Medical physics最新文献

{"title":"AAPM task group report 288: Recommendations for guiding radiotherapy event narratives","authors":"Bruce Thomadsen,&nbsp;Ajay Kapur,&nbsp;Bette Blankenship,&nbsp;Barrett Caldwell,&nbsp;Lindsey Claps,&nbsp;Joanne Cunningham,&nbsp;Jennifer Elee,&nbsp;Suzanne Evans,&nbsp;Eric Ford,&nbsp;Debbie Gilley,&nbsp;Sandra Hayden,&nbsp;Kathleen Hintenlang,&nbsp;Rishabh Kapoor,&nbsp;John Kildea,&nbsp;Linda Kroger,&nbsp;Ksenija Kujundzic,&nbsp;Qing Liang,&nbsp;Sasa Mutic,&nbsp;Anita O'Donovan,&nbsp;Michael O'Hara,&nbsp;Zoubir Ouhib,&nbsp;Jatinder Palta,&nbsp;Todd Pawlicki,&nbsp;William Salter,&nbsp;Stacey Schmidt,&nbsp;Sugata Tripathi","doi":"10.1002/mp.17282","DOIUrl":"10.1002/mp.17282","url":null,"abstract":"<p>Incident reporting and learning systems provide an opportunity to identify systemic vulnerabilities that contribute to incidents and potentially degrade quality. The narrative of an incident is intended to provide a clear, easy to understand description of an incident. Unclear, incomplete or poorly organized narratives compromise the ability to learn from them. This report provides guidance for drafting effective narratives, with particular attention to the use of narratives in incident reporting and learning systems (IRLS). Examples are given that compare effective and less than effective narratives. This report is mostly directed to organizations that maintain IRLS, but also may be helpful for individuals who desire to write a useful narrative for entry into such a system. Recommendations include the following: (1) Systems should allow a one- or two-sentence, free-text synopsis of an incident without guessing at causes; (2) Information included should form a sequence of events with chronology; and (3) Reporting and learning systems should consider using the headings suggested to guide the reporter through the narrative: (a) incident occurrences and actions by role; (b) prior circumstances and actions; (c) method by which the incident was identified; (d) equipment related details if relevant; (e) recovery actions by role; (f) relevant time span between responses; (g) and how individuals affected during or immediately after incident. When possible and appropriate, supplementary information including relevant data elements should be included using numerical scales or drop-down choices outside of the narrative. Information that should not be included in the narrative includes: (a) patient health information (PHI); (b) conjecture or blame; (c) jargon abbreviations or details without specifying their significance; (d) causal analysis.</p>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 9","pages":"5858-5872"},"PeriodicalIF":3.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141790682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Toward real-time, volumetric dosimetry for FLASH-capable clinical synchrocyclotrons using protoacoustic imaging","authors":"Siqi Wang,&nbsp;Gilberto Gonzalez,&nbsp;Daniel Rocky Owen,&nbsp;Leshan Sun,&nbsp;Yan Liu,&nbsp;Townsend Zwart,&nbsp;Yong Chen,&nbsp;Liangzhong Xiang","doi":"10.1002/mp.17318","DOIUrl":"10.1002/mp.17318","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Radiation delivery with ultra-high dose rate (FLASH) radiotherapy (RT) holds promise for improving treatment outcomes and reducing side effects but poses challenges in radiation delivery accuracy due to its ultra-high dose rates. This necessitates the development of novel imaging and verification technologies tailored to these conditions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Our study explores the effectiveness of proton-induced acoustic imaging (PAI) in tracking the Bragg peak in three dimensions and in real time during FLASH proton irradiations, offering a method for volumetric beam imaging at both conventional and FLASH dose rates.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We developed a three-dimensional (3D) PAI technique using a 256-element ultrasound detector array for FLASH dose rate proton beams. In the study, we tested protoacoustic signal with a beamline of a FLASH-capable synchrocyclotron, setting the distal 90% of the Bragg peak around 35 mm away from the ultrasound array. This configuration allowed us to assess various total proton radiation doses, maintaining a consistent beam output of 21 pC/pulse. We also explored a spectrum of dose rates, from 15 Gy/s up to a FLASH rate of 48 Gy/s, by administering a set number of pulses. Furthermore, we implemented a three-dot scanning beam approach to observe the distinct movements of individual Bragg peaks using PAI. All these procedures utilized a proton beam energy of 180 MeV to achieve the maximum possible dose rate.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our findings indicate a strong linear relationship between protoacoustic signal amplitudes and delivered doses (<i>R</i>² = 0.9997), with a consistent fit across different dose rates. The technique successfully provided 3D renderings of Bragg peaks at FLASH rates, validated through absolute Gamma index values.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The protoacoustic system demonstrates effectiveness in 3D visualization and tracking of the Bragg peak during FLASH proton therapy, representing a notable advancement in proton therapy quality assurance. This method promises enhancements in protoacoustic image guidance and real-time dosimetry, paving the way for more accurate and effective treatments in ultra-high dose rate therapy environments.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 11","pages":"8496-8505"},"PeriodicalIF":3.2,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141790683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient application of deep learning-based elective lymph node regions delineation for pelvic malignancies","authors":"Feng Wen,&nbsp;Jie Zhou,&nbsp;Zhebin Chen,&nbsp;Meng Dou,&nbsp;Yu Yao,&nbsp;Xin Wang,&nbsp;Feng Xu,&nbsp;Yali Shen","doi":"10.1002/mp.17330","DOIUrl":"10.1002/mp.17330","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>While there are established international consensuses on the delineation of pelvic lymph node regions (LNRs), significant inter- and intra-observer variabilities persist. Contouring these clinical target volumes for irradiation in pelvic malignancies is both time-consuming and labor-intensive.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>The purpose of this study was to develop a deep learning model of pelvic LNRs delineation for patients with pelvic cancers.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Planning computed tomography (CT) studies of 160 patients with pelvic primary malignancies (including rectal, prostate, and cervical cancer) were retrospectively collected and divided into training set (<i>n</i> = 120) and testing set (<i>n</i> = 40). Six pelvic LNRs, including abdominal presacral, pelvic presacral, internal iliac nodes, external iliac nodes, obturator nodes, and inguinal nodes were delineated by two radiation oncologists as ground truth (Gt) contours. The cascaded multi-heads U-net (CMU-net) was constructed based on the Gt contours from training cohort, which was subsequently verified in the testing cohort. The automatic delineation of six LNRs (Auto) was evaluated using dice similarity coefficient (DSC), average surface distance (ASD), 95th percentile Hausdorff distance (HD95), and a 7-point scale score.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In the testing set, the DSC of six pelvic LNRs by CMU-net model varied from 0.851 to 0.942, ASD from 0.381 to 1.037 mm, and HD95 from 2.025 to 3.697 mm. No significant differences were founded in these three parameters between postoperative and preoperative cases. 95.9% and 96.2% of auto delineations by CMU-net model got a score of 1–3 by two expert radiation oncologists, respectively, meaning only minor edits needed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The CMU-net was successfully developed for automated delineation of pelvic LNRs for pelvic malignancies radiotherapy with improved contouring efficiency and highly consistent, which might justify its implementation in radiotherapy work flow.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 10","pages":"7057-7066"},"PeriodicalIF":3.2,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141775648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Open access segmentations of intraoperative brain tumor ultrasound images","authors":"Bahareh Behboodi,&nbsp;Francois-Xavier Carton,&nbsp;Matthieu Chabanas,&nbsp;Sandrine de Ribaupierre,&nbsp;Ole Solheim,&nbsp;Bodil K. R. Munkvold,&nbsp;Hassan Rivaz,&nbsp;Yiming Xiao,&nbsp;Ingerid Reinertsen","doi":"10.1002/mp.17317","DOIUrl":"10.1002/mp.17317","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>Registration and segmentation of magnetic resonance (MR) and ultrasound (US) images could play an essential role in surgical planning and resectioning brain tumors. However, validating these techniques is challenging due to the scarcity of publicly accessible sources with high-quality ground truth information. To this end, we propose a unique set of segmentations (RESECT-SEG) of cerebral structures from the previously published RESECT dataset to encourage a more rigorous development and assessment of image-processing techniques for neurosurgery.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Acquisition and Validation Methods</h3>\u0000 \u0000 <p>The RESECT database consists of MR and intraoperative US (iUS) images of 23 patients who underwent brain tumor resection surgeries. The proposed RESECT-SEG dataset contains segmentations of tumor tissues, <i>sulci</i>, <i>falx cerebri</i>, and resection cavity of the RESECT iUS images. Two highly experienced neurosurgeons validated the quality of the segmentations.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Data Format and Usage Notes</h3>\u0000 \u0000 <p>Segmentations are provided in 3D NIFTI format in the OSF open-science platform: https://osf.io/jv8bk.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Potential Applications</h3>\u0000 \u0000 <p>The proposed RESECT-SEG dataset includes segmentations of real-world clinical US brain images that could be used to develop and evaluate segmentation and registration methods. Eventually, this dataset could further improve the quality of image guidance in neurosurgery.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 9","pages":"6525-6532"},"PeriodicalIF":3.2,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17317","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A feasibility study of dosimetry for breast cancer radiotherapy based on body surface changes","authors":"Yongjin Deng,&nbsp;Minmin Qiu,&nbsp;Jiajian Zhong,&nbsp;Zhenhua Xiao,&nbsp;Yong Bao,&nbsp;Botian Huang","doi":"10.1002/mp.17331","DOIUrl":"10.1002/mp.17331","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>The requirement for precise and effective delivery of the actual dose to the patient grows along with the complexity of breast cancer radiotherapy. Dosimetry during treatment has become a crucial component of guaranteeing the efficacy and security.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To propose a dosimetry method during breast cancer radiotherapy based on body surface changes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 29 left breast cancer radiotherapy cases were retroactively retrieved from an earlier database for analysis. Non-rigid image registration and dose recalculation of the planning computed tomography (CT) referring to the Cone-beam computed tomography were performed to obtain dose changes. The study used 3D point cloud feature extraction to characterize body surface changes. Based on the correlation proof, a mapping model is developed between body surface changes and dose changes using neural network framework. The MSE metrics, the Euclidean distances of feature points and the 3D gamma pass rate metric were used to assess the prediction accuracy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A strong correlation exist between body surface changes and dose changes (first canonical correlation coefficient = 0.950). For the dose deformation field and dose amplitude difference in the test set, the MSE of the predicted and actual values were 0.136 pixels and 0.229 cGy, respectively. After deforming the planning dose into a deformed one, the feature points’ Euclidean distance between it and the recalculated dose changes from 9.267 ± 1.879 mm to 0.456 ± 0.374 mm. The 3D gamma pass rate of 90% or higher for the 2 mm/2% criteria were achieved by 80.8% of all cases, with a minimum pass rate of 75.9% and a maximum pass rate of 99.6%. Pass rate for the 3 mm/2% criteria ranged from 87.8% to 99.8%, with 92.3% of the cases having a pass rate of 90% or higher.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study provides a dosimetry method that is non-invasive, real-time, and requires no additional dose for breast cancer radiotherapy.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 11","pages":"8482-8495"},"PeriodicalIF":3.2,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomic feature robustness in CT scans affected by fiducial marker induced streak artifacts for patients with hepatocellular carcinoma","authors":"Damon Verstappen,&nbsp;David Pasquier,&nbsp;Yi Chen,&nbsp;Phillipe Lambin,&nbsp;Henry C. Woodruff","doi":"10.1002/mp.17329","DOIUrl":"10.1002/mp.17329","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Stereotactic body radiation therapy for hepatocellular carcinoma necessitates the implantation of gold fiducial markers in the liver, resulting in artifacts on computed tomography (CT) scans, which affect radiomic feature values.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This report aims to assess the effect of these artifacts on radiomic features and how removing CT slices affects radiomic features extracted from 3D regions of interest (ROI).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>First, the range variation in 38 tumor contours unaffected by artifacts was assessed after sequentially and randomly removing 25%, 50%, 75% of slices. Subsequently, the agreement of feature values before and after removing ROI slices containing artifacts from 186 patients’ CT scans was assessed with Lin's concordance correlation coefficient (CCC) and a Wilcoxon signed-rank test.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In artifact-free tumor volumes, at least 71% of features remain robust with up to 50% of slices removed, while 56% remains robust with up to 75% of slices removed. When comparing contours before and after removing slices containing artifacts, around a third of features in the tumor contour and surrounding area remain robust (CCC &gt; 0.9), compared to 44% in the healthy liver. Concerning the tumor, 13% (Gray Level Size Zone Matrix) to 61% (first order) of the features remain robust (CCC &gt; 0.9). Over 90% of features differ significantly as assessed by Wilcoxon signed-rank test, however.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study demonstrates that removing slices containing artifacts is a feasible solution for the CT fiducial problem in this patient population and provides insight into which features are affected.</p>\u0000 </section>\u0000 </div>","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 11","pages":"8362-8370"},"PeriodicalIF":3.2,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17329","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141763658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comprehensive stereotactic radiosurgery platform characterization: A novel end-to-end approach with anthropomorphic 3D dosimetry","authors":"Yi-Fang Wang,&nbsp;John Adamovics,&nbsp;Cheng-Shie Wuu","doi":"10.1002/mp.17321","DOIUrl":"10.1002/mp.17321","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;Stereotactic radiosurgery (SRS) is a widely employed strategy for intracranial metastases, utilizing linear accelerators and volumetric modulated arc therapy (VMAT). Ensuring precise linear accelerator performance is crucial, given the small planning target volume (PTV) margins. Rapid dose falloff is vital to minimize brain radiation necrosis. Despite advances in SRS planning, tools for end-to-end testing of SRS treatments are lacking, hindering confidence in the procedure.&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 introduces a novel end-to-end three-dimensional (3D) anthropomorphic dosimetry system for characterization of a radiosurgery platform, aiming to measure planning metrics, dose gradient index (DGI), brain volumes receiving at least 10 and 12 Gy (V10, V12), as well as assess delivery uncertainties in multitarget treatments. The study also compares metrics from benchmark plans to enhance understanding and confidence in SRS treatments.&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 developed anthropomorphic 3D dosimetry system includes a modified Stereotactic End-to-End Verification (STEEV) phantom with a customized insert integrating 3D dosimeters and a fiber optic CT scanner. Labview and MATLAB programs handle optical scanning, image preprocessing, and dosimetric analysis. SlicerRT is used for 3D dose visualization and analysis. A film stack insert was used to validate the 3D dosimeter measurements at specific slices. Benchmark plans were developed and measured to investigate off-axis errors, dose spillage, small field dosimetry, and multi-target delivery.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The accuracy of the developed 3D dosimetry system was rigorously assessed using radiochromic films. Two two-dimensional (2D) dose planes, extracted from the 3D dose distribution, were compared with film measurements, resulting in high passing rates of 99.9% and 99.6% in gamma tests. The mean relative dose difference between film and 3D dosimeter measurements was −1%, with a standard deviation of 2.2%, well within dosimeter uncertainties. In the first module, evaluating single-isocenter multitarget treatments, a 1.5 mm dose distribution shift was observed when targets were 7 cm off-axis. This shift was attributed to machine mechanical errors and image-guided system uncertainties, indicating potential limitations in conventional gamma tests. The second module investigated discrepancies in intermediate-to-low dose spillage, revealing higher measured doses in the connecting region between closely positioned targets. This discrepancy was linked to uncertainties in treatmen","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 11","pages":"8524-8537"},"PeriodicalIF":3.2,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141750081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ultra-low dose hip CT-based automated measurement of volumetric bone mineral density at proximal femoral subregions","authors":"Xiaoliu Zhang,&nbsp;Syed Ahmed Nadeem,&nbsp;Paul A. DiCamillo,&nbsp;Amal Shibli-Rahhal,&nbsp;Elizabeth A. Regan,&nbsp;R. Graham Barr,&nbsp;Eric A. Hoffman,&nbsp;Alejandro P. Comellas,&nbsp;Punam K. Saha","doi":"10.1002/mp.17319","DOIUrl":"10.1002/mp.17319","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;Forty to fifty percent of women and 13%–22% of men experience an osteoporosis-related fragility fracture in their lifetimes. After the age of 50 years, the risk of hip fracture doubles in every 10 years. x-Ray based DXA is currently clinically used to diagnose osteoporosis and predict fracture risk. However, it provides only 2-D representation of bone and is associated with other technical limitations. Thus, alternative methods are needed.&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;To develop and evaluate an ultra-low dose (ULD) hip CT-based automated method for assessment of volumetric bone mineral density (vBMD) at proximal femoral subregions.&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;An automated method was developed to segment the proximal femur in ULD hip CT images and delineate femoral subregions. The computational pipeline consists of deep learning (DL)-based computation of femur likelihood map followed by shape model-based femur segmentation and finite element analysis-based warping of a reference subregion labeling onto individual femur shapes. Finally, vBMD is computed over each subregion in the target image using a calibration phantom scan. A total of 100 participants (50 females) were recruited from the Genetic Epidemiology of COPD (COPDGene) study, and ULD hip CT imaging, equivalent to 18 days of background radiation received by U.S. residents, was performed on each participant. Additional hip CT imaging using a clinical protocol was performed on 12 participants and repeat ULD hip CT was acquired on another five participants. ULD CT images from 80 participants were used to train the DL network; ULD CT images of the remaining 20 participants as well as clinical and repeat ULD CT images were used to evaluate the accuracy, generalizability, and reproducibility of segmentation of femoral subregions. Finally, clinical CT and repeat ULD CT images were used to evaluate accuracy and reproducibility of ULD CT-based automated measurements of femoral vBMD.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Dice scores of accuracy (&lt;i&gt;n&lt;/i&gt; = 20), reproducibility (&lt;i&gt;n&lt;/i&gt; = 5), and generalizability (&lt;i&gt;n&lt;/i&gt; = 12) of ULD CT-based automated subregion segmentation were 0.990, 0.982, and 0.977, respectively, for the femoral head and 0.941, 0.970, and 0.960, respectively, for the femoral neck. ULD CT-based regional vBMD showed Pearson and concordance correlation coefficients of 0.994 and 0.977, respectively, and a root-mean-square coefficient of variation (RMSCV) (%) of 1.39% with the clinical CT-derived refe","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 11","pages":"8213-8231"},"PeriodicalIF":3.2,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17319","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141750085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting major adverse cardiac events using radiomics nomogram of pericoronary adipose tissue based on CCTA: A multi-center study","authors":"Zhaoheng Huang,&nbsp;Saikit Lam,&nbsp;Zihe Lin,&nbsp;Linjia Zhou,&nbsp;Liangchen Pei,&nbsp;Anyi Song,&nbsp;Tianle Wang,&nbsp;Yuanpeng Zhang,&nbsp;Rongxing Qi,&nbsp;Sheng Huang","doi":"10.1002/mp.17324","DOIUrl":"10.1002/mp.17324","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;The evolution of coronary atherosclerotic heart disease (CAD) is intricately linked to alterations in the pericoronary adipose tissue (PCAT). In recent epochs, characteristics of the PCAT have progressively ascended as focal points of research in CAD risk stratification and individualized clinical decision-making. Harnessing radiomic methodologies allows for the meticulous extraction of imaging features from these adipose deposits. Coupled with machine learning paradigms, we endeavor to establish predictive models for the onset of major adverse cardiovascular events (MACE).&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;To appraise the predictive utility of radiomic features of PCAT derived from coronary computed tomography angiography (CCTA) in forecasting MACE.&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;We retrospectively incorporated data from 314 suspected or confirmed CAD patients admitted to our institution from June 2019 to December 2022. An additional cohort of 242 patients from two external institutions was encompassed for external validation. The endpoint under consideration was the occurrence of MACE after a 1-year follow-up. MACE was delineated as cardiovascular mortality, newly diagnosed myocardial infarction, hospitalization (or re-hospitalization) for heart failure, and coronary target vessel revascularization occurring more than 30 days post-CCTA examination. All enrolled patients underwent CCTA scanning. Radiomic features were meticulously extracted from the optimal diastolic phase axial slices of CCTA images. Feature reduction was achieved through a composite feature selection algorithm, laying the groundwork for the radiomic signature model. Both univariate and multivariate analyses were employed to assess clinical variables. A multifaceted logistic regression analysis facilitated the crafting of a clinical-radiological-radiomic combined model (or nomogram). Receiver operating characteristic (ROC) curves, calibration, and decision curve analyses (DCA) were delineated, with the area under the ROC curve (AUCs) computed to gauge the predictive prowess of the clinical model, radiomic model, and the synthesized ensemble.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A total of 12 radiomic features closely associated with MACE were identified to establish the radiomic model. Multivariate logistic regression results demonstrated that smoking, age, hypertension, and dyslipidemia were significantly correlated with MACE. In the integrated nomogram, which amalgamated clinical, imaging, and radiomic parameters, the diagnostic performance was as follows: 0.970 AUC, 0.949 accuracy (AC","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 11","pages":"8348-8361"},"PeriodicalIF":3.2,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141750084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and first implementation of a novel multi-modality cardiac motion and dosimetry phantom for radiotherapy applications","authors":"Kenneth W. Gregg,&nbsp;Chase Ruff,&nbsp;Grant Koenig,&nbsp;Kalin I. Penev,&nbsp;Andrew Shepard,&nbsp;Grace Kreissler,&nbsp;Margo Amatuzio,&nbsp;Cameron Owens,&nbsp;Prashant Nagpal,&nbsp;Carri K. Glide-Hurst","doi":"10.1002/mp.17315","DOIUrl":"10.1002/mp.17315","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;Cardiac applications in radiation therapy are rapidly expanding including magnetic resonance guided radiation therapy (MRgRT) for real-time gating for targeting and avoidance near the heart or treating ventricular tachycardia (VT).&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 work describes the development and implementation of a novel multi-modality and magnetic resonance (MR)-compatible cardiac phantom.&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 patient-informed 3D model was derived from manual contouring of a contrast-enhanced Coronary Computed Tomography Angiography scan, exported as a Stereolithography model, then post-processed to simulate female heart with an average volume. The model was 3D-printed using Elastic50A to provide MR contrast to water background. Two rigid acrylic modules containing cardiac structures were designed and assembled, retrofitting to an MR-safe programmable motor to supply cardiac and respiratory motion in superior-inferior directions. One module contained a cavity for an ion chamber (IC), and the other was equipped with multiple interchangeable cavities for plastic scintillation detectors (PSDs). Images were acquired on a 0.35 T MR-linac for validation of phantom geometry, motion, and simulated online treatment planning and delivery. Three motion profiles were prescribed: patient-derived cardiac (sine waveform, 4.3 mm peak-to-peak, 60 beats/min), respiratory (cos&lt;sup&gt;4&lt;/sup&gt; waveform, 30 mm peak-to-peak, 12 breaths/min), and a superposition of cardiac (sine waveform, 4 mm peak-to-peak, 70 beats/min) and respiratory (cos&lt;sup&gt;4&lt;/sup&gt; waveform, 24 mm peak-to-peak, 12 breaths/min). The amplitude of the motion profiles was evaluated from sagittal cine images at eight frames/s with a resolution of 2.4 mm × 2.4 mm. Gated dosimetry experiments were performed using the two module configurations for calculating dose relative to stationary. A CT-based VT treatment plan was delivered twice under cone-beam CT guidance and cumulative stationary doses to multi-point PSDs were evaluated.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;No artifacts were observed on any images acquired during phantom operation. Phantom excursions measured 49.3 ± 25.8%/66.9 ± 14.0%, 97.0 ± 2.2%/96.4 ± 1.7%, and 90.4 ± 4.8%/89.3 ± 3.5% of prescription for cardiac, respiratory, and cardio-respiratory motion profiles for the 2-chamber (PSD) and 12-substructure (IC) phantom modules respectively. In the gated experiments, the cumulative dose was &lt;2% from expected using the IC module. Real-time dose meas","PeriodicalId":18384,"journal":{"name":"Medical physics","volume":"51 10","pages":"7479-7491"},"PeriodicalIF":3.2,"publicationDate":"2024-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/mp.17315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141750082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}