Xiaobing Fan, Aritrick Chatterjee, Milica Medved, Tatjana Antic, Aytekin Oto, Gregory S. Karczmar
A new approach to analysis of prostate hybrid multidimensional MRI (HM-MRI) data was introduced in this study. HM-MRI data were acquired for a combination of a few echo times (TEs) and a few b-values. Naturally, there is a matrix associated with HM-MRI data for each image pixel. To process the data, we first linearized HM-MRI data by taking the natural logarithm of the imaging signal intensity. Subsequently, a hybrid symmetric matrix was constructed by multiplying the matrix for each pixel by its own transpose. The eigenvalues for each pixel could then be calculated from the hybrid symmetric matrix. In order to compare eigenvalues between patients, three b-values and three TEs were used, because this was smallest number of b-values and TEs among all patients. The results of eigenvalues were displayed as qualitative color maps for easier visualization. For quantitative analysis, the ratio (λr) of eigenvalues (λ1, λ2, λ3) was defined as λr = (λ1/λ2)/λ3 to compare region of interest (ROI) between prostate cancer (PCa) and normal tissue. The results show that the combined eigenvalue maps show PCas clearly and these maps are quite different from apparent diffusion coefficient (ADC) and T2 maps of the same prostate. The PCa has significant larger λr, smaller ADC and smaller T2 values than normal prostate tissue (p < 0.001). This suggests that the matrix-based method for analyzing HM-MRI data provides new information that may be clinically useful. The method is easy to use and could be easily implemented in clinical practice. The eigenvalues are associated with combination of ADC and T2 values, and could aid in the identification and staging of PCa.
本研究介绍了一种分析前列腺混合多维磁共振成像（HM-MRI）数据的新方法。HM-MRI 数据是在几个回波时间（TE）和几个 b 值的组合下获得的。自然，每个图像像素都有一个与 HM-MRI 数据相关的矩阵。为了处理这些数据，我们首先对成像信号强度取自然对数，对 HM-MRI 数据进行线性化处理。然后，通过将每个像素的矩阵乘以其自身的转置，构建混合对称矩阵。然后就可以根据混合对称矩阵计算出每个像素的特征值。为了比较不同患者的特征值，使用了三个 b 值和三个 TE，因为这是所有患者中 b 值和 TE 数量最少的。特征值的结果以定性彩色图的形式显示，以便于可视化。在定量分析中，特征值（λ1、λ2、λ3）的比率（λr）被定义为 λr = (λ1/λ2)/λ3 以比较前列腺癌（PCa）和正常组织的感兴趣区（ROI）。结果显示，综合特征值图能清晰显示 PCa，这些图与同一前列腺的表观扩散系数（ADC）和 T2 图截然不同。与正常前列腺组织相比，PCa 的 λr 明显较大，ADC 较小，T2 值也较小（p＜0.05）。
{"title":"Introduction to matrix-based method for analyzing hybrid multidimensional prostate MRI data","authors":"Xiaobing Fan, Aritrick Chatterjee, Milica Medved, Tatjana Antic, Aytekin Oto, Gregory S. Karczmar","doi":"10.1002/acm2.14544","DOIUrl":"10.1002/acm2.14544","url":null,"abstract":"<p>A new approach to analysis of prostate hybrid multidimensional MRI (HM-MRI) data was introduced in this study. HM-MRI data were acquired for a combination of a few echo times (TEs) and a few <i>b</i>-values. Naturally, there is a matrix associated with HM-MRI data for each image pixel. To process the data, we first linearized HM-MRI data by taking the natural logarithm of the imaging signal intensity. Subsequently, a hybrid symmetric matrix was constructed by multiplying the matrix for each pixel by its own transpose. The eigenvalues for each pixel could then be calculated from the hybrid symmetric matrix. In order to compare eigenvalues between patients, three <i>b</i>-values and three TEs were used, because this was smallest number of <i>b</i>-values and TEs among all patients. The results of eigenvalues were displayed as qualitative color maps for easier visualization. For quantitative analysis, the ratio (<i>λ</i><sub>r</sub>) of eigenvalues (<i>λ</i><sub>1</sub>, <i>λ</i><sub>2</sub>, <i>λ</i><sub>3</sub>) was defined as <i>λ</i><sub>r</sub> = (<i>λ</i><sub>1</sub>/<i>λ</i><sub>2</sub>)/<i>λ</i><sub>3</sub> to compare region of interest (ROI) between prostate cancer (PCa) and normal tissue. The results show that the combined eigenvalue maps show PCas clearly and these maps are quite different from apparent diffusion coefficient (ADC) and T2 maps of the same prostate. The PCa has significant larger <i>λ</i><sub>r</sub>, smaller ADC and smaller T2 values than normal prostate tissue (<i>p</i> < 0.001). This suggests that the matrix-based method for analyzing HM-MRI data provides new information that may be clinically useful. The method is easy to use and could be easily implemented in clinical practice. The eigenvalues are associated with combination of ADC and T2 values, and could aid in the identification and staging of PCa.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11713853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142681962","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}
Xiaohan Yuan, Chaoqiong Ma, Mingzhe Hu, Richard L. J. Qiu, Elahheh Salari, Reema Martini, Xiaofeng Yang
The integration of machine learning (ML) with radiotherapy has emerged as a pivotal innovation in outcome prediction, bringing novel insights amid unique challenges. This review comprehensively examines the current scope of ML applications in various treatment contexts, focusing on treatment outcomes such as patient survival, disease recurrence, and treatment-induced toxicity. It emphasizes the ascending trajectory of research efforts and the prominence of survival analysis as a clinical priority. We analyze the use of several common medical imaging modalities in conjunction with clinical data, highlighting the advantages and complexities inherent in this approach. The research reflects a commitment to advancing patient-centered care, advocating for expanded research on abdominal and pancreatic cancers. While data collection, patient privacy, standardization, and interpretability present significant challenges, leveraging ML in radiotherapy holds remarkable promise for elevating precision medicine and improving patient care outcomes.
机器学习（ML）与放疗的结合已成为结果预测领域的一项关键创新，在独特的挑战中带来了新的见解。本综述全面探讨了当前机器学习在各种治疗环境中的应用范围，重点关注患者生存、疾病复发和治疗诱发毒性等治疗结果。它强调了研究工作的上升轨迹以及生存分析作为临床优先事项的突出地位。我们分析了几种常见医学成像模式与临床数据的结合使用，强调了这种方法固有的优势和复杂性。这项研究反映出我们致力于推进以患者为中心的护理，倡导扩大对腹部和胰腺癌症的研究。虽然数据收集、患者隐私、标准化和可解释性方面存在重大挑战，但在放射治疗中利用 ML 有助于提高精准医疗水平和改善患者护理效果。
{"title":"Machine learning in image-based outcome prediction after radiotherapy: A review","authors":"Xiaohan Yuan, Chaoqiong Ma, Mingzhe Hu, Richard L. J. Qiu, Elahheh Salari, Reema Martini, Xiaofeng Yang","doi":"10.1002/acm2.14559","DOIUrl":"10.1002/acm2.14559","url":null,"abstract":"<p>The integration of machine learning (ML) with radiotherapy has emerged as a pivotal innovation in outcome prediction, bringing novel insights amid unique challenges. This review comprehensively examines the current scope of ML applications in various treatment contexts, focusing on treatment outcomes such as patient survival, disease recurrence, and treatment-induced toxicity. It emphasizes the ascending trajectory of research efforts and the prominence of survival analysis as a clinical priority. We analyze the use of several common medical imaging modalities in conjunction with clinical data, highlighting the advantages and complexities inherent in this approach. The research reflects a commitment to advancing patient-centered care, advocating for expanded research on abdominal and pancreatic cancers. While data collection, patient privacy, standardization, and interpretability present significant challenges, leveraging ML in radiotherapy holds remarkable promise for elevating precision medicine and improving patient care outcomes.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11712300/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142667930","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}
Heng Zhou, Cheng Yan, Min Ji, Zhang Shi, Chun Yang, Mengsu Zeng
<div> <section> <h3> Objective</h3> <p>The routine patient arm position differs between coronary CT angiography (CTA) and craniocervical CTA protocols. To investigate the clinical feasibility of supported bridge position (SBP) in combined coronary and craniocervical CTA.</p> </section> <section> <h3> Methods</h3> <p>Prospective enrollment included patients with suspected coronary artery disease (CAD) or craniocervical artery disease (CCAD) from February 2022 to November 2022. Patients were divided into three groups: coronary or craniocervical CTA according to CAD or CCAD using standard position (group 1), combined CTA with naturally arm-down position (group 2) and SBP (group 3). Statistical analysis of objective image quality, such as noise and contrast-to-noise ratio (CNR), subjective image quality, patient position and radiation dose was performed among the three groups.</p> </section> <section> <h3> Results</h3> <p>Two hundred and one patients (median age, 67 years; 138 men) were included. In terms of CNR for cardiac segment, group 1 and group 3 had no statistical difference, both significantly higher than group 2 (group 1, 12.56 ± 2.05; group 2, 10.4 ± 2.43; group 3, 11.94 ± 2.22; <i>P</i> < 0.05). Subjective image evaluation revealed no statistically significant differences among the three groups of coronary arteries (<i>P</i> > 0.05). Additionally, the lateral project value of scout images at the heart level indicated a significant difference (119.48 ± 12.19, 182.34 ± 25.09, and 140.58 ± 19.68 of patients, for group 1, group 2, and group 3, respectively, <i>P</i> < 0.05). No statistical differences were observed in <span></span><math> <semantics> <msub> <mi>CTDI</mi> <mi>vol</mi> </msub> <annotation>${mathrm{CTDI}}_{{mathrm{vol}}}$</annotation> </semantics></math> between group 1 and group 3 (cardiac scan, 15.77 [15.07–16.37] mGy vs. 14.88 [12.19–18.81] mGy; craniocervical scan, 7.85 [7.69–8.01] mGy vs. 7.88 [7.88–7.89] mGy; all <i>P</i> > 0.05). However, group 2 had a higher dose (19.54 [16.86–22.85] mGy and 10.87 [10.86–10.87] mGy, for cardiac and craniocervical scans, respectively).</p> </section> <section> <h3> Conclusions</h3> <p>In comparison with a naturally arm-down position, SBP, which aligns the humerus bones with the spinal column, can provide diagnostic image quality at routine dose level of standard position CTA.</p> </section>
{"title":"Supported bridge position in one-stop coronary and craniocervical CT angiography: A randomized clinical trial","authors":"Heng Zhou, Cheng Yan, Min Ji, Zhang Shi, Chun Yang, Mengsu Zeng","doi":"10.1002/acm2.14561","DOIUrl":"10.1002/acm2.14561","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>The routine patient arm position differs between coronary CT angiography (CTA) and craniocervical CTA protocols. To investigate the clinical feasibility of supported bridge position (SBP) in combined coronary and craniocervical CTA.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Prospective enrollment included patients with suspected coronary artery disease (CAD) or craniocervical artery disease (CCAD) from February 2022 to November 2022. Patients were divided into three groups: coronary or craniocervical CTA according to CAD or CCAD using standard position (group 1), combined CTA with naturally arm-down position (group 2) and SBP (group 3). Statistical analysis of objective image quality, such as noise and contrast-to-noise ratio (CNR), subjective image quality, patient position and radiation dose was performed among the three groups.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Two hundred and one patients (median age, 67 years; 138 men) were included. In terms of CNR for cardiac segment, group 1 and group 3 had no statistical difference, both significantly higher than group 2 (group 1, 12.56 ± 2.05; group 2, 10.4 ± 2.43; group 3, 11.94 ± 2.22; <i>P</i> < 0.05). Subjective image evaluation revealed no statistically significant differences among the three groups of coronary arteries (<i>P</i> > 0.05). Additionally, the lateral project value of scout images at the heart level indicated a significant difference (119.48 ± 12.19, 182.34 ± 25.09, and 140.58 ± 19.68 of patients, for group 1, group 2, and group 3, respectively, <i>P</i> < 0.05). No statistical differences were observed in <span></span><math>\u0000 <semantics>\u0000 <msub>\u0000 <mi>CTDI</mi>\u0000 <mi>vol</mi>\u0000 </msub>\u0000 <annotation>${mathrm{CTDI}}_{{mathrm{vol}}}$</annotation>\u0000 </semantics></math> between group 1 and group 3 (cardiac scan, 15.77 [15.07–16.37] mGy vs. 14.88 [12.19–18.81] mGy; craniocervical scan, 7.85 [7.69–8.01] mGy vs. 7.88 [7.88–7.89] mGy; all <i>P</i> > 0.05). However, group 2 had a higher dose (19.54 [16.86–22.85] mGy and 10.87 [10.86–10.87] mGy, for cardiac and craniocervical scans, respectively).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>In comparison with a naturally arm-down position, SBP, which aligns the humerus bones with the spinal column, can provide diagnostic image quality at routine dose level of standard position CTA.</p>\u0000 </section>\u0000 ","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11713983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638970","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}
Suman Gautam, Alexander F. I. Osman, Dylan Richeson, Somayeh Gholami, Binod Manandhar, Sharmin Alam, William Y. Song
<div> <section> <h3> Background</h3> <p>Formulating a clinically acceptable plan within the time-constrained clinical setting of brachytherapy poses challenges to clinicians. Deep learning based dose prediction methods have shown favorable solutions for enhancing efficiency, but development has primarily been on external beam radiation therapy. Thus, there is a need for translation to brachytherapy.</p> </section> <section> <h3> Purpose</h3> <p>This study proposes a dose prediction model utilizing an attention-gating mechanism and a 3D UNET for cervical cancer high-dose-rate intracavitary brachytherapy treatment planning with tandem-and-ovoid/ring applicators.</p> </section> <section> <h3> Methods</h3> <p>A multi-institutional data set consisting of 77 retrospective clinical brachytherapy plans was utilized in this study. The data were preprocessed and augmented to increase the number of plans to 252. A 3D UNET architecture with attention gates was constructed and trained for mapping the contour information to dose distribution. The trained model was evaluated on a testing data set using various metrics, including dose statistics and dose-volume indices. We also trained a baseline UNET model for a fair comparison.</p> </section> <section> <h3> Results</h3> <p>The attention-gated 3D UNET model exhibited competitive accuracy in predicting dose distributions similar to the ground truth. The average values of the mean absolute errors were 0.46 ± 11.71 Gy (vs. 0.47 ± 9.16 Gy for a baseline UNET) in CTV<sub>HR</sub>, 0.55 ± 0.67 Gy (vs. 0.70 ± 1.54 Gy for a baseline UNET) in bladder, 0.42 ± 0.46 Gy (vs. 0.49 ± 1.34 Gy for a baseline UNET) in rectum, and 0.31 ± 0.65 Gy (vs. 0.20 ± 3.76 Gy for a baseline UNET) in sigmoid. Our results showed that the mean individual differences in ΔD<sub>2cc</sub> for bladder, rectum, and sigmoid were 0.38 ± 1.19 (<i>p</i> = 0.50), 0.43 ± 0.71 (<i>p</i> = 0.41), and −0.47 ± 0.79 (<i>p</i> = 0.30) Gy, respectively. Similarly, the mean individual differences in ΔD<sub>1cc</sub> for bladder, rectum, and sigmoid were 0.09 ± 1.21 (<i>p</i> = 0.36), 0.20 ± 0.95 (<i>p</i> = 0.24), and −0.21 ± 0.59 (<i>p</i> = 0.30) Gy. The mean individual differences for ΔD<sub>90</sub>, ΔV<sub>100%</sub>, ΔV<sub>150%</sub>, and ΔV<sub>200%</sub> of the CTV<sub>HR</sub> were −0.45 ± 2.42 (<i>p</i> = 0.26) Gy, 0.55 ± 9.42% (<i>p</i> = 0.78), 0.82 ± 4.21% (<i>p</i> = 0.81), and −0.80 ± 10.48% (<i>p</i> = 0.36), respectively. The model requires less than 5 s to predict a full 3D dose distribution for a new patient plan.</p> </section>
{"title":"Attention 3D UNET for dose distribution prediction of high-dose-rate brachytherapy of cervical cancer: Intracavitary applicators","authors":"Suman Gautam, Alexander F. I. Osman, Dylan Richeson, Somayeh Gholami, Binod Manandhar, Sharmin Alam, William Y. Song","doi":"10.1002/acm2.14568","DOIUrl":"10.1002/acm2.14568","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Formulating a clinically acceptable plan within the time-constrained clinical setting of brachytherapy poses challenges to clinicians. Deep learning based dose prediction methods have shown favorable solutions for enhancing efficiency, but development has primarily been on external beam radiation therapy. Thus, there is a need for translation to brachytherapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study proposes a dose prediction model utilizing an attention-gating mechanism and a 3D UNET for cervical cancer high-dose-rate intracavitary brachytherapy treatment planning with tandem-and-ovoid/ring applicators.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A multi-institutional data set consisting of 77 retrospective clinical brachytherapy plans was utilized in this study. The data were preprocessed and augmented to increase the number of plans to 252. A 3D UNET architecture with attention gates was constructed and trained for mapping the contour information to dose distribution. The trained model was evaluated on a testing data set using various metrics, including dose statistics and dose-volume indices. We also trained a baseline UNET model for a fair comparison.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The attention-gated 3D UNET model exhibited competitive accuracy in predicting dose distributions similar to the ground truth. The average values of the mean absolute errors were 0.46 ± 11.71 Gy (vs. 0.47 ± 9.16 Gy for a baseline UNET) in CTV<sub>HR</sub>, 0.55 ± 0.67 Gy (vs. 0.70 ± 1.54 Gy for a baseline UNET) in bladder, 0.42 ± 0.46 Gy (vs. 0.49 ± 1.34 Gy for a baseline UNET) in rectum, and 0.31 ± 0.65 Gy (vs. 0.20 ± 3.76 Gy for a baseline UNET) in sigmoid. Our results showed that the mean individual differences in ΔD<sub>2cc</sub> for bladder, rectum, and sigmoid were 0.38 ± 1.19 (<i>p</i> = 0.50), 0.43 ± 0.71 (<i>p</i> = 0.41), and −0.47 ± 0.79 (<i>p</i> = 0.30) Gy, respectively. Similarly, the mean individual differences in ΔD<sub>1cc</sub> for bladder, rectum, and sigmoid were 0.09 ± 1.21 (<i>p</i> = 0.36), 0.20 ± 0.95 (<i>p</i> = 0.24), and −0.21 ± 0.59 (<i>p</i> = 0.30) Gy. The mean individual differences for ΔD<sub>90</sub>, ΔV<sub>100%</sub>, ΔV<sub>150%</sub>, and ΔV<sub>200%</sub> of the CTV<sub>HR</sub> were −0.45 ± 2.42 (<i>p</i> = 0.26) Gy, 0.55 ± 9.42% (<i>p</i> = 0.78), 0.82 ± 4.21% (<i>p</i> = 0.81), and −0.80 ± 10.48% (<i>p</i> = 0.36), respectively. The model requires less than 5 s to predict a full 3D dose distribution for a new patient plan.</p>\u0000 </section>\u0000","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14568","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638935","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}
We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy.
Methods
A total of 76 patients of abdominal and pelvic tumors were prospectively selected. The Catphan504 was acquired with the same conditions as the standard phantom test set. We used a CycleGAN-based model for image enhancement. Normal dose CT (NDCT), ultra-low dose CT (LDCT) and deep learning enhanced CT (DLR) were evaluated by subjective and objective analyses in terms of imaging quality, HU accuracy, and image signal-to-noise ratio (SNR).
Results
The image noise of DLR was significantly reduced, and the contrast-to-noise ratio (CNR) was significantly improved compared to the LDCT. The most significant improvement was the acrylic which represented soft tissue in CNR from 1.89 to 3.37, improving by 76%, nearly approaching the NDCT, and in low-density resolution from 7.64 to 12.6, improving by 64%. The spatial frequencies of MTF10 and MTF50 in DLR were 4.28 and 2.35 cycles/mm in DLR, respectively, which are higher than LDCT 3.87 and 2.12 cycles/mm, and even slightly higher than NDCT 4.15 and 2.28 cycles/mm. The accuracy and stability of HU values of DLR were similar to NDCT. The image quality evaluation of the two doctors agreed well with DLR and NDCT. A two-by-two comparison between groups showed that the differences in image scores of LDCT compared with NDCT and DLR were all statistically significant (p < 0.05), and the subjective scores of DLR were close to NDCT.
Conclusion
The image quality of DLR was close to NDCT with reduced radiation dose, which can fully meet the needs of conventional image-guided adaptive radiotherapy (ART) and achieve the quality requirements of clinical radiotherapy. The proposed method provided a technical basis for LDCT-guided ART.
{"title":"Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy","authors":"Hua Jiang, Songbing Qin, Lecheng Jia, Ziquan Wei, Weiqi Xiong, Wentao Xu, Wei Gong, Wei Zhang, Liqin Yu","doi":"10.1002/acm2.14560","DOIUrl":"10.1002/acm2.14560","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Objective</h3>\u0000 \u0000 <p>We investigated the feasibility of deep learning-based ultra-low dose kV-fan-beam computed tomography (kV-FBCT) image enhancement algorithm for clinical application in abdominal and pelvic tumor radiotherapy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 76 patients of abdominal and pelvic tumors were prospectively selected. The Catphan504 was acquired with the same conditions as the standard phantom test set. We used a CycleGAN-based model for image enhancement. Normal dose CT (NDCT), ultra-low dose CT (LDCT) and deep learning enhanced CT (DLR) were evaluated by subjective and objective analyses in terms of imaging quality, HU accuracy, and image signal-to-noise ratio (SNR).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The image noise of DLR was significantly reduced, and the contrast-to-noise ratio (CNR) was significantly improved compared to the LDCT. The most significant improvement was the acrylic which represented soft tissue in CNR from 1.89 to 3.37, improving by 76%, nearly approaching the NDCT, and in low-density resolution from 7.64 to 12.6, improving by 64%. The spatial frequencies of MTF10 and MTF50 in DLR were 4.28 and 2.35 cycles/mm in DLR, respectively, which are higher than LDCT 3.87 and 2.12 cycles/mm, and even slightly higher than NDCT 4.15 and 2.28 cycles/mm. The accuracy and stability of HU values of DLR were similar to NDCT. The image quality evaluation of the two doctors agreed well with DLR and NDCT. A two-by-two comparison between groups showed that the differences in image scores of LDCT compared with NDCT and DLR were all statistically significant (<i>p</i> < 0.05), and the subjective scores of DLR were close to NDCT.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The image quality of DLR was close to NDCT with reduced radiation dose, which can fully meet the needs of conventional image-guided adaptive radiotherapy (ART) and achieve the quality requirements of clinical radiotherapy. The proposed method provided a technical basis for LDCT-guided ART.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"25 12","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11633815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142620927","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}
Samuel D. Rusu, Blake R. Smith, Joel J. St-Aubin, Nathan Shaffer, Daniel Ellis Hyer
Purpose
MRI-guided adaptive radiotherapy can directly monitor the anatomical positioning of the intended target during treatment with no additional imaging dose. Elekta has recently released its comprehensive motion management (CMM) solution that enables automatic radiation beam-gating on the Unity MR-Linac. Easily visualized targets that are distinct from the surrounding anatomy can be used to drive automatic gating decisions from the MRI cine imaging. However, poorly visualized targets can compromise the tracking and gating capabilities and may require surrogate tracking structures. This work presents strategies to generate robust tracking surrogates for a variety of treatment sites, enabling a wider application of CMM.
Methods
Surrogate tracking strategies were developed from a cohort of patients treated using the CMM system on the Unity MR-Linac for treatment sites of the lung, pancreas, liver, and prostate. These sites posed challenging visualization or tracking of the primary target thereby compromising the tracking accuracy. Surrogate structures were developed using site-specific strategies to improve the imaging textured detail within the tracking volume while avoiding the dynamic overwhelming hypo- or hyper-intense anatomical structures. These surrogate volumes were applied within the anatomical positioning monitoring system as a proxy that drove the CMM gating decisions on the treatment unit.
Results
Robust site-specific surrogate structures were developed. Surrogate tracking structures for centrally located thoracic targets were created by expanding the target peripherally away from the heart and great vessels and into the lung. Pancreas surrogates required a vertically expanded column intersecting with the inferior liver edge. For the liver and prostate, surrogate structures consisted of a uniform expansion of the target, with liver surrogates intersecting the proximal liver edge or diaphragm while avoiding nearby ribs.
Conclusion
These surrogate strategies have enabled the gating of complex moving targets among different treatment sites at our institution.
{"title":"Surrogate gating strategies for the Elekta Unity MR-Linac gating system","authors":"Samuel D. Rusu, Blake R. Smith, Joel J. St-Aubin, Nathan Shaffer, Daniel Ellis Hyer","doi":"10.1002/acm2.14566","DOIUrl":"10.1002/acm2.14566","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>MRI-guided adaptive radiotherapy can directly monitor the anatomical positioning of the intended target during treatment with no additional imaging dose. Elekta has recently released its comprehensive motion management (CMM) solution that enables automatic radiation beam-gating on the Unity MR-Linac. Easily visualized targets that are distinct from the surrounding anatomy can be used to drive automatic gating decisions from the MRI cine imaging. However, poorly visualized targets can compromise the tracking and gating capabilities and may require surrogate tracking structures. This work presents strategies to generate robust tracking surrogates for a variety of treatment sites, enabling a wider application of CMM.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Surrogate tracking strategies were developed from a cohort of patients treated using the CMM system on the Unity MR-Linac for treatment sites of the lung, pancreas, liver, and prostate. These sites posed challenging visualization or tracking of the primary target thereby compromising the tracking accuracy. Surrogate structures were developed using site-specific strategies to improve the imaging textured detail within the tracking volume while avoiding the dynamic overwhelming hypo- or hyper-intense anatomical structures. These surrogate volumes were applied within the anatomical positioning monitoring system as a proxy that drove the CMM gating decisions on the treatment unit.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Robust site-specific surrogate structures were developed. Surrogate tracking structures for centrally located thoracic targets were created by expanding the target peripherally away from the heart and great vessels and into the lung. Pancreas surrogates required a vertically expanded column intersecting with the inferior liver edge. For the liver and prostate, surrogate structures consisted of a uniform expansion of the target, with liver surrogates intersecting the proximal liver edge or diaphragm while avoiding nearby ribs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>These surrogate strategies have enabled the gating of complex moving targets among different treatment sites at our institution.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/acm2.14566","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142620930","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}
Siju C. George, Santiago Aguirre, Nichole M. Maughan, Ranjini Tolakanahalli, E. James Jebaseelan Samuel, Sven L. Gallo, Jacqueline E. Zoberi, Yongsook C. Lee
<div> <section> <h3> Purpose</h3> <p>This study investigates potential failure modes and conducts failure mode and effects analysis (FMEA) and fault tree analysis (FTA) on the administration of <span></span><math> <semantics> <mrow> <msup> <mrow></mrow> <mn>177</mn> </msup> <mi>Lu</mi> </mrow> <annotation>$^{177}{rm Lu}$</annotation> </semantics></math> DOTATATE (LUTATHERA) and <span></span><math> <semantics> <mrow> <msup> <mrow></mrow> <mn>177</mn> </msup> <mi>Lu</mi> </mrow> <annotation>$^{177}{rm Lu}$</annotation> </semantics></math> PSMA-617 (PLUVICTO). The quality management (QM) process in radiopharmaceutical therapies (RPTs) requires collaboration between nuclear medicine (NM) and radiation oncology (RO) departments. As part of a multi-institutional study, we surveyed various departments to identify and analyze failure modes, leading to a proposed comprehensive QM program. RPT teams in RO or NM clinics can benefit from this study by continually improving their practice.</p> </section> <section> <h3> Methods</h3> <p>We reviewed the literature to investigate the administration of Pluvicto and Lutathera, focusing on prospective procedural failures and potential failure modes (PFMs) and their outcomes. We distributed an FMEA survey to multiple experienced centers in <span></span><math> <semantics> <mrow> <msup> <mrow></mrow> <mn>177</mn> </msup> <mi>Lu</mi> </mrow> <annotation>$^{177}{rm Lu}$</annotation> </semantics></math>-based RPTs and calculated risk priority number (RPN) for various PFM. We conducted an FTA using this information to pinpoint the root causes of potential failures.</p> </section> <section> <h3> Results</h3> <p>The findings from the literature review and survey responses on the prospective study have identified several critical areas at risk of failure. These areas include non-optimized treatment delivery, inadequate patient monitoring, and lack of safety training, leading to
目的：本研究调查了177 Lu $^{177}{rm Lu}$ DOTATATE（LUTATHERA）和177 Lu $^{177}{rm Lu}$ PSMA-617 （PLUVICTO）的潜在失效模式，并进行了失效模式与效应分析（FMEA）和故障树分析（FTA）。放射性药物治疗（RPT）的质量管理（QM）过程需要核医学（NM）和放射肿瘤学（RO）部门之间的合作。作为一项多机构研究的一部分，我们对各部门进行了调查，以确定和分析失败模式，从而提出了一项全面的质量管理计划。放射肿瘤科或 NM 诊所的 RPT 团队可以从这项研究中受益，不断改进他们的实践：我们查阅了相关文献，调查了 Pluvicto 和 Lutathera 的使用情况，重点关注了前瞻性程序故障和潜在故障模式 (PFM) 及其结果。我们向多个经验丰富的中心发放了基于177 Lu $^{177}{rm Lu}$ RPT的FMEA调查表，并计算了各种PFM的风险优先级（RPN）。我们利用这些信息进行了一次 FTA，以找出潜在故障的根本原因：结果：文献综述和前瞻性研究调查的结果确定了几个存在故障风险的关键领域。这些领域包括未优化治疗实施、患者监测不足以及缺乏安全培训，从而导致治疗实施后患者排出的剂量造成辐射污染。根据 FMEA 结果创建了一个分段 FTA，重点关注 RPN 值较高的辐射污染：通过找出故障的根本原因并对现有的质量管理措施提出有针对性的改进建议，该分析提高了基于 177 Lu $^{177}{rm Lu}$ 的 RPT 治疗的安全性。鉴于RPT前瞻性风险分析研究的数量有限，我们的研究提出了进行更多此类研究的必要性，并推荐了将本研究应用于其他RPT的方法。
{"title":"Enhancing safety: Multi-institutional FMEA and FTA on \u0000 \u0000 \u0000 \u0000 \u0000 177\u0000 \u0000 Lu\u0000 \u0000 $^{177}{rm Lu}$\u0000 -based radio-pharmaceutical therapy","authors":"Siju C. George, Santiago Aguirre, Nichole M. Maughan, Ranjini Tolakanahalli, E. James Jebaseelan Samuel, Sven L. Gallo, Jacqueline E. Zoberi, Yongsook C. Lee","doi":"10.1002/acm2.14550","DOIUrl":"10.1002/acm2.14550","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study investigates potential failure modes and conducts failure mode and effects analysis (FMEA) and fault tree analysis (FTA) on the administration of <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow></mrow>\u0000 <mn>177</mn>\u0000 </msup>\u0000 <mi>Lu</mi>\u0000 </mrow>\u0000 <annotation>$^{177}{rm Lu}$</annotation>\u0000 </semantics></math> DOTATATE (LUTATHERA) and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow></mrow>\u0000 <mn>177</mn>\u0000 </msup>\u0000 <mi>Lu</mi>\u0000 </mrow>\u0000 <annotation>$^{177}{rm Lu}$</annotation>\u0000 </semantics></math> PSMA-617 (PLUVICTO). The quality management (QM) process in radiopharmaceutical therapies (RPTs) requires collaboration between nuclear medicine (NM) and radiation oncology (RO) departments. As part of a multi-institutional study, we surveyed various departments to identify and analyze failure modes, leading to a proposed comprehensive QM program. RPT teams in RO or NM clinics can benefit from this study by continually improving their practice.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We reviewed the literature to investigate the administration of Pluvicto and Lutathera, focusing on prospective procedural failures and potential failure modes (PFMs) and their outcomes. We distributed an FMEA survey to multiple experienced centers in <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mrow></mrow>\u0000 <mn>177</mn>\u0000 </msup>\u0000 <mi>Lu</mi>\u0000 </mrow>\u0000 <annotation>$^{177}{rm Lu}$</annotation>\u0000 </semantics></math>-based RPTs and calculated risk priority number (RPN) for various PFM. We conducted an FTA using this information to pinpoint the root causes of potential failures.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The findings from the literature review and survey responses on the prospective study have identified several critical areas at risk of failure. These areas include non-optimized treatment delivery, inadequate patient monitoring, and lack of safety training, leading to","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11712884/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142620919","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}
Xinyi Li, Yang Sheng, Qingrong Jackie Wu, Yaorong Ge, David M. Brizel, Yvonne M. Mowery, Dongrong Yang, Fang-Fang Yin, Qiuwen Wu
Background and purpose
To describe the clinical commissioning of an in-house artificial intelligence (AI) treatment planning platform for head-and-neck (HN) Intensity Modulated Radiation Therapy (IMRT).
Materials and methods
The AI planning platform has three components: (1) a graphical user interface (GUI) is built within the framework of a commercial treatment planning system (TPS). The GUI allows AI models to run remotely on a designated workstation configured with GPU acceleration. (2) A template plan is automatically prepared involving both clinical and AI considerations, which include contour evaluation, isocenter placement, and beam/collimator jaw placement. (3) A well-orchestrated suite of AI models predicts optimal fluence maps, which are imported into TPS for dose calculation followed by an optional automatic fine-tuning. Six AI models provide flexible tradeoffs in parotid sparing and Planning Target Volume (PTV)-organ-at-risk (OAR) preferences. Planners could examine the plan dose distribution and make further modifications as clinically needed. The performance of the AI plans was compared to the corresponding clinical plans.
Results
The average plan generation time including manual operations was 10–15 min per case, with each AI model prediction taking ∼1 s. The six AI plans form a wide range of tradeoff choices between left and right parotids and between PTV and OARs compared with corresponding clinical plans, which correctly reflected their tradeoff designs.
Conclusion
The in-house AI IMRT treatment planning platform was developed and is available for clinical use at our institution. The process demonstrates outstanding performance and robustness of the AI platform and provides sufficient validation.
{"title":"Clinical commissioning and introduction of an in-house artificial intelligence (AI) platform for automated head and neck intensity modulated radiation therapy (IMRT) treatment planning","authors":"Xinyi Li, Yang Sheng, Qingrong Jackie Wu, Yaorong Ge, David M. Brizel, Yvonne M. Mowery, Dongrong Yang, Fang-Fang Yin, Qiuwen Wu","doi":"10.1002/acm2.14558","DOIUrl":"10.1002/acm2.14558","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background and purpose</h3>\u0000 \u0000 <p>To describe the clinical commissioning of an in-house artificial intelligence (AI) treatment planning platform for head-and-neck (HN) Intensity Modulated Radiation Therapy (IMRT).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Materials and methods</h3>\u0000 \u0000 <p>The AI planning platform has three components: (1) a graphical user interface (GUI) is built within the framework of a commercial treatment planning system (TPS). The GUI allows AI models to run remotely on a designated workstation configured with GPU acceleration. (2) A template plan is automatically prepared involving both clinical and AI considerations, which include contour evaluation, isocenter placement, and beam/collimator jaw placement. (3) A well-orchestrated suite of AI models predicts optimal fluence maps, which are imported into TPS for dose calculation followed by an optional automatic fine-tuning. Six AI models provide flexible tradeoffs in parotid sparing and Planning Target Volume (PTV)-organ-at-risk (OAR) preferences. Planners could examine the plan dose distribution and make further modifications as clinically needed. The performance of the AI plans was compared to the corresponding clinical plans.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The average plan generation time including manual operations was 10–15 min per case, with each AI model prediction taking ∼1 s. The six AI plans form a wide range of tradeoff choices between left and right parotids and between PTV and OARs compared with corresponding clinical plans, which correctly reflected their tradeoff designs.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The in-house AI IMRT treatment planning platform was developed and is available for clinical use at our institution. The process demonstrates outstanding performance and robustness of the AI platform and provides sufficient validation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11712748/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583191","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}
This study aimed to develop and evaluate the validity and reliability of dentists' radiation protection knowledge, attitudes, and practices (DRP-KAPs) questionnaire.
Methods
This study was conducted using a stepwise approach. In the first step, items were generated to determine the relevant content and domains after a thorough literature review. In the second step, the content validity of the questionnaire was assessed by seven experts using face and content validity. The content validity index for relevance and clarity (I-CVI, S-CVI/Av, and S-CVI/UA) and the content validity ratio (CVR) were performed. Then, the questionnaire was pre-tested with 10 dentists for face validation. In the third step, reliability was assessed using internal consistency (Kuder-Richardson-20 (KR-20) and Cronbach's alpha) and test–retest (Kappa and intraclass correlation coefficient [ICC]) methods by filling out the questionnaire by 100 dentists practicing in the Rabat-Salé-Kénitra region, in Morocco.
Results
The finalized DRP-KAPs questionnaire contains 41 items covering knowledge, attitudes and practices (KAPs). The I-CVIs, S-CVI/UA and S-CVI/AV and CVR values of the 41 items were ≥0.86 for each item, ≥0.82, ≥0.97, and ≥0.71, respectively. With regard to internal consistency reliability, the KR-20 coefficients for the knowledge and practice domains were 0.70 and 0.68, respectively, and the Cronbach alpha for the attitude domain was 0.73. The DRP-KAPs questionnaire has good reliability with the ICC coefficients for attitude items ranging from 0.57 to 0.95 and Kappa coefficients for knowledge and practice items ranging from 0.64 to 1 and 0.77 to 1, respectively.
Conclusion
The developed DRP-KAPs questionnaire was found to be a noteworthy tool for assessing radiation protection among dentists, with acceptable internal consistency, good ICC and Kappa coefficients, and good content validity indices.
{"title":"Development and validation of a questionnaire on radiation protection knowledge, attitudes, and practices among Moroccan dentists","authors":"Naoual Elmorabit, Majdouline Obtel, Mohamed Azougagh, Asmaa Marrakchi, Oum Keltoum Ennibi","doi":"10.1002/acm2.14555","DOIUrl":"10.1002/acm2.14555","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study aimed to develop and evaluate the validity and reliability of dentists' radiation protection knowledge, attitudes, and practices (DRP-KAPs) questionnaire.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This study was conducted using a stepwise approach. In the first step, items were generated to determine the relevant content and domains after a thorough literature review. In the second step, the content validity of the questionnaire was assessed by seven experts using face and content validity. The content validity index for relevance and clarity (I-CVI, S-CVI/Av, and S-CVI/UA) and the content validity ratio (CVR) were performed. Then, the questionnaire was pre-tested with 10 dentists for face validation. In the third step, reliability was assessed using internal consistency (Kuder-Richardson-20 (KR-20) and Cronbach's alpha) and test–retest (Kappa and intraclass correlation coefficient [ICC]) methods by filling out the questionnaire by 100 dentists practicing in the Rabat-Salé-Kénitra region, in Morocco.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The finalized DRP-KAPs questionnaire contains 41 items covering knowledge, attitudes and practices (KAPs). The I-CVIs, S-CVI/UA and S-CVI/AV and CVR values of the 41 items were ≥0.86 for each item, ≥0.82, ≥0.97, and ≥0.71, respectively. With regard to internal consistency reliability, the KR-20 coefficients for the knowledge and practice domains were 0.70 and 0.68, respectively, and the Cronbach alpha for the attitude domain was 0.73. The DRP-KAPs questionnaire has good reliability with the ICC coefficients for attitude items ranging from 0.57 to 0.95 and Kappa coefficients for knowledge and practice items ranging from 0.64 to 1 and 0.77 to 1, respectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The developed DRP-KAPs questionnaire was found to be a noteworthy tool for assessing radiation protection among dentists, with acceptable internal consistency, good ICC and Kappa coefficients, and good content validity indices.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11714091/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142583200","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}
Wesley Rivais, Louis Constine, Matthew Pacella, Neil Joyce, Maimuna Nagey, Matthew Webster, Jihyung Yoon, Hyunuk Jung, Sean Tanny, Olga Maria Dona Lemus, Dandan Zheng
Purpose
To compare the effect of two linacs designs on fetal dose sparing on a pregnant patient, including estimation of the fetal dose, and the effect of a lead apron.
Methods
A patient with a high-grade sarcoma located in the right knee/lower thigh was prescribed 51 Gy (1.7 Gy/Fx) with a simultaneous-integrated-boost (SIB) of 60 Gy to a smaller volume, starting in the 26th gestational week. Volumetric modulated radiation therapy (VMAT) plans with 6MV-FFF were developed using identical dosimetric constraints on a Varian Truebeam Edge with HD-MLC and a Varian Halcyon with double-stacked MLC. Based on patient dimension measurements, an anthropomorphic phantom was constructed using a Rando phantom and saline bags in the patient's Vac-Lok bag. Phantom measurements were performed using OSLDs and TLDs placed at three different planes, corresponding to the pubis, the umbilicus, and the fundus based on patient measurements and projected gestational age, to estimate the fetal dose. Three experimental scenarios were measured, each with CBCT-based image guidance for an accurate, reproducible setup: Edge, Halcyon, and Halcyon with a tri-folded lead apron (0.5 mm × 3 = 1.5 mm Pb) over the phantom abdomen.
Results
Plan quality and total MUs are comparable between the Edge and Halcyon plans. The OSLD-measured whole-course dose to the pubis, umbilicus, and fundus were 18.8, 13.1, and 11.7 cGy, respectively, on Halcyon, on average 27.8% lower than Edge. The repeatability within either dosimeter was good, although TLD showed systematically lower doses. Importantly, both dosimetry systems showed a lower measured fetal dose for the Halcyon plan compared with the Edge plan. Adding a tri-folded lead apron over the abdomen did not meaningfully lower the measured dose.
Conclusion
In this case study, Halcyon demonstrated a better sparing of out-of-field fetal dose compared to TrueBeam Edge. It was shown that lead aprons do not provide additional fetal dose sparing.
{"title":"Comparative analysis of fetal dose sparing between a C-arm linac and an O-ring linac in a SIB-VMAT sarcoma treatment for a pregnant patient: A technical note and case report","authors":"Wesley Rivais, Louis Constine, Matthew Pacella, Neil Joyce, Maimuna Nagey, Matthew Webster, Jihyung Yoon, Hyunuk Jung, Sean Tanny, Olga Maria Dona Lemus, Dandan Zheng","doi":"10.1002/acm2.14556","DOIUrl":"10.1002/acm2.14556","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To compare the effect of two linacs designs on fetal dose sparing on a pregnant patient, including estimation of the fetal dose, and the effect of a lead apron.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A patient with a high-grade sarcoma located in the right knee/lower thigh was prescribed 51 Gy (1.7 Gy/Fx) with a simultaneous-integrated-boost (SIB) of 60 Gy to a smaller volume, starting in the 26th gestational week. Volumetric modulated radiation therapy (VMAT) plans with 6MV-FFF were developed using identical dosimetric constraints on a Varian Truebeam Edge with HD-MLC and a Varian Halcyon with double-stacked MLC. Based on patient dimension measurements, an anthropomorphic phantom was constructed using a Rando phantom and saline bags in the patient's Vac-Lok bag. Phantom measurements were performed using OSLDs and TLDs placed at three different planes, corresponding to the pubis, the umbilicus, and the fundus based on patient measurements and projected gestational age, to estimate the fetal dose. Three experimental scenarios were measured, each with CBCT-based image guidance for an accurate, reproducible setup: Edge, Halcyon, and Halcyon with a tri-folded lead apron (0.5 mm × 3 = 1.5 mm Pb) over the phantom abdomen.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Plan quality and total MUs are comparable between the Edge and Halcyon plans. The OSLD-measured whole-course dose to the pubis, umbilicus, and fundus were 18.8, 13.1, and 11.7 cGy, respectively, on Halcyon, on average 27.8% lower than Edge. The repeatability within either dosimeter was good, although TLD showed systematically lower doses. Importantly, both dosimetry systems showed a lower measured fetal dose for the Halcyon plan compared with the Edge plan. Adding a tri-folded lead apron over the abdomen did not meaningfully lower the measured dose.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>In this case study, Halcyon demonstrated a better sparing of out-of-field fetal dose compared to TrueBeam Edge. It was shown that lead aprons do not provide additional fetal dose sparing.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"26 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11713124/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142574998","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}
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