Journal of Applied Clinical Medical Physics最新文献

{"title":"Commissioning and implementation of a pencil-beam algorithm with a Lorentz correction as a secondary dose calculation algorithm for an Elekta Unity 1.5T MR linear accelerator.","authors":"Sameer Taneja, Hesheng Wang, David L Barbee, Paulina Galavis, Mario Serrano Sosa, David Byun, Michael Zelefsky, Ting Chen","doi":"10.1002/acm2.14590","DOIUrl":"https://doi.org/10.1002/acm2.14590","url":null,"abstract":"<p><strong>Purpose: </strong>To commission a beam model in ClearCalc (Radformation Inc.) for use as a secondary dose calculation algorithm and to implement its use into an adaptive workflow for an MR-linear accelerator.</p><p><strong>Methods: </strong>A beam model was developed using commissioning data for an Elekta Unity MR-linear accelerator and entered into ClearCalc. The beam model consisted of absolute dose calculation settings, output factors, percent depth-dose (PDD) curves, mutli-leaf collimator (MLC) transmission and dose leaf gap error, and cryostat corrections. Beam profiles were hard-coded by the manufacturer into the beam model and were compared with Monaco-derived profiles. The beam model was tested by comparing point doses in a homogenous phantom obtained through measurements using an ionization chamber in water, Monaco, and ClearCalc for various field sizes, source-surface distances (SSDs), and point locations. Additional testing including point dose verification for test plans using a heterogeneous phantom and patient plans. Post clinical implementation, performance of ClearCalc was evaluated for the first 41 patients treated, which included 215 adaptive plans.</p><p><strong>Results: </strong>PDDs generated using ClearCalc fell within 1.2% of measurements. Field profile comparison between ClearCalc and Monaco showed an average pass rate of 98% using a 3%/3 mm gamma criteria. Measured cryostat corrections used in the beam model showed a maximum deviation from unity of 1.4%. Point dose and field monitor units (MUs) comparisons in a homogenous phantom (N = 22), heterogeneous phantoms (N = 22), and patient plans (N = 57) all passed with a threshold of 5%/5MU. Clinically, ClearCalc was implemented as a physics check post adaptive planning completed prior to beam delivery. Point dose and field MUs showed good agreement at a 5%/5MU threshold for prostate stereotactic body radiation therapy (SBRT), pelvic lymph nodes, rectum, and prostate and lymph node plans.</p><p><strong>Discussion: </strong>This work demonstrated commissioning and clinical implementation of ClearCalc into an adaptive planning workflow. No primary or adaptive plan failures were reported with proper beam model testing.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14590"},"PeriodicalIF":2.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769256","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":"Verification of daily dose recalculation accuracy for an adaptive radiation therapy monitoring tool in helical tomotherapy for nasopharyngeal carcinoma.","authors":"Yawitta Maneepan, Anirut Watcharawipha, Imjai Chitapanarux, Somsak Wanwilairat, Wannapha Nobnop","doi":"10.1002/acm2.14601","DOIUrl":"https://doi.org/10.1002/acm2.14601","url":null,"abstract":"<p><strong>Purpose: </strong>PreciseART, an adaptive radiation therapy (ART) software for helical tomotherapy (HT), was integrated into the Precision treatment planning system (Accuray, Inc., Sunnyvale, CA). It supports automatic monitoring of dose variations to both the target and organs at risk (OARs) throughout the treatment course. This study aims to verify the accuracy of PreciseART's automatic dose recalculation and assess the effectiveness of its notification function.</p><p><strong>Methods: </strong>The Radixact X9's kVCT image-guided system (ClearRT) was used to acquire daily images for dose recalculations. We assessed the accuracy of PreciseART's automatic dose recalculation by comparing it with the treatment planning system (TPS) recalculation for the PTV70, spinal cord, and bilateral parotid glands. We also evaluated the efficacy of its notification function by comparing each dose metric for each notification color to TPS recalculation, assessing its role as a trigger tool.</p><p><strong>Results: </strong>In the phantom study, dosimetric analysis indicated no statistically significant differences between TPS and PreciseART recalculations (p > 0.05), with dose variations under 1.5%. Similarly, in the patient study (n = 10), no significant dosimetric discrepancies were found (p > 0.05), with a maximum variation of 2.3%. The notification system performed effectively, providing accurate notifications in accordance with predefined dose criteria.</p><p><strong>Conclusions: </strong>PreciseART's daily dose recalculation demonstrated good agreement with TPS recalculation, and its notification function is effective for identifying dose threshold compliance, supporting its use in clinical ART.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14601"},"PeriodicalIF":2.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769184","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":"Low dose threshold for measuring cardiac functional metrics using four-dimensional CT with deep learning.","authors":"Pengwei Wu, Kyle Kim, Lauren Severance, Elliot McVeigh, Jed Douglas Pack","doi":"10.1002/acm2.14593","DOIUrl":"https://doi.org/10.1002/acm2.14593","url":null,"abstract":"<p><strong>Background: </strong>Four-dimensional CT is increasingly used for functional cardiac imaging, including prognosis for conditions such as heart failure and post myocardial infarction. However, radiation dose from an acquisition spanning the full cardiac cycle remains a concern. This work investigates the possibility of dose reduction in 4DCT using deep learning (DL)-based segmentation techniques as an objective observer.</p><p><strong>Methods: </strong>A 3D residual U-Net was developed for segmentation of left ventricle (LV) myocardium and blood pool. Two networks were trained: Standard DL (trained with only standard-dose [SD] data) and Noise-Robust DL (additionally trained with low-dose data). The primary goal of the proposed DL methods is to serve as an unbiased and consistent observer for functional analysis performance. Functional cardiac metrics including ejection fraction (EF), global longitudinal strain (GLS), circumferential strain (CS), and wall thickness (WT), were measured for an external test set of 250 Cardiac CT volumes reconstructed at five different dose levels.</p><p><strong>Results: </strong>Functional metrics obtained from DL segmentations of standard dose images matched well with those from expert manual analysis. Utilizing Standard-DL, absolute difference between DL-derived metrics obtained with standard dose data and 100 mA (corresponding to ∼76 ± 13% dose reduction) data was less than 0.8 ± 1.0% for EF, GLS, and CS, and 5.6 ± 6.7% for Average WT. Performance variation of Noise-Robust DL remained acceptable at even 50 mA.</p><p><strong>Conclusion: </strong>We demonstrate that on average radiation dose can be reduced by a factor of 5 while introducing minimal changes to global functional metrics (especially EF, GLS, and CS). The robustness to reduced image quality can be further boosted by using emulated low-dose data in the DL training set.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14593"},"PeriodicalIF":2.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142768987","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":"Assessing population-based to personalized planning strategies for head and neck adaptive radiotherapy.","authors":"Justin Visak, Chien-Yi Liao, Xinran Zhong, Biling Wang, Sean Domal, Hui-Ju Wang, Austen Maniscalco, Arnold Pompos, Dan Nyguen, David Parsons, Andrew Godley, Weiguo Lu, Steve Jiang, Dominic Moon, David Sher, Mu-Han Lin","doi":"10.1002/acm2.14576","DOIUrl":"https://doi.org/10.1002/acm2.14576","url":null,"abstract":"<p><strong>Purpose: </strong>Optimal head-and-neck cancer (HNC) treatment planning requires accurate and feasible planning goals to meet dosimetric constraints and generate robust online adaptive treatment plans. A new x-ray-based adaptive radiotherapy (ART) treatment planning system (TPS) version 2.0 emulator includes novel methods to drive the planning process including the revised intelligent optimization engine algorithm (IOE2). HNC is among the most challenging and complex sites and heavily depends on planner skill and experience to successfully generate a reference plan. Therefore, we evaluate the new TPS performance via conventionally accepted planning strategies with/without artificial intelligence (AI) and knowledge-based planning (KBP).</p><p><strong>Methods: </strong>Our institution has a pre-clinical release of the Varian Ethos2.0 TPS emulator which includes several changes that may affect current planning strategies. Twenty definitive and post-operative HNC patients were retrospectively selected with a two or three-level simultaneous integrated boost (SIB) dosing scheme. Patients were replanned in the emulator using population-based, KBP-guided with/without human intervention and AI-guided planning goals. These planning strategies were compared both dosimetrically and for plan deliverability.</p><p><strong>Results: </strong>All strategies generally demonstrated acceptable plan quality with KBP- and AI-guided goals offering enhanced dosimetric sparing in organs-at-risk (OAR). The average contralateral parotid gland mean dose was 20.0 ± 6.1 Gy (p < 0.001) for population-based and 15.0 ± 6.1 Gy (p = n.s.) for KBP-with human intervention versus 15.1 ± 7.4 Gy for clinical plans. Target coverage, minimum dose, and plan hotspot were acceptable in all cases. KBP-enabled strategy demonstrated higher modulation and faster optimization time than both population-based and AI-guided strategies.</p><p><strong>Conclusion: </strong>Simply entering population, automatic KBP-enabled or AI-generated planning goals into the new Ethos2.0 TPS produced dosimetrically compliant plans, with AI-guided goals demonstrating the most OAR sparing. Several of these approaches are easy to translate to other treatment sites and will help lower the barrier to entry for x-ray-based online-ART.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14576"},"PeriodicalIF":2.0,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769254","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":"Future clinical medical physics division should have fewer medical physicists and more medical physics assistants.","authors":"Minsun Kim, Hyejoo Kang, Yi Rong","doi":"10.1002/acm2.14592","DOIUrl":"https://doi.org/10.1002/acm2.14592","url":null,"abstract":"","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14592"},"PeriodicalIF":2.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142768951","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":"AAPM MPLA case: Betrayal versus disillusionment.","authors":"William N Duggar, Dongxu Wang, Irina Vergalasova, Cassandra Stambaugh, Leonard Kim","doi":"10.1002/acm2.14554","DOIUrl":"https://doi.org/10.1002/acm2.14554","url":null,"abstract":"<p><p>This work of fiction depicts a scenario in which a faculty member felt they were criticized unfairly and inappropriately for honesty on a faculty survey, which reflected poorly on administration. The faculty member was left struggling with how to respond to conflicting feelings and perception of misaligned goals and mission. Simultaneously, the department chair felt they were blindsided by issues that could have been addressed without the embarrassment of a poor survey. The intended use of this case, through group discussion, self-study, or role-play, is to encourage readers to discuss the situation at hand, inspire professionalism and leadership thinking, and allow the practice of conflict management. Facilitator's notes are available upon request to the MPLA Cases Subcommittee. This case study falls under the scope of and is supported by the Medical Physics Leadership Academy (MPLA), a committee in the American Association of Physicists in Medicine (AAPM).</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14554"},"PeriodicalIF":2.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769251","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":"Five-year evaluation of linear accelerator-based SRS platform isocentricity.","authors":"Yohan A Walter, Anne N Hubbard, Phillip F Durham, Hsinshun T Wu","doi":"10.1002/acm2.14597","DOIUrl":"https://doi.org/10.1002/acm2.14597","url":null,"abstract":"<p><p>Linear accelerator (LINAC)-based stereotactic radiosurgery (SRS) has become a mainstay in the management of intracranial tumors. However, the high fractional doses and sharp gradients used in SRS place heavy demands on geometric accuracy. Image guidance systems such as ExacTrac (ETX, Brainlab AG, Munich, Germany) have been developed to facilitate position verification at nonzero table angles. Though convenient, potential loss of mechanical rigidity between the imaging and treatment systems can be cause for concern, as the ETX system is not mounted to the rotating gantry. In this retrospective study, we analyzed 518 Winston-Lutz (WL) tests performed in the last 5 years with ETX alignment on our Elekta Versa HD (Elekta AB, Stockholm, Sweden) linear accelerator to determine the achievable limits of precision and stability over time for our LINAC-based SRS platform. Results demonstrated remarkable stability over time. 3D and directional misalignments never exceeded 1.0 mm over the study period; however, table rotation was shown to be the most significant source of positional uncertainty. Gantry sag, as measured by gun-to-target misalignments at the gantry-0 and gantry-180-degree positions, was consistent, measuring 1.23 ± 0.18 mm over the study period. Measured accuracy was well within acceptable tolerances for cranial SRS treatment delivery. Notably, the use of the ETX system for intrafraction repositioning effectively eliminates couch walkout, the most significant source of uncertainty identified in this study. Our results thus corroborate safe SRS treatment delivery on our Versa HD with ExacTrac image guidance.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14597"},"PeriodicalIF":2.0,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142768901","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":"Evaluating the dosimetric impact of deep-learning-based auto-segmentation in prostate cancer radiotherapy: Insights into real-world clinical implementation and inter-observer variability.","authors":"Najmeh Arjmandi, Mohammad Amin Mosleh-Shirazi, Shokoufeh Mohebbi, Shahrokh Nasseri, Alireza Mehdizadeh, Zohreh Pishevar, Sare Hosseini, Amin Amiri Tehranizadeh, Mehdi Momennezhad","doi":"10.1002/acm2.14569","DOIUrl":"https://doi.org/10.1002/acm2.14569","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to investigate the dosimetric impact of deep-learning-based auto-contouring for clinical target volume (CTV) and organs at risk (OARs) delineation in prostate cancer radiotherapy planning. Additionally, we compared the geometric accuracy of auto-contouring system to the variability observed between human experts.</p><p><strong>Methods: </strong>We evaluated 28 planning CT volumes, each with three contour sets: reference original contours (OC), auto-segmented contours (AC), and expert-defined manual contours (EC). We generated 3D-CRT and intensity-modulated radiation therapy (IMRT) plans for each contour set and compared their dosimetric characteristics using dose-volume histograms (DVHs), homogeneity index (HI), conformity index (CI), and gamma pass rate (3%/3 mm).</p><p><strong>Results: </strong>The geometric differences between automated contours and both their original manual reference contours and a second set of manually generated contours are smaller than the differences between two manually contoured sets for bladder, right femoral head (RFH), and left femoral head (LFH) structures. Furthermore, dose distribution accuracy using planning target volumes (PTVs) derived from automatically contoured CTVs and auto-contoured OARs demonstrated consistency with plans based on reference contours across all evaluated cases for both 3D-CRT and IMRT plans. For example, in IMRT plans, the average D₉₅ for PTVs was 77.71 ± 0.53 Gy for EC plans, 77.58 ± 0.69 Gy for OC plans, and 77.62 ± 0.38 Gy for AC plans. Automated contouring significantly reduced contouring time, averaging 0.53 ± 0.08 min compared to 24.9 ± 4.5 min for manual delineation.</p><p><strong>Conclusion: </strong>Our automated contouring system can reduce inter-expert variability and achieve dosimetric accuracy comparable to gold standard reference contours, highlighting its potential for streamlining clinical workflows. The quantitative analysis revealed no consistent trend of increasing or decreasing PTVs derived from automatically contoured CTVs and OAR doses due to automated contours, indicating minimal impact on treatment outcomes. These findings support the clinical feasibility of utilizing our deep-learning-based auto-contouring model for prostate cancer radiotherapy planning.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14569"},"PeriodicalIF":2.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142768128","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":"LightAWNet: Lightweight adaptive weighting network based on dynamic convolutions for medical image segmentation.","authors":"Xiaoyan Wang, Jianhao Yu, Bangze Zhang, Xiaojie Huang, Xiaoting Shen, Ming Xia","doi":"10.1002/acm2.14584","DOIUrl":"https://doi.org/10.1002/acm2.14584","url":null,"abstract":"<p><strong>Purpose: </strong>The complexity of convolutional neural networks (CNNs) can lead to improved segmentation accuracy in medical image analysis but also results in increased network complexity and training challenges, especially under resource limitations. Conversely, lightweight models offer efficiency but often sacrifice accuracy. This paper addresses the challenge of balancing efficiency and accuracy by proposing LightAWNet, a lightweight adaptive weighting neural network for medical image segmentation.</p><p><strong>Methods: </strong>We designed LightAWNet with an efficient inverted bottleneck encoder block optimized by spatial attention. A two-branch strategy is employed to separately extract detailed and spatial features for fusion, enhancing the reusability of model feature maps. Additionally, a lightweight optimized up-sampling operation replaces traditional transposed convolution, and channel attention is utilized in the decoder to produce more accurate outputs efficiently.</p><p><strong>Results: </strong>Experimental results on the LiTS2017, MM-WHS, ISIC2018, and Kvasir-SEG datasets demonstrate that LightAWNet achieves state-of-the-art performance with only 2.83 million parameters. Our model significantly outperforms existing methods in terms of segmentation accuracy, highlighting its effectiveness in maintaining high performance with reduced complexity.</p><p><strong>Conclusions: </strong>LightAWNet successfully balances efficiency and accuracy in medical image segmentation. The innovative use of spatial attention, dual-branch feature extraction, and optimized up-sampling operations contribute to its superior performance. These findings offer valuable insights for the development of resource-efficient yet highly accurate segmentation models in medical imaging. The code will be made available at https://github.com/zjmiaprojects/lightawnet upon acceptance for publication.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14584"},"PeriodicalIF":2.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142768954","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":"Quality and mechanical efficiency of automated knowledge-based planning for volumetric-modulated arc therapy in head and neck cancer.","authors":"Sangutid Thongsawad, Sasikarn Chamchod, Kornkanok Chawengsaksopak, Wilai Masanga, Aphisara Deeharing, Sarinya Bawornpatarapakorn, Thitiwan Prachanukul, Chirapha Tannanonta, Nuntawat Udee","doi":"10.1002/acm2.14588","DOIUrl":"https://doi.org/10.1002/acm2.14588","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to examine the effectiveness of the automated RapidPlan in assessing plan quality and to explore how beam complexity affects the mechanical performance of volumetric modulated arc therapy for head and neck cancers.</p><p><strong>Materials and methods: </strong>The plans were first generated using automated RapidPlan with scripting application programming interface (API) and then further refined through manual optimization (RP+MP) to improve plan quality. The quality of 20 plans was assessed, taking into account dose statistics and clinical plan acceptability. The impact of beam complexity on mechanical performance was analyzed using parameters such as leaf speed (LS), leaf acceleration (LA), mean-field area (MFA), cross-axis score (CAS), closed leaf score (CLS), small aperture score (SAS), and monitor units per control point (MU/CP). Patient-specific quality assurance (PSQA) was conducted to determine differences between the RP+MP and original plans.</p><p><strong>Results: </strong>No differences in the heterogeneity index and conformity number were observed between the RP+MP and original plans. The RP+MP plan was superior to the original plan for sparing the left cochlea, left and right internal auditory canals, chiasm, and left optic nerve. Significant differences (p < 0.05) were identified in CAS, SAS₁ _mm, SAS₂ _mm, and SAS_10mm. However, there was no significant difference in PSQA between the RP+MP and original plans. The RP+MP plan without any modifications was clinically acceptable in 45% of cases.</p><p><strong>Conclusion: </strong>The automated RP with scripting API followed by MP (RP+MP) yielded a high-quality plan in terms of dose statistics and clinical acceptability. The RP+MP plan yielded a higher CAS and SAS compared with the original plan. Nevertheless, there was no significant difference in PSQA between the RP+MP and original plans.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14588"},"PeriodicalIF":2.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142768989","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}