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Reduction of radiation dose to the eye lens during common CT examinations of the head, paranasal sinus, and cervical spine in emergency settings: A phantom study. 急诊情况下头部、副鼻窦和颈椎普通CT检查时对晶状体的辐射剂量降低:一项幻象研究
IF 2.2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-02-01 DOI: 10.1002/acm2.70486
Sowitchaya Huakham, Wirachad Sripoori, Raksumon Suksi, Thawatchai Thaikan, Thanyawee Pengpan

Background: Computed tomography (CT) examinations of the head, paranasal sinus (PNS), and cervical spine (C-spine) are frequently performed in emergency settings, raising concerns about radiation exposure to the radiosensitive eye lens. Overexposure can cause radiation-induced ocular damage. To address this concern, organ dose modulation (ODM) has emerged as a promising technique for reducing eye lens dose in CT examinations.

Purpose: This study aimed to evaluate radiation exposure to the eye lens and objective image quality metrics for head, PNS, and C-spine CT examinations using fixed tube current, automatic tube current modulation (ATCM), and ODM techniques.

Methods: Eye lens doses were measured using nanoDot optically stimulated luminescence dosimeters (OSLDs) placed bilaterally to the eye lens of a whole-body anthropomorphic phantom (PBU-60). CT scans were performed using a Revolution EX CT scanner with three scanning techniques: fixed tube current, ATCM, and ODM. The phantom was scanned twice for each examination type (head, PNS, and C-spine) with all three techniques. Eye lens dose reductions with the ODM technique were quantified relative to fixed tube current and ATCM techniques. Image quality was quantitatively evaluated in terms of image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR).

Results: Mean eye lens doses ± standard deviation (SD) using the ODM technique were 38.44 ± 1.37, 17.92 ± 1.01, and 9.77 ± 0.38 mGy for head, PNS, and C-spine, respectively. These eye lens doses were reduced by 4.28%, 21.33%, and 47.97% compared to the fixed tube current techniques and by 19.40%, 24.70%, and 13.69% compared to the ATCM techniques, for head, PNS, and C-spine, respectively. These dose reductions were achieved while maintaining image quality metrics (image noise, SNR, and CNR) with no statistically significant differences (p > 0.05) compared to fixed tube current and ATCM techniques.

Conclusion: Implementation of the ODM technique resulted in significant eye lens dose reduction (4.28%-47.97%) across head, PNS, and C-spine CT examinations with no significant differences in image noise, SNR, and CNR compared to both fixed tube current and ATCM techniques. ODM demonstrates potential as a practical dose optimization strategy for routine emergency head and neck CT imaging. Further studies with subjective image quality assessment are recommended to evaluate clinical diagnostic acceptability in hospital settings.

背景:头部、副鼻窦(PNS)和颈椎(C-spine)的计算机断层扫描(CT)检查经常在紧急情况下进行,这引起了对辐射敏感眼晶状体辐射暴露的担忧。过度暴露会引起辐射引起的眼部损伤。为了解决这一问题,器官剂量调节(ODM)已经成为一种很有前途的技术,用于减少CT检查中的晶状体剂量。目的:本研究旨在评估使用固定管电流、自动管电流调制(ATCM)和ODM技术进行头部、PNS和颈椎CT检查时,眼晶状体的辐射暴露和客观图像质量指标。方法:采用纳米点光刺激发光剂量计(osld)测量眼晶状体剂量,该剂量计放置在全身拟人幻影(PBU-60)的眼晶状体两侧。CT扫描使用Revolution EX CT扫描仪,采用三种扫描技术:固定管电流、ATCM和ODM。采用所有三种技术对每个检查类型(头部、PNS和颈椎)的幻肢进行两次扫描。相对于固定管电流和ATCM技术,对ODM技术的眼晶状体剂量减少量进行了量化。通过图像噪声、信噪比(SNR)和噪声对比比(CNR)对图像质量进行定量评价。结果:使用ODM技术,头部、PNS和颈椎的平均晶状体剂量±标准差(SD)分别为38.44±1.37、17.92±1.01和9.77±0.38 mGy。与固定管电流技术相比,这些眼球晶状体剂量分别减少4.28%、21.33%和47.97%,与ATCM技术相比,头部、PNS和颈椎的晶状体剂量分别减少19.40%、24.70%和13.69%。与固定管电流和ATCM技术相比,在保持图像质量指标(图像噪声、信噪比和CNR)的同时实现了这些剂量的降低,没有统计学上的显著差异(p > 0.05)。结论:与固定管电流和ATCM技术相比,ODM技术的实施使头部、PNS和颈椎CT检查的晶体剂量显著降低(4.28%-47.97%),图像噪声、信噪比和CNR无显著差异。ODM显示了作为常规急诊头颈部CT成像的实用剂量优化策略的潜力。建议进一步研究主观图像质量评估,以评估医院设置的临床诊断可接受性。
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引用次数: 0
Acceptance testing of a 0.35 T MR-Linac: procedures, QA baseline, and system limitations. 0.35 T MR-Linac的验收测试:程序、QA基线和系统限制。
IF 2.2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-02-01 DOI: 10.1002/acm2.70488
Mateb Al Khalifa, Tianjun Ma, Haya Aljuaid, Siyong Kim, William Y Song

Purpose: This study describes and evaluates the acceptance procedure for a ViewRay (VR) MRIdian 0.35T MR-Linac, emphasizing key challenges, limitations, and recommendations to enhance clinical performance and accuracy.

Methods: A comprehensive acceptance test was conducted at a single institution, following the manufacturer's protocols and aligned with established acceptance guidelines. Specific tools and phantoms were used to assess three primary components: mechanical, dosimetric, and Magnetic Resonance Imaging (MRI).

Results: Overall, the test outcomes satisfied the manufacturer's specifications. However, certain issues were identified: high couch attenuation at specific gantry angles (leading to their exclusion from treatment), variations in magnetic field homogeneity at different gantry angles, and discrepancies between TPS calculations and measurements for field output factors smaller than 0.83 cm × 0.83 cm.

Conclusion: This work provides a detailed account of the acceptance testing procedure and establishes a QA baseline for 0.35T MR-Linac systems. In doing so, it also identifies key system limitations, such as couch attenuation, magnetic field inhomogeneity, and small-field output discrepancies, underscoring the need for careful gantry angle selection, field homogeneity optimization, and meticulous validation of very small fields.

目的:本研究描述并评估了ViewRay (VR) mrridian 0.35T MR-Linac的接受程序,强调了关键的挑战、限制和建议,以提高临床表现和准确性。方法:在单一机构进行全面验收测试,遵循制造商的协议,并与既定的验收指南保持一致。使用特定的工具和模型来评估三个主要组成部分:机械,剂量学和磁共振成像(MRI)。结果:总体而言,测试结果满足制造商的规格。然而,也发现了一些问题:特定龙门架角度下的高沙发衰减(导致其被排除在处理之外),不同龙门架角度下磁场均匀性的变化,以及小于0.83 cm × 0.83 cm的场输出因子的TPS计算与测量之间的差异。结论:这项工作提供了验收测试程序的详细说明,并建立了0.35T MR-Linac系统的QA基线。在此过程中,它还确定了关键的系统限制,例如couch衰减,磁场不均匀性和小场输出差异,强调需要仔细选择龙门角度,场均匀性优化以及对非常小的场进行细致的验证。
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引用次数: 0
Evaluating equivalent square field size definitions for rectangular small fields. 评估矩形小场的等效方形场大小定义。
IF 2.2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-02-01 DOI: 10.1002/acm2.70500
Ignasi Méndez, Mateb Al Khalifa, Haya Aljuaid, Božidar Casar

Background: In the IAEA TRS-483 Code of Practice (CoP), rectangular small field sizes are approximated to equivalent square small fields using the definition proposed by Cranmer-Sargison et al. However, the CoP estimates the uncertainties associated with this formula only for rectangular fields with dimensions within the range 0.7 < X / Y < 1.4 $0.7 < X/Y < 1.4$ .

Purpose: The objective of the present study was to compare the accuracy of the Cranmer-Sargison definition with other formulas for equivalent square small fields in the context of measuring field output factors (FOFs) for rectangular small fields, both within and outside the range covered by the CoP.

Methods: Measurements were conducted using Gafchromic EBT4 radiochromic films. The models compared included Cranmer-Sargison, Sterling, Superellipse, Sterling-Partial Superellipse, Sterling-Superellipse, Vadash and Bjärngard, and Fogliata. The most accurate definition of equivalent square field size was identified as the one yielding the lowest discrepancy between measured and analytical values, with the log-likelihood of the measurements selected as the metric. Analytical values were derived using the function introduced by Sauer and Wilbert, which relates FOFs to equivalent square field sizes.

Results: The best results were achieved with the Fogliata model, followed in terms of accuracy by the Sterling-Partial Superellipse model. The Sterling-Superellipse and Vadash and Bjärngard models came next. It should be noted that the Sterling-Partial Superellipse and Sterling-Superellipse models rely solely on the geometric shape of the irradiation field size, whereas the Fogliata and Vadash and Bjärngard models incorporate a fitting parameter. The Sterling definition, while less accurate than these models, improved upon the Cranmer-Sargison definition and retained computational simplicity. Finally, the Cranmer-Sargison and Superellipse models exhibited the largest discrepancies.

Conclusions: This study identified several definitions of equivalent square small field size that could refine the IAEA TRS-483 CoP by improving the accuracy of field output correction factors for rectangular small fields. Among these definitions, the Fogliata model obtained the best results.

背景:在IAEA TRS-483操作规范(CoP)中,矩形小场尺寸使用Cranmer-Sargison等人提出的定义近似为等效方形小场。然而,CoP仅对尺寸在0.7 X/Y 1.4$ 0.7 < X/Y < 1.4$范围内的矩形场估算与该公式相关的不确定性。目的:本研究的目的是在测量矩形小场的场输出因子(fof)的背景下,比较Cranmer-Sargison定义与其他等效方形小场公式的准确性,包括在CoP覆盖范围内和之外。方法:采用Gafchromic EBT4放射线致变色薄膜进行测定。比较的模型包括Cranmer-Sargison、Sterling、Superellipse、Sterling- partial Superellipse、Sterling-Superellipse、Vadash和Bjärngard以及Fogliata。最准确的等效方场大小定义被确定为测量值和分析值之间产生最小差异的定义,并选择测量值的对数似然作为度量。利用Sauer和Wilbert引入的函数推导出解析值,该函数将fof与等效的平方场大小联系起来。结果:Fogliata模型的准确率最高,Sterling-Partial Superellipse模型次之。接下来是Sterling-Superellipse、Vadash和Bjärngard模型。值得注意的是,Sterling-Partial Superellipse和Sterling-Superellipse模型仅依赖于辐照场尺寸的几何形状,而Fogliata和Vadash以及Bjärngard模型则包含了一个拟合参数。斯特林定义虽然不如这些模型精确,但在克兰默-萨吉森定义的基础上进行了改进,并保持了计算的简单性。最后,Cranmer-Sargison模型和Superellipse模型表现出最大的差异。结论:本研究确定了几种等效方形小场尺寸的定义,通过提高矩形小场输出校正系数的精度,可以对IAEA TRS-483 CoP进行细化。在这些定义中,Fogliata模型获得了最好的结果。
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引用次数: 0
Multi-institutional validation of hypersight CBCT-based dose calculation on O-ring linacs. 基于cbct的o型环直线机剂量计算的多机构验证。
IF 2.2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-02-01 DOI: 10.1002/acm2.70512
Chih-Yuan Lin, Yi-Ling Chen, Chia-Chi Chang, Yin-Hsun Hu, Chia-Peng Pan, Fang-Hui Liu, Hsiang-Ping Chao, Yang-Wei Hsieh, Yu-Wei Lin, Chi-Yuan Yeh, Tzu-Yuan Chao, Shih-Ming Hsu

Background: Conventional cone-beam computed tomography (CBCT) systems are limited by suboptimal image quality, inaccurate Hounsfield unit (HU) calibration, and reduced reliability for dose calculation. HyperSight CBCT on the Halcyon platform offers improved HU accuracy, expanded field-of-view (FOV), and enhanced image quality.

Purpose: This study aimed to assess the dosimetric accuracy of treatment planning using HyperSight CBCT through phantom-based dose verification.

Methods: This study included three institutions equipped with the HyperSight imaging system on the Halcyon platform, with all procedures performed after acceptance testing and calibration. Each institution generated HU-to-density calibration curves using computed tomography (CT) scanners and standardized phantoms, and corresponding CBCT for planning (CBCTp) scans were also acquired. Additional CBCTp scans were acquired using a consistent phantom model (062 M) across the three institutions. Reference treatment plans were created on CT images and transferred to CBCTp and CBCT datasets for dose recalculation using identical parameters. Dosimetric assessment included gamma analysis and comparisons of DVH-based dosimetric metrics for relevant regions of interest (ROIs). End-to-end testing with an anthropomorphic phantom was performed using ion chamber measurements and film dosimetry at brain, bone, and thorax locations.

Results: HU-to-density curves showed consistent behavior across institutions, with larger variability only at higher densities. CBCTp calibrations agreed well with vendor references. DVH-based dosimetric metrics showed differences generally within 1% for both CBCTp and CBCT when compared with CT. Across institutions, gamma analysis of both CBCTp and CBCT yielded high passing rates (≥ 98.5% at 3%/2 mm). End-to-end testing with film dosimetry showed that CBCT-based plans agreed with measured doses within ± 4%, while CT-based plans were within ± 3%. Ion chamber measurements showed all dose differences within ± 2.3%, with both CBCTp and CBCT within ± 1.0% of CT.

Conclusions: HyperSight CBCT provides accurate dose calculations when properly calibrated. Phantom-based validation demonstrated sub-2% deviations and strong agreement with CT, supporting its clinical use in adaptive radiotherapy.

背景:传统的锥束计算机断层扫描(CBCT)系统受到图像质量不理想、Hounsfield单位(HU)校准不准确以及剂量计算可靠性降低的限制。HyperSight CBCT在Halcyon平台上提供了更高的HU精度、扩大的视场(FOV)和增强的图像质量。目的:本研究旨在通过基于幻象的剂量验证来评估HyperSight CBCT治疗计划的剂量学准确性。方法:在Halcyon平台上安装HyperSight成像系统的三家机构,所有程序均在验收测试和校准后进行。每个机构都使用计算机断层扫描(CT)扫描仪和标准化模型生成了HU-to-density校准曲线,并获得了相应的CBCT规划(CBCTp)扫描。在三个机构使用一致的幻影模型(062 M)获得额外的CBCTp扫描。在CT图像上创建参考治疗方案,并将其转移到CBCTp和CBCT数据集中,使用相同的参数重新计算剂量。剂量学评估包括伽马分析和相关感兴趣区域(roi)基于dvh的剂量学指标的比较。在脑、骨和胸腔位置使用离子室测量和膜剂量法对拟人化幻影进行端到端测试。结果:hu -密度曲线在各个机构中表现出一致的行为,只有在较高的密度下才有较大的变异性。CBCTp校准与供应商的参考资料一致。与CT相比,基于dvh的剂量学指标显示CBCTp和CBCT的差异通常在1%以内。在所有机构中,CBCTp和CBCT的伽玛分析都获得了高通过率(≥98.5%,3%/ 2mm)。胶片剂量学的端到端测试显示,基于cbct的方案与测量剂量在±4%内一致,而基于ct的方案在±3%内一致。离子室测量显示,所有剂量差异在±2.3%内,CBCTp和CBCT均在CT的±1.0%内。结论:HyperSight CBCT在适当校准时提供准确的剂量计算。基于幻影的验证显示偏差低于2%,与CT高度一致,支持其在适应性放疗中的临床应用。
{"title":"Multi-institutional validation of hypersight CBCT-based dose calculation on O-ring linacs.","authors":"Chih-Yuan Lin, Yi-Ling Chen, Chia-Chi Chang, Yin-Hsun Hu, Chia-Peng Pan, Fang-Hui Liu, Hsiang-Ping Chao, Yang-Wei Hsieh, Yu-Wei Lin, Chi-Yuan Yeh, Tzu-Yuan Chao, Shih-Ming Hsu","doi":"10.1002/acm2.70512","DOIUrl":"https://doi.org/10.1002/acm2.70512","url":null,"abstract":"<p><strong>Background: </strong>Conventional cone-beam computed tomography (CBCT) systems are limited by suboptimal image quality, inaccurate Hounsfield unit (HU) calibration, and reduced reliability for dose calculation. HyperSight CBCT on the Halcyon platform offers improved HU accuracy, expanded field-of-view (FOV), and enhanced image quality.</p><p><strong>Purpose: </strong>This study aimed to assess the dosimetric accuracy of treatment planning using HyperSight CBCT through phantom-based dose verification.</p><p><strong>Methods: </strong>This study included three institutions equipped with the HyperSight imaging system on the Halcyon platform, with all procedures performed after acceptance testing and calibration. Each institution generated HU-to-density calibration curves using computed tomography (CT) scanners and standardized phantoms, and corresponding CBCT for planning (CBCTp) scans were also acquired. Additional CBCTp scans were acquired using a consistent phantom model (062 M) across the three institutions. Reference treatment plans were created on CT images and transferred to CBCTp and CBCT datasets for dose recalculation using identical parameters. Dosimetric assessment included gamma analysis and comparisons of DVH-based dosimetric metrics for relevant regions of interest (ROIs). End-to-end testing with an anthropomorphic phantom was performed using ion chamber measurements and film dosimetry at brain, bone, and thorax locations.</p><p><strong>Results: </strong>HU-to-density curves showed consistent behavior across institutions, with larger variability only at higher densities. CBCTp calibrations agreed well with vendor references. DVH-based dosimetric metrics showed differences generally within 1% for both CBCTp and CBCT when compared with CT. Across institutions, gamma analysis of both CBCTp and CBCT yielded high passing rates (≥ 98.5% at 3%/2 mm). End-to-end testing with film dosimetry showed that CBCT-based plans agreed with measured doses within ± 4%, while CT-based plans were within ± 3%. Ion chamber measurements showed all dose differences within ± 2.3%, with both CBCTp and CBCT within ± 1.0% of CT.</p><p><strong>Conclusions: </strong>HyperSight CBCT provides accurate dose calculations when properly calibrated. Phantom-based validation demonstrated sub-2% deviations and strong agreement with CT, supporting its clinical use in adaptive radiotherapy.</p>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 2","pages":"e70512"},"PeriodicalIF":2.2,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146206998","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}
引用次数: 0
Approaches to needle navigation in interstitial brachytherapy using infrared tracking and radiography 间质性近距离治疗中使用红外线追踪和x线摄影的针导航方法。
IF 2.2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-31 DOI: 10.1002/acm2.70496
Veronika Kreß, Ricarda Merten, Christoph Bert, Vratislav Strnad, Rainer Fietkau, Stefanie Corradini, Andre Karius

Background

Intraoperative cone-beam computed tomography (CBCT) provides a valuable option for accurate three-dimensional applicator positioning in gynecologic brachytherapy, but is associated with radiation exposure and increased intervention time especially in case of repeated CBCT imaging being required for creating a sufficient implant arrangement.

Purpose

To reduce the need for multiple CBCT scans for corresponding applicator verification, this work proposes two methods for needle path navigation, including corrections of potential bending in situ, by combining infrared tracking with planar x-ray imaging for enabling accurate intraoperative needle guidance.

Methods

An examined 200 mm brachytherapy needle was rigidly mounted on an infrared-reflective tracking tool to enable real time tracking. Two planar x-ray images, acquired from varying distinct angles, were used to determine the exact 3D position of the needle tip region via backprojection. A spline was fitted through the obtained coordinates to reconstruct the full needle path. Based on this, only a single initial CBCT scan was required to visualize the predicted needle path within this scan. Additionally, a second approach for needle prediction was presented focusing on only one planar x-ray image by incorporating prior needle bending information from the initial CBCT scan. Both methods were evaluated in preclinical studies and validated against a corresponding ground-truth obtained from CBCT.

Results

The proposed method considering two planar x-ray images successfully reconstructed the needle path with deviations of less than 1 mm from the CBCT reference scan, when using at least 20° offset between the x-ray image acquisitions. The single-scan approach, using prior bending information, yielded promising results with deviations at the tip of below 1.3 mm.

Conclusions

Both described methods demonstrated their feasibility in preclinical studies, showing potential to improve and accelerate clinical implantation workflows by means of needle navigation in the future.

背景:术中锥形束计算机断层扫描(CBCT)在妇科近距离治疗中提供了一个有价值的选择,用于准确的三维应用定位,但与辐射暴露和增加的干预时间有关,特别是在需要重复CBCT成像以创建足够的植入物排列的情况下。目的:为了减少对多次CBCT扫描进行相应涂抹器验证的需要,本工作提出了两种针路导航方法,包括原位校正潜在弯曲,通过结合红外跟踪和平面x射线成像,实现术中准确的针路引导。方法:将检查好的200mm近距离治疗针牢固地安装在红外反射跟踪工具上,实现实时跟踪。从不同角度获取的两张平面x射线图像通过反向投影确定针尖区域的确切三维位置。通过得到的坐标拟合样条,重建整个针径。基于此,只需要一次初始CBCT扫描就可以在扫描中可视化预测的针头路径。此外,提出了第二种方法,通过结合初始CBCT扫描的先前针头弯曲信息,仅关注一个平面x射线图像。两种方法都在临床前研究中进行了评估,并根据CBCT获得的相应的基本事实进行了验证。结果:考虑两张平面x线图像,当x线图像之间使用至少20°偏移时,该方法成功地重建了与CBCT参考扫描偏差小于1 mm的针径。单次扫描方法,利用先前的弯曲信息,产生了令人满意的结果,尖端偏差小于1.3 mm。结论:两种方法在临床前研究中都证明了它们的可行性,显示出未来通过针头导航改善和加速临床植入工作流程的潜力。
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引用次数: 0
Evaluating consistency of radiomic features derived from CT images: A cross-center phantom study 评估CT图像放射学特征的一致性:一项跨中心幻像研究。
IF 2.2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-31 DOI: 10.1002/acm2.70482
Lorna Tu, Hervé H. F. Choi, Haley Clark, Bradford Gill, Scott Young, Samantha A. M. Lloyd

Purpose

To investigate the consistency of radiomic features extracted from computed tomography (CT) scans across CT radiotherapy simulators geographically spread across a Canadian province using a simplified lung radiomic phantom, and to determine whether it is appropriate to combine multicenter imaging data into a single dataset.

Methods

An inexpensive phantom was created using foam with a density similar to lung and a plastic vial insert filled with water. It was imaged at six provincial radiotherapy treatment centers using eight GE CT radiotherapy simulators and routine lung stereotactic ablative radiotherapy planning CT acquisition protocols. Radiomic features were extracted from regions of interest using Imaging Biomarker Explorer radiomics software and compared using Kruskal Wallis H tests, intraclass correlation coefficient (ICC), and coefficient of variation (CV).

Results

Image acquisition parameters were similar across centers. At the population level, no significant inconsistencies between radiomic features originating from different centers or from within the same center were observed (Bonferroni-corrected p > 0.05; ICC > 0.941). On average, 52.5% of features were considered consistent (CV ≤ 0.10).

Conclusions

The proposed phantom was transported across widespread centers without detectable damage and demonstrates potential for easy quality assurance checks on radiomic feature consistency within a multi-institutional setting. Our analysis suggests that some features should be omitted or standardized before combining provincial imaging data into a harmonized lung radiotherapy dataset. These preliminary findings lay the groundwork for further investigation into provincial radiomic feature consistency and potential application to multicenter clinical studies. Owing to potential differences in imaging protocols, a consistency evaluation should be performed before undertaking radiomic analysis of data combined from different institutions.

目的:利用简化的肺放射学模型,研究从分布在加拿大一个省的CT放疗模拟器的CT扫描中提取的放射学特征的一致性,并确定将多中心成像数据合并为单个数据集是否合适。方法:使用密度与肺相似的泡沫和充满水的塑料瓶插入物制作廉价的假体。在6个省级放疗治疗中心使用8台GE CT放疗模拟器和常规肺立体定向消融放疗计划CT采集方案对其进行成像。使用Imaging Biomarker Explorer放射组学软件从感兴趣的区域提取放射组学特征,并使用Kruskal Wallis H检验、类内相关系数(ICC)和变异系数(CV)进行比较。结果:各中心图像采集参数相似。在种群水平上,来自不同中心或同一中心的放射学特征之间没有观察到显著的不一致性(Bonferroni-corrected p > 0.05; ICC > 0.941)。平均52.5%的特征被认为是一致的(CV≤0.10)。结论:所提出的假体在广泛的中心运输时没有可检测到的损伤,并证明了在多机构环境中对放射性特征一致性进行简单质量保证检查的潜力。我们的分析表明,在将省级影像数据合并为统一的肺部放疗数据集之前,应该省略或标准化一些特征。这些初步发现为进一步研究省域放射学特征的一致性和在多中心临床研究中的潜在应用奠定了基础。由于成像方案可能存在差异,在对来自不同机构的合并数据进行放射学分析之前,应进行一致性评估。
{"title":"Evaluating consistency of radiomic features derived from CT images: A cross-center phantom study","authors":"Lorna Tu,&nbsp;Hervé H. F. Choi,&nbsp;Haley Clark,&nbsp;Bradford Gill,&nbsp;Scott Young,&nbsp;Samantha A. M. Lloyd","doi":"10.1002/acm2.70482","DOIUrl":"10.1002/acm2.70482","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>To investigate the consistency of radiomic features extracted from computed tomography (CT) scans across CT radiotherapy simulators geographically spread across a Canadian province using a simplified lung radiomic phantom, and to determine whether it is appropriate to combine multicenter imaging data into a single dataset.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>An inexpensive phantom was created using foam with a density similar to lung and a plastic vial insert filled with water. It was imaged at six provincial radiotherapy treatment centers using eight GE CT radiotherapy simulators and routine lung stereotactic ablative radiotherapy planning CT acquisition protocols. Radiomic features were extracted from regions of interest using Imaging Biomarker Explorer radiomics software and compared using Kruskal Wallis H tests, intraclass correlation coefficient (ICC), and coefficient of variation (CV).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Image acquisition parameters were similar across centers. At the population level, no significant inconsistencies between radiomic features originating from different centers or from within the same center were observed (Bonferroni-corrected <i>p </i>&gt; 0.05; ICC &gt; 0.941). On average, 52.5% of features were considered consistent (CV ≤ 0.10).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The proposed phantom was transported across widespread centers without detectable damage and demonstrates potential for easy quality assurance checks on radiomic feature consistency within a multi-institutional setting. Our analysis suggests that some features should be omitted or standardized before combining provincial imaging data into a harmonized lung radiotherapy dataset. These preliminary findings lay the groundwork for further investigation into provincial radiomic feature consistency and potential application to multicenter clinical studies. Owing to potential differences in imaging protocols, a consistency evaluation should be performed before undertaking radiomic analysis of data combined from different institutions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12860509/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096967","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}
引用次数: 0
Machine learning for optimizing mAs in KUB radiography with metal implants 利用机器学习优化金属植入KUB x线摄影中的mAs。
IF 2.2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-30 DOI: 10.1002/acm2.70493
Wen-Xuan Chen, Jen-Pei Su, Shih‑Hua Huang, Sin‑Rong Huang, Ming-Chung Chou
<div> <section> <h3> Background and purpose</h3> <p>Kidney–ureter–bladder (KUB) radiography is a common examination that exposes patients to a higher radiation dose and increased cancer risk; therefore, it is important to estimate suitable exposure factors for each patient prior to radiography. The present study aimed to utilize machine learning (ML) approach to predicting the suitable milliampere-seconds (mAs) and reducing overexposure in patients with metal implants during KUB radiography.</p> </section> <section> <h3> Methods</h3> <p>A phantom was used to understand the effect of metal implants on radiation exposure during KUB radiography with automatic exposure control (AEC) technique. Subsequently, we retrospectively enrolled 619 subjects, including 56 with metal implants and 563 without, from one hospital (group A) and 323 subjects, including 89 with metal implants and 234 without, from another hospital (group B). All subjects underwent both KUB radiography and physiological examinations on the same day. Data on body parameters and exposure factors were retrieved from hospital database. To train the prediction model, the dataset of group A without metal implants was randomly divided into 80% and 20% for training and testing sets, respectively. Five different ML algorithms were utilized to train the prediction model using 10-fold cross-validation. The correlation coefficients (CC), mean average error (MAE), normalized root mean squared errors (nRMSE), and R-square (R<sup>2</sup>) were compared to find the optimal model. For external validation, the dataset of group B was randomly separated into 80% and 20% for training and testing sets, respectively. The training sets of both groups were combined for transfer learning, and the testing set of the group B was used to assess the optimal model. Furthermore, the final model was utilized to predict an appropriate mAs for patients with metal implants in both groups. Statistical analysis was performed to understand the differences between datasets, phantom settings, and ML models. Comparisons were considered significance if <i>p</i> < 0.05.</p> </section> <section> <h3> Results</h3> <p>The phantom experiment demonstrated that the metal plate significantly increased the mAs and reached exposure (REX) values when using AEC technique during KUB radiography. The comparison of patient data showed that the patients with metal implants had significantly higher mAs and REX than those without in both groups. In group A, the ML comparisons showed that the artificial neural network (ANN) model outperformed other ML models in predicting mAs based on the testing set, exhibiting the highest CC of 0.791 ± 0.007 a
背景和目的:肾-输尿管-膀胱(KUB) x线摄影是一种常见的检查,使患者暴露于更高的辐射剂量和增加的癌症风险;因此,在x线摄影前对每位患者估计合适的暴露因子是很重要的。本研究旨在利用机器学习(ML)方法来预测合适的毫安秒(mAs),并减少金属植入物患者在KUB放射摄影期间的过度暴露。方法:采用假体研究自动曝光控制(AEC)技术对KUB放射成像中金属植入物对辐射暴露的影响。随后,我们回顾性地从一家医院(A组)招募了619名受试者,其中56名植入金属种植体,563名未植入金属种植体;从另一家医院(B组)招募了323名受试者,其中89名植入金属种植体,234名未植入金属种植体。所有受试者在同一天接受KUB x线摄影和生理检查。身体参数和暴露因素数据从医院数据库检索。为了训练预测模型,将不含金属植入物的A组数据集随机分为80%和20%作为训练集和测试集。使用五种不同的ML算法进行10倍交叉验证来训练预测模型。比较相关系数(CC)、平均误差(MAE)、归一化均方根误差(nRMSE)和r平方(R2),寻找最优模型。为了进行外部验证,将B组的数据集随机分为80%和20%,分别用于训练集和测试集。将两组的训练集合并进行迁移学习,并使用B组的测试集来评估最优模型。此外,最后的模型用于预测两组金属种植体患者的合适mAs。进行统计分析以了解数据集、幻影设置和ML模型之间的差异。结果:幻影实验表明,在KUB x线摄影中使用AEC技术时,金属板显着增加了mAs并达到了暴露(REX)值。两组患者资料比较显示,金属种植体患者的mAs和REX均显著高于未种植体患者。在A组,人工神经网络(ANN)模型在基于测试集预测mAs方面优于其他ML模型,其CC最高为0.791±0.007,R2为0.6193±0.010。在B组中,基于迁移学习的外部验证表明,该ANN模型在测试集中的CC为0.837±0.051,R2为0.823±0.007。对于金属种植体患者,ANN模型预测的mAs明显低于两组使用AEC技术获得的mAs。结论:我们得出的结论是,ML方法适用于建立模型,以预测适当的mAs和减少金属种植体患者在KUB x线摄影期间的过度暴露。
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引用次数: 0
A prospective hazard analysis of real-time adaptive helical tomotherapy 实时适应性螺旋断层治疗的前瞻性风险分析。
IF 2.2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-30 DOI: 10.1002/acm2.70478
Jonathan Hindmarsh, Scott Crowe, Jemma Walsh, Tanya Kairn, Sonja Dieterich, Jeremy Booth, Paul Keall
<div> <section> <h3> Background</h3> <p>Following the release in 2016 of the report of the American Association of Physicists in Medicine Task Group 100, there has been growing interest in the use of prospective hazard analysis in radiation therapy. System Theoretic Process Analysis (STPA) is an emerging technique in this domain that is particularly suited to processes that involve time sensitive collaboration, decision-making and/or automation.</p> </section> <section> <h3> Purpose</h3> <p>The goal of this research was to use STPA to evaluate existing processes and procedures with an aim to identify improvements, gaps or unforeseen risks stemming from implementing real-time adaptive treatment on a helical tomotherapy platform.</p> </section> <section> <h3> Methods</h3> <p>The Radixact treatment delivery system (Accuray Inc., Sunnyvale, CA, USA), an evolution of the Tomotherapy platform, incorporates upgrades such as the Synchrony system for real-time motion monitoring and treatment adaptation. In collaboration with a team from the radiation oncology department of a large public hospital, a prospective hazard analysis focused on the real-time adaptive capabilities of the Radixact Synchrony system was conducted using STPA. The system boundaries were defined and a control structure model comprising sub-systems and control actions was developed. Unsafe control actions were identified and broad-based causal scenarios were generated. The causal scenarios that were novel, specific to Synchrony or challenging to mitigate were selected for further analysis regarding impacts and potential causes, following which mitigation strategies were proposed, taking into consideration the hierarchy of controls.</p> </section> <section> <h3> Results</h3> <p>A control structure model encompassing the entire patient journey was developed, incorporating all the hardware and software components and human decision makers. The model consisted of 12 sub-systems and 21 control actions, resulting in 108 unsafe control actions and 595 causal scenarios. Sixty-one causal scenarios were selected for further analysis, for which mitigation strategies were proposed based on the hierarchy of controls. These included the development of better reference documentation, the systematic testing of the sensitivity of tracking performance to changes in tracking parameters, guidance around setting and documenting tracking parameters, and documentation review.</p> </section> <section> <h3> Conclusions</h3>
背景:继2016年美国物理学家协会医学任务小组100报告发布后,人们对在放射治疗中使用前瞻性危害分析的兴趣越来越大。系统理论过程分析(STPA)是该领域的一项新兴技术,特别适用于涉及时间敏感的协作、决策和/或自动化的过程。目的:本研究的目的是使用STPA来评估现有的流程和程序,目的是确定在螺旋断层治疗平台上实施实时自适应治疗所产生的改进、差距或不可预见的风险。方法:Radixact治疗输送系统(Accuray Inc., Sunnyvale, CA, USA)是Tomotherapy平台的一种演变,包含了同步系统等升级,用于实时运动监测和治疗适应。与一家大型公立医院放射肿瘤科的团队合作,使用STPA对Radixact同步系统的实时适应能力进行了前瞻性危害分析。定义了系统边界,建立了由子系统和控制动作组成的控制结构模型。确定了不安全的控制行动,并产生了广泛的因果情景。选择新颖、特定于同步性或难以缓解的因果情景,对影响和潜在原因进行进一步分析,然后考虑到控制的层次结构,提出缓解战略。结果:建立了一个涵盖整个患者旅程的控制结构模型,包括所有硬件和软件组件以及人工决策者。该模型由12个子系统和21个控制动作组成,产生108个不安全控制动作和595个因果场景。选择61种因果情景进行进一步分析,并根据控制层次提出缓解战略。这些包括开发更好的参考文档,系统地测试跟踪性能对跟踪参数变化的敏感性,围绕设置和记录跟踪参数的指导,以及文档审查。结论:STPA被有效地用于评估Radixact同步系统的实时适应性放射治疗能力,从而深入了解该系统在整个患者旅程中如何变得不安全。虽然该研究侧重于Radixact同步和实时适应性放射治疗,但该研究提供了一个可转移的STPA应用示例,从分析初始化到缓解,可以为放射治疗中的其他安全性评估提供信息。
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引用次数: 0
Deep learning-based lung volume estimation with dynamic chest radiography 基于深度学习的动态胸片肺容量估计。
IF 2.2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-29 DOI: 10.1002/acm2.70487
Nozomi Ishihara, Rie Tanaka, Haruto Kikuno, Noriyuki Ohkura, Isao Matsumoto

Background

Dynamic chest radiography (DCR) is a recently developed low-dose pulmonary functional imaging method that can be performed in a general X-ray room. DCR provides sequential images during respiration, and the measured changes in lung area are a promising diagnostic indicator of lung function.

Purpose

To investigate lung volume estimation using deep learning from DCR images during respiration and evaluate its accuracy in comparison with previously proposed estimation methods.

Methods

Two convolutional neural networks (CNNs), VGG19 and DenseNet121, were trained using DCR image datasets from 257 patients, with reference lung volumes derived from corresponding computed tomography (CT) images. The performance of the models was evaluated using mean absolute error (MAE) and mean absolute percentage error (MAPE), and compared against that of a conventional linear regression model. Correlation between the estimated and reference lung volumes was assessed using Pearson's correlation coefficient (r) and the degrees-of-freedom-adjusted coefficient of determination (Rf2). Forced vital capacity (FVC) was also estimated by subtracting the lung volume at maximum exhalation from that at maximum inhalation.

Results

The VGG19 and DenseNet121 models demonstrated superior performance in estimating whole lung volume (combined right and left lung) compared to the linear regression method. Specifically, MAE was 373/376 mL, MAPE was 8.1%/7.9%, r was 0.88/0.90, and Rf2 was 0.76/0.80 for VGG19/DenseNet121, respectively. In contrast, the linear regression model yielded an MAE of 568 mL, MAPE of 12.4%, r of 0.84, and Rf2 of 0.69. Although the Rf2 values for DCR-derived FVC using VGG19 and DenseNet121 indicated moderate correlation, the MAE and MAPE were relatively high at 1.3/1.4 L and 41.1%/47.0%, respectively.

Conclusion

The proposed deep learning-based approach for lung volume estimation from DCR images outperformed the conventional linear regression method. Further improvements in CNN model architecture and the incorporation of guided forced respiratory maneuvers may enhance the potential for image-based pulmonary function testing.

背景:动态胸部x线摄影(DCR)是最近发展起来的一种低剂量肺功能成像方法,可在普通x线室进行。DCR提供呼吸过程的连续图像,测量肺面积的变化是一种很有前途的肺功能诊断指标。目的:研究利用呼吸过程中DCR图像的深度学习估计肺体积,并与先前提出的估计方法进行比较,评估其准确性。方法:使用257例患者的DCR图像数据集和相应CT图像的参考肺体积,对VGG19和DenseNet121两个卷积神经网络(cnn)进行训练。使用平均绝对误差(MAE)和平均绝对百分比误差(MAPE)评估模型的性能,并与传统线性回归模型的性能进行比较。使用Pearson相关系数(r)和自由度调整后的决定系数(Rf2)评估估计肺容量和参考肺容量之间的相关性。用力肺活量(FVC)也通过最大呼气量减去最大吸气量来估计。结果:与线性回归方法相比,VGG19和DenseNet121模型在估计全肺体积(左右肺联合)方面表现出更好的性能。其中,VGG19/DenseNet121的MAE为373/376 mL, MAPE为8.1%/7.9%,r为0.88/0.90,Rf2为0.76/0.80。线性回归模型的MAE为568 mL, MAPE为12.4%,r为0.84,Rf2为0.69。虽然使用VGG19和DenseNet121的dcr衍生FVC的Rf2值显示中等相关性,但MAE和MAPE相对较高,分别为1.3/1.4 L和41.1%/47.0%。结论:基于深度学习的DCR图像肺容量估计方法优于传统的线性回归方法。CNN模型架构的进一步改进和引导强迫呼吸操作的结合可能会增强基于图像的肺功能测试的潜力。
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引用次数: 0
Spatially aware radiomics integrating anatomical knowledge to improve lymph node malignancy prediction in head and neck cancer 空间感知放射组学整合解剖学知识提高头颈部肿瘤淋巴结恶性预测。
IF 2.2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Pub Date : 2026-01-27 DOI: 10.1002/acm2.70483
Liyuan Chen, Sepeadeh Radpour, Michael Dohopolski, David Sher, Jing Wang

Background

Radiomics holds the potential to improve the diagnostic evaluation of equivocal lymph nodes in head and neck cancer (HNC). While conventional radiomics models utilize features such as intensity, geometry, and texture of individual lymph node, they often neglect key spatial and anatomical characteristics tied to lymphatic dissemination patterns.

Purpose

In this study, we propose a novel spatially aware radiomics model that integrates anatomical knowledge and clinical factors to enhance lymph node malignancy prediction.

Methods

A total of 1389 lymph nodes (1119 benign and 270 malignant), contoured on CT scans from 192 HNC patients were included. Two models were developed: a baseline model using conventional radiomics features and an enhanced model incorporating five additional spatial and anatomical features, such as primary tumor type, lymph node level, the laterality of the primary tumor, the laterality of the lymph node, and the distance from the lymph node to the primary tumor. Sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), negative predictive value (NPV) and the area under the receiver operating characteristic curve (AUC) criteria were used to evaluate the model performance.

Results

The proposed spatially aware radiomics model significantly outperformed the baseline model. The baseline model achieved SEN = 0.915, SPE = 0.756, ACC = 0.787, PPV = 0.475, NPV = 0.974, and AUC = 0.931. The enhanced model achieved SEN = 0.919, SPE = 0.845, ACC = 0.860, PPV = 0.589, NPV = 0.977, and AUC = 0.953. Statistical testing confirmed a significant improvement in both accuracy (p = 3.71 × 10−20) and AUC (p = 1.13 × 10−4).

Conclusions

This study demonstrates that incorporating lymphatic anatomy and clinical context into radiomics models significantly improves predictive performance. The proposed approach enhances interpretability, aligns with clinical workflows, and holds promises for personalized radiation therapy planning.

背景:放射组学具有提高头颈癌(HNC)模棱两可淋巴结诊断评价的潜力。虽然传统的放射组学模型利用了个体淋巴结的强度、几何形状和纹理等特征,但它们往往忽略了与淋巴传播模式相关的关键空间和解剖学特征。目的:在本研究中,我们提出了一种新的空间感知放射组学模型,该模型将解剖学知识和临床因素结合起来,以增强淋巴结恶性肿瘤的预测。方法:选取192例HNC患者,CT扫描共1389个淋巴结(良性1119个,恶性270个)。建立了两种模型:使用常规放射组学特征的基线模型和包含5个额外空间和解剖特征的增强模型,如原发肿瘤类型、淋巴结水平、原发肿瘤的侧边性、淋巴结的侧边性以及淋巴结到原发肿瘤的距离。采用敏感性(SEN)、特异性(SPE)、准确性(ACC)、阳性预测值(PPV)、阴性预测值(NPV)和受试者工作特征曲线下面积(AUC)标准评价模型的性能。结果:提出的空间感知放射组学模型显著优于基线模型。基线模型的SEN = 0.915, SPE = 0.756, ACC = 0.787, PPV = 0.475, NPV = 0.974, AUC = 0.931。增强模型的SEN = 0.919, SPE = 0.845, ACC = 0.860, PPV = 0.589, NPV = 0.977, AUC = 0.953。统计检验证实准确率(p = 3.71 × 10-20)和AUC (p = 1.13 × 10-4)均有显著提高。结论:本研究表明,将淋巴解剖和临床背景纳入放射组学模型可显著提高预测性能。所提出的方法增强了可解释性,与临床工作流程一致,并有望实现个性化放射治疗计划。
{"title":"Spatially aware radiomics integrating anatomical knowledge to improve lymph node malignancy prediction in head and neck cancer","authors":"Liyuan Chen,&nbsp;Sepeadeh Radpour,&nbsp;Michael Dohopolski,&nbsp;David Sher,&nbsp;Jing Wang","doi":"10.1002/acm2.70483","DOIUrl":"10.1002/acm2.70483","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Radiomics holds the potential to improve the diagnostic evaluation of equivocal lymph nodes in head and neck cancer (HNC). While conventional radiomics models utilize features such as intensity, geometry, and texture of individual lymph node, they often neglect key spatial and anatomical characteristics tied to lymphatic dissemination patterns.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>In this study, we propose a novel spatially aware radiomics model that integrates anatomical knowledge and clinical factors to enhance lymph node malignancy prediction.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>A total of 1389 lymph nodes (1119 benign and 270 malignant), contoured on CT scans from 192 HNC patients were included. Two models were developed: a baseline model using conventional radiomics features and an enhanced model incorporating five additional spatial and anatomical features, such as primary tumor type, lymph node level, the laterality of the primary tumor, the laterality of the lymph node, and the distance from the lymph node to the primary tumor. Sensitivity (SEN), specificity (SPE), accuracy (ACC), positive predictive value (PPV), negative predictive value (NPV) and the area under the receiver operating characteristic curve (AUC) criteria were used to evaluate the model performance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The proposed spatially aware radiomics model significantly outperformed the baseline model. The baseline model achieved SEN = 0.915, SPE = 0.756, ACC = 0.787, PPV = 0.475, NPV = 0.974, and AUC = 0.931. The enhanced model achieved SEN = 0.919, SPE = 0.845, ACC = 0.860, PPV = 0.589, NPV = 0.977, and AUC = 0.953. Statistical testing confirmed a significant improvement in both accuracy (<i>p</i> = 3.71 × 10<sup>−20</sup>) and AUC (<i>p</i> = 1.13 × 10<sup>−4</sup>).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study demonstrates that incorporating lymphatic anatomy and clinical context into radiomics models significantly improves predictive performance. The proposed approach enhances interpretability, aligns with clinical workflows, and holds promises for personalized radiation therapy planning.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14989,"journal":{"name":"Journal of Applied Clinical Medical Physics","volume":"27 2","pages":""},"PeriodicalIF":2.2,"publicationDate":"2026-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12836286/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146052141","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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Journal of Applied Clinical Medical Physics
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