2 mm gamma passing rate &gt;98 %), while thorax-and-abdomen cases exhibited the largest differences, suggesting caution in sCT algorithm usage for this site. Significant systematic differences were observed in the clinical target volume and organ-at-risk doses in head-and-neck and pelvis cases, highlighting the importance of using the correct algorithm.</p></div><div><h3>Conclusions</h3><p>This study provided treatment site-specific recommendations for sCT algorithm selection in proton therapy. The findings offered insights for proton beam centers implementing adaptive radiotherapy workflows.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S240563162400109X/pdfft?md5=ba78c750d5b28de5c476ae16c45a73a8&pid=1-s2.0-S240563162400109X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142129785","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}

{"title":"Optimizing volumetric modulated arc therapy prostate planning using an automated Fine-Tuning process through dynamic adjustment of optimization parameters","authors":"Hasan Cavus ,&nbsp;Thierry Rondagh ,&nbsp;Alexandra Jankelevitch ,&nbsp;Koen Tournel ,&nbsp;Marc Orlandini ,&nbsp;Philippe Bulens ,&nbsp;Laurence Delombaerde ,&nbsp;Kenny Geens ,&nbsp;Wouter Crijns ,&nbsp;Brigitte Reniers","doi":"10.1016/j.phro.2024.100619","DOIUrl":"10.1016/j.phro.2024.100619","url":null,"abstract":"<div><p>In radiotherapy treatment planning, optimization is essential for achieving the most favorable plan by adjusting optimization criteria. This study introduced an innovative approach to automatically fine-tune optimization parameters for volumetric modulated arc therapy prostate planning, ensuring all constraints were met. A knowledge-based planning model was invoked, and the fine-tuning process was applied through an in-house developed script. Among 25 prostate plans, this fine-tuning increased the number of plans meeting all constraints from 10/25 to 22/25, with a reduction in mean monitor units per gray without increasing plan’s complexity. This automation improved efficiency by saving time and resources in treatment planning.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000897/pdfft?md5=67b6d4c77a5c201be301070fcdd99b0c&pid=1-s2.0-S2405631624000897-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141963060","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}

{"title":"Balancing benefits and limitations of linear energy transfer optimization in carbon ion radiotherapy for large sacral chordomas","authors":"Giovanni Parrella ,&nbsp;Giuseppe Magro ,&nbsp;Agnieszka Chalaszczyk ,&nbsp;Marco Rotondi ,&nbsp;Mario Ciocca ,&nbsp;Lars Glimelius ,&nbsp;Maria R. Fiore ,&nbsp;Chiara Paganelli ,&nbsp;Ester Orlandi ,&nbsp;Silvia Molinelli ,&nbsp;Guido Baroni","doi":"10.1016/j.phro.2024.100624","DOIUrl":"10.1016/j.phro.2024.100624","url":null,"abstract":"<div><h3>Background and Purpose</h3><p>A low linear energy transfer (LET) in the target can reduce the effectiveness of carbon ion radiotherapy (CIRT). This study aimed at exploring benefits and limitations of LET optimization for large sacral chordomas (SC) undergoing CIRT.</p></div><div><h3>Materials and Methods</h3><p>Seventeen cases were used to tune LET-based optimization, and seven to independently test interfraction plan robustness. For each patient, a reference plan was optimized on biologically-weighted dose cost functions. For the first group, 7 LET-optimized plans were obtained by increasing the gross tumor volume (GTV) minimum LET_d (minLET_d) in the range 37–55 keV/μm, in steps of 3 keV/μm. The optimal LET-optimized plan (LET_OPT) was the one maximizing LET_d, while adhering to clinical acceptability criteria. Reference and LET_OPT plans were compared through dose and LETd metrics (D<em>_x</em>, L<em>_x</em> to x% volume) for the GTV, clinical target volume (CTV), and organs at risk (OARs). The 7 held-out cases were optimized setting minLET_d to the average GTV L_98% of the investigation cohort. Both reference and LET_OPT plans were recalculated on re-evaluation CTs and compared.</p></div><div><h3>Results</h3><p>GTV L_98% increased from (31.8 ± 2.5)keV/μm to (47.6 ± 3.1)keV/μm on the LET_OPT plans, while the fraction of GTV receiving over 50 keV/μm increased on average by 36% (p &lt; 0.001), without affecting target coverage goals, or impacting LET_d and dose to OARs. The interfraction analysis showed no significant worsening with minLET_d set to 48 keV/μm.</p></div><div><h3>Conclusion</h3><p>LET_d optimization for large SC could boost the LET_d in the GTV without significantly compromising plan quality, potentially improving the therapeutic effects of CIRT for large radioresistant tumors.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000940/pdfft?md5=be0c154498e3ac43bff0bd6054502b2c&pid=1-s2.0-S2405631624000940-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964623","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}