Physics and Imaging in Radiation Oncology最新文献

{"title":"The effect of editing clinical contours on deep-learning segmentation accuracy of the gross tumor volume in glioblastoma","authors":"Kim M. Hochreuter ,&nbsp;Jintao Ren ,&nbsp;Jasper Nijkamp ,&nbsp;Stine S. Korreman ,&nbsp;Slávka Lukacova ,&nbsp;Jesper F. Kallehauge ,&nbsp;Anouk K. Trip","doi":"10.1016/j.phro.2024.100620","DOIUrl":"10.1016/j.phro.2024.100620","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Deep-learning (DL) models for segmentation of the gross tumor volume (GTV) in radiotherapy are generally based on clinical delineations which suffer from inter-observer variability. The aim of this study was to compare performance of a DL-model based on clinical glioblastoma GTVs to a model based on a single-observer edited version of the same GTVs.</p></div><div><h3>Materials and methods</h3><p>The dataset included imaging data (Computed Tomography (CT), T1, contrast-T1 (T1C), and fluid-attenuated-inversion-recovery (FLAIR)) of 259 glioblastoma patients treated with post-operative radiotherapy between 2012 and 2019 at a single institute. The clinical GTVs were edited using all imaging data. The dataset was split into 207 cases for training/validation and 52 for testing.</p><p>GTV segmentation models (nnUNet) were trained on clinical and edited GTVs separately and compared using Surface Dice with 1 mm tolerance (sDSC_1mm). We also evaluated model performance with respect to extent of resection (EOR), and different imaging combinations (T1C/T1/FLAIR/CT, T1C/FLAIR/CT, T1C/FLAIR, T1C/CT, T1C/T1, T1C). A Wilcoxon test was used for significance testing.</p></div><div><h3>Results</h3><p>The median (range) sDSC_1mm of the clinical-GTV-model and edited-GTV-model both evaluated with the edited contours, was 0.76 (0.43–0.94) vs. 0.92 (0.60–0.98) respectively (p &lt; 0.001). sDSC_1mm was not significantly different between patients with a biopsy, partial, and complete resection. T1C as single input performed as good as use of imaging combinations.</p></div><div><h3>Conclusions</h3><p>High segmentation accuracy was obtained by the DL-models. Editing of the clinical GTVs significantly increased DL performance with a relevant effect size. DL performance was robust for EOR and highly accurate using only T1C.</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/S2405631624000903/pdfft?md5=e88e04622fe9ccd80053c813dfb9b1cc&pid=1-s2.0-S2405631624000903-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953363","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":"Machine learning and lean six sigma for targeted patient-specific quality assurance of volumetric modulated arc therapy plans","authors":"Nicola Lambri ,&nbsp;Damiano Dei ,&nbsp;Giulia Goretti ,&nbsp;Leonardo Crespi ,&nbsp;Ricardo Coimbra Brioso ,&nbsp;Marco Pelizzoli ,&nbsp;Sara Parabicoli ,&nbsp;Andrea Bresolin ,&nbsp;Pasqualina Gallo ,&nbsp;Francesco La Fauci ,&nbsp;Francesca Lobefalo ,&nbsp;Lucia Paganini ,&nbsp;Giacomo Reggiori ,&nbsp;Daniele Loiacono ,&nbsp;Ciro Franzese ,&nbsp;Stefano Tomatis ,&nbsp;Marta Scorsetti ,&nbsp;Pietro Mancosu","doi":"10.1016/j.phro.2024.100617","DOIUrl":"10.1016/j.phro.2024.100617","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Radiotherapy plans with excessive complexity exhibit higher uncertainties and worse patient-specific quality assurance (PSQA) results, while the workload of measurement-based PSQA can impact the efficiency of the radiotherapy workflow. Machine Learning (ML) and Lean Six Sigma, a process optimization method, were implemented to adopt a targeted PSQA approach, aiming to reduce workload, risk of failures, and monitor complexity.</p></div><div><h3>Materials and methods</h3><p>Lean Six Sigma was applied using DMAIC (define, measure, analyze, improve, and control) steps. Ten complexity metrics were computed for 69,811 volumetric modulated arc therapy (VMAT) arcs from 28,612 plans delivered in our Institute (2013–2021). Outlier complexities were defined as &gt;95th-percentile of the historical distributions, stratified by treatment. An ML model was trained to predict the gamma passing rate (GPR-3 %/1mm) of an arc given its complexity. A decision support system was developed to monitor the complexity and expected GPR. Plans at risk of PSQA failure, either extremely complex or with average GPR &lt;90 %, were identified. The tool’s impact was assessed after nine months of clinical use.</p></div><div><h3>Results</h3><p>Among 1722 VMAT plans monitored prospectively, 29 (1.7 %) were found at risk of failure. Planners reacted by performing PSQA measurement and re-optimizing the plan. Occurrences of outlier complexities remained stable within 5 %. The expected GPR increased from a median of 97.4 % to 98.2 % (Mann-Whitney p &lt; 0.05) due to plan re-optimization.</p></div><div><h3>Conclusions</h3><p>ML and Lean Six Sigma have been implemented in clinical practice enabling a targeted measurement-based PSQA approach for plans at risk of failure to improve overall quality and patient safety.</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/S2405631624000873/pdfft?md5=97f18d2b09662feebc335b8b11e5294b&pid=1-s2.0-S2405631624000873-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964132","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":"Safety and efficiency of a fully automatic workflow for auto-segmentation in radiotherapy using three commercially available deep learning-based applications","authors":"Hasan Cavus ,&nbsp;Philippe Bulens ,&nbsp;Koen Tournel ,&nbsp;Marc Orlandini ,&nbsp;Alexandra Jankelevitch ,&nbsp;Wouter Crijns ,&nbsp;Brigitte Reniers","doi":"10.1016/j.phro.2024.100627","DOIUrl":"10.1016/j.phro.2024.100627","url":null,"abstract":"<div><p>Advancements in radiotherapy auto-segmentation necessitate reliable and efficient workflows. Therefore, a standardized fully automatic workflow was developed for three commercially available deep learning-based auto-segmentation applications and compared to a manual workflow for safety and efficiency. The workflow underwent safety evaluation with failure mode and effects analysis. Notably, eight failure modes were reduced, including seven with severity factors ≥7, indicating the effect on patients, and two with Risk Priority Number value &gt;125, which assesses relative risk level. Efficiency, measured by mouse clicks, showed zero clicks with the automatic workflow. This automation illustrated improvement in both safety and efficiency of workflow.</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/S2405631624000976/pdfft?md5=2b3abdc79a31bbca036b2178ac496af9&pid=1-s2.0-S2405631624000976-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993842","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":"Parametric response mapping of co-registered intravoxel incoherent motion magnetic resonance imaging and positron emission tomography in locally advanced cervical cancer undergoing concurrent chemoradiation therapy","authors":"Dante P.I. Capaldi ,&nbsp;Jen-Yeu Wang ,&nbsp;Lianli Liu ,&nbsp;Vipul R. Sheth ,&nbsp;Elizabeth A. Kidd ,&nbsp;Dimitre H. Hristov","doi":"10.1016/j.phro.2024.100630","DOIUrl":"10.1016/j.phro.2024.100630","url":null,"abstract":"<div><h3>Background and Purpose</h3><p>Intravoxel-incoherent-motion (IVIM) magnetic-resonance-imaging (MRI) and positron-emission-tomography (PET) have been investigated independently but not voxel-wise to evaluate tumor microenvironment in cervical carcinoma patients. Whether regionally combined information of IVIM and PET offers additional predictive benefit over each modality independently has not been explored. Here, we investigated parametric-response-mapping (PRM) of co-registered PET and IVIM in cervical cancer patients to identify sub-volumes that may predict tumor shrinkage to concurrent-chemoradiation-therapy (CCRT).</p></div><div><h3>Materials and Methods</h3><p>Twenty cervical cancer patients (age: 63[41–85]) were retrospectively evaluated. Diffusion-weighted-images (DWIs) were acquired on 3.0 T MRIs using a free-breathing single-shot-spin echo-planar-imaging (EPI) sequence. Pre- and on-treatment (∼after four-weeks of CCRT) MRI and pre-treatment FDG-PET/CT were acquired. IVIM model-fitting on the DWIs was performed using a Bayesian-fitting simplified two-compartment model. Three-dimensional rigidly-registered maps of PET/CT standardized-uptake-value (SUV) and IVIM diffusion-coefficient (<em>D</em>) and perfusion-fraction (<em>f</em>) were generated. Population-means of PET-SUV, IVIM-<em>D</em> and IVIM-<em>f</em> from pre-treatment-scans were calculated and used to generate PRM via a voxel-wise joint-histogram-analysis to classify voxels as high/low metabolic-activity and with high/low (hi/lo) cellular-density. Similar PRM maps were generated for SUV and <em>f</em>.</p></div><div><h3>Results</h3><p>Tumor-volume (p &lt; 0.001) significantly decreased, while IVIM-<em>f</em> (p = 0.002) and IVIM-<em>D</em> (p = 0.03) significantly increased on-treatment. Pre-treatment tumor-volume (r = -0.45,p = 0.04) and PRM-SUV^hi<em>D</em>^lo (r = -0.65,p = 0.002) negatively correlated with ΔGTV, while pre-treatment IVIM-<em>D</em> (r = 0.64,p = 0.002), PRM-SUV^lo<em>f</em>^hi (r = 0.52,p = 0.02), and PRM-SUV^lo<em>D</em>^hi (r = 0.74,p &lt; 0.001) positively correlated with ΔGTV.</p></div><div><h3>Conclusion</h3><p>IVIM and PET was performed on cervical cancer patients undergoing CCRT and we observed that both IVIM-<em>f</em> and IVIM-<em>D</em> increased during treatment. Additionally, PRM was applied, and sub-volumes were identified that were related to ΔGTV.</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/S2405631624001003/pdfft?md5=9ebf67427560b68162bc21c29c446c95&pid=1-s2.0-S2405631624001003-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142048389","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":"Progressive auto-segmentation for cone-beam computed tomography-based online adaptive radiotherapy","authors":"Hengrui Zhao,&nbsp;Xiao Liang,&nbsp;Boyu Meng,&nbsp;Michael Dohopolski,&nbsp;Byongsu Choi,&nbsp;Bin Cai,&nbsp;Mu-Han Lin,&nbsp;Ti Bai,&nbsp;Dan Nguyen,&nbsp;Steve Jiang","doi":"10.1016/j.phro.2024.100610","DOIUrl":"10.1016/j.phro.2024.100610","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Accurate and automated segmentation of targets and organs-at-risk (OARs) is crucial for the successful clinical application of online adaptive radiotherapy (ART). Current methods for cone-beam computed tomography (CBCT) auto-segmentation face challenges, resulting in segmentations often failing to reach clinical acceptability. Current approaches for CBCT auto-segmentation overlook the wealth of information available from initial planning and prior adaptive fractions that could enhance segmentation precision.</p></div><div><h3>Materials and methods</h3><p>We introduce a novel framework that incorporates data from a patient’s initial plan and previous adaptive fractions, harnessing this additional temporal context to significantly refine the segmentation accuracy for the current fraction’s CBCT images. We present LSTM-UNet, an innovative architecture that integrates Long Short-Term Memory (LSTM) units into the skip connections of the traditional U-Net framework to retain information from previous fractions. The models underwent initial pre-training with simulated data followed by fine-tuning on a clinical dataset.</p></div><div><h3>Results</h3><p>Our proposed model’s segmentation predictions yield an average Dice similarity coefficient of 79% from 8 Head &amp; Neck organs and targets, compared to 52% from a baseline model without prior knowledge and 78% from a baseline model with prior knowledge but no memory.</p></div><div><h3>Conclusions</h3><p>Our proposed model excels beyond baseline segmentation frameworks by effectively utilizing information from prior fractions, thus reducing the effort of clinicians to revise the auto-segmentation results. Moreover, it works together with registration-based methods that offer better prior knowledge. Our model holds promise for integration into the online ART workflow, offering precise segmentation capabilities on synthetic CT images.</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/S2405631624000800/pdfft?md5=f95835cfab39bce24fc884853673897d&pid=1-s2.0-S2405631624000800-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141638858","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":"Validation of quantitative magnetic resonance imaging techniques in head and neck healthy structures involved in the salivary and swallowing function: Accuracy and repeatability","authors":"F. Guerreiro ,&nbsp;P.J. van Houdt ,&nbsp;R.J.M. Navest ,&nbsp;N. Hoekstra ,&nbsp;M. de Jong ,&nbsp;B.J. Heijnen ,&nbsp;S.E. Zijlema ,&nbsp;B. Verbist ,&nbsp;U.A. van der Heide ,&nbsp;E. Astreinidou","doi":"10.1016/j.phro.2024.100608","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100608","url":null,"abstract":"<div><h3>Background and Purpose</h3><p>Radiation-induced damage to the organs at risk (OARs) in head-and-neck cancer (HNC) patient can result in long-term complications. Quantitative magnetic resonance imaging (qMRI) techniques such as diffusion-weighted imaging (DWI), DIXON for fat fraction (FF) estimation and T₂ mapping could potentially provide a spatial assessment of such damage. The goal of this study is to validate these qMRI techniques in terms of accuracy in phantoms and repeatability in-vivo across a broad selection of healthy OARs in the HN region.</p></div><div><h3>Materials and Methods</h3><p>Scanning was performed at a 3 T diagnostic MRI scanner, including the calculation of apparent diffusion coefficient (ADC) from DWI, FF and T₂ maps. Phantoms were scanned to estimate the qMRI techniques bias using Bland-Altman statistics. Twenty-six healthy subjects were scanned twice in a test–retest study to determine repeatability. Repeatability coefficients (RC) were calculated for the parotid, submandibular, sublingual and tubarial salivary glands, oral cavity, pharyngeal constrictor muscle and brainstem. Additionally, a linear mixed-effect model analysis was used to evaluate the effect of subject-specific characteristics on the qMRI values.</p></div><div><h3>Results</h3><p>Bias was 0.009x10^-3 mm²/s for ADC, -0.7 % for FF and -7.9 ms for T₂. RCs ranged 0.11–0.25x10^-3 mm²/s for ADC, 1.2–6.3 % for FF and 2.5–6.3 ms for T₂. A significant positive linear relationship between age and the FF and T₂ for some of the OARs was found.</p></div><div><h3>Conclusion</h3><p>These qMRI techniques are feasible, accurate and repeatable, which is promising for treatment response monitoring and/or differentiating between healthy and unhealthy tissues due to radiation-induced damage in HNC patients.</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/S2405631624000782/pdfft?md5=6c7372e0c0e896428978c7b16c9908f3&pid=1-s2.0-S2405631624000782-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594637","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":"Preclinical prototype validation and characterization of a thermobrachytherapy system for interstitial hyperthermia and high-dose-rate brachytherapy","authors":"Ioannis Androulakis ,&nbsp;Rob M.C. Mestrom ,&nbsp;Sergio Curto ,&nbsp;Inger-Karine K. Kolkman-Deurloo ,&nbsp;Gerard C. van Rhoon","doi":"10.1016/j.phro.2024.100606","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100606","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Integrating simultaneous interstitial hyperthermia in high-dose-rate brachytherapy treatments (HDR-BT) is expected to lead to enhanced therapeutic effect. However, there is currently no device available for such an integration. In this study, we presented and validated the thermobrachytherapy (TBT) preclinical prototype system that is able to seamlessly integrate into the HDR-BT workflow.</p></div><div><h3>Materials and methods</h3><p>The TBT system consisted of an advanced radiofrequency power delivery and control system, dual-function interstitial applicators, and integrated connection and impedance matching system. The efficiency and minimum heating ability of the system was calculated performing calorimetric experiments. The effective-heating-length and heating pattern was evaluated using single-applicator split phantom experiments. The heating independence between applicators, the ability of the system to adaptable and predictable temperature steering was evaluated using multi-applicator split phantom experiments.</p></div><div><h3>Results</h3><p>The system satisfied interstitial hyperthermia requirements. It demonstrated 50 % efficiency and ability to reach 6 °C temperature increase in 6 min. Effective-heating-length of the applicator was 43.7 mm, following the initial design. Heating pattern interference between applicators was lower than recommended. The system showed its ability to generate diverse heating patterns by adjusting the phase and amplitude settings of each electrode, aligning well with simulations (minimum agreement of 88 %).</p></div><div><h3>Conclusions</h3><p>The TBT preclinical prototype system complied with IHT requirements, and agreed well with design criteria and simulations, hence performing as expected. The preclinical prototype TBT system can now be scaled to an in-vivo validation prototype, including an adaptable impedance matching solution, appropriate number of channels, and ensuring biocompatibility and regulatory compliance.</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/S2405631624000769/pdfft?md5=068eae7f1186c6f0fa5fe4dffd5533bf&pid=1-s2.0-S2405631624000769-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594638","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 systematic review of 4D magnetic resonance imaging techniques for abdominal radiotherapy treatment planning","authors":"Lamyaa Aljaafari ,&nbsp;David Bird ,&nbsp;David L. Buckley ,&nbsp;Bashar Al-Qaisieh ,&nbsp;Richard Speight","doi":"10.1016/j.phro.2024.100604","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100604","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Four-dimensional magnetic resonance imaging (4DMRI) has gained interest as an alternative to the current standard for motion management four-dimensional tomography (4DCT) in abdominal radiotherapy treatment planning (RTP). This review aims to assess the 4DMRI literature in abdomen, focusing on technical considerations and the validity of using 4DMRI for patients within radiotherapy protocols.</p></div><div><h3>Materials and methods</h3><p>The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search was performed across the Medline, Embase, Scopus, and Web of Science databases, covering all years up to December 31, 2023. The studies were grouped into two categories: 4DMRI reconstructed from 3DMRI acquisition; and 4DMRI reconstructed from multi-slice 2DMRI acquisition.</p></div><div><h3>Results</h3><p>A total of 39 studies met the inclusion criteria and were analysed to provide key findings. Key findings were 4DMRI had the potential to improve abdominal RTP for patients by providing accurate tumour definition and motion assessment compared to 4DCT. 4DMRI reconstructed from 3DMRI acquisition showed promise as a feasible approach for motion management in abdominal RTP regarding spatial resolution. Currently,the slice thickness achieved on 4DMRI reconstructed from multi-slice 2DMRI acquisitions was unsuitable for clinical purposes. Lastly, the current barriers for clinical implementation of 4DMRI were the limited availability of validated commercial solutions and the lack of larger cohort comparative studies to 4DCT for target delineation and plan optimisation.</p></div><div><h3>Conclusion</h3><p>4DMRI showed potential improvements in abdominal RTP, but standards and guidelines for the use of 4DMRI in radiotherapy were required to demonstrate clinical benefits.</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/S2405631624000745/pdfft?md5=b91ec6c4d2ad12fdf4baa92b0aec37c2&pid=1-s2.0-S2405631624000745-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141594636","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":"Clinical application of real-time tumor-tracking for stereotactic volumetric modulated arc therapy for liver tumors","authors":"Naoki Miyamoto ,&nbsp;Norio Katoh ,&nbsp;Takahiro Kanehira ,&nbsp;Kohei Yokokawa ,&nbsp;Ryusuke Suzuki ,&nbsp;Yusuke Uchinami ,&nbsp;Hiroshi Taguchi ,&nbsp;Daisuke Abo ,&nbsp;Hidefumi Aoyama","doi":"10.1016/j.phro.2024.100623","DOIUrl":"10.1016/j.phro.2024.100623","url":null,"abstract":"<div><p>Real-time tumor-tracking volumetric modulated arc therapy (RT-VMAT) enabling beam-gating based on continuous X-ray tracking of the three-dimensional position of internal markers is relevant for moving tumors. Dose-volume characteristics and treatment time were evaluated in ten consecutive patients who underwent liver stereotactic body radiation therapy with RT-VMAT. Target dose conformity and sparing of the stomach and the intestine were improved comparing RT-VMAT with RT-3D conformal radiotherapy. The mean treatment time for each fraction was less than 10 min. RT-VMAT could be effective, especially for targets located adjacent to organs at risk.</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/S2405631624000939/pdfft?md5=cf3707e6bfe09fe028209ce2dd045420&pid=1-s2.0-S2405631624000939-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141953362","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":"Artificial intelligence-generated targets and inter-observer variation in online adaptive radiotherapy of bladder cancer","authors":"Lina M. Åström ,&nbsp;Patrik Sibolt ,&nbsp;Hannah Chamberlin ,&nbsp;Eva Serup-Hansen ,&nbsp;Claus E. Andersen ,&nbsp;Marcel van Herk ,&nbsp;Lene S. Mouritsen ,&nbsp;Marianne C. Aznar ,&nbsp;Claus P. Behrens","doi":"10.1016/j.phro.2024.100640","DOIUrl":"10.1016/j.phro.2024.100640","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Daily target re-delineation in online adaptive radiotherapy (oART) introduces uncertainty. The aim of this study was to evaluate artificial intelligence (AI) generated contours and inter-observer target variation among radiotherapy technicians in cone-beam CT (CBCT) guided oART of bladder cancer.</p></div><div><h3>Materials and methods</h3><p>For each of 10 consecutive patients treated with oART for bladder cancer, one CBCT was randomly selected and retrospectively included. The bladder (CTV-T) was AI-segmented (CTV-T_AI). Seven radiotherapy technicians independently reviewed and edited CTV-T_AI, generating CTV-T_ADP. Contours were benchmarked against a ground truth contour (CTV-T_GT) delineated blindly from scratch. CTV-T_ADP and CTV-T_AI were compared to CTV-T_GT using volume, dice similarity coefficient, and bidirectional local distance. Dose coverage (D_99%&gt;95 %) of CTV-T_GT was evaluated for treatment plans optimized for CTV-T_AI and CTV-T_ADP with clinical margins. Inter-observer variation among CTV-T_ADP was assessed using coefficient of variation and generalized conformity index.</p></div><div><h3>Results</h3><p>CTV-T_GT ranged from 48.7 cm³ to 211.6 cm³. The median [range] volume difference was 4.5 [−17.8, 42.4] cm³ for CTV-T_ADP and −15.5 [−54.2, 4.3] cm³ for CTV-T_AI, compared to CTV-T_GT. Corresponding dice similarity coefficients were 0.87 [0.71, 0.95] and 0.84 [0.64, 0.95]. CTV-T_GT was adequately covered in 68/70 plans optimized on CTV-T_ADP and in 6/10 plans optimized on CTV-T_AI with clinical margins. The median [range] coefficient of variation was 0.08 [0.05, 0.11] and generalized conformity index was 0.78 [0.71, 0.88] among CTV-T_ADP.</p></div><div><h3>Conclusions</h3><p>Target re-delineation in CBCT-guided oART of bladder cancer demonstrated non-isotropic inter-observer variation. Manual adjustment of AI-generated contours was necessary to cover ground truth targets.</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/S2405631624001106/pdfft?md5=65ea09c6823d780c0e3cf276f0b90698&pid=1-s2.0-S2405631624001106-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136831","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}