Journal of Applied Clinical Medical Physics应用临床医学物理杂志
Journal of Applied Clinical Medical Physics(英文缩写 J APPL CLIN MED PHYS),ISSN 1526-9914,eISSN 1526-9914,中文译名:应用临床医学物理杂志 是一本学术期刊。本页汇总该期刊的最新影响因子、分区信息以及最新收录于 PubMed 的文献,帮助您快速了解期刊全貌。
发文量统计区间：2025-10-01 至 2026-09-30，按本站收录文献的发表日期统计。
期刊介绍
历年影响因子趋势
| JCR 数据年份 | 影响因子 | JCR 分区 |
|---|---|---|
| 2021 | 2.243 | Q3 |
| 2022 | 2.100 | Q3 |
| 2023 | 2.000 | Q3 |
| 2024 | 2.200 | Q2 |
| 2025 | 2.500 | Q2 |
Journal of Applied Clinical Medical Physics 最新收录文献
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1. Automatic single-isocenter multiple-target cranial stereotactic treatment plan optimization via planning system scripting.
PMID:日期:2026-09-30Single-isocenter multiple-target stereotactic radiation treatments delivered on C-arm linear accelerators are increasingly common due to practical advantages in both accessibility and speed compared to multiple isocenter treatments. Optimization of high-quality treatment plans can be a time-consuming process requiring substantial manual effort. In our institution, the planning workflow for these treatments was entirely manual and involved the generation of optimization structures and repeated recalculation and input of optimization objectives during the iterative optimization process. Although commercial automated planning solutions are available, their implementation may impose operational constraints, including reliance on specific immobilization and image/surface guidance hardware, motivating the development of an immobilization-independent automation tool for cranial stereotactic treatment plan optimization. To develop and implement a treatment planning optimization tool for multiple-target cranial stereotactic treatments in the Varian Eclipse treatment planning system, to reduce manual planner input and planning time while improving plan quality. A software tool was written using the Varian Eclipse Scripting Application Programming Interface to automatically generate target-specific ring structures and facilitate the iterative process of plan generation. Twenty cases were retrospectively re-planned with this tool and compared with the corresponding clinical plans to evaluate plan quality using qualitative and quantitative metrics of conformality and complexity. Automated plans were verified using portal dosimetry to ensure clinical deliverability. For ten cases, a timing study was performed to compare optimization time between the software tool and manual re-optimization. The automatic optimization tool produced plans with similar modulation and complexity, but consistently lower dosimetric falloff metrics (R and Paddick Gradient Index) than the corresponding clinical plans. All plans passed patient-specific QA (portal dosimetry) following institutional practice. For the ten cases included in the timing study, the software had a mean runtime of 5.8 min (range: 2-12 min), with variation depending on case characteristics such as number of targets and arcs employed. In comparison, manual plan generation required 21.5 min on average (range: 4-49 min). The automated planning tool produced clinically acceptable, deliverable plans with better dose falloff compared to the previous manual planning approach. The tool is estimated to save an average of 15 min of optimization time per plan. A treatment planning optimization tool has been developed that provides improvement in both dosimetric plan quality and treatment planning efficiency for single-isocenter multiple-target cranial stereotactic treatments.
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2. Deep learning-based dose prediction for stereotactic prostate cancer radiotherapy with CyberKnife.
PMID:日期:2026-09-30Deep learning-based (DL) approaches have gained interest in predicting dose distributions in radiotherapy of prostate cancer treated with volumetric modulated arc therapy and intensity-modulated radiation therapy. Meanwhile, research on predicting dose distributions in high-precision stereotactic radiotherapy treatments has remained relatively underrepresented. We aimed to expand the previous studies by developing a DL-based framework for predicting dose distributions for robotic, stereotactic prostate cancer radiotherapy. We harnessed a U-Net-based convolutional neural network for predicting clinically achievable dose distributions based on CT images, delineated structures, and distance information from the planning target volume. A dataset of 462 patients treated with CyberKnife (Accuray Inc.) utilizing an Iris collimator was divided into training (70%, n = 323), validation (10%, n = 46), and test (20%, n = 93) sets. In the independent test set, the mean absolute error between the mean doses of predictions and clinical plans was 0.63 Gy for the rectum and 1.04 Gy for the bladder. The proposed U-Net-based model demonstrated the ability to learn and reproduce characteristic dose distributions in CyberKnife prostate cancer radiotherapy. The model may provide patient-specific dose estimates for setting initial planning objectives to assist in automating treatment planning and improving inter-planner consistency.
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3. A technology-specific risk analysis of surface guided radiation therapy.
PMID:日期:2026-09-30Surface Guided Radiation Therapy (SGRT) has become an increasingly important tool that complements x-ray imaging to improve patient safety for setup, respiratory tracking, and motion monitoring. As SGRT is integrated into clinical workflows, its complexity can introduce potential risk of process-related errors. These risks may depend on workflow design, system integration, equipment configuration, and technology specific features. As SGRT continues to expand across a wider range of treatment sites and clinical applications, comprehensive evaluation of institutional workflows is recommended to support safe and effective implementation. This study aimed to identify and evaluate safety risks associated with the clinical use of SGRT using Failure Mode and Effects Analysis (FMEA). Emphasis was placed on workflow processes, system integration, and equipment-specific characteristics within a clinical environment to inform risk mitigation strategies and support safe implementation. A multidisciplinary team performed an FMEA of SGRT related procedures and workflows. A process map was developed to define the scope of clinical applications, including tattoo free setup, free-breathing and deep-inspiration breath-hold (DIBH) breast treatments, prone patient positioning and real-time motion monitoring for stereotactic body radiation therapy (SBRT) patients across body sites. For each process step, team members identified potential failure modes associated with the clinical workflow and environment, including the integration of a C-RAD SGRT system with Elekta linear accelerators. Scoring was performed according to AAPM TG-100 guidelines, using severity (S), occurrence (O), and detectability (D) to calculate the Risk Priority Number (RPN). Failure modes were then ranked by RPN, and those with scores greater than or equal to 100 were selected for further analysis and development of mitigation strategies. Thirty-eight failure modes were identified, with nine having RPN scores greater than or equal to 100 (S = 5-8, O = 2-5, and D = 5-9). High risk failure modes were most associated with the DIBH workflow, particularly respiratory trace acquisition, respiratory trace configuration, and x-ray image verification. Additional high risk failure modes were identified in treatment preparation and system quality assurance processes, including manual data import, template selection, calibration, and daily QA procedures. These risks were primarily associated with workflow-dependent processes involving user interaction and coordination between integrated clinical systems. This FMEA identified workflow, system integration and equipment related vulnerabilities associated with SGRT implementation. The findings emphasize the importance of risk assessments tailored to specific clinical workflows and treatment environments and support the development of targeted mitigation strategies for safe clinical use.
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6. Application of synthetic computed tomography based on a deep learning model in pelvic radiotherapy planning: A feasibility study.
PMID:日期:2026-09-28While computed tomography (CT) remains an important imaging modality in radiotherapy planning, magnetic resonance imaging (MRI)-only workflow, often realized via the generation of synthetic computed tomography (sCT), is desired due to superior soft-tissue contrast and less susceptibility to registration errors. The aim of this study was to investigate the feasibility of applying sCT, generated from MRI using a deep learning model, to pelvic radiotherapy planning. By comparing differences between conventional CT and sCT in terms of image quality, dosimetric parameters, and three-dimensional (3D) dose distributions, the clinical potential of an MRI-only radiotherapy workflow was evaluated. Fifteen patients who underwent both pelvic CT and MRI examinations were retrospectively enrolled. For the same patients, sCT images were generated from the MRI using a pretrained pelvic deep learning model integrated in the syngo.via VB60A software platform. Treatment plans originally created on planning CT (pCT) images were re-calculated on corresponding sCT datasets. Subsequently, the mean absolute error (MAE) of CT numbers, Dice similarity coefficients (DSCs) for major structures, dose-volume histogram (DVH) parameters for the planning target volume (PTV) and major organs at risk (OARs), and 3D dose distributions were compared between plans calculated on pCT and sCT. In terms of image quality, sCT demonstrated comparable CT number accuracy with pCT for soft tissue structures, with MAEs ranging from 6.8 to 8.5 Hounsfield units (HU) for the PTV, bladder, and rectum. Higher MAEs were observed for bony structures (femoral heads and bone marrow), with average MAEs ranging between 17 and 19 HU. The DSC was 0.94 ± 0.01 for the right femoral head, 0.93 ± 0.01 for the left femoral head, and 0.85 ± 0.03 for bone marrow. Regarding dosimetric comparison, the mean absolute differences in key dosimetric parameters for the PTV (D98%, D95%, and Dmean) were all below 0.6%, remaining within clinically acceptable limits. Among the OARs, except for relatively large differences for the maximum and minimum doses in the bladder, the mean dose (Dmean) differences for the rectum, femoral heads, and bone marrow were minimal (mean absolute value ≤ 0.34%), indicating high reliability. Analysis of 3D dose distributions using a 2% 2 mm γ criterion revealed an average γ passing rate of 95.92% between sCT and pCT, satisfying routine clinical quality assurance requirements. The findings of our study demonstrate the feasibility of applying sCT generated from MRI using a deep learning model to pelvic radiotherapy planning. The MRI-only workflow proposed on this basis eliminates systematic errors introduced by CT-MRI image registration, optimizes the clinical workflow, and fully exploits the superior soft-tissue contrast of MRI to improve target delineation accuracy. The proposed approach provides a novel technical paradigm for achieving more precise and efficient individualized radiotherapy.
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7. Automated quality assurance of rigid brain CT/MR image registration using a 3D convolutional neural network.
PMID:日期:2026-09-28Accurate CT/MR registration is important in stereotactic brain radiotherapy, where small spatial errors may affect target localization and treatment planning. In many clinical settings, registration quality is still assessed primarily through manual visual inspection. However, time and workflow constraints may limit the consistency and depth of manual evaluation. This study aimed to develop a 3D convolutional neural network (3D CNN)-based quality assurance (QA) method for rigid brain CT/MR registration using paired sub-volumes and a registration-level mean probability score (μ). The proposed method classifies local CT/MR registration quality, while the final registration-level assessment is based on μ across the sampled sub-volumes. The study included 209 patients. Of these, 202 comprised the primary cohort and were divided patient-wise into 128 training, 33 validation, and 41 test patients. For each patient, 25 skull-based CT/MR sub-volumes of 42 × 42 × 42 voxels were used as a two-channel CNN input. High-quality examples were obtained from clinically approved registrations, while low-quality examples were generated using rigid perturbations of the complete MR volume. The mean predicted probability across the 25 sub-volumes was used as the registration-level score μ, and the validation-derived threshold of 0.5871 was fixed for testing. Seven additional patients, separate from the 202-patient cohort, were evaluated after model development and threshold selection were completed using registrations before and after physician correction. On the held-out test set, patch-level accuracy was 85.90%, with an ROC AUC of 0.929 and average precision of 0.920. At the registration level, accuracy was 97.56% and ROC AUC was 0.999. All 41 low-quality registrations were correctly flagged, while 39 of 41 high-quality registrations were correctly accepted. In the additional clinical evaluation, all seven initial registrations requiring correction were classified as low quality, while six of seven corrected registrations were classified as high quality, resulting in an overall accuracy of 92.86%. The proposed method combined local CNN predictions into a registration-level μ score and showed strong performance on held-out and clinical cases. It may provide a quantitative screening measure for rigid brain CT/MR registration QA while maintaining clinical review as the final decision step.
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8. Can physics review catch prescription and PTV margin errors in fully automated radiotherapy plans? A hazard study.
PMID:日期:2026-09-28End-to-end AI-based automated contouring and radiotherapy planning systems promise substantial gains in efficiency and consistency but also introduce new safety risks, including user input errors that occur early in the workflow. This study evaluates the ability of standard physics plan review to detect user errors in prescription and planning target volume (PTV) margin entry in a fully automated planning workflow. A prototype of the Radiation Planning Assistant (RPA) was used to generate fully automated prostate cancer treatment plans for 20 patients. Five cases intentionally included incorrect prescriptions or non-standard PTV margins. Five clinical medical physicists independently performed physics plan reviews in a commercial treatment planning system, assuming a standard clinical workflow and without access to user guides or service request forms. Reviews were conducted in two phases, with and without margin information embedded in structure names. A third phase evaluated whether adding reference expansion structures improved margin error detection. Incorrect prescriptions were detected in 80% of cases, with no false-positive identification of correct prescriptions. In contrast, detection of incorrect PTV margins was highly inconsistent. Two physicists did not evaluate margins and missed all margin errors. Three physicists identified all intentional margin errors but demonstrated poor specificity, frequently flagging correct cases as incorrect (specificity range: 13%-63%). Including margin information in structure names and adding reference expansion structures did not improve specificity or overall detection performance. Physics plan review was unreliable for detecting incorrect PTV margins in a fully automated planning workflow, even when additional cues were provided. Although based on a limited dataset, these findings indicate that traditional plan review processes alone are insufficient to mitigate this failure mode. Alternative risk-reduction strategies-such as timely workflow-embedded alerts, targeted checklists, automated verification tools, and careful expectation setting-are likely required to improve safety in AI-driven radiotherapy planning systems.
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9. Development and evaluation of a water-profile-based calibration method for 2D ionization chamber arrays in medical linear accelerator quality assurance.
PMID:日期:2026-09-26To develop and evaluate a water-profile-based (WP) calibration method for two-dimensional (2D) ionization chamber arrays used in medical linear accelerator (linac) quality assurance (QA), and to compare its accuracy, efficiency, and cross-energy applicability with the manufacturer-provided wide-field (WF) calibration method. Beam profiles were measured using an IC Profiler (ICP) and a 3D water scanner on three TrueBeam linacs (TB1-TB3) at five photon energies (4, 6, 10 MV flattened; 6, 10 MV flattening-filter-free [FFF]) with a 20 × 20 cm field size at a depth of 10 cm. A total of 240 profiles were acquired across eight independent measurement sessions. The WP method derived adjusted calibration factors (ACFs) by directly matching ICP detector responses to water-scanner reference profiles, and was evaluated under three strategies: (i) machine-specific (single linac reference), (ii) machine-integrated (reference pooled across three linacs), and (iii) cross-energy applicability across 20 inter-energy combinations. Performance metrics included per-detector relative differences (mean absolute error [MAE] and standard deviation [SD]) and beam profile characterization (symmetry and flatness). Paired t-tests (n = 30) compared the machine-integrated WP method with the WF method. The WF method yielded a per-detector MAE of 0.31%, with symmetry and flatness MAEs of 0.49% and 0.44%, respectively. The WP method significantly reduced these uncertainties to 0.15-0.16% (per-detector), 0.21-0.27% (symmetry), and 0.27-0.28% (flatness), with medium-to-large effect sizes (Cohen's d = 0.55-0.99; all p < 0.01). Cross-energy application of WP-derived ACFs yielded a per-detector MAE of 0.25%, higher than that of energy-specific WP calibration but lower than that of the WF method in this dataset. Total calibration time decreased from approximately 75 min (WF) to 10-15 min (WP). For the tested 20 × 20 cm field on TrueBeam linacs with a CC13/Blue Phantom2 water-scanner reference, the proposed WP calibration method provided closer agreement with the water-scanner reference and a substantially shorter calibration time than the manufacturerprovided WF method. Machine-integrated calibration is recommended for routine multi-linac implementation, while energy-specific ACFs should be used whenever feasible to maximize accuracy.
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10. Fine wine, art, and medical physicist bibliometrics: Things that improve with age.
PMID:日期:2026-09-26Bibliometric indicators are widely used to evaluate faculty productivity, yet these metrics change with career stage, evolving authorship practices, and technological shifts in how science is produced. This study examines patterns of scholarly output among radiation therapy medical physics faculty at leading cancer centers and provides a framework and quantitative metrics for managing career age- and era-based effects. Peer-reviewed articles (1969-2025) were collected from Scopus for 397 Ph.D. medical physics faculty at the 20 U.S. cancer centers most highly ranked by US News and World Report in 2025. The bibliometric measures included annual rates of authorships ( ), first/last authorships ( ), monograph-equivalents ( ), as well as the cumulative h-index and contribution-normalized ha-index. Career age was defined by year of first publication. Productivity distributions were evaluated by career age using empirical cumulative distribution functions derived from 2016-2025 data. Era-dependent fluctuations (1990-2025) were examined using age-normalized metrics and trends in authorship team size. Tabulated bibliometrics as a function of career age are provided for evaluation of individual performance. Productivity increased substantially with career age, peaking in late career. Senior faculty produced approximately two-fold higher annual output than early-career peers. At career ages of 1, 10, 20, 30, and 40 years, the median bibliometric rates were = (1.2, 1.6, 2.5, 2.5,1.8) year, = (0.4, 0.4, 0.5, 0.5,0.2) year, = (0.2, 0.2, 0.3, 0.3,0.3) year, h-index = (0, 7, 15, 23,43), and ha-index = (0, 3.3, 6.0, 9.4,15.0). Era-based effects were substantial. Between 1990 and 2025, median authors per paper increased from 4 to 10, inflating authorship-based metrics. In contrast, contribution-weighted measures (FL, ME, ha-index) demonstrated greater temporal stability. Scholarly productivity in radiation therapy medical physics is strongly dependent on career age, with peak output occurring near the end of a typical career before declining modestly near retirement. Career-age-based percentile benchmarks provide a more balanced framework for evaluating academic performance than unadjusted bibliometric comparisons, especially for early-career investigators. Due to changes in authorship norms over time, first/last authorships, monograph equivalents, and ha-index may provide more accurate and equitable assessments of research performance than authorships or h-index.