Journal of Applied Clinical Medical Physics最新文献

{"title":"Robust automated method of spatial resolution measurement in radiotherapy CT simulation images","authors":"Pavel Govyadinov, Rick. R. Layman, Tucker Netherton, Raymond Mumme, Aaron. K. Jones, Laurence. E. Court, Moiz Ahmad","doi":"10.1002/acm2.70006","DOIUrl":"10.1002/acm2.70006","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Variation in imaging protocol, patient positioning, and the presence of artifacts can vary image quality in CT images used for radiotherapy planning. Automated methods for spatial resolution (SR) estimation exist but require further investigation and validation for wider adoption.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To validated previously existing algorithm for SR estimation and introduce improvements that make it robust to patient positioning, CT protocol, site, and artifacts.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Method</h3>\u0000 \u0000 <p>A reference algorithm based on the previous gold standard was recreated and modified to improve robustness. The algorithms were tested on three different datasets: (1) a cylindrical ACR CT QC phantom scanned using a Siemens SOMATOM Definition Edge scanner and reconstructed using 61 different kernels, (2) a set of anthropomorphic phantoms scanned with the presence of artifacts common to clinical acquisitions such as blankets and immobilization devices, and (3) a clinical patient dataset of head and neck (HN) CT scans (nine patients) and spine/pelvis (10 patients). The robustness of both algorithms was tested on the clinical patient data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Over the range of tested kernels, both algorithms were accurate when the ground truth MTF f<sub>50</sub> was within the range 0.2–0.7 mm<sup>−1</sup> in the cylindrical phantom datasets with an RMS error of 10.3% and 3.8% for the reference and modified versions of the algorithm, respectively, as compared to the ground truth. In the anthropomorphic phantom datasets the reference algorithm showed an 8.4% and 30.0% difference from ground truth for the Pelvic and HN phantoms, respectively, while the modified algorithm showed 4.9% and 3.9% percent difference from ground truth. In the clinical dataset the reference algorithm estimated a mean f<sub>50</sub> value of 0.21 ± 0.03 mm<sup>−1</sup> and 0.25 ± 0.03 mm<sup>−1</sup> for pelvis/spine while the reference algorithm estimated mean of 0.28 ± 0.02 and 0.29 ± 0.01 mm<sup>−1</sup> for HN and pelvis/spine, respectively, as compared to the ground truth found to be 0.28 mm<sup>−1</sup> on the cylindrical phantom.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The SR algorithm was validated cylindrical/anthropomorphic phantoms and clinical CT scans. Further modifications were tested and showed improved accuracy in more challenging CT acquisitions.</p>\u0000 ","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143412943","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 and failure analysis of four commercial deep learning-based autosegmentation software for abdominal organs at risk","authors":"Mingdong Fan, Tonghe Wang, Yang Lei, Pretesh R. Patel, Sean Dresser, Beth Bradshaw Ghavidel, Richard L. J. Qiu, Jun Zhou, Kirk Luca, Oluwatosin Kayode, Jeffrey D. Bradley, Xiaofeng Yang, Justin Roper","doi":"10.1002/acm2.70010","DOIUrl":"10.1002/acm2.70010","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Deep learning-based segmentation of organs-at-risk (OAR) is emerging to become mainstream in clinical practice because of the superior performance over atlas and model-based autocontouring methods. While several commercial deep learning-based autosegmentation solutions are now available, the implementation of these tools is still at such a primitive stage that acceptance criteria are underdeveloped due to a lack of knowledge about the systems’ segmentation tendencies and failure modes. As the starting point of the iterative process of clinical implementation, this study focuses on the outlier analysis of four commercial autocontouring tools for the abdominal OARs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Materials and methods</h3>\u0000 \u0000 <p>The autosegmentation software, developed by Limbus AI, MIM Contour ProtégéAI, Radformation AutoContour, and Siemens syngo.via, were used to segment 111 patient cases. Geometric segmentation accuracy was quantitatively compared with clinical contours using the dice similarity coefficient (DSC) and 95% Hausdorff distance (HD95). The outliers from quantitative evaluations of each software were analyzed for the liver, stomach, and kidneys with the possible causes of outliers summarized into six categories: (1) difference in contouring style or guideline, (2) image acquisition and quality, (3) abnormal anatomy of the OAR, (4) abnormal anatomy of abutting organs/tissues, (5) external/internal devices, and (6) other causes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>For the liver segmentation, the most prominent cause of discrepancies for Limbus, which occurred in four of its six outliers, was the existence of biliary stent or internal/external biliary drain as well as the resulting pneumobilia. Siemens included the abutting organs that shared CT numbers similar to those of the liver in 5/8 outliers. 12 of 13 Radformation's liver segmentation outliers included the heart and/or stomach while MIM not only included the stomach in the presence of barium in 5/11 outliers, but also produced fragmented contours in 5/11 other cases. Only Limbus and Radformation provided stomach segmentation, and imaging with barium contrast directly caused incomplete stomach delineation in 10/12 Limbus outliers and 21/25 Radformation outliers. As for the kidneys, Radformation and Siemens consistently followed the RTOG contouring guidelines, whereas the institutional contours excluded the renal pelvis in some cases, resulting in 19/25 Radformation outliers and 18/23 Siemens outliers. By contrast, Limbus contours appeared to follow different contouring guidelines that exclude the renal pelvis. Fragmented kidney contours wer","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70010","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143414392","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":"Patient setup variation on Elekta Unity and its impact on adaptive planning","authors":"Maggie Yan,&nbsp;Erika Kollitz,&nbsp;Sheng-Hsuan Sun,&nbsp;Kathryn Hitchcock,&nbsp;Alexandra De Leo,&nbsp;Amanda Schwarz,&nbsp;Luke Maloney,&nbsp;Jonathan Li,&nbsp;Chihray Liu,&nbsp;Guanghua Yan","doi":"10.1002/acm2.70016","DOIUrl":"10.1002/acm2.70016","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The unique design of the MR-linac may restrict the use of effective immobilization devices, resulting in significant patient setup variations (PSVs). The purpose of this study is to analyze the PSVs on the Elekta Unity system and investigate their impact on adaptive planning.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The PSVs for 10 brain, 10 pancreas, five prostate, and five rectum patients previously treated on Elekta Unity were analyzed. The five prostate and five pancreas plans were selected to investigate the impact of PSVs on adaptive planning. The reference scans were shifted by 1, 2, and 3 cm in the left–right (LR) and superior–inferior (SI) directions to simulate PSVs. Both the adaptive-to-position (ATP) and adaptive-to-shape (ATS) workflows were executed. The adaptive planning time, number of monitor units (MUs), and dosimetric metrics quantifying target coverage and organ-at-risks (OARs) sparing were compared.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>For brain treatments, the average/maximum PSVs were −0.2 ± 0.3 cm/0.8 cm (LR), 0.3 ± 0.7 cm/1.8 cm (SI), and 0.8 ± 0.7 cm/1.8 cm in the anterior–posterior (AP) direction. For pancreas treatments, the PSVs are −0.1 ± 1.0 cm/3.8 cm (LR), −0.1 ± 0.8 cm/3.5 cm (SI), and 0.3 ± 0.3 cm/1.3 cm (AP). Pelvis treatments had similar PSVs as pancreas treatments. The ATS workflow took two to three times longer than the ATP workflow. The only trend observed was that the plan MUs increased slightly (&lt; 10%) with PSVs in the ATP workflow for prostate patients. Both workflows effectively reproduced target coverage and OAR sparing, regardless of the magnitude of the PSVs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Significant PSVs were observed on Elekta Unity due to suboptimal patient immobilization. Using prostate and pancreas treatments as examples, we demonstrated that adaptive planning can effectively accommodate such PSVs. Nevertheless, efforts should be made to minimize PSVs—particularly rotations—to mitigate intra-fraction motion and reduce treatment time.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70016","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143408072","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":"American Association of Physicists in Medicine (AAPM) chapter climate check: Mixed methods analysis of survey responses","authors":"Ashley J. Cetnar,&nbsp;Ghada Aldosary,&nbsp;Meghan C. Koo,&nbsp;Holly Lincoln,&nbsp;Angélica Pérez-Andújar,&nbsp;Surendra Prajapati,&nbsp;Samantha J. Simiele,&nbsp;Kristi R. G. Hendrickson","doi":"10.1002/acm2.14600","DOIUrl":"10.1002/acm2.14600","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>The American Association of Physicists in Medicine (AAPM) recently shared results and recommendations from its first Equity, Diversity, and Inclusion (EDI) Climate Survey, which was designed to assess the climate at the workplace, the AAPM organization, and the AAPM regional chapter level. This work further explores the status of EDI at the regional chapter level.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>AAPM's EDI Survey was distributed to 5500 members and had a response rate of 25%. In the survey, three open-ended comment boxes were provided for feedback, including one for regional AAPM members. Sixty-four percent of respondents indicated they were part of a regional chapter, and 6% provided written responses to the regional chapter question. Responses were analyzed using a mixed methods approach with an exploratory sequential design. Two phases were conducted; the first relied on a Grounded Theory quantitative systemic approach, and the second applied qualitative analysis. Chapter member demographic data were collected to support findings.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Survey respondents provided open comments and feedback on their regional chapter's climate. Data are summarized as five themes: positive experiences, negative experiences, challenges within chapters, diversity and inclusion, and changes observed. Experiences of regional chapters were rated positively by 75% of respondents. Respondents found their chapters were welcoming, and some noted their great chapter leadership. A number of incidents of sexual harassment, bullying, and discrimination incidences were also shared. Other respondents observed exclusion based on their gender, race, highest degree, and medical physics specialty. Chapter leadership data aligned with these claims, with most leaders to-date being white males, doctoral degree holders, and/or specializing in radiation therapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>AAPM chapters provide rewarding professional opportunities. This study has highlighted positive and negative experiences reported by its members. The major themes identified can guide chapter leaders to continue to cultivate welcoming communities for regional AAPM members.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14600","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143398993","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":"Investigation of factors related to treatment planning of x-ray SBRT and scanning carbon-ion radiation therapy for early-stage lung cancer patients","authors":"Yuya Miyasaka,&nbsp;Sung Hyun Lee,&nbsp;Hikaru Souda,&nbsp;Hongbo Chai,&nbsp;Miyu Ishizawa,&nbsp;Takuya Ono,&nbsp;Takashi Ono,&nbsp;Hiraku Sato,&nbsp;Takeo Iwai","doi":"10.1002/acm2.14618","DOIUrl":"10.1002/acm2.14618","url":null,"abstract":"<p>This study aimed to compare the treatment plans of x-ray SBRT and scanning carbon ion radiation therapy (CIRT) for localized lung tumors, and to evaluate the dose dependence of tumor size tumor-to-heart distance. For phantom verification, we used a chest phantom with a spherical simulated tumor. Treatment plans for 3-dimensional conformal radiation therapy (3D-CRT), volumetric modulated arc therapy (VMAT), and CIRT were created. GTVs were created in sizes ranging from 0.5 to 5 cm in diameter, and the dependence of the lung dose on GTV diameter was evaluated for each treatment plan. For patient validation, 30 cases of localized lung tumors were analyzed. 3D-CRT, VMAT, and CIRT treatment plans were developed, and DVH parameters were evaluated for each GTV size and GTV-to-heart distance. In both phantom and patient validations, the OAR doses were the lowest for CIRT. The lung dose increased with increasing GTV diameter for all three treatment plans. CIRT had the smallest ratio of lung dose increase to GTV diameter increase among the three treatment plans. Heart dose in CIRT was independent of GTV size and GTV-to-heart distance Conclusions: The results of the present study suggested that the use of scanning CIRT can reduce the OAR dose while guaranteeing the tumor dose compared to x-ray SBRT. In addition, it was suggested that CIRT can treat patients with large GTV sizes while maintaining low lung and heart dose.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 4","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14618","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143398996","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 semi-supervised domain adaptation method with scale-aware and global-local fusion for abdominal multi-organ segmentation","authors":"Kexin Han,&nbsp;Qiong Lou,&nbsp;Fang Lu","doi":"10.1002/acm2.70008","DOIUrl":"10.1002/acm2.70008","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Abdominal multi-organ segmentation remains a challenging task. Semi-supervised domain adaptation (SSDA) has emerged as an innovative solution. However, SSDA frameworks based on UNet struggle to capture multi-scale and global information.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Our work aimed to propose a novel SSDA method to achieve more accurate abdominal multi-organ segmentation with limited labeled target domain data, which has a superior ability to capture the multi-scale features and integrate local and global information effectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>The proposed network is based on UNet. In the encoder part, a scale-aware with domain-specific batch normalization (SAD) module is integrated to adaptively extract multi-scale features and to get better generalization across source and target domains. In the bottleneck part, a global-local fusion (GLF) module is utilized for capturing and integrating both local and global information. They are integrated into the framework of self-ensembling mean-teacher (SE-MT) to enhance the model's capability to learn common features across source and target domains.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>To validate the performance of the proposed model, we evaluated it on the public CHAOS and BTCV datasets. For CHAOS, the proposed method obtains an average DSC of 88.97% and ASD of 1.12 mm with only 20% labeled target data. For BTCV, it achieves an average DSC of 88.95% and ASD of 1.13 mm with 20% labeled target data. Compared with the state-of-the-art methods, DSC and ASD increased by at least 0.72% and 0.33 mm on CHAOS, 1.29% and 0.06 mm on BTCV, respectively. Ablation studies were also conducted to verify the contribution of each component of the model. The proposed method achieves a DSC improvement of 3.17% over the baseline with 20% labeled target data.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The proposed SSDA method for abdominal multi-organ segmentation has a powerful ability to extract multi-scale and more global features, significantly improving segmentation accuracy and robustness.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143382221","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":"Primer on disability: Why accessibility is important for all medical physicists","authors":"Lindsay E. Jones,&nbsp;Grace Eliason,&nbsp;Shivani Gupta,&nbsp;Elizabeth G. Jeong,&nbsp;Abigail Besemer,&nbsp;David A. Sterling,&nbsp;Jessica M. Fagerstrom","doi":"10.1002/acm2.70003","DOIUrl":"10.1002/acm2.70003","url":null,"abstract":"<p>Disability and accessibility remain under-addressed topics despite the increasing prevalence of disabled people in the workforce. In the field of medical physics, there is growing evidence that the proportion of people with one or more disabilities mirrors that of the US population. Addressing disability and accessibility is a crucial facet of the American Association of Physicists in Medicine's 2018 strategic goal to “champion equity, diversity, and inclusion in the field of medical physics.” This review aims to provide guidance on disability-related topics in the context of the medical physics profession. An overview of current knowledge and recommendations is provided on essential topics such as how to comply with federal law, handle accommodation requests, and discuss disability using appropriate language. To that end, background information such as definitions, models, and classifications of disability is included. Beyond the essentials, this review also applies disability-related concepts to improve overall efficiency and productivity, attract diverse talent, and demonstrate leadership as an individual or organization.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 3","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.70003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143382361","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 complexity of safety: Embracing systems engineering in radiation oncology","authors":"Lawrence M. Wong,&nbsp;Todd Pawlicki","doi":"10.1002/acm2.14622","DOIUrl":"10.1002/acm2.14622","url":null,"abstract":"&lt;p&gt;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?&lt;/p&gt;&lt;p&gt;AAPM TG-100 was published about 8 years ago to help make radiation oncology safer and more efficient.&lt;span&gt;&lt;sup&gt;1&lt;/sup&gt;&lt;/span&gt; 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.&lt;/p&gt;&lt;p&gt;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.&lt;span&gt;&lt;sup&gt;2, 3&lt;/sup&gt;&lt;/span&gt; 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.&lt;/p&gt;&lt;p&gt;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.&lt;span&gt;&lt;sup&gt;3&lt;/sup&gt;&lt;/span&gt; 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}