Physics and Imaging in Radiation Oncology最新文献

{"title":"Automatic segmentation for magnetic resonance imaging guided individual elective lymph node irradiation in head and neck cancer patients","authors":"Floris C.J. Reinders ,&nbsp;Mark H.F. Savenije ,&nbsp;Mischa de Ridder ,&nbsp;Matteo Maspero ,&nbsp;Patricia A.H. Doornaert ,&nbsp;Chris H.J. Terhaard ,&nbsp;Cornelis P.J. Raaijmakers ,&nbsp;Kaveh Zakeri ,&nbsp;Nancy Y. Lee ,&nbsp;Eric Aliotta ,&nbsp;Aneesh Rangnekar ,&nbsp;Harini Veeraraghavan ,&nbsp;Marielle E.P. Philippens","doi":"10.1016/j.phro.2024.100655","DOIUrl":"10.1016/j.phro.2024.100655","url":null,"abstract":"<div><h3>Background and purpose</h3><div>In head and neck squamous cell carcinoma (HNSCC) patients, the radiation dose to nearby organs at risk can be reduced by restricting elective neck irradiation from lymph node levels to individual lymph nodes. However, manual delineation of every individual lymph node is time-consuming and error prone. Therefore, automatic magnetic resonance imaging (MRI) segmentation of individual lymph nodes was developed and tested using a convolutional neural network (CNN).</div></div><div><h3>Materials and methods</h3><div>In 50 HNSCC patients (UMC-Utrecht), individual lymph nodes located in lymph node levels Ib-II-III-IV-V were manually segmented on MRI by consensus of two experts, obtaining ground truth segmentations. A 3D CNN (nnU-Net) was trained on 40 patients and tested on 10. Evaluation metrics were Dice Similarity Coefficient (DSC), recall, precision, and F1-score. The segmentations of the CNN was compared to segmentations of two observers. Transfer learning was used with 20 additional patients to re-train and test the CNN in another medical center.</div></div><div><h3>Results</h3><div>nnU-Net produced automatic segmentations of elective lymph nodes with median DSC: 0.72, recall: 0.76, precision: 0.78, and F1-score: 0.78. The CNN had higher recall compared to both observers (p = 0.002). No difference in evaluation scores of the networks in both medical centers was found after re-training with 5 or 10 patients.</div></div><div><h3>Conclusion</h3><div>nnU-Net was able to automatically segment individual lymph nodes on MRI. The detection rate of lymph nodes using nnU-Net was higher than manual segmentations. Re-training nnU-Net was required to successfully transfer the network to the other medical center.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"Article 100655"},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533611","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":"Evaluation of deep learning-based target auto-segmentation for Magnetic Resonance Imaging-guided cervix brachytherapy","authors":"Rita Simões,&nbsp;Eva C. Rijkmans,&nbsp;Eva E. Schaake,&nbsp;Marlies E. Nowee,&nbsp;Sandra van der Velden,&nbsp;Tomas Janssen","doi":"10.1016/j.phro.2024.100669","DOIUrl":"10.1016/j.phro.2024.100669","url":null,"abstract":"<div><h3>Background and purpose</h3><div>The target structures for cervix brachytherapy are segmented by radiation oncologists using imaging and clinical information. At the first fraction, this is performed manually from scratch. For subsequent fractions the first fraction segmentations are rigidly propagated and edited manually. This process is time-consuming while patients wait immobilized. In this work, we evaluate the potential clinical impact of using population-based and patient-specific auto-segmentations as a starting point for target segmentation of the second fraction.</div></div><div><h3>Materials and method</h3><div>For twenty-eight patients with locally advanced cervical cancer, treated with MRI-guided brachytherapy, auto-segmentations were retrospectively generated for the second fraction image using two approaches: 1) population-based model, 2) patient-specific models fine-tuned on first fraction information. A radiation oncologist manually edited the auto-segmentations to assess model-induced bias. Pairwise geometric and dosimetric comparisons were performed for the automatic, edited and clinical structures. The time spent editing the auto-segmentations was compared to the current clinical workflow.</div></div><div><h3>Results</h3><div>The edited structures were more similar to the automatic than to the clinical structures. The geometric and dosimetric differences between the edited and the clinical structures were comparable to the inter-observer variability investigated in literature. Editing the auto-segmentations was faster than the manual segmentation performed during our clinical workflow. Patient-specific auto-segmentations required less edits than population-based structures.</div></div><div><h3>Conclusions</h3><div>Auto-segmentation introduces a bias in the manual delineations but this bias is clinically irrelevant. Auto-segmentation, particularly patient-specific fine-tuning, is a time-saving tool that can improve treatment logistics and therefore reduce patient burden during the second fraction of cervix brachytherapy.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"Article 100669"},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593794","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":"Feasibility and potential clinical benefit of dose de-escalation in stereotactic ablative radiotherapy for lung cancer lesions with ground glass opacities","authors":"Carla Cases ,&nbsp;Meritxell Mollà ,&nbsp;Marcelo Sánchez ,&nbsp;Mariana Benegas ,&nbsp;Marc Ballestero ,&nbsp;Sergi Serrano-Rueda ,&nbsp;Gabriela Antelo ,&nbsp;Carles Gomà","doi":"10.1016/j.phro.2024.100681","DOIUrl":"10.1016/j.phro.2024.100681","url":null,"abstract":"<div><h3>Introduction</h3><div>Treatment of neoplasic lung nodules with ground glass opacities (GGO) faces two primary challenges. First, the standard practice of treating GGOs as solid nodules, which effectively controls the tumor locally, but might increase associated toxicities. The second is the potential for dose calculation errors related to increased heterogeneity. This study addresses the optimization of a dose de-escalation regime for stereotactic ablative radiotherapy (SABR) for GGO lesions.</div></div><div><h3>Materials and Methods</h3><div>We used the CT scans of 35 patients (40 lesions) with some degree of GGO component treated at our institution between 2017 and 2021. We first assessed the dose calculation accuracy as a function of the GGO component of the lesion. We then analysed the advantages of a dose de-escalation regime in terms of lung dose reduction (Dmean, V20Gy and V300GyBED3) and plan robustness.</div></div><div><h3>Results</h3><div>We found a positive correlation between the presence of GGO and the dose calculation errors in a phantom scenario. These differences are reduced for patient data and in the presence of breathing motion. When using a de-escalation regime, significant reductions were achieved in mean lung dose, V20Gy and V300GyBED3. This study also revealed that lower doses in GGO areas lead to more stable fluence patterns, increasing treatment robustness.</div></div><div><h3>Conclusions</h3><div>The study lays the foundation for an eventual use of dose de-escalation in SABR for treating lung lesions with GGO, potentially leading to equivalent local control while reducing associated toxicities. These findings lay the groundwork for future clinical trials.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"Article 100681"},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11663960/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883145","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":"Feasibility of quantitative relaxometry for prostate target localization and response assessment in magnetic resonance-guided online adaptive stereotactic body radiotherapy","authors":"Ergys Subashi ,&nbsp;Eve LoCastro ,&nbsp;Sarah Burleson ,&nbsp;Aditya Apte ,&nbsp;Michael Zelefsky ,&nbsp;Neelam Tyagi","doi":"10.1016/j.phro.2024.100678","DOIUrl":"10.1016/j.phro.2024.100678","url":null,"abstract":"<div><h3>Purpose</h3><div>Multiparametric magnetic resonance imaging (MRI) is known to provide predictors for malignancy and treatment outcome. The inclusion of these datasets in workflows for online adaptive planning remains under investigation. We demonstrate the feasibility of longitudinal relaxometry in online MR-guided adaptive stereotactic body radiotherapy (SBRT) to the prostate and dominant intra-prostatic lesion (DIL).</div></div><div><h3>Methods</h3><div>Fifty patients with intermediate-risk prostate cancer were included in the study. The clinical target volume (CTV) was defined as the prostate gland plus 1 cm of seminal vesicles. The gross tumor volume (GTV) was defined as the DIL identified on multiparametric MRI. Online adaptive radiotherapy was delivered in a 1.5 T MR-Linac using a prescription of 800 cGy/900 cGy × 5 fractions to the CTV + 3 mm/GTV + 2 mm. Relaxometry and diffusion-weighted imaging were implemented using clinically available sequences. Test-retest measurements were performed in eight patients, at each treatment fraction. Bias and uncertainty in relaxometry measurements were also assessed using a reference phantom.</div></div><div><h3>Results</h3><div>The bias in longitudinal/transverse relaxation times was negligible while uncertainty was within 3 %. Test-retest measurements demonstrate that bias/uncertainty in patient T1 and T2 were comparable to bias/uncertainty estimated in the phantom. Mean T1 and T2 relaxation were significantly different between the prostate and DIL. The correlation between T1, T2, and diffusion was significant in the DIL, but not in the prostate. During treatment, mean T1 in the DIL approaches mean T1 in the prostate.</div></div><div><h3>Conclusions</h3><div>Longitudinal relaxometry for online MR-guided adaptive SBRT is feasible in a high-field MR-Linac and may provide complementary information for target delineation and response assessment.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"Article 100678"},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883148","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":"Pro-active risk analysis of an in-house developed deep learning based autoplanning tool for breast Volumetric Modulated Arc Therapy","authors":"Liesbeth Vandewinckele ,&nbsp;Chahrazad Benazzouz ,&nbsp;Laurence Delombaerde ,&nbsp;Laure Pape ,&nbsp;Truus Reynders ,&nbsp;Aline Van der Vorst ,&nbsp;Dylan Callens ,&nbsp;Jan Verstraete ,&nbsp;Adinda Baeten ,&nbsp;Caroline Weltens ,&nbsp;Wouter Crijns","doi":"10.1016/j.phro.2024.100677","DOIUrl":"10.1016/j.phro.2024.100677","url":null,"abstract":"<div><h3>Background and Purpose:</h3><div>With the increasing amount of in-house created deep learning models in radiotherapy, it is important to know how to minimise the risks associated with the local clinical implementation prior to clinical use. The goal of this study is to give an example of how to identify the risks and find mitigation strategies to reduce these risks in an implemented workflow containing a deep learning based planning tool for breast Volumetric Modulated Arc Therapy.</div></div><div><h3>Materials and Methods:</h3><div>The deep learning model ran on a private Google Cloud environment for adequate computational capacity and was integrated into a workflow that could be initiated within the clinical Treatment Planning System (TPS). A proactive Failure Mode and Effect Analysis (FMEA) was conducted by a multidisciplinary team, including physicians, physicists, dosimetrists, technologists, quality managers, and the research and development team. Failure modes categorised as ‘Not acceptable’ and ‘Tolerable’ on the risk matrix were further examined to find mitigation strategies.</div></div><div><h3>Results:</h3><div>In total, 39 failure modes were defined for the total workflow, divided over four steps. Of these, 33 were deemed ‘Acceptable’, five ‘Tolerable’, and one ‘Not acceptable’. Mitigation strategies, such as a case-specific Quality Assurance report, additional scripted checks and properties, a pop-up window, and time stamp analysis, reduced the failure modes to two ‘Tolerable’ and none in the ‘Not acceptable’ region.</div></div><div><h3>Conclusions:</h3><div>The pro-active risk analysis revealed possible risks in the implemented workflow and led to the implementation of mitigation strategies that decreased the risk scores for safer clinical use.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"Article 100677"},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11697787/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142932913","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":"Monte Carlo simulated correction factors for high dose rate brachytherapy postal dosimetry audit methodology","authors":"Krzysztof Chelminski ,&nbsp;Alexis Dimitriadis ,&nbsp;Roua Abdulrahim ,&nbsp;Pavel Kazantsev ,&nbsp;Evelyn Granizo-Roman ,&nbsp;Jonathan Kalinowski ,&nbsp;Shirin Abbasi Enger ,&nbsp;Godfrey Azangwe ,&nbsp;Mauro Carrara ,&nbsp;Jamema Swamidas","doi":"10.1016/j.phro.2024.100657","DOIUrl":"10.1016/j.phro.2024.100657","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Full-scatter conditions in water are impractical for postal dosimetry audits in brachytherapy. This work presents a method to obtain correction factors that account for deviations from full-scatter water-equivalent conditions for a small plastic phantom.</div></div><div><h3>Material and Methods</h3><div>A 16 × 8 × 3 cm phantom (PMMA) with a radiophotoluminescent dosimeter (RPLD) at the centre and two catheters on either side was simulated using Monte Carlo (MC) to calculate correction factors accounting for the lack of scatter, non-water equivalence of the RPLD and phantom, source model and backscatter for HDR ⁶⁰Co and ¹⁹²Ir sources.</div></div><div><h3>Results</h3><div>The correction factors for non-water equivalence, lack of full scatter, and the use of PMMA were 1.062 ± 0.013, 1.059 ± 0.008 and 0.993 ± 0.009 for ¹⁹²Ir and 1.129 ± 0.005, 1.009 ± 0.005 and 1.005 ± 0.005 for ⁶⁰Co respectively. Water-equivalent backscatter thickness of 5 cm was found to be adequate and increasing thickness of backscatter did not have an influence on the RPLD dose. The mean photon energy in the RPLD for four HDR ¹⁹²Ir sources was 279 ± 2 keV in full scatter conditions and 295 ± 1 keV in the audit conditions. For ⁶⁰Co source the corresponding mean energies were 989 ± 1 keV and 1022 ± 1 keV respectively.</div></div><div><h3>Conclusions</h3><div>Correction factors were obtained through the MC simulations for conditions deviating from TG-43, including the amount of back scatter, and the optimum audit set up. Additionally, the influence of different source models on the correction factors was negligible and demonstrates their generic applicability.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"Article 100657"},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560794","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":"External validation of a multimodality deep-learning normal tissue complication probability model for mandibular osteoradionecrosis trained on 3D radiation distribution maps and clinical variables","authors":"Laia Humbert-Vidan ,&nbsp;Christian R. Hansen ,&nbsp;Vinod Patel ,&nbsp;Jørgen Johansen ,&nbsp;Andrew P. King ,&nbsp;Teresa Guerrero Urbano","doi":"10.1016/j.phro.2024.100668","DOIUrl":"10.1016/j.phro.2024.100668","url":null,"abstract":"<div><h3>Background and purpose</h3><div>While the inclusion of spatial dose information in deep learning (DL)-based normal-tissue complication probability (NTCP) models has been the focus of recent research studies, external validation is still lacking. This study aimed to externally validate a DL-based NTCP model for mandibular osteoradionecrosis (ORN) trained on 3D radiation dose distribution maps and clinical variables.</div></div><div><h3>Methods and materials</h3><div>A 3D DenseNet-40 convolutional neural network (3D-mDN40) was trained on clinical and radiation dose distribution maps on a retrospective class-balanced matched cohort of 184 subjects. A second model (3D-DN40) was trained on dose maps only and both DL models were compared to a logistic regression (LR) model trained on DVH metrics and clinical variables. All models were externally validated by means of their discriminative ability and calibration on an independent dataset of 82 subjects.</div></div><div><h3>Results</h3><div>No significant difference in performance was observed between models. In internal validation, these exhibited similar Brier scores around 0.2, Log Loss values of 0.6–0.7 and ROC AUC values around 0.7 (internal) and 0.6 (external). Differences in clinical variable distributions and their effect sizes were observed between internal and external cohorts, such as smoking status (0.6 vs. 0.1) and chemotherapy (0.1 vs. −0.5), respectively.</div></div><div><h3>Conclusion</h3><div>To our knowledge, this is the first study to externally validate a multimodality DL-based ORN NTCP model. Utilising mandible dose distribution maps, these models show promise for enhancing spatial risk assessment and guiding dental and oncological decision-making, though further research is essential to address overfitting and domain shift for reliable clinical use.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"Article 100668"},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142660813","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":"The influence of cardiac substructure dose on survival in a large lung cancer stereotactic radiotherapy cohort using a robust personalized contour analysis","authors":"Luuk H.G. van der Pol ,&nbsp;Jacquelien Pomp ,&nbsp;Firdaus A.A. Mohamed Hoesein ,&nbsp;Bas W. Raaymakers ,&nbsp;Joost J.C. Verhoeff ,&nbsp;Martin F. Fast","doi":"10.1016/j.phro.2024.100686","DOIUrl":"10.1016/j.phro.2024.100686","url":null,"abstract":"<div><div>Background/Purpose: Radiation-induced cardiac toxicity in lung cancer patients has received increased attention since RTOG 0617. However, large cohort studies with accurate cardiac substructure (CS) contours are lacking, limiting our understanding of the potential influence of individual CSs. Here, we analyse the correlation between CS dose and overall survival (OS) while accounting for deep learning (DL) contouring uncertainty, <span><math><mrow><mi>α</mi><mtext>/</mtext><mi>β</mi></mrow></math></span> uncertainty and different modelling approaches. Materials/Methods: This single institution, retrospective cohort study includes 730 patients (early-stage tumours (I or II). All treated: 2009–2019), who received stereotactic body radiotherapy (≥ 5 Gy per fraction). A DL model was trained on 70 manually contoured patients to create 12 cardio-vascular structures. Structures with median dice score above 0.8 and mean surface distance (MSD) &lt;2 mm during testing, were further analysed. Patientspecific CS dose was used to find the correlation between CS dose and OS with elastic net and random survival forest models (with and without confounding clinical factors). The influence of delineation-induced dose uncertainty on OS was investigated by expanding/contracting the DL-created contours using the MSD ± 2 standard deviations. Results: Eight CS contours met the required performance level. The left atrium (LA) mean dose was significant for OS and an LA mean dose of 3.3 Gy (in EQD2) was found as a significant dose stratum. Conclusion: Explicitly accounting for input parameter uncertainty in lung cancer survival modelling was crucial in robustly identifying critical CS dose parameters. Using this robust methodology, LA mean dose was revealed as the most influential CS dose parameter.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"Article 100686"},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11663986/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883152","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":"Magnetic resonance-guided stereotactic body radiation therapy for pancreatic oligometastases from renal cell carcinoma","authors":"Jonna K. van Vulpen ,&nbsp;Hidde Eijkelenkamp ,&nbsp;Guus Grimbergen ,&nbsp;Frank J. Wessels ,&nbsp;Sasja F. Mulder ,&nbsp;Gert J. Meijer ,&nbsp;Martijn P.W. Intven","doi":"10.1016/j.phro.2024.100683","DOIUrl":"10.1016/j.phro.2024.100683","url":null,"abstract":"<div><div>Stereotactic body radiation therapy (SBRT) may be a non-invasive strategy to treat patients with pancreatic oligometastases from renal cell carcinoma (RCC). We analyzed 11 patients treated with MR-guided SBRT to 31 pancreatic oligometastases. At a median follow-up of 31.6 months, 1-year and 2-year freedom from local progression was 100 % and 95 % (95 % CI 86–100 %), respectively. Moreover, 1-year and 2-year freedom from systemic therapy was 91 % (95 %CI 75–100 %) and 82 % (95 % CI 62–100 %), respectively. MR-guided SBRT may be a safe and effective treatment option for pancreatic oligometastases from RCC.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"Article 100683"},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11650256/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142847867","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":"Accuracy, repeatability, and reproducibility of water-fat magnetic resonance imaging in a phantom and healthy volunteer","authors":"Anouk Corbeau ,&nbsp;Pien van Gastel ,&nbsp;Piotr A. Wielopolski ,&nbsp;Nick de Jong ,&nbsp;Carien L. Creutzberg ,&nbsp;Uulke A. van der Heide ,&nbsp;Stephanie M. de Boer ,&nbsp;Eleftheria Astreinidou","doi":"10.1016/j.phro.2024.100651","DOIUrl":"10.1016/j.phro.2024.100651","url":null,"abstract":"<div><div>Bone marrow (BM) damage due to chemoradiotherapy can increase BM fat in cervical cancer patients. Water-fat magnetic resonance (MR) scans were performed on a phantom and a healthy female volunteer to validate proton density fat fraction accuracy, reproducibility, and repeatability across different vendors, field strengths, and protocols. Phantom measurements showed a high accuracy, high repeatability, and excellent reproducibility. Volunteer measurements had an excellent intra- and interreader reliability, good repeatability, and moderate to good reproducibility. Water-fat MRI show potential for quantification of longitudinal vertebral BM fat changes. Further studies are needed to validate and extend these findings for broader clinical applicability.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"Article 100651"},"PeriodicalIF":3.4,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142533614","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}