Journal of Applied Clinical Medical Physics最新文献

{"title":"Promotion and tenure for medical physicists should be based on article specific measures and not on journal impact factor","authors":"Samantha Hedrick,&nbsp;Jinzhong Yang,&nbsp;Yi Rong","doi":"10.1002/acm2.14537","DOIUrl":"10.1002/acm2.14537","url":null,"abstract":"<p>In the evolving progress of academic medicine, the metrics by which we measure success are both crucial and contentious. Among these, the Journal Impact Factor (JIF) has long been a dominant metric, often serving as a shorthand for the quality and significance of research. For many institutions, JIF plays a pivotal role in decisions regarding promotion, tenure, and funding, positioning it as a key indicator of academic achievement. However, as we delve deeper into the complexities of scholarly impact, questions arise: Is the JIF truly a fair measure of quality of an individual author or value of their article? Or should we, instead, focus on article-specific metrics that more accurately reflect the true impact of the work? This month's debate seeks to explore these questions from both perspectives. We have Dr. Samantha Hedrick, who argues in favor of article-specific measures as a more accurate reflection of scholarly contribution, while Dr. Jinzhong Yang defends the established role of the JIF as a useful, if imperfect, tool in academic evaluation.</p><p>Samantha Hedrick, PhD, DABR received her B.S. in Nuclear Engineering from the University of Missouri-Rolla and received her M.S. and PhD in Nuclear Engineering from the University of Missouri. She then completed a two-year CAMPEP accredited residency at Washington University in St. Louis. She is currently the Director of Medical Physics at the Thompson Proton Center, specializing in pencil beam scanning proton therapy treatment planning, scripting, and safety improvements.</p><p>Jinzhong Yang, PhD is an Assistant Professor in the Department of Radiation Physics at the University of Texas MD Anderson Cancer Center. He is the lead physicist of the MR-Linac program at MD Anderson. He earned his PhD in Electrical Engineering from Lehigh University in 2006 and received a postdoctoral training at University of Pennsylvania. His research focuses on artificial intelligence in medical image computing for radiation oncology applications, MR-guided online adaptive radiotherapy, and quantitative imaging biomarkers for treatment outcome prediction. He has published over 130 peer-reviewed journal articles, nine book chapters, and edited a book.</p><p>The authors declare no conflicts of interest.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"25 12","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11633813/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142466126","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 new method to evaluate fluoroscopic system collimator performance","authors":"Joseph R. Steiner,&nbsp;Courtney K. Morrison,&nbsp;Mayur Vaya,&nbsp;Nicholas Bevins,&nbsp;Jeremy Christophel,&nbsp;Matt Vanderhoek","doi":"10.1002/acm2.14536","DOIUrl":"10.1002/acm2.14536","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Introduction</h3>\u0000 \u0000 <p>Fluoroscopy uses collimators to limit the radiation field size. Collimators are often evaluated annually during equipment performance evaluations to maintain compliance with regulatory and/or accreditation bodies. A method to evaluate and quantify fluoroscopy collimator performance was developed.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A radiation field and displayed image measurement device consisting of radiopaque rulers and radiochromic film strips was placed on the x-ray source assembly exit window to evaluate fluoroscopy collimator performance. This method was used to evaluate collimator performance on 79 fluoroscopic imaging systems including fixed C-arms, mobile C-arms, mini C-arms, and radiographic fluoroscopic systems.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The excess length (EL), excess width (EW), and sum EL + EW of the radiation field relative to the displayed image were measured and compared to the limits specified in 21CFR1020.32. Four systems exceeded these limits. Placing the radiation measurement device at the x-ray source assembly exit window relative to the image receptor cover increased the film exposure rate by a factor up to 14.6. The time required to set up and complete the fluoroscopy collimator performance measurements using this method ranged from 5 to 10 min.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This method provides an easily implemented quantitative measure of fluoroscopy system collimator performance that satisfies regulatory and accreditation body requirements.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"25 12","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11633780/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390774","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":"Retrospective assessment of HDR brachytherapy dose calculation methods in locally advanced cervical cancer patients: AcurosBV vs. AAPM TG43 formalism.","authors":"Billie Ann Radcliffe, Yongbok Kim, Julie Raffi, Diandra N Ayala-Peacock, Sarah J Stephens, Junzo Chino, Sheridan Meltsner, Oana Craciunescu","doi":"10.1002/acm2.14549","DOIUrl":"https://doi.org/10.1002/acm2.14549","url":null,"abstract":"<p><strong>Purpose: </strong>This retrospective analysis was completed to investigate the use of a model-based dose calculation algorithm (MBDCA) AcurosBV, for use in HDR BT treatments for locally advanced cervical cancer treated with tandem and ovoid applicators with interstitial needles.</p><p><strong>Methods: </strong>A cohort of 32 patients receiving post-EBRT HDR brachytherapy boost with a prescription dose of 5.5 Gy × 5 fractions to the high-risk clinical target volume (CTV_HR) were selected for this study. For standard TG43 dose calculation, applicators were manually digitized on the planning images, while for AcurosBV calculations, solid renderings of Titanium Fletcher Suite Delclos (FSD) applicators included in BrachyVision were matched to those used clinically and Ti needles were manually digitized. The dose was recalculated using Varian's AcurosBV 13.5 and dose-to-medium-in-medium (D_m,m) was reported. EQD2 values for targets and organs at risk were compared between dose calculation formalisms. D_90% and D_98% values were reported for the high and intermediate-risk CTVs, and <math> <semantics> <msub><mrow><mspace></mspace> <mi>D</mi></mrow> <mrow><mrow><mn>2</mn> <mspace></mspace> <mi>c</mi></mrow> <msup><mi>m</mi> <mn>3</mn></msup> </mrow> </msub> <annotation>${mathrm{ D}}_{{mathrm{2 c}}{{mathrm{m}}}^{mathrm{3}}}$</annotation></semantics> </math> values were reported for OARs including bladder, rectum, sigmoid, bowel, and vagina. Due to variability within the patient cohort, the dosimetric impact of AcurosBV was investigated corresponding to planning image modality (CT vs. CBCT), presence of Ti needles, and contrast within vaginal balloons used to stabilize implants. AcurosBV showed lower dosimetric values for all plans compared to TG43.</p><p><strong>Results: </strong>The average ± standard deviation of dosimetric reduction in D_90% was 4.33 ± 0.09% for CTV_HR and 4.12 ± 0.09% for CTV_IR. The reduction to OARs <math> <semantics> <msub><mrow><mspace></mspace> <mi>D</mi></mrow> <mrow><mrow><mn>2</mn> <mspace></mspace> <mi>c</mi></mrow> <msup><mi>m</mi> <mn>3</mn></msup> </mrow> </msub> <annotation>${mathrm{ D}}_{{mathrm{2 c}}{{mathrm{m}}}^{mathrm{3}}}$</annotation></semantics> </math> was: 4.99 ± 0.15% for bladder, 7.87 ± 0.16% for rectum, 5.79 ± 0.17% for sigmoid, 6.91 ± 0.14% for bowel, and 4.55 ± 0.14% for vagina.</p><p><strong>Conclusions: </strong>AcurosBV should be utilized for HDR BT GYN cases, treated with tandem and ovoid applicators, with high degrees of heterogeneity and calculated in tandem with TG43.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14549"},"PeriodicalIF":2.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated quality control analysis for American College of Radiology (ACR) digital mammography (DM) phantom images","authors":"Zeyad Alawaji,&nbsp;Seyedamir Tavakoli Taba,&nbsp;Lucy Cartwright,&nbsp;William Rae","doi":"10.1002/acm2.14548","DOIUrl":"10.1002/acm2.14548","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To develop and validate an automated software analysis method for mammography image quality assessment of the American College of Radiology (ACR) digital mammography (DM) phantom images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Twenty-seven DICOM images were acquired using Fuji mammography systems. All images were evaluated by three expert medical physicists using the Royal Australian and New Zealand College of Radiologists (RANZCR) mammography quality control guideline. To enhance the robustness and sensitivity assessment of our algorithm, an additional set of 12 images from a Hologic mammography system was included to test various phantom positional adjustments. The software automatically chose multiple regions of interest (ROIs) for analysis. A template matching method was primarily used for image analysis, followed by an additional method that locates and scores each target object (speck groups, fibers, and masses).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The software performance shows a good to excellent agreement with the average scoring of observers (intraclass correlation coefficient [ICC] of 0.75, 0.79, 0.82 for speck groups, fibers, and masses, respectively). No significant differences were found in the scoring of target objects between human observers and the software. Both methods achieved scores meeting the pass criteria for speck groups and masses. Expert observers allocated lower scores to fiber objects, with diameters less than 0.61 mm, when compared to the software. The software was able to accurately score objects when the phantom position changed by up to 25 mm laterally, up to 5 degrees rotation, and overhanging the chest wall edge by up to 15 mm.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Automated software analysis is a feasible method that may help improve the consistency and reproducibility of mammography image quality assessment with reduced reliance on human interaction and processing time.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"25 12","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11633796/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390777","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 deep learning approaches for multimodal image segmentation of liver cancer","authors":"Chaopeng Wu,&nbsp;Qiyao Chen,&nbsp;Haoyu Wang,&nbsp;Yu Guan,&nbsp;Zhangyang Mian,&nbsp;Cong Huang,&nbsp;Changli Ruan,&nbsp;Qibin Song,&nbsp;Hao Jiang,&nbsp;Jinghui Pan,&nbsp;Xiangpan Li","doi":"10.1002/acm2.14540","DOIUrl":"10.1002/acm2.14540","url":null,"abstract":"<p>This review examines the recent developments in deep learning (DL) techniques applied to multimodal fusion image segmentation for liver cancer. Hepatocellular carcinoma is a highly dangerous malignant tumor that requires accurate image segmentation for effective treatment and disease monitoring. Multimodal image fusion has the potential to offer more comprehensive information and more precise segmentation, and DL techniques have achieved remarkable progress in this domain. This paper starts with an introduction to liver cancer, then explains the preprocessing and fusion methods for multimodal images, then explores the application of DL methods in this area. Various DL architectures such as convolutional neural networks (CNN) and U-Net are discussed and their benefits in multimodal image fusion segmentation. Furthermore, various evaluation metrics and datasets currently used to measure the performance of segmentation models are reviewed. While reviewing the progress, the challenges of current research, such as data imbalance, model generalization, and model interpretability, are emphasized and future research directions are suggested. The application of DL in multimodal image segmentation for liver cancer is transforming the field of medical imaging and is expected to further enhance the accuracy and efficiency of clinical decision making. This review provides useful insights and guidance for medical practitioners.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"25 12","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11633801/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390775","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":"Enhancing stereotactic ablative boost radiotherapy dose prediction for bulky lung cancer: A multi-scale dilated network approach with scale-balanced structure loss.","authors":"Lei Huang, Xianshu Gao, Yue Li, Feng Lyu, Yan Gao, Yun Bai, Mingwei Ma, Siwei Liu, Jiayan Chen, Xueying Ren, Shiyu Shang, Xuanfeng Ding","doi":"10.1002/acm2.14546","DOIUrl":"https://doi.org/10.1002/acm2.14546","url":null,"abstract":"<p><strong>Purpose: </strong>Partial stereotactic ablative boost radiotherapy (P-SABR) effectively treats bulky lung cancer; however, the planning process for P-SABR requires repeated dose calculations. To improve planning efficiency, we proposed a novel deep learning method that utilizes limited data to accurately predict the three-dimensional (3D) dose distribution of the P-SABR plan for bulky lung cancer.</p><p><strong>Methods: </strong>We utilized data on 74 patients diagnosed with bulky lung cancer who received P-SABR treatment. The patient dataset was randomly divided into a training set (51 plans) with augmentation, validation set (7 plans), and testing set (16 plans). We devised a 3D multi-scale dilated network (MD-Net) and integrated a scale-balanced structure loss into the loss function. A comparative analysis with a classical network and other advanced networks with multi-scale analysis capabilities and other loss functions was conducted based on the dose distributions in terms of the axial view, average dose scores (ADSs), and average absolute differences of dosimetric indices (AADDIs). Finally, we analyzed the predicted dosimetric indices against the ground-truth values and compared the predicted dose-volume histogram (DVH) with the ground-truth DVH.</p><p><strong>Results: </strong>Our proposed dose prediction method for P-SABR plans for bulky lung cancer demonstrated strong performance, exhibiting a significant improvement in predicting multiple indicators of regions of interest (ROIs), particularly the gross target volume (GTV). Our network demonstrated increased accuracy in most dosimetric indices and dose scores in different ROIs. The proposed loss function significantly enhanced the predictive performance of the dosimetric indices. The predicted dosimetric indices and DVHs were equivalent to the ground-truth values.</p><p><strong>Conclusion: </strong>Our study presents an effective model based on limited datasets, and it exhibits high accuracy in the dose prediction of P-SABR plans for bulky lung cancer. This method has potential as an automated tool for P-SABR planning and can help optimize treatments and improve planning efficiency.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14546"},"PeriodicalIF":2.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative MRI biomarker for classification of clinically significant prostate cancer: Calibration for reproducibility across echo times","authors":"Karoline Kallis,&nbsp;Christopher C. Conlin,&nbsp;Courtney Ollison,&nbsp;Michael E. Hahn,&nbsp;Rebecca Rakow-Penner,&nbsp;Anders M. Dale,&nbsp;Tyler M. Seibert","doi":"10.1002/acm2.14514","DOIUrl":"10.1002/acm2.14514","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;The purpose of the present study is to develop a calibration method to account for differences in echo times (TE) and facilitate the use of restriction spectrum imaging restriction score (RSIrs) as a quantitative biomarker for the detection of clinically significant prostate cancer (csPCa).&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;This study included 197 consecutive patients who underwent MRI and biopsy examination; 97 were diagnosed with csPCa (grade group ≥ 2). RSI data were acquired three times during the same session: twice at minimum TE ~75 ms and once at TE = 90 ms (TEmin&lt;sub&gt;1&lt;/sub&gt;, TEmin&lt;sub&gt;2&lt;/sub&gt;, and TE90, respectively). A linear regression model was determined to match the C-maps of TE90 to the reference C-maps of TEmin&lt;sub&gt;1&lt;/sub&gt; within the interval ranging from 95th to 99th percentile of signal intensity within the prostate. RSIrs comparisons were made at the 98th percentile within each patient's prostate.&lt;/p&gt;\u0000 \u0000 &lt;p&gt;We compared RSIrs from calibrated TE90 (RSIrs&lt;sub&gt;TE90corr&lt;/sub&gt;) and uncorrected TE90 (RSIrs&lt;sub&gt;TE90&lt;/sub&gt;) to RSIrs from reference TEmin&lt;sub&gt;1&lt;/sub&gt; (RSIrs&lt;sub&gt;TEmin1&lt;/sub&gt;) and repeated TEmin&lt;sub&gt;2&lt;/sub&gt; (RSIrs&lt;sub&gt;TEmin2&lt;/sub&gt;). Calibration performance was evaluated with sensitivity, specificity and area under the ROC curve (AUC).&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;Scaling factors for C&lt;sub&gt;1&lt;/sub&gt;, C&lt;sub&gt;2&lt;/sub&gt;, C&lt;sub&gt;3&lt;/sub&gt;, and C&lt;sub&gt;4&lt;/sub&gt; were estimated as 1.68, 1.33, 1.02, and 1.13, respectively. In non-csPCa cases, the 98th percentile of RSIrs&lt;sub&gt;TEmin2&lt;/sub&gt; and RSIrs&lt;sub&gt;TEmin1&lt;/sub&gt; differed by 0.27 ± 0.86SI (mean ± standard deviation), whereas RSIrs&lt;sub&gt;TE90&lt;/sub&gt; differed from RSIrs&lt;sub&gt;TEmin1&lt;/sub&gt; by 1.82 ± 1.20SI. After calibration, this bias was reduced to -0.51 ± 1.21SI, representing a 72% reduction in absolute error. For patients with csPCa, the difference was 0.54 ± 1.98SI between RSIrs&lt;sub&gt;TEmin2&lt;/sub&gt; and RSIrs&lt;sub&gt;TEmin1&lt;/sub&gt; and 2.28 ± 2.06SI between RSIrs&lt;sub&gt;TE90&lt;/sub&gt; and RSIrs&lt;sub&gt;TEmin1&lt;/sub&gt;. After calibration, the mean difference decreased to -1.03SI, a 55% reduction in absolute error. At the Youden index for patient-level classification of csPCa (8.94SI), RSIrs&lt;sub&gt;TEmin1&lt;/sub&gt; has a sensitivity of 66% and a specificity of 72%.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;The proposed linear calibration method produces similar quantitative biomarker values for acquisitions with different TE, reducing TE-induced error by 72% and 55% for non-csPCa and csPCa, respectively.&lt;/p&gt;\u0000 &lt;/sect","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"25 11","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11539966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390780","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":"Long-term performance monitoring of a-Si 1200 electronic portal imaging device for dosimetric applications.","authors":"Ivan Kutuzov, Ryan Rivest, Eric VanUytven, Boyd McCurdy","doi":"10.1002/acm2.14551","DOIUrl":"https://doi.org/10.1002/acm2.14551","url":null,"abstract":"<p><strong>Purpose: </strong>Recently, dosimetri applications of the electronic portal imaging device (EPID) in radiotherapy have gained popularity. Confidence in the robust and reliable dosimetric performance of EPID detectors is essential for their clinical use. This study aimed to evaluate the dosimetric performance of the a-Si 1200 EPID and assess the long-term stability of its response.</p><p><strong>Methods: </strong>Weekly measurements were performed on two clinically used TrueBeam linear accelerators (linacs) equipped with a-Si 1200 EPID detectors over a 2-year period. They included dark and flood calibration fields, and EPID response to an open field corrected for the long-term machine output drift measured with the secondary absolute dosimeters: an ion chamber and an ion chamber array. All measurements were performed using five photon beam energies and two imaging modes: continuous and dosimetry. The measurements were analyzed for constancy and the presence of long-term trends. Comparisons were made between the two linacs for each beam energy. Pixel sensitivity matrices (PSM) were determined semi-annually and analyzed for long-term constancy for both treatment machines.</p><p><strong>Results: </strong>The long-term variation of the dark and flood field signals, integrated across the EPID plane, over the entire observation period did not exceed 0.17% and 0.79%, respectively. The output-corrected EPID response showed long-term variation from 0.28% to 0.36%, depending on beam energy, while the short-term variation was 0.04%-0.07% for EPID and 0.02%-0.06% for secondary dosimeters. The long-term variation of secondary dosimeters was 0.2%-0.3%. PSMs were found to be stable to within 1% for 97.8% of pixels and 2% for 100% of pixels.</p><p><strong>Conclusion: </strong>Techniques to monitor and assess the long-term performance of the a-Si 1200 EPID as a dosimeter were developed and implemented using two TrueBeam linacs. The long-term variation of the EPID response was within clinical tolerance indicated in AAPM TG-142 report, and the detector was shown to be stable and reproducible for routine clinical dosimetry.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":" ","pages":"e14551"},"PeriodicalIF":2.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AAPM BTSC Report 377.B: Physicist brachytherapy training in 2022 – A survey of therapeutic medical physics residents","authors":"Samantha J. Simiele,&nbsp;Manik Aima,&nbsp;Christopher S. Melhus,&nbsp;Susan L. Richardson","doi":"10.1002/acm2.14501","DOIUrl":"10.1002/acm2.14501","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>A survey of medical physics residency program directors was conducted in Spring 2021 to examine the current state of brachytherapy (BT) training during residency. In this related work, a subsequent survey of therapeutic medical physics residents in 2022 was conducted to assess the confidence and experience of the trainees. Concerns for access to high-quality and diverse training in BT have escalated in importance due to recent declines in BT utilization.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A survey consisting of 26 questions was designed by a working unit of the Brachytherapy Subcommittee of the American Association of Physicists in Medicine (AAPM) and approved for distribution by the Executive Committee of the AAPM. The survey was distributed to current trainees and recent graduates of the Commission on Accreditation of Medical Physics Education Programs accredited therapeutic medical physics residency programs by the AAPM. The participant response was anonymously recorded in an online platform and subsequently analyzed using spreadsheet software.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The survey was distributed to 796 current medical physics residents or recent graduates over the course of 6 weeks in February and March of 2022. The survey received 736 views and a total of 182 responses were collected, with 165 respondents completing the survey in full. Among those responses, 110 had completed their residency training, with program start dates ranging from calendar years 2015 to 2021. Individual responses from the survey takers (including partial survey submissions) were evaluated and analyzed to compile results.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Medical physics residents reported the highest levels of confidence and caseload for gynecological BT procedures when compared with other surveyed treatment techniques. This indicates opportunities to improve training and increase access to clinical caseload are needed in order to improve competency and confidence. Time constraints (clinical and rotation-based) were indicated as impediments to BT proficiency. Medical physics residents reported enthusiasm for additional training opportunities in BT, and it is evident that additional structure and programs are required to ensure adequate access to BT training during residency.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"25 11","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11539968/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142390776","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":"Ranking attention multiple instance learning for lymph node metastasis prediction on multicenter cervical cancer MRI","authors":"Shan Jin,&nbsp;Hongming Xu,&nbsp;Yue Dong,&nbsp;Xiaofeng Wang,&nbsp;Xinyu Hao,&nbsp;Fengying Qin,&nbsp;Ranran Wang,&nbsp;Fengyu Cong","doi":"10.1002/acm2.14547","DOIUrl":"10.1002/acm2.14547","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>In the current clinical diagnostic process, the gold standard for lymph node metastasis (LNM) diagnosis is histopathological examination following surgical lymphadenectomy. Developing a non-invasive and preoperative method for predicting LNM is necessary and holds significant clinical importance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We develop a ranking attention multiple instance learning (RA-MIL) model that integrates convolutional neural networks (CNNs) and ranking attention pooling to diagnose LNM from T2 MRI. Our RA-MIL model applies the CNNs to derive imaging features from 2D MRI slices and employs ranking attention pooling to create patient-level feature representation for diagnostic classification. Based on the MIL and attention theory, informative regions of top-ranking MRI slices from LNM-positive patients are visualized to enhance the interpretability of automatic LNM prediction. This retrospective study collected 300 female patients with cervical cancer who underwent T2-weighted magnetic resonance imaging (MRI) scanning and histopathological diagnosis from one hospital (289 patients) and one open-source dataset (11 patients).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Our RA-MIL model delivers promising LNM prediction performance, achieving the area under the receiver operating characteristic curve (AUC) of 0.809 on the internal test set and 0.833 on the public dataset. Experiments show significant improvements in LNM status prediction using the proposed RA-MIL model compared with other state-of-the-art (SOTA) comparative deep learning models.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The developed RA-MIL model has the potential to serve as a non-invasive auxiliary tool for preoperative LNM prediction, offering visual interpretability regarding informative MRI slices and regions in LNM-positive patients.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"25 12","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11633800/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142380913","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}