Journal of Applied Clinical Medical Physics最新文献

{"title":"Stereotactic radiosurgery for multiple small brain metastases using gamma knife versus single-isocenter VMAT: Normal brain dose based on lesion number and size.","authors":"Abram Abdou, Timoteo Almeida, Elizabeth Bossart, Irene Monterroso, Eric A Mellon","doi":"10.1002/acm2.70065","DOIUrl":"https://doi.org/10.1002/acm2.70065","url":null,"abstract":"<p><strong>Purpose: </strong>The study evaluates rapid linear accelerator (Linac) single isocenter stereotactic radiosurgery (SRS) with Hyperarc for large target numbers. We compared to Gamma Knife (GK), which suffers from long treatment times and investigated causes of differences.</p><p><strong>Methods: </strong>Linac SRS and GK treatment plans for patients receiving 18 Gy to the gross tumor volume (GTV) were evaluated for mean brain dose and volume of brain receiving 12 Gy or more (V12 Gy) as toxicity correlates. Further investigations included patient-based and simulations of 1-33 brain metastases to compare the ability of Linac SRS and GK to separate adjacent and distant lesions.</p><p><strong>Results: </strong>For three patients (33, 33, and 18 metastases), GK reduced mean brain dose (2.89 Gy, 2.38 Gy, 2.79 Gy) compared to 2.5 mm microMLCs (4.36 Gy, 4.75 Gy, 4.26 Gy, p = 0.027) and 5 mm MLCs (4.71 Gy, 5.22 Gy, 4.60 Gy, p = 0.024). GK also improved V12 Gy (13.29 cc, 11.62 cc, 33.79 cc) compared to microMLC (25.31 cc, 30.91 cc, 54.71 cc, p = 0.019) and MLC (31.69 cc, 33.68 cc, 54.71 cc). This must be balanced with GK treatment times (5-11 h). GK achieved 50% prescription line separation at smaller distances (1.8-7.6 mm) than microMLC (7.7-10.2 mm) or MLC (8.8-12.2 mm) for 0.5-1.0 cm targets (4-8 mm collimator single shot). For 1.5 cm targets (16 mm shot) results were mixed (GK 5.4-17 mm, microMLC 9.5-11.2 mm, MLC 9.5-11.3 mm). A 7.7 cm simulation cube was then incrementally filled with 0.5 cm or 1.0 cm equidistant targets. GK plan mean brain dose increased 0.04 Gy/target (1.08 Gy mean/27 targets) compared to 0.14 Gy/target for microMLC (3.78 Gy mean/27 targets) for 0.5 cm targets, with differences diminishing for 1.0 cm targets (GK 0.15 Gy/target, microMLC 0.17 Gy/target).</p><p><strong>Conclusions: </strong>For numerous small metastases GK improves dosimetry but has exceedingly long treatment times. GK improves dose separation for adjacent lesions < 1.0 cm and conformity for small (∼0.5 cm) targets. GK and Linac differences are small for individual targets but compound over many targets. V12 Gy limits in the NCIC CE.7 trial protocol mandate dose modifications for Linac SRS but not GK.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e70065"},"PeriodicalIF":2.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143663626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A systematic review of electron FLASH dosimetry and beam control mechanisms utilized with modified non-clinical LINACs","authors":"Justin DeFrancisco,&nbsp;Siyong Kim","doi":"10.1002/acm2.70051","DOIUrl":"10.1002/acm2.70051","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>FLASH has been shown to spare normal tissue toxicity while maintaining tumor control. However, existing irradiation platforms and dosimetry are not compatible. Consequently, an abundance of FLASH delivery devices and new dosimetry across all modalities has been created. Many review articles concluded that dosimetry is modality-dependent. Focusing on electrons, researchers have modified clinical LINACs to enable FLASH dose rates. Modified LINACs caused the development of unique control systems that have yet to be characterized. Improvement could be made when considering the organization of reviews.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To systematically perform a literature survey on electron FLASH dosimetry and beam control mechanisms with modified LINACs, detail where articles originated, and organize the results.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A literature survey was performed from two websites using specified keywords and sifted results to find articles that fit the criteria. The results were organized in tables and summaries effectively by matching up dosimeters with their measurement goal, referring to their specific models, outlining the irradiation conditions they were tested in, and detailing their calibration procedure. Furthermore, included was the unique topic of control mechanisms.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Twenty-eight matches were found. Various dosimeters were examined to measure absorbed dose, beam characteristics (BC), dose per pulse (DPP), and pulse counting (PC). Specific detectors and the irradiation conditions are organized and presented in a table. Each model's pros and cons are presented in another table for further consideration. A third table is provided to detail beam control methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Dosimetry is majorly film-based for absorbed dose and beam characteristic measurements. Many candidates for dosimeters for the use of DPP and PC have been tested, but they have yet to be tested without limitations. Beam control mechanisms primarily consist of unacceptable delivery errors. Many suggestions for improvement were given, mainly consisting of finding new dosimeters and modulating the dose DPP.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143663621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Closing the gap in plan quality: Leveraging deep-learning dose prediction for adaptive radiotherapy","authors":"Sean J. Domal,&nbsp;Austen Maniscalco,&nbsp;Justin Visak,&nbsp;Michael Dohopolski,&nbsp;Dominic Moon,&nbsp;Vladimir Avkshtol,&nbsp;Dan Nguyen,&nbsp;Steve Jiang,&nbsp;David Sher,&nbsp;Mu- Han Lin","doi":"10.1002/acm2.70045","DOIUrl":"10.1002/acm2.70045","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Balancing quality and efficiency has been a challenge for online adaptive therapy. Most systems start the online re-optimization with the original planning goals. While some systems allow planners to modify the planning goals, achieving a high-quality plan within time constraints remains a common barrier. This study aims to bolster plan quality by leveraging a deep-learning dose prediction model to predict new planning goals that account for inter-fractional anatomical changes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Fine-tuned patient-specific (FT-PS) models were clinically evaluated to accurately predict dose for 23 adaptive fractions of 15 head-and-neck (H&amp;N) patients treated with Ethos ART. The original adapted plan from the adaptive treatment session was used as the quality baseline. Based on physician-approved adaptive treatment contours, the FT-PS model predicted subsequent planning goals for high-impact organs at risk (OARs). These goals were retrospectively re-optimized in Ethos to compare the original adapted plan (IOE-Auto Plan) with the newly re-optimized plan (AI-guided IOE Plan). A physician blindly selected the preferred plan.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Dose savings were observed for nine high impact OAR's including the constrictor, ipsilateral/contralateral parotid, ipsilateral/contralateral submandibular gland, oral cavity, and esophagus, mandible and larynx with a maximum value of 5.47 Gy. Of the 23 plans reviewed in the blind observer study, 19 re-optimized plans were chosen over the original adapted session plan.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our preliminary results demonstrate the feasibility of utilizing an AI dose predictor to predict optimal planning goals with anatomical changes, thereby improving adaptive plan quality. This method is feasible for both online and offline adaptive radiotherapy (ART) and has the potential to significantly enhance treatment outcomes for head-and-neck (H&amp;N) cancer patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143663625","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of dosimetric and spatial accuracy of a virtual cone technique for radiosurgery using linac-integrated CBCT-based polymer gel dosimetry.","authors":"Tenzin Kunkyab, Michael Lamey, Andrew Jirasek, Michael Kudla, Nathan Becker, Benjamin Mou, Derek Hyde","doi":"10.1002/acm2.70081","DOIUrl":"https://doi.org/10.1002/acm2.70081","url":null,"abstract":"<p><strong>Purpose: </strong>This study evaluates the dosimetric and geometric precision of a virtual cone technique using CBCT-based polymer gel dosimetry, enabling radiation delivery, and imaging readout within an identical spatial coordinate system.</p><p><strong>Methods: </strong>We created a C# script for a virtual cone technique that generates a treatment plan with 10 gantry arcs at 0°, 36°, 72°, 288°, and 324° couch angles, with 2 arcs per couch angle using 45° and 135° collimator angles. Two verification plans using Eclipse v15.6 (AcurosXB) were created with 20 Gy at the maximum dose for: (1) a cylindrical gel, with an additional calibration region; (2) a 3D printed anthropomorphic skull phantom with a gel insert. The 50% isodose (10 Gy) width through the central axis of the axial and sagittal planes (SPs) were measured for the gel experiment. The distance between the centers-of-masses of the 10 Gy isodose region of the plan and the gel (skull phantom) were calculated for an end-to-end spatial accuracy test.</p><p><strong>Results: </strong>The maximum point dose measured with gel was within 1% of the plan, though the gel measured 50% isodose widths of 5.56 <math> <semantics><mrow><mspace></mspace> <mo>±</mo> <mspace></mspace></mrow> <annotation>$; pm ;$</annotation></semantics> </math> 0.02 mm, 5.65  <math> <semantics><mrow><mo>±</mo> <mspace></mspace></mrow> <annotation>$ pm ;$</annotation></semantics> </math> 0.04 mm, 4.23  <math> <semantics><mrow><mo>±</mo> <mspace></mspace></mrow> <annotation>$ pm ;$</annotation></semantics> </math> 0.01 mm for axial (anterior-posterior), axial (left-right), sagittal (superior-inferior) respectively, which were slightly narrower than Eclipse (1.29 mm maximum difference in the SP due to CBCT slice thickness). The center-of-mass distance was 0.66 mm for the gel experiment, and 0.94 mm for complete end-to-end testing with the anthropomorphic phantom, including CBCT setup (kV-MV isocenter uncertainty).</p><p><strong>Conclusion: </strong>The 50% isodose width of the gel measurement was 5.15 mm (mean), which was tighter than our Eclipse v15.6 beam model. The end-to-end spatial accuracy test, only achievable with gel dosimetry using CBCT readout, resulted in sub-millimeter accuracy. This study demonstrates the value of gel dosimetry in verifying the dosimetric and spatial accuracy of this high precision, stereotactic technique.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e70081"},"PeriodicalIF":2.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A method for measuring spatial resolution based on clinical chest CT sequence images","authors":"Ying Liu,&nbsp;Jingying Shen,&nbsp;Haowei Zhang,&nbsp;Haikuan Liu","doi":"10.1002/acm2.70078","DOIUrl":"10.1002/acm2.70078","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aimed to develop and validate a method for characterizing the spatial resolution of clinical chest computed tomography (CT) sequence images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>An algorithm for characterizing spatial resolution based on clinical chest CT sequence images was developed in Matlab (2021b). The algorithm was validated using CT sequence images from a custom-made chest automatic tube current modulation (ATCM) phantom and clinically reconstructed chest CT sequence images. A region of interest (ROI) was automatically established at the edges of CT image subject to calculate the edge spread function (ESF). The ESF curves from consecutive CT images within the same sequence were fitted into a curve, and the line spread function (LSF) was derived through differentiation. A Fourier transformation of the LSF curve was conducted to obtain the modulation transfer function (MTF). The method's effectiveness was verified by comparing the 50% MTF and 10% MTF values with those calculated using IndoQCT (22a) software. The method was also applied to clinical CT images to calculate MTF values for various reconstructions, confirming its sensitivity by determining spatial resolution of clinically reconstructed images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Validation experiments based on the phantom CT sequence images demonstrated that the MTF values calculated using the proposed method had an average difference of within ± 5% compared to the results obtained with IndoQCT. Validation experiments with clinical CT sequence images indicated that the method effectively reflects differences and variations in spatial resolution of images under different reconstruction kernels, with the MTF values for B10f-B50f and D10f-D50f exhibiting a consistent increase.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>A method for measuring spatial resolution using clinical chest CT sequence images was developed. This method provides a direct means of spatial resolution characterization for clinical CT datasets and a more accurate representation of CT imaging quality, effectively reflects variations across different reconstruction convolution kernels, demonstrating its sensitivity.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143657000","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geometric and dosimetric evaluation of a commercial AI auto-contouring tool on multiple anatomical sites in CT scans.","authors":"Robert N Finnegan, Alexandra Quinn, Patrick Horsley, Joseph Chan, Maegan Stewart, Regina Bromley, Jeremy Booth","doi":"10.1002/acm2.70067","DOIUrl":"https://doi.org/10.1002/acm2.70067","url":null,"abstract":"<p><p>Current radiotherapy practices rely on manual contouring of CT scans, which is time-consuming, prone to variability, and requires highly trained experts. There is a need for more efficient and consistent contouring methods. This study evaluated the performance of the Varian Ethos AI auto-contouring tool to assess its potential integration into clinical workflows. This retrospective study included 223 patients with treatment sites in the pelvis, abdomen, thorax, and head and neck regions. The Ethos AI tool generated auto-contours on each patients' pre-treatment planning CT, and 45 unique structures were included across the study cohort. Multiple measures of geometric similarity were computed, including surface Dice Similarity Coefficient (sDSC) and mean distance to agreement (MDA). Dosimetric concordance was evaluated by comparing mean dose and maximum 2 cm³ dose (D_{2 cc}) between manual and AI contours. Ethos AI demonstrated high geometric accuracy for well-defined structures like the bladder, lungs, and femoral heads. Smaller structures and those with less defined boundaries, such as optic nerves and duodenum, showed lower agreement. Over 70% of auto-contours demonstrated a sDSC > 0.8, and 74% had MDA < 2.5 mm. Geometric accuracy generally correlated with dosimetric concordance, however differences in contour definitions did result in some structures exhibiting dose deviations. The Ethos AI auto-contouring tool offers promising accuracy and reliability for many anatomical structures, supporting its use in planning workflows. Auto-contouring errors, although rare, highlight the importance of ongoing QA and expert manual oversight.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e70067"},"PeriodicalIF":2.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143648641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commissioning evaluation of a deviceless 4DCT scanner","authors":"Hunter Tillery,&nbsp;Cheyann Windsor,&nbsp;Christopher Aguilera","doi":"10.1002/acm2.70048","DOIUrl":"10.1002/acm2.70048","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The utilization of four-dimensional computed tomography (4DCT) for radiation therapy has not seen major advances to the method of data binning since shortly after inception. Recently there is increased interest in the utilization of an alternative binning method rather than more established techniques. At this point routine quality assurance and commissioning of 4DCT have been well studied and established with traditional binning methods. Due to this new “deviceless” technique relying on algorithms instead of an external breathing signal, established dynamic phantoms and equipment typically used in the commissioning and quality assurance workflow have proven to no longer be compatible.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A commercially available phantom was modified to include components that the deviceless 4D algorithm uses for binning. Typical 4DCT commissioning datasets were acquired and reconstructed using both deviceless and device-based binning techniques. Both regular and irregular breathing curves were evaluated for performance, similar to what would be seen with typical radiation therapy patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Deviceless and device-based binning methods performed similarly and well for regular breathing curves. As datasets became more irregular, the deviceless algorithm was better able to reconstruct 4DCTs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Commissioning datasets for both device-based and deviceless 4DCTs were evaluated to test if modifications to a commercially available phantom would allow for an accurate comparison between binning systems. It was shown that not only did these modifications work but also highlighted a difference in the way that these systems binned data, which could be applied to patients with breathing irregularities.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143648722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic image-based treatment planning for online adaptive ultra-hypofractionated prostate cancer radiotherapy with MR-Linac.","authors":"Yuan Xu, Ningning Lu, Qiao Li, Kuo Men, Xinming Zhao, Jianrong Dai","doi":"10.1002/acm2.70075","DOIUrl":"https://doi.org/10.1002/acm2.70075","url":null,"abstract":"<p><strong>Purpose: </strong>A new workflow was investigated for Elekta Unity MR-Linac by removing the computed tomography (CT)-simulation step and using diagnostic CT (DCT) for reference plan generation.</p><p><strong>Materials and methods: </strong>Ten patients with ultra-hypofractionated prostate cancer treated with magnetic resonance imaging (MRI)-guided adaptive radiotherapy were retrospectively enrolled. Targets and organs at risk (OARs) were recontoured on DCT, and Hounsfield unit conversions to relative electron density were calibrated for DCT. Reference plans were reoptimized and recalculated using DCT for Unity. Subsequent adaptive plans were designed through an adapt-to-shape workflow to edit targets and OARs via daily MRI to generate a new treatment plan. Bulk electron density information for Unity adaptive plan was compared between planning CT (PCT) and DCT for volumes of interest. Dosimetric parameters were evaluated between PCT- and DCT-based reference and adaptive plans for target coverage and OAR dose constraints.</p><p><strong>Results: </strong>Bulk relative electron density differences between PCT and DCT were within ±1% for targets and OARs, excepting the rectum. PCT and DCT-based reference plans did not significantly differ in mean target coverages or for OARs in dosimetric difference except for V_36 Gy of the rectum. PCT- and DCT-based adaptive plans did not significantly differ for most dosimetric parameters of targets and OARs except for V_29 Gy and V_36 Gy of the rectum, V_18.1 Gy of the bladder, and D_50% of the urethra.</p><p><strong>Conclusions: </strong>By removing the CT simulation step, it is feasible to use DCT for designing reference and adaptive plans in the Unity ATS workflow. The workflow increased adaptive radiotherapy efficiency and decreased patient waiting time and additional radiation dose.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e70075"},"PeriodicalIF":2.0,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facilitating 1.5T MR-Linac adoption: Workflow strategies and practical tips","authors":"Madeline Michel,&nbsp;Zelda Paquier,&nbsp;Christelle Bouchart,&nbsp;Akos Gulyban,&nbsp;Nicolas Jullian,&nbsp;Dirk Van Gestel,&nbsp;Sara Poeta,&nbsp;Nick Reynaert,&nbsp;Philippe Martinive,&nbsp;Robbe Van Den Begin","doi":"10.1002/acm2.70073","DOIUrl":"10.1002/acm2.70073","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>MR-guided radiotherapy (MRgRT) offers new opportunities but also introduces workflow complexities requiring dedicated optimization. Implementing magnetic resonance linear accelerator (MR-Linac) technology comes with challenges such as prolonged treatment times and workflow integration issues.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>We present here our experience and share practical tips and tricks to streamline MR-Linac implementation, optimize workflow efficiency, and improve coordination.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The first 150 patients treated with a 1.5T MR-Linac Unity^® at our institution were analyzed. Treatments were assessed based on session recordings, difficulties encountered were identified, and solutions documented.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A total of 726 fractions were delivered, with a mean treatment time of 48 minutes. Key optimizations included standardized operating procedures (SOPs) and structured briefing sheets, leading to reduced delays and improved treatment consistency.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Strategic workflow standardization and optimized communication tools significantly improved the ability to deliver high-quality, patient-centered care by streamlining processes and enhancing coordination among team members. These insights provide practical guidance for centers integrating MR-Linac technology.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70073","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143648725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced dose prediction for head and neck cancer artificial intelligence-driven radiotherapy based on transfer learning with limited training data","authors":"Hui-Ju Wang,&nbsp;Austen Maniscalco,&nbsp;David Sher,&nbsp;Mu-Han Lin,&nbsp;Steve Jiang,&nbsp;Dan Nguyen","doi":"10.1002/acm2.70012","DOIUrl":"10.1002/acm2.70012","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Training deep learning dose prediction models for the latest cutting-edge radiotherapy techniques, such as AI-based nodal radiotherapy (AINRT) and Daily Adaptive AI-based nodal radiotherapy (DA-AINRT), is challenging due to limited data. This study aims to investigate the impact of transfer learning on the predictive performance of an existing clinical dose prediction model and its potential to enhance emerging radiotherapy approaches for head and neck cancer patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>We evaluated the impact and benefits of transfer learning by fine-tuning a Hierarchically Densely Connected U-net on both AINRT and DA-AINRT patient datasets, creating Model_AINRT (Study 1) and Model_DA-AINRT (Study 2). These models were compared against pretrained and baseline models trained from scratch. In Study 3, both fine-tuned models were tested using DA-AINRT patients' final adaptive sessions to assess Model_AINRT ’s effectiveness on DA-AINRT patients, given that the primary difference is planning target volume (PTV) sizes between AINRT and DA-AINRT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Result</h3>\u0000 \u0000 <p>Studies 1 and 2 revealed that the transfer learning model accurately predicted the mean dose within 0.71% and 0.86% of the prescription dose on the test data. This outperformed the pretrained and baseline models, which showed PTV mean dose prediction errors of 2.29% and 1.1% in Study 1, and 2.38% and 2.86% in Study 2 (<i>P</i> &lt; 0.05). Additionally, Study 3 demonstrated significant improvements in PTV dose prediction error with Model_DA-AINRT, with a mean dose difference of 0.86% ± 0.73% versus 2.26% ± 1.65% (<i>P</i> &lt; 0.05). This emphasizes the importance of training models for specific patient cohorts to achieve optimal outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Applying transfer learning to dose prediction models significantly improves prediction accuracy for PTV while maintaining similar dose performance in predicting organ-at-risk (OAR) dose compared to pretrained and baseline models. This approach enhances dose prediction models for novel radiotherapy methods with limited training data.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 5","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70012","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143634042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}