Journal of Applied Clinical Medical Physics最新文献

{"title":"Correlation of size specific dose estimates with organ dose in abdominal spectral computed tomography","authors":"Tian Qin, Qingting Sun, Yihan Fan, Mengting Wang, Jing Wang, Yankui Chang, Baohui Liang","doi":"10.1002/acm2.70845","DOIUrl":"10.1002/acm2.70845","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>In abdominal spectral Computed Tomography (CT), organ dose assessment is important for radiation safety but remains challenging due to the complexity of Monte Carlo (MC) simulations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aimed to compare the correlations of three size-specific dose estimate (SSDE) metrics (including the central slice SSDE<sub>center</sub>, the mean of all slices SSDE<sub>mean</sub>, and the organ-specific SSDE<sub>organ</sub>) with organ doses and to establish a rapid estimation method for organ dose using linear regression in abdominal spectral CT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Monte Carlo N-Particle (MCNP6) was used to model the General Electric (GE) Revolution CT scanner, including its bowtie filter, radiation source, and simulations of tube voltage switching and the X-ray tube trajectory. Digital Imaging and Communications in Medicine (DICOM) data from 30 patients undergoing abdominal spectral CT with identical scan parameters were processed using 3D Slicer. Radiation-sensitive organs were delineated. Following this, digital models were constructed and subsequently voxelized. All patient external body contours were delineated using ImageJ software. Three SSDE metrics were calculated for all patients. Linear regression analysis was performed to correlate organ doses with the three SSDE metrics and to derive estimation coefficients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The results indicated that in abdominal spectral CT examinations on a Revolution scanner, SSDE<sub>organ</sub> exhibited the strongest correlation with organ dose. The R<sup>2</sup> values for the liver, stomach, pancreas, spleen, kidneys, spinal cord, and bladder were 0.89, 0.61, 0.81, 0.85, 0.88, 0.94, and 0.68, respectively. The corresponding estimation coefficients derived from SSDE<sub>organ</sub> for these organs were 0.42, 0.36, 0.37, 0.41, 0.36, 0.26, and 0.26, respectively. The estimation errors for the conversion coefficient combined with SSDE<sub>organ</sub>, relative to the MC simulation results, were 7.34% for the liver, 6.09% for the kidneys, 13.25% for the spleen, 2.97% for the spinal cord, 13.61% for the pancreas, 17.64% for the stomach, and 18.56% for the bladder.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>For abdominal spectral CT, SSDE<sub>organ</sub> demonstrated the strongest correlation with individual organ doses. These findings enable rapid and accurat","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13639253/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149109819","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":"Application of synthetic computed tomography based on a deep learning model in pelvic radiotherapy planning: A feasibility study.","authors":"Feng Lin, Jinyan Hu, Zhao Li, Jun Wang, Wenying Zhang, Aiping Wen, Mao Li, Yunfei Hu, Yang Wang","doi":"10.1002/acm2.70829","DOIUrl":"10.1002/acm2.70829","url":null,"abstract":"<p><strong>Background: </strong>While computed tomography (CT) remains an important imaging modality in radiotherapy planning, magnetic resonance imaging (MRI)-only workflow, often realized via the generation of synthetic computed tomography (sCT), is desired due to superior soft-tissue contrast and less susceptibility to registration errors.</p><p><strong>Purpose: </strong>The aim of this study was to investigate the feasibility of applying sCT, generated from MRI using a deep learning model, to pelvic radiotherapy planning. By comparing differences between conventional CT and sCT in terms of image quality, dosimetric parameters, and three-dimensional (3D) dose distributions, the clinical potential of an MRI-only radiotherapy workflow was evaluated.</p><p><strong>Methods: </strong>Fifteen patients who underwent both pelvic CT and MRI examinations were retrospectively enrolled. For the same patients, sCT images were generated from the MRI using a pretrained pelvic deep learning model integrated in the syngo.via VB60A software platform. Treatment plans originally created on planning CT (pCT) images were re-calculated on corresponding sCT datasets. Subsequently, the mean absolute error (MAE) of CT numbers, Dice similarity coefficients (DSCs) for major structures, dose-volume histogram (DVH) parameters for the planning target volume (PTV) and major organs at risk (OARs), and 3D dose distributions were compared between plans calculated on pCT and sCT.</p><p><strong>Results: </strong>In terms of image quality, sCT demonstrated comparable CT number accuracy with pCT for soft tissue structures, with MAEs ranging from 6.8 to 8.5 Hounsfield units (HU) for the PTV, bladder, and rectum. Higher MAEs were observed for bony structures (femoral heads and bone marrow), with average MAEs ranging between 17 and 19 HU. The DSC was 0.94 ± 0.01 for the right femoral head, 0.93 ± 0.01 for the left femoral head, and 0.85 ± 0.03 for bone marrow. Regarding dosimetric comparison, the mean absolute differences in key dosimetric parameters for the PTV (D98%, D95%, and Dmean) were all below 0.6%, remaining within clinically acceptable limits. Among the OARs, except for relatively large differences for the maximum and minimum doses in the bladder, the mean dose (Dmean) differences for the rectum, femoral heads, and bone marrow were minimal (mean absolute value ≤ 0.34%), indicating high reliability. Analysis of 3D dose distributions using a 2% 2 mm γ criterion revealed an average γ passing rate of 95.92% between sCT and pCT, satisfying routine clinical quality assurance requirements.</p><p><strong>Conclusions: </strong>The findings of our study demonstrate the feasibility of applying sCT generated from MRI using a deep learning model to pelvic radiotherapy planning. The MRI-only workflow proposed on this basis eliminates systematic errors introduced by CT-MRI image registration, optimizes the clinical workflow, and fully exploits the superior soft-tissue contrast of MRI to imp","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 10","pages":"e70829"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13619458/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149074726","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":"Automated quality assurance of rigid brain CT/MR image registration using a 3D convolutional neural network.","authors":"Zeinab A Chahrour, Nour F Obeid, Ali El-Zaart, Wassila El Kanawati, Abbas Mkanna, Rida Assaf, Bilal H Shahine","doi":"10.1002/acm2.70828","DOIUrl":"10.1002/acm2.70828","url":null,"abstract":"<p><strong>Background: </strong>Accurate CT/MR registration is important in stereotactic brain radiotherapy, where small spatial errors may affect target localization and treatment planning. In many clinical settings, registration quality is still assessed primarily through manual visual inspection. However, time and workflow constraints may limit the consistency and depth of manual evaluation.</p><p><strong>Purpose: </strong>This study aimed to develop a 3D convolutional neural network (3D CNN)-based quality assurance (QA) method for rigid brain CT/MR registration using paired sub-volumes and a registration-level mean probability score (μ). The proposed method classifies local CT/MR registration quality, while the final registration-level assessment is based on μ across the sampled sub-volumes.</p><p><strong>Methods: </strong>The study included 209 patients. Of these, 202 comprised the primary cohort and were divided patient-wise into 128 training, 33 validation, and 41 test patients. For each patient, 25 skull-based CT/MR sub-volumes of 42 ×  42 ×  42 voxels were used as a two-channel CNN input. High-quality examples were obtained from clinically approved registrations, while low-quality examples were generated using rigid perturbations of the complete MR volume. The mean predicted probability across the 25 sub-volumes was used as the registration-level score μ, and the validation-derived threshold of 0.5871 was fixed for testing. Seven additional patients, separate from the 202-patient cohort, were evaluated after model development and threshold selection were completed using registrations before and after physician correction.</p><p><strong>Results: </strong>On the held-out test set, patch-level accuracy was 85.90%, with an ROC AUC of 0.929 and average precision of 0.920. At the registration level, accuracy was 97.56% and ROC AUC was 0.999. All 41 low-quality registrations were correctly flagged, while 39 of 41 high-quality registrations were correctly accepted. In the additional clinical evaluation, all seven initial registrations requiring correction were classified as low quality, while six of seven corrected registrations were classified as high quality, resulting in an overall accuracy of 92.86%.</p><p><strong>Conclusions: </strong>The proposed method combined local CNN predictions into a registration-level μ score and showed strong performance on held-out and clinical cases. It may provide a quantitative screening measure for rigid brain CT/MR registration QA while maintaining clinical review as the final decision step.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 10","pages":"e70828"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13619455/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149074662","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":"Automatic single-isocenter multiple-target cranial stereotactic treatment plan optimization via planning system scripting.","authors":"Kurtis H Dekker, Jennifer A Everaars","doi":"10.1002/acm2.70832","DOIUrl":"https://doi.org/10.1002/acm2.70832","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Single-isocenter multiple-target stereotactic radiation treatments delivered on C-arm linear accelerators are increasingly common due to practical advantages in both accessibility and speed compared to multiple isocenter treatments. Optimization of high-quality treatment plans can be a time-consuming process requiring substantial manual effort. In our institution, the planning workflow for these treatments was entirely manual and involved the generation of optimization structures and repeated recalculation and input of optimization objectives during the iterative optimization process.  Although commercial automated planning solutions are available, their implementation may impose operational constraints, including reliance on specific immobilization and image/surface guidance hardware, motivating the development of an immobilization-independent automation tool for cranial stereotactic treatment plan optimization.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;To develop and implement a treatment planning optimization tool for multiple-target cranial stereotactic treatments in the Varian Eclipse treatment planning system, to reduce manual planner input and planning time while improving plan quality.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A software tool was written using the Varian Eclipse Scripting Application Programming Interface to automatically generate target-specific ring structures and facilitate the iterative process of plan generation. Twenty cases were retrospectively re-planned with this tool and compared with the corresponding clinical plans to evaluate plan quality using qualitative and quantitative metrics of conformality and complexity. Automated plans were verified using portal dosimetry to ensure clinical deliverability. For ten cases, a timing study was performed to compare optimization time between the software tool and manual re-optimization.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The automatic optimization tool produced plans with similar modulation and complexity, but consistently lower dosimetric falloff metrics (R&lt;sub&gt;50%&lt;/sub&gt; and Paddick Gradient Index) than the corresponding clinical plans. All plans passed patient-specific QA (portal dosimetry) following institutional practice. For the ten cases included in the timing study, the software had a mean runtime of 5.8 min (range: 2-12 min), with variation depending on case characteristics such as number of targets and arcs employed. In comparison, manual plan generation required 21.5 min on average (range: 4-49 min).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Discussion: &lt;/strong&gt;The automated planning tool produced clinically acceptable, deliverable plans with better dose falloff compared to the previous manual planning approach. The tool is estimated to save an average of 15 min of optimization time per plan.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;A treatment planning optimization tool has been developed that provides improvement in both dosimetric plan quality and treatment planning efficiency for s","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 10","pages":"e70832"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626862/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149109903","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":"Correction to \"Retrospective analysis of adaptation frequencies and factors in offline in offline adaptive proton therapy\".","authors":"","doi":"10.1002/acm2.70796","DOIUrl":"10.1002/acm2.70796","url":null,"abstract":"","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 10","pages":"e70796"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13622933/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149080741","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":"Deep learning-based dose prediction for stereotactic prostate cancer radiotherapy with CyberKnife.","authors":"Hilla Magga, Henri Korkalainen, Tuomas Virén, Janne Heikkilä, Jan Seppälä, Akseli Leino","doi":"10.1002/acm2.70784","DOIUrl":"https://doi.org/10.1002/acm2.70784","url":null,"abstract":"<p><strong>Background: </strong>Deep learning-based (DL) approaches have gained interest in predicting dose distributions in radiotherapy of prostate cancer treated with volumetric modulated arc therapy and intensity-modulated radiation therapy. Meanwhile, research on predicting dose distributions in high-precision stereotactic radiotherapy treatments has remained relatively underrepresented.</p><p><strong>Purpose: </strong>We aimed to expand the previous studies by developing a DL-based framework for predicting dose distributions for robotic, stereotactic prostate cancer radiotherapy.</p><p><strong>Methods: </strong>We harnessed a U-Net-based convolutional neural network for predicting clinically achievable dose distributions based on CT images, delineated structures, and distance information from the planning target volume. A dataset of 462 patients treated with CyberKnife (Accuray Inc.) utilizing an Iris collimator was divided into training (70%, n = 323), validation (10%, n = 46), and test (20%, n = 93) sets.</p><p><strong>Results: </strong>In the independent test set, the mean absolute error between the mean doses of predictions and clinical plans was 0.63 Gy for the rectum and 1.04 Gy for the bladder.</p><p><strong>Conclusions: </strong>The proposed U-Net-based model demonstrated the ability to learn and reproduce characteristic dose distributions in CyberKnife prostate cancer radiotherapy. The model may provide patient-specific dose estimates for setting initial planning objectives to assist in automating treatment planning and improving inter-planner consistency.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 10","pages":"e70784"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626863/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149109830","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":"A technology-specific risk analysis of surface guided radiation therapy.","authors":"Victoria Bry, Angelia Tran, Tamara Egan, Eric Ford, Juergen Meyer","doi":"10.1002/acm2.70837","DOIUrl":"https://doi.org/10.1002/acm2.70837","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Surface Guided Radiation Therapy (SGRT) has become an increasingly important tool that complements x-ray imaging to improve patient safety for setup, respiratory tracking, and motion monitoring. As SGRT is integrated into clinical workflows, its complexity can introduce potential risk of process-related errors. These risks may depend on workflow design, system integration, equipment configuration, and technology specific features. As SGRT continues to expand across a wider range of treatment sites and clinical applications, comprehensive evaluation of institutional workflows is recommended to support safe and effective implementation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;This study aimed to identify and evaluate safety risks associated with the clinical use of SGRT using Failure Mode and Effects Analysis (FMEA). Emphasis was placed on workflow processes, system integration, and equipment-specific characteristics within a clinical environment to inform risk mitigation strategies and support safe implementation.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;A multidisciplinary team performed an FMEA of SGRT related procedures and workflows. A process map was developed to define the scope of clinical applications, including tattoo free setup, free-breathing and deep-inspiration breath-hold (DIBH) breast treatments, prone patient positioning and real-time motion monitoring for stereotactic body radiation therapy (SBRT) patients across body sites. For each process step, team members identified potential failure modes associated with the clinical workflow and environment, including the integration of a C-RAD SGRT system with Elekta linear accelerators. Scoring was performed according to AAPM TG-100 guidelines, using severity (S), occurrence (O), and detectability (D) to calculate the Risk Priority Number (RPN). Failure modes were then ranked by RPN, and those with scores greater than or equal to 100 were selected for further analysis and development of mitigation strategies.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Thirty-eight failure modes were identified, with nine having RPN scores greater than or equal to 100 (S = 5-8, O = 2-5, and D = 5-9). High risk failure modes were most associated with the DIBH workflow, particularly respiratory trace acquisition, respiratory trace configuration, and x-ray image verification. Additional high risk failure modes were identified in treatment preparation and system quality assurance processes, including manual data import, template selection, calibration, and daily QA procedures. These risks were primarily associated with workflow-dependent processes involving user interaction and coordination between integrated clinical systems.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This FMEA identified workflow, system integration and equipment related vulnerabilities associated with SGRT implementation. The findings emphasize the importance of risk assessments tailored to specific clinical workflows and treatment environment","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 10","pages":"e70837"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13626861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149110093","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":"The future of brachytherapy physics: Innovation, quality, and the next generation of clinical practice.","authors":"Nataliya Kovalchuk, Åsa Carlsson Tedgren, Frank-André Siebert, Mark J Rivard, Alexandra Rink, Yi Rong","doi":"10.1002/acm2.70834","DOIUrl":"10.1002/acm2.70834","url":null,"abstract":"","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 10","pages":"e70834"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13619461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149074693","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":"Can physics review catch prescription and PTV margin errors in fully automated radiotherapy plans? A hazard study.","authors":"Laurence E Court, Raphael Douglas, Parmeswaran Diagaradjane, Vindu W Kathriarachchi, Shiquin S, Ergys Subashi, Yao Zhao, Lifei Zhang, Tucker Netherton","doi":"10.1002/acm2.70759","DOIUrl":"10.1002/acm2.70759","url":null,"abstract":"<p><strong>Purpose: </strong>End-to-end AI-based automated contouring and radiotherapy planning systems promise substantial gains in efficiency and consistency but also introduce new safety risks, including user input errors that occur early in the workflow. This study evaluates the ability of standard physics plan review to detect user errors in prescription and planning target volume (PTV) margin entry in a fully automated planning workflow.</p><p><strong>Methods: </strong>A prototype of the Radiation Planning Assistant (RPA) was used to generate fully automated prostate cancer treatment plans for 20 patients. Five cases intentionally included incorrect prescriptions or non-standard PTV margins. Five clinical medical physicists independently performed physics plan reviews in a commercial treatment planning system, assuming a standard clinical workflow and without access to user guides or service request forms. Reviews were conducted in two phases, with and without margin information embedded in structure names. A third phase evaluated whether adding reference expansion structures improved margin error detection.</p><p><strong>Results: </strong>Incorrect prescriptions were detected in 80% of cases, with no false-positive identification of correct prescriptions. In contrast, detection of incorrect PTV margins was highly inconsistent. Two physicists did not evaluate margins and missed all margin errors. Three physicists identified all intentional margin errors but demonstrated poor specificity, frequently flagging correct cases as incorrect (specificity range: 13%-63%). Including margin information in structure names and adding reference expansion structures did not improve specificity or overall detection performance.</p><p><strong>Conclusions: </strong>Physics plan review was unreliable for detecting incorrect PTV margins in a fully automated planning workflow, even when additional cues were provided. Although based on a limited dataset, these findings indicate that traditional plan review processes alone are insufficient to mitigate this failure mode. Alternative risk-reduction strategies-such as timely workflow-embedded alerts, targeted checklists, automated verification tools, and careful expectation setting-are likely required to improve safety in AI-driven radiotherapy planning systems.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 10","pages":"e70759"},"PeriodicalIF":2.5,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13619456/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149074700","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":"SRS/SBRT commissioning process with a hybrid beam data model in new preconfigured CyberKnife system","authors":"Mikoto Tamura,&nbsp;Yasumasa Nishimura,&nbsp;Tomohiro Matsuura,&nbsp;Yuta Inosaka,&nbsp;Yoshiyuki Sadahira,&nbsp;Ayano Akamine,&nbsp;Kyomi Inoue,&nbsp;Seiki Nishino,&nbsp;Hirokazu Mizuno,&nbsp;Iori Sumida,&nbsp;John M. Noll","doi":"10.1002/acm2.70822","DOIUrl":"https://doi.org/10.1002/acm2.70822","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A new preconfigured CyberKnife system was introduced to improve the SRS/SBRT commissioning efficiency. The vendor provides a reference beam data set, termed “beam data model (BDM)”, and recommends its validation and combination with measurement data to develop a hybrid beam data model (h-BDM). However, the validation method by the user and the clinical acceptability with the h-BDM has not been established.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study reports the first clinical validation of the BDM and aims to demonstrate the SRS/SBRT commissioning process with the h-BDM is clinically acceptable.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The BDM was validated by comparing with the measurements of tissue phantom ratios (TPRs), off-center ratios (OCRs), and output factors (OFs) for fixed and Iris collimators and MLC in CyberKnife S7 system. Dose difference (DD) and distance-to-agreement (DTA) between BDM and measurement data were assessed against predefined thresholds: DDs of 1.0% for TPRs; DDs of 1.0% at d&lt;sub&gt;15mm&lt;/sub&gt; and d&lt;sub&gt;100mm&lt;/sub&gt; and 1.5% at d&lt;sub&gt;300mm&lt;/sub&gt; for central regions in OCRs; and DTAs of 0.3–1.0 mm for penumbra regions. The h-BDM was constructed by supplementing the BDM with the measurement beam data for all OFs, and for the TPRs and OCRs of the three smallest field sizes with each collimator. Additional replacements were also made when discrepancies exceeded the proposed thresholds based on clinical usability considerations. The conventional measurement beam data model (m-BDM) was also developed with only measurement data. Single-beam validation was performed in homogeneous water-equivalent phantom and heterogeneous phantoms, including lung-equivalent slabs, by comparing between the measured and calculated point doses with h-BDM and m-BDM at multiple depths. Finally, patient-specific quality assurance (PSQA) was performed for SRS/SBRT plans. In phantom case, SRS/SBRT plans were created for a single spherical target on the CT images of the StereoPHAN phantom and anthropomorphic lung phantom for brain and lung sites, respectively. For the SBRT plans of spine and prostate sites, the C-shape and test-prostate structure sets, provided by the AAPM TG 119, were employed. In clinical case, six SRS/SBRT plans for each patients’ CT images in brain, lung, spine, and prostate sites, were employed and the PSQA plans were generated using the CT images of the StereoPHAN phantom, anthropomorphic lung phantom, and the I'mRT phantom. The point dose and γ pass rate were evaluated using micro ionization chamber and radiochromic film, respectively.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 ","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 10","pages":""},"PeriodicalIF":2.5,"publicationDate":"2026-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70822","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149048431","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}