Physics and Imaging in Radiation Oncology最新文献

{"title":"Recommendations for reporting and evaluating proton therapy beyond dose and constant relative biological effectiveness.","authors":"Armin Lühr, Dirk Wagenaar, Daniëlle B P Eekers, Lars Glimelius, Steven J M Habraken, Semi Harrabi, Miranda C A Kramer, Ranald I Mackay, Ana Vaniqui, Alexandru Dasu, Damien C Weber","doi":"10.1016/j.phro.2024.100692","DOIUrl":"10.1016/j.phro.2024.100692","url":null,"abstract":"<p><strong>Background and purpose: </strong>In proton therapy, a relative biological effectiveness (RBE) of 1.1 is used to convert proton dose into an equivalent photon dose. However, RBE varies with tissue type, fraction dose, and beam quality parameters beyond dose such as linear energy transfer (LET) raising concerns about increased local effectiveness and potential toxicity. This work aims to harmonize quantities used for clinical consideration of variable RBE for proton therapy.</p><p><strong>Materials and methods: </strong>A survey was distributed to proton centres to determine agreement on RBE-related concerns and clinical implementations. A subsequent clinical expert meeting facilitated by the European Particle Therapy Network was held to achieve consensus and to make clinical recommendations how to prescribe and report beyond using dose and constant RBE.</p><p><strong>Results: </strong>The survey was answered by 17 out of 23 centres contacted (74%). For proton RBE, most concerns existed regarding toxicity in serial organs, while the assumption of an RBE of 1.1 was considered valid for targets. Most physicists intended to consider a physical quantity beyond dose in clinical decision making.</p><p><strong>Conclusions: </strong>A constant RBE of 1.1 was the consensus for prescribing dose. However, current practice of recording and reporting dose in proton therapy must be complemented: the recommended quantity beyond dose was the dose-averaged LET in water from primary and secondary protons, normalized to unit density. This will facilitate analyses of treatment data on effectiveness beyond dose and between centres. No consensus on a single variable RBE model was found. More clinical training on proton RBE is needed.</p>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"100692"},"PeriodicalIF":3.4,"publicationDate":"2024-12-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11750264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013292","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":"Normal tissue complication probability model for severe radiation-induced lymphopenia in patients with pancreatic cancer treated with concurrent chemoradiotherapy.","authors":"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","doi":"10.1016/j.phro.2024.100690","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100690","url":null,"abstract":"<p><strong>Background and purpose: </strong>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).</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"100690"},"PeriodicalIF":3.4,"publicationDate":"2024-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733268/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143013289","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":"Translation of dynamic contrast-enhanced imaging onto a magnetic resonance-guided linear accelerator in patients with head and neck cancer.","authors":"Michael J Dubec, Michael Berks, James Price, Lisa McDaid, John Gaffney, Ross A Little, Susan Cheung, Marcel van Herk, Ananya Choudhury, Julian C Matthews, Andrew McPartlin, Geoff J M Parker, David L Buckley, James P B O'Connor","doi":"10.1016/j.phro.2024.100689","DOIUrl":"10.1016/j.phro.2024.100689","url":null,"abstract":"<p><strong>Background and purpose: </strong>Magnetic resonance imaging - linear accelerator (MRI-linac) systems permit imaging of tumours to guide treatment. Dynamic contrast enhanced (DCE)-MRI allows investigation of tumour perfusion. We assessed the feasibility of performing DCE-MRI on a 1.5 T MRI-linac in patients with head and neck cancer (HNC) and measured biomarker repeatability and sensitivity to radiotherapy effects.</p><p><strong>Materials and methods: </strong>Patients were imaged on a 1.5 T MRI-linac or a 1.5 T diagnostic MR system twice before treatment. DCE-MRI parameters including K^trans were calculated, with the optimum pharmacokinetic model identified using corrected Akaike information criterion. Repeatability was assessed by within-subject coefficient of variation (wCV). Treatment effects were assessed as change measured at week 2 of radiotherapy.</p><p><strong>Results: </strong>14 patients were recruited (6 scanned on diagnostic MR and 8 on MRI-linac), with a total of 24 lesions. Baseline K^trans estimates were comparable on both MR systems; 0.13 [95 %CI: 0.10 to 0.16] min^-1 (diagnostic MR) and 0.15 [0.12 to 0.18] min^-1 (MRI-linac). wCV values were 22.6 % [95 % CI: 16.2 to 37.3 %] (diagnostic MR) and 11.7 % [8.4 to 19.3 %] (MRI-linac). Combined cohort increase in K^trans was significant (p < 0.01). Similar results were seen for other DCE-MRI parameters.</p><p><strong>Conclusions: </strong>DCE-MRI is feasible on a 1.5 T MRI-linac system in patients with HNC. Parameter estimates, repeatability, and sensitivity to treatment were similar to those measured on a conventional diagnostic MR system. These data support performing DCE-MRI in studies on the MRI-linac to assess treatment response and adaptive guidance based on tumour perfusion.</p>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"100689"},"PeriodicalIF":3.4,"publicationDate":"2024-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11721217/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972457","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 artificial intelligence-based autosegmentation for a high-performance cone-beam computed tomography imaging system in the pelvic region.","authors":"Judith H Sluijter, Agustinus J A J van de Schoot, Abdelmounaim El Yaakoubi, Maartje de Jong, Martine S van der Knaap-van Dongen, Britt Kunnen, Nienke D Sijtsema, Joan J Penninkhof, Kim C de Vries, Steven F Petit, Maarten L P Dirkx","doi":"10.1016/j.phro.2024.100687","DOIUrl":"10.1016/j.phro.2024.100687","url":null,"abstract":"<p><strong>Background and purpose: </strong>A novel ring-gantry cone-beam computed tomography (CBCT) imaging system shows improved image quality compared to its conventional version, but its effect on autosegmentation is unknown. This study evaluates the impact of this high-performance CBCT on autosegmentation performance, inter-observer variability, contour correction times and delineation confidence, compared to the conventional CBCT.</p><p><strong>Materials and methods: </strong>Twenty prostate cancer patients were enrolled in this prospective clinical study. Per patient, one pair of high-performance CBCT and conventional CBCT scans was included. Three observers manually corrected contours generated by the artificial intelligence (AI) model for prostate, seminal vesicles, bladder, rectum and bowel. Differences between AI-based and manual corrected contours were quantified using Dice Similarity Coefficient (DSC) and 95th percentile of Hausdorff distance (HD95). Autosegmentation performance and interobserver variation were compared using a random effects model; correction times and confidence scores using a paired <i>t</i>-test and Wilcoxon signed-rank test, respectively.</p><p><strong>Results: </strong>Autosegmentation performance showed small, but statistically insignificant differences. Interobserver variability, assessed by the intraclass correlation coefficient, was significantly different across most organs, but these were considered clinically irrelevant (maximum difference = 0.08). Mean contour correction times were similar for both CBCT systems (11:03 versus 11:12 min; p = 0.66). Delineation confidence scores were significantly higher with the high-performance CBCT scans for prostate, seminal vesicles and rectum (4.5 versus 3.5, 4.3 versus 3.5, 4.8 versus 4.3; all p < 0.001).</p><p><strong>Conclusion: </strong>The high-performance CBCT did not (clinically) improve autosegmentation performance, inter-observer variability or contour correction time compared to conventional CBCT. However, it clearly enhanced user confidence in organ delineation for prostate, seminal vesicles and rectum.</p>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"100687"},"PeriodicalIF":3.4,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11721864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972456","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":"Impact of annotation imperfections and auto-curation for deep learning-based organ-at-risk segmentation.","authors":"Victor I J Strijbis, O J Gurney-Champion, Berend J Slotman, Wilko F A R Verbakel","doi":"10.1016/j.phro.2024.100684","DOIUrl":"10.1016/j.phro.2024.100684","url":null,"abstract":"<p><strong>Background and purpose: </strong>Segmentation imperfections (noise) in radiotherapy organ-at-risk segmentation naturally arise from specialist experience and image quality. Using clinical contours can result in sub-optimal convolutional neural network (CNN) training and performance, but manual curation is costly. We address the impact of simulated and clinical segmentation noise on CNN parotid gland (PG) segmentation performance and provide proof-of-concept for an easily implemented auto-curation countermeasure.</p><p><strong>Methods and materials: </strong>The impact of segmentation imperfections was investigated by simulating noise in clean, high-quality segmentations. Curation efficacy was tested by removing lowest-scoring Dice similarity coefficient (DSC) cases early during CNN training, both in simulated (5-fold) and clinical (10-fold) settings, using our full radiotherapy clinical cohort (RTCC; N = 1750 individual PGs). Statistical significance was assessed using Bonferroni-corrected Wilcoxon signed-rank tests. Curation efficacies were evaluated using DSC and mean surface distance (MSD) on in-distribution and out-of-distribution data and visual inspection.</p><p><strong>Results: </strong>The curation step correctly removed median(range) 98(90-100)% of corrupted segmentations and restored the majority (1.2 %/1.3 %) of DSC lost from training with 30 % corrupted segmentations. This effect was masked when using typical (non-curated) validation data. In RTCC, 20 % curation showed improved model generalizability which significantly improved out-of-distribution DSC and MSD (p < 1.0e-12, p < 1.0e-6). Improved consistency was observed in particularly the medial and anterior lobes.</p><p><strong>Conclusions: </strong>Up to 30% case removal, the curation benefit outweighed the training variance lost through curation. Considering the notable ease of implementation, high sensitivity in simulations and performance gains already at lower curation fractions, as a conservative middle ground, we recommend 15% curation of training cases when training CNNs using clinical PG contours.</p>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"100684"},"PeriodicalIF":3.4,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11667007/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886228","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, Jacquelien Pomp, Firdaus A A Mohamed Hoesein, Bas W Raaymakers, Joost J C Verhoeff, Martin F Fast","doi":"10.1016/j.phro.2024.100686","DOIUrl":"10.1016/j.phro.2024.100686","url":null,"abstract":"<p><strong>Background/purpose: </strong>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, <math><mrow><mi>α</mi> <mtext>/</mtext> <mi>β</mi></mrow> </math> uncertainty and different modelling approaches.</p><p><strong>Materials/methods: </strong>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) <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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"100686"},"PeriodicalIF":3.4,"publicationDate":"2024-12-01","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":"Feasibility and potential clinical benefit of dose de-escalation in stereotactic ablative radiotherapy for lung cancer lesions with ground glass opacities.","authors":"Carla Cases, Meritxell Mollà, Marcelo Sánchez, Mariana Benegas, Marc Ballestero, Sergi Serrano-Rueda, Gabriela Antelo, Carles Gomà","doi":"10.1016/j.phro.2024.100681","DOIUrl":"10.1016/j.phro.2024.100681","url":null,"abstract":"<p><strong>Introduction: </strong>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.</p><p><strong>Materials and methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"100681"},"PeriodicalIF":3.4,"publicationDate":"2024-11-29","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":"Definition of a framework for volumetric modulated arc therapy plan quality assessment with integration of dose-, complexity-, and robustness metrics.","authors":"Tina Orovwighose, Bernhard Rhein, Oliver Schramm, Oliver Jäkel, Vania Batista","doi":"10.1016/j.phro.2024.100685","DOIUrl":"10.1016/j.phro.2024.100685","url":null,"abstract":"<p><strong>Background and purpose: </strong>Conventionally, the quality of radiotherapy treatment plans is assessed through visual inspection of dose distributions and dose-volume histograms. This study developed a framework to evaluate plan quality using dose, complexity, and robustness metrics. Additionally, a method for predicting plan robustness metrics using dose and complexity metrics was introduced for cases where plan robustness evaluation is unavailable or impractical.</p><p><strong>Materials and methods: </strong>The framework and prediction models were developed and validated using 103-bronchial Volumetric Modulated Arc Therapy (VMAT)-plans. The application of the framework was demonstrated using 25-VMAT-plans. To identify significant metrics for plan evaluation, 122-metrics were analysed and narrowed down using multivariate Spearman correlation. Metric limits were set with Statistical process control (SPC). Robustness metrics were predicted using multivariable or single linear regression models based on dose-and complexity-metrics.</p><p><strong>Results: </strong>Twenty-five-metrics were selected based on the amount and strength of correlations. R₉₅(dose coverage) and HI_95/5(homogeneity index) stood out among the dose-metrics, while the complexity-metrics showed similar correlations. Average scenarios dose at 95 % Clinical Target Volume D95_mean(CTV) and Errorbar-based Volume-Histograms (EVH) were notable for robustness metrics. Approximately 99 % of evaluated metrics fell within established SPC limits. The prediction model for D95_mean(CTV) showed good performance (adjusted R² = 0.88, mean squared error (MSE) = 3.84 × 10^-6), while the model for EVH demonstrated moderate reliability (adjusted R² = 0.52, MSE = 0.2). No statistically significant differences were found between the predicted (using dose-and complexity-metrics) and calculated robustness metrics (EVH (<i>p-value</i> = 0.9) and D95_mean(CTV) (<i>p-value</i> = 1)).</p><p><strong>Conclusions: </strong>The developed framework enables early detection of sub-optimal, complex and non-robust treatment plans. The predictive model can be used when robustness evaluations are impractical.</p>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"100685"},"PeriodicalIF":3.4,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11663972/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883131","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, Hidde Eijkelenkamp, Guus Grimbergen, Frank J Wessels, Sasja F Mulder, Gert J Meijer, Martijn P W Intven","doi":"10.1016/j.phro.2024.100683","DOIUrl":"10.1016/j.phro.2024.100683","url":null,"abstract":"<p><p>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.</p>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"100683"},"PeriodicalIF":3.4,"publicationDate":"2024-11-28","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":"Experience with remote electronic portal imaging device-based dosimetric auditing for static and rotational intensity modulated radiotherapy.","authors":"Peter B Greer, Joerg Lehmann, Alisha Moore","doi":"10.1016/j.phro.2024.100674","DOIUrl":"10.1016/j.phro.2024.100674","url":null,"abstract":"<p><p>The aim of this work was to evaluate results of a remote electronic portal imaging based dosimetric auditing method using Task-Group 218 clinical gamma evaluation criteria (3%,2 mm, 10% dose threshold). For intensity modulated radiation therapy the results were (mean ± 1 SD) 97.9 ± 4.5% with 31/34 audits passing (optimal level, <math><mo>≥</mo></math> 95%) and 3/34 audits failing (action level, <math><mo><</mo></math> 90%). For volumetric modulated arc therapy the results were 98.5 ± 2.3% with 32/36 audits passing (optimal level) and 4/36 passing (tolerance level, <math><mo>≥</mo></math> 90% and <math><mo><</mo></math> 95%). The audit has been successfully applied globally for clinical trial quality assurance.</p>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"32 ","pages":"100674"},"PeriodicalIF":3.4,"publicationDate":"2024-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11663962/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142883132","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}