Journal of Applied Clinical Medical Physics最新文献

{"title":"The complexity of safety: Embracing systems engineering in radiation oncology","authors":"Lawrence M. Wong, Todd Pawlicki","doi":"10.1002/acm2.14622","DOIUrl":"10.1002/acm2.14622","url":null,"abstract":"<p>In the past 30 years, radiation oncology has posted remarkable milestones such as the introduction of intensity modulated treatments, image- and surface-guided localization, FLASH, and online plan adaptation based on the anatomy of the day. Indeed, the complexity of clinical practice is increasing exponentially, and with increasing complexity comes increasing risk. How do we best grapple with these changes while continuing to focus on patient safety improvement in radiation oncology?</p><p>AAPM TG-100 was published about 8 years ago to help make radiation oncology safer and more efficient.<span><sup>1</sup></span> Despite those laudable intentions, AAPM TG-100′s emphasis on risk-based analysis techniques is not sufficient to address the impact of complexity on safety in radiation oncology. The fact that many errors are caused by workflow and process deviations, rather than device or software failures, highlights the limitations of an approach that has a large focus on estimating failure probabilities. The complexity of radiation oncology, which includes the variability of human behavior, requires a more nuanced and multifaceted approach to ensuring patient safety. Systems engineering (or systems thinking) can help answer this and many other questions critical to understanding of how to deal with the complex system that is radiation oncology.</p><p>Recognizing that a system is complex is an important first step in expanding our safety toolbox beyond probability-based risk analysis techniques to understanding safety. Complex systems involve a high degree of interconnectedness, interdependence, and non-linearity among system components.<span><sup>2, 3</sup></span> What occurs in one step of the radiation oncology process of care can have disastrous consequences in another step even though neither step has failed. For example, consider a prescription for palliative treatment to the spine (T11 – L1) using 6 MV and of total dose of 3000 cGy at 300 cG/fx to the isocenter using an isocentric setup and the beam was planned (and delivered) to enter the patient from the anterior with the isocenter located in the vertebral body. In this example, no step in the process has explicitly failed but this is clearly an error. It is the interconnectedness and interdependence of the steps that contributed to the error.</p><p>In complex systems, surprising phenomena can arise that cannot be predicted by examining individual parts in isolation, through a concept known as emergence. Emergence is a foundational concept of systems thinking. It is the idea that some system properties only exist when the whole system is considered but not at the level of its individual components.<span><sup>3</sup></span> Emergent properties come about through the interactions between system components. An example is that treatment effectiveness can only be assessed when the entire radiation oncology system is considered but not at the level of the subsystems such as treatment planning o","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143364627","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 implementation of knowledge-based planning with partial OAR contours for prostate radiotherapy","authors":"Ositomiwa O. Osipitan,&nbsp;David Wiant,&nbsp;Han Liu","doi":"10.1002/acm2.70004","DOIUrl":"10.1002/acm2.70004","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Intra- and inter-observer contour uncertainty is a continuous challenge in treatment planning for radiotherapy. Our proposed solution to address this challenge is the use of partial contours for treatment planning, focusing on uninvolved or non-overlapping portions of the organs-at-risk (OARs) with the planning target volume (PTV).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The partial contours systematically eliminate overlapping regions. The partial contours were evaluated against fully contoured OARs. We incorporated advanced tools like knowledge-based planning (KBP) to create treatment plans and artificial intelligence (AI) to create auto-segmented contours. We developed two models, Rapid Plan (RP) and Rapid Plan partial uninvolved (RP_Part_Un), using 70 previous clinically approved volumetric arc therapy (VMAT) plans each prescribed with 70 Gy/28 fractions. From these models, we created three plans, RP, RP_Part_Un, and MIM AI_Part_Un. In this retrospective study, 60 prostate patients were analyzed using the three plans. For determining OAR sparing, <i>D</i>_max and <i>D</i>_mean along the percent volume receiving a dose over a range (V₁₀ Gy V₇₀ Gy) between each plan were compared. Geometric evaluations, dice similarity coefficient (DSC), and overlay index (OI) between the OAR contours from partial-contoured manual structure sets and partial-contoured AI structure sets were analyzed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>When comparing the <i>DSC</i> and <i>OI</i> for full contours to the partial contours, in both groups, all comparisons were significantly increased for both organs. This indicated the partial contours had a higher degree of concordance. In patients with SpaceOAR, RP_Part_Un plans exhibited significantly reduced bladder <i>D</i>_max and <i>D</i>_mean compared to RP plans, while rectum <i>D</i>_max and D_mean showed no significant differences. For patients without SpaceOAR, RP_Part_Un significantly lowered rectum <i>D</i>_mean. MIM AI_Part_Un plans demonstrated lower rectum <i>D</i>_max in both patient groups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Partial contours, defined at a specified distance from the PTV, yielded dosimetry comparable to fully contoured plans, highlighting their potential efficacy in treatment planning.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143364632","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":"Improving VMAT dose calculation accuracy and planning quality via a GPU-accelerated Fourier transform dose calculation algorithm","authors":"Kenny Guida,&nbsp;Chaoqiong Ma,&nbsp;Joy Patel,&nbsp;Krishna Reddy,&nbsp;H. Harold Li","doi":"10.1002/acm2.70002","DOIUrl":"10.1002/acm2.70002","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Inverse planning typically utilizes fast, less accurate dose calculation algorithms <i>during</i> the iterative optimization process, thus leading to dose calculation errors (DCEs) and suboptimal plans that often require dose normalization and/or plan re-optimization.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>A graphic processing unit (GPU) accelerated Fourier transform dose calculation (FTDC) was recently commissioned at our institution during the Eclipse treatment planning system (Varian Medical Systems) v18.0 upgrade. We hypothesize that FTDC could reduce DCEs and planning failure rates (PFRs) compared to its predecessor, multi-resolution dose calculation (MRDC), while improving efficiency through utilization of GPUs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Fifty lung SBRT plans were optimized with MRDC and FTDC dose calculation algorithms. Acuros XB (AXB) was then used for final dose calculations. DCEs for target and organ-at-risk (OAR) were calculated as the percent difference between AXB and dose calculated at the final optimization step. Plan quality was assessed using an in-house planning scorecard where PFRs were calculated as the percentage of plans that had a plan score less than 90% with optimal plans scored at 100%.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>FTDC showed excellent agreement with AXB in terms of planning target volume (PTV) coverage, as PTV D95% DCE_FTDC averaged 0.8% ± 0.9%, compared to DCE_MRDC’s −2.5% ± 3.2%. DCEs for thoracic OARs were reduced with less variation when optimizing with FTDC as compared to MRDC. FTDC had a PFR of 10% (5 out of 50) versus MRDC's 32% (16 out of 50). The subsequent re-optimization rate resulted from a plan normalization of 3% or greater was 4% for FTDC compared to MRDC's 38%. FTDC with GPU acceleration reduced optimization time by 75% on average compared to MRDC without GPU acceleration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>FTDC shows more accurate dose calculation accuracy compared to MRDC. Its use during the optimization process improved planning quality and efficiency assisted with GPUs.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70002","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143364612","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":"Benchmarking MapRT and first clinical experience: A novel solution for collision-free non-coplanar treatment planning","authors":"Mathieu Gonod,&nbsp;Ilyas Achag,&nbsp;Jad Farah,&nbsp;Léone Aubignac,&nbsp;Igor Bessieres","doi":"10.1002/acm2.14572","DOIUrl":"10.1002/acm2.14572","url":null,"abstract":"<p>In recent years, complex re-irradiations and stereotactic treatments have triggered the use of non-coplanar treatments for better dose conformality, entailing risks of collision between the machine and the patient, couch, or immobilization device. To ensure the plans deliverability without collisions, time-consuming actions are typically performed, including dry runs, in-room couch rotations, and beam configuration tests during planning. To overcome these challenges, a new tool called MapRT (VisionRT Ltd., London, UK) was developed. MapRT predicts a clearance map based on a patients' 3D model (acquired with dedicated cameras at the CT simulation) and pre-established machine models. This work evaluates the accuracy of MapRT using a 30 × 35 × 40 cm³ phantom and 64 gantry/couch collision coordinates on a Truebeam Linac (Varian, Palo Alto, USA). Collision coordinates were recorded for gantry and couch rotations. The agreement of real collision coordinates and MapRT's predictions was evaluated for different buffer margins around the couch/patient models customizable in MapRT. Results of the first clinical implementation of MapRT were also reported. With no buffer margin, MapRT's predictions and experimental collision coordinates showed small average differences but with large standard deviations for gantry (-0.5°±6.2°) and couch (-0.1°±4.8°) collision coordinates. When excluding the kV imaging components, these values were of -0.8°±3.5° for gantry and 0.4°±4.4° for couch. Finally, a 3 cm buffer margin allows for 100% accurate predictions by MapRT of gantry-to-phantom and gantry-to-couch collisions. Among the ∼900 treatment plans checked with MapRT, 22 collisions could be avoided while another 6 plans still incurred a collision but these are mainly due to users' oversights. MapRT easily predict collisions in complex treatment planning. This work demonstrated its reliability using a 3 cm buffer margin. MapRT is a promising tool for increasing security, time saving and workflow improvement.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143189330","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":"Dosimetric sensitivity of an enhanced leaf model (ELM) for individual versus averaged machines","authors":"Rafail Panagi,&nbsp;Rhydian Caines,&nbsp;Carl G. Rowbottom","doi":"10.1002/acm2.14621","DOIUrl":"10.1002/acm2.14621","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>With the introduction of a new multi-leaf collimator (MLC) enhanced leaf model (ELM) in the Varian Eclipse™ treatment planning system, there is currently limited data regarding the dosimetric sensitivity to real-world variation in the ELM parameters, and its clinical relevance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To characterize the variation in ELM parameters across a large department with ten linear accelerators and investigate the feasibility of using a single machine-averaged ELM for treatment planning. This could achieve time and resource savings from reduced quality assurance, while allowing easy transfer of patients between machines.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Clinical plans of a range of sites (head and neck, prostate, breast, lung, and brain), techniques (VMAT, IMRT, SBRT, and SRS), and energies (6 MV, 6 MV FFF, 10 MV, and 10 MV FFF) were recalculated on Varian TrueBeam™ (120 MLC) and Varian EDGE™ (HD120 MLC), with machine-specific ELM beam models, an averaged machine and an outlier machine model. A range of clinically relevant metrics relating to target coverage (e.g. PTV D_98%, D_50%, D_2%) and OAR doses (dosimetric, volumetric, conformity, and gradient indices) were evaluated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>For the target metrics, the maximum percentage deviation from the mean was 0.422%, 0.157%, and 1.956% for the cases of the individual machines, the averaged machine and the outlier machine correspondingly, while the maximum absolute dose differences were 0.28 Gy, 0.07 Gy, and 0.38 Gy. For the OAR metrics, the maximum deviation from the mean was 1.833%, 0.204%, and 5.722% for the individual, averaged, and outlier machines, while the maximum absolute dose differences were 0.41 Gy, 0.10 Gy, and 0.97 Gy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>For machines that are well matched in terms of dosimetry for transmission and sweeping gap fields, the use of an averaged machine model is unlikely to introduce clinically significant dosimetric differences to treatment plans.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14621","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143189345","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":"High-precision localization of radiation isocenter using Winston-Lutz test: Impact of collimator angle, phantom position, and field size","authors":"Weiliang Du","doi":"10.1002/acm2.70000","DOIUrl":"10.1002/acm2.70000","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aimed to evaluate the impact of collimator angle, ball bearing (BB) phantom position, and field size on the accuracy of Winston-Lutz (WL) test–derived radiation isocenters.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>WL tests were performed on four TrueBeam linear accelerators. Fifty-six images (eight gantry angles multiplied by seven collimator angles) were acquired for each WL test. Images with different sets of collimator angles were used to compute the radiation isocenters. The resulting radiation isocenters were correlated with the collimator angles. Then, the BB position and radiation field size were varied for the subsequent WL tests. The calculated BB shifts were compared with the known shifts, and the radiation isocenters were compared between different field sizes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The use of a single collimator angle led to errors of as much as 0.4 mm in the calculated radiation isocenters. Systematic differences were observed between the radiation isocenters derived with collimator angle 0° and those derived with 90° and/or 270°. A commonly used opposing collimator angle pair, 90° and 270°, resulted in a vertical 0.1 mm offset of the radiation isocenters toward the ceiling. Oblique opposite or mixed collimator angles yielded radiation isocenter errors less than 0.1 mm. The BB shifts derived from WL tests were less than 0.1 mm from the known shifts. The radiation isocenters varied by less than 0.1 mm between field sizes ranging from 2 × 2 cm² to 20 × 20 cm².</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Oblique opposing collimator angle pairs should be considered to minimize errors in localizing radiation isocenters. Uncertainty in BB positioning could be eliminated if the BB is used as a static reference point in space. The field size had no significant effect on the radiation isocenters. With careful design of WL test parameters and image processing, it is possible to achieve a precision of 0.1 mm in localizing radiation isocenters using WL tests.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70000","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143189353","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 review of accident models and incident analysis techniques","authors":"Lawrence M. Wong,&nbsp;Todd Pawlicki","doi":"10.1002/acm2.14623","DOIUrl":"10.1002/acm2.14623","url":null,"abstract":"<p>This review article aims to provide an overview of accident models and incident analysis techniques in the context of radiation oncology. Accident models conceptualize the mechanisms through which accidents occur. Chain-of-event models and systemic models are two main categories of accident models and differ in how accident causation is portrayed. Chain-of-event models focus on the linear sequence of events leading up to an accident, whereas systemic models emphasize the nonlinear relationships between the components in a complex system. The article then introduces various incident analysis techniques, including root cause analysis (RCA), London Protocol, AcciMap, and Causal Analysis Based on Systems Theory (CAST), which are based on these accident models.  The techniques based on the chain-of-event model can be effective in identifying causal factors, safety interventions, and improving safety.  The other techniques based on the systemic models inherently facilitate an examination of how the influence of personal conditions, environmental conditions, and information exchange between different aspects of a system contributed to an accident.  To improve incident analysis, it is essential to translate unsafe human behavior into decision-making flaws and the underlying contextual factors. Where resources allow, it is also crucial to systematically link frontline contributions to organizational and societal aspects of the system and incorporate expertise in safety science and human factors into the analysis team.  The article also touches on related concepts such as Perrow's Normal Accident Theory (NAT), Functional Resonance Analysis Method (FRAM), and Bowtie Analysis, which are not based on specific accident models but have been used for safety improvement in radiation oncology. Overall, different incident analysis techniques have strengths and weaknesses. Taking a systems approach to incident analysis requires a shift from linear thinking to a more nuanced understanding of complex systems. However, the approach also brings unique value and can help improve safety as radiation oncology further gains complexity.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14623","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143080110","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":"Modeling dosimetric benefits from daily adaptive RT for gynecological cancer patients with and without knowledge-based dose prediction","authors":"Rupesh Ghimire,&nbsp;Lance Moore,&nbsp;Daniela Branco,&nbsp;Dominique L. Rash,&nbsp;Jyoti S. Mayadev,&nbsp;Xenia Ray","doi":"10.1002/acm2.14596","DOIUrl":"10.1002/acm2.14596","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Purpose&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Daily online adaptive radiotherapy (ART) improves dose metrics for gynecological cancer patients, but the on-treatment process is resource-intensive requiring longer appointments and additional time from the entire adaptive team. To optimize resource allocation, we propose a model to identify high-priority patients.&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;For 49 retrospective cervical and endometrial cancer patients, we calculated two initial plans: the treated standard-of-care (Initial&lt;sub&gt;SOC&lt;/sub&gt;) and a reduced margin initial plan (Initial&lt;sub&gt;ART&lt;/sub&gt;) for adapting with the Ethos treatment planning system. Daily doses corresponding to standard and reduced margins (Daily&lt;sub&gt;SOC&lt;/sub&gt; and Daily&lt;sub&gt;ART&lt;/sub&gt;) were determined by re-segmenting the anatomy based on the treatment CBCT and calculating dose on a synthetic CT. These initial and daily doses were used to estimate the ART benefit (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;Δ&lt;/mi&gt;\u0000 &lt;mi&gt;D&lt;/mi&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;mi&gt;i&lt;/mi&gt;\u0000 &lt;mi&gt;l&lt;/mi&gt;\u0000 &lt;mi&gt;y&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;${{Delta}}Daily$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;= Daily&lt;sub&gt;SOC&lt;/sub&gt;-Daily&lt;sub&gt;ART&lt;/sub&gt;) versus initial plan differences (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;Δ&lt;/mi&gt;\u0000 &lt;mi&gt;I&lt;/mi&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 &lt;mi&gt;i&lt;/mi&gt;\u0000 &lt;mi&gt;t&lt;/mi&gt;\u0000 &lt;mi&gt;i&lt;/mi&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;mi&gt;l&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;${{Delta}}Initial$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;= Initial&lt;sub&gt;SOC&lt;/sub&gt;–Initial&lt;sub&gt;ART&lt;/sub&gt;) via multivariate linear regression. Dosimetric benefits were modeled with initial plan differences (&lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;Δ&lt;/mi&gt;\u0000 &lt;mi&gt;I&lt;/mi&gt;\u0000 &lt;mi&gt;n&lt;/mi&gt;\u0000 &lt;mi&gt;i&lt;/mi&gt;\u0000 &lt;mi&gt;t&lt;/mi&gt;\u0000 &lt;mi&gt;i&lt;/mi&gt;\u0000 &lt;mi&gt;a&lt;/mi&gt;\u0000 &lt;mi&gt;l&lt;/mi&gt;\u0000 &lt;/mrow&gt;\u0000 &lt;annotation&gt;${{Delta}}Initial$&lt;/annotation&gt;\u0000 &lt;/semantics&gt;&lt;/math&gt;) of &lt;span&gt;&lt;/span&gt;&lt;math&gt;\u0000 &lt;semantics&gt;\u0000 &lt;mrow&gt;\u0000 &lt;mi&gt;","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14596","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143046827","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 AI-based auto-contouring tools in radiotherapy: A single-institution study","authors":"Tingyu Wang,&nbsp;James Tam,&nbsp;Thomas Chum,&nbsp;Cyril Tai,&nbsp;Deborah C. Marshall,&nbsp;Michael Buckstein,&nbsp;Jerry Liu,&nbsp;Sheryl Green,&nbsp;Robert D. Stewart,&nbsp;Tian Liu,&nbsp;Ming Chao","doi":"10.1002/acm2.14620","DOIUrl":"10.1002/acm2.14620","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Accurate delineation of organs at risk (OARs) is crucial yet time-consuming in the radiotherapy treatment planning workflow. Modern artificial intelligence (AI) technologies had made automation of OAR contouring feasible. This report details a single institution's experience in evaluating two commercial auto-contouring software tools and making well-informed decisions about their clinical adoption.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A cohort of 36 patients previously treated at our institution were selected for the software performance assessment. Fifty-eight OAR structures from seven disease sites were automatically segmented with each tool. Five radiation oncologists with different specialties qualitatively scored the automatic OAR contours’ clinical usability by a 4-level scale (0–3), termed as quality score (QS), representing from “0: not usable” to “3: directly usable for a clinic.” Additionally, quantitative comparison with clinically approved contours using Dice similarity coefficient (DSC) and the 95% Hausdorff distance (HD95) was performed in complement to QS from physicians.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Result</h3>\u0000 \u0000 <p>Software A achieved an average QS of 2.17 ± 0.69, comparable to Software B's average QS of 2.17 ± 0.72. Software B performed better with more OARs (42 vs. 37) that required minor or no modification than Software A. Major modifications were needed for 13 out of 58 automated contours from both tools. Both DSC and HD95 scores for the two tools were comparable to each other, with DSC: 0.67 ± 0.23 versus 0.66 ± 0.21 and HD95: 13.07 ± 15.84 versus 15.55 ± 18.45 for Software A and Software B, respectively. Correlation coefficients between the physician score and the quantitative metrics suggested that the contouring results from Software A aligned more closely with the physician's evaluations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Based on our study, either software tool could produce clinically acceptable contours for about 65% of the OAR structures. However, further refinement is necessary for several challenging OARs to improve model performance.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14620","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143005942","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":"Online correction of intrafraction motion during volumetric modulated arc therapy for prostate radiotherapy using fiducial-based kV imaging: A cohort study quantifying the frequency of shifts and analysis of men at highest risk","authors":"Lucas M. Serra,&nbsp;Tianming Wu,&nbsp;Mark C. Korpics,&nbsp;Kamil Yenice,&nbsp;Stanley L. Liauw","doi":"10.1002/acm2.14603","DOIUrl":"10.1002/acm2.14603","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Various methods exist to correct for intrafraction motion (IFM) of the prostate during radiotherapy. We sought to characterize setup corrections in our practice informed by the TrueBeam Advanced imaging package, and analyze factors associated with IFM.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>132 men received radiation therapy for prostate cancer with a volumetric modulated arc therapy technique. All patients underwent planning CT immediately following transrectal placement of 3 fiducial markers. The most common RT course was 20 fractions (range: 17–44). Triggered kV images were acquired every 15 seconds over 2–3 full arcs using an onboard imaging system. IFM correction was considered when if any two fiducial markers in a single kV image were observed to be outside beyond a 3 mm tolerance margin. A manual 2D/3D match was performed using the fiducial markers from the single triggered kV image to obtain a suggested couch shift. Shift data for three (x, y, z) planes were extracted from the record and verify system and expressed as a single 3-dimensional translation. Shift percent (SP) was defined as the number of instances of an intrafraction correction divided by the total number of fractions for a given patient.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Over 2659 fractions of radiation, IFM was observed and corrected for 582 times across 463 (17%) fractions, and at least one shift was made over the course of treatment in 77% of men. Univariate analysis revealed that larger rectal volume or width, smaller prostate volume, and use of ADT were associated with SP &gt; 20% (p &lt; 0.05). Men with a rectal width &gt;3.6 cm were more likely to have IFM corrected (SP &gt; 20% 47% vs 18%, p = 0.0016). On multivariate analysis, only rectal volume and width were associated with IFM.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In this cohort study, 17% of fractions were interrupted to apply at least one couch shift. Men treated with shorter courses of therapy, such as stereotactic body radiation therapy, or men at high risk for IFM (e.g. larger rectal size) may warrant more careful consideration regarding the implications of IFM.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14603","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143005947","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}