Medical Dosimetry最新文献

{"title":"A phantom geometry approach to develop a RapidPlan knowledge-based planning model","authors":"Himank Kalra MSc ,&nbsp;Raj Pal Singh PhD ,&nbsp;Anuj Kumar PhD","doi":"10.1016/j.meddos.2025.11.002","DOIUrl":"10.1016/j.meddos.2025.11.002","url":null,"abstract":"<div><div>Knowledge-based radiation therapy (KBRT) models, including RapidPlan (RP), significantly improve treatment planning by enhancing both consistency and plan quality. This study focuses on the development and validation of an RP model utilizing phantom geometries that simulate prostate patient anatomies for optimized dosimetry. A library of 240 volumetric modulated arc therapy (VMAT) plans was developed using customized phantom geometries to simulate prostate anatomy with varying sizes for the planning target volume (PTV), bladder, and rectum. All plans were optimized following standard clinical constraints that ensure at least 95 % target coverage while adhering to the ALARA principle for organ protection. RP model was validated on data from 30 prostate cancer patients with three dose regimens: 50 Gy in 25 fractions (reference), 60 Gy in 20 fractions, and 78 Gy in 28 fractions. Plans for the 60 Gy and 78 Gy cohorts were compared with and without additional optimization objectives to evaluate improvements in sparing organs at risk (OAR). The RP model, trained on 240 phantom plans, showed strong target coverage, with 95 % of the PTV receiving at least 95 % of the prescribed dose (mean PTV dose: 48.25 ± 0.13 Gy in phantoms; 48.02 ± 0.26 Gy in patients). For the 78 Gy in 28 fraction prescription, rectal sparing was significant, with Rectum V65Gy (%) decreasing from 15.4 % to 10.1 % (<em>p</em> = 0.001) and V40Gy (%) from 32.6 % to 24.5 % (<em>p</em> = 0.004). In contrast, bladder dose reductions were minimal and not statistically significant. For the 60 Gy in 20 fraction prescription, bladder sparing was notable, with Bladder V48Gy (%) decreasing from 16.8 % to 9.7 % (<em>p</em> = 0.005) and V56.8 Gy (%) from 11.4 % to 5.0 % (<em>p</em> = 0.009), while rectal dose improvements remained modest and non-significant. Significant changes were defined as <em>p</em> &lt; 0.05. The RP model, trained on a library of phantom-based plans aligned with body dimensions and dose constraints, demonstrated strong target coverage and effective sparing of organs-at-risk (OAR) across various prescription regimens. Validation with 30 prostate cancer patients confirmed that this method provides clinically relevant dose predictions. Though phantom datasets cannot fully replicate the complexities of patient anatomy, this approach enables scalable training of knowledge-based planning (KBP) models, reducing variability among planners and maintaining plan quality when real patient data is scarce. This RP model, based on phantom geometries, delivers accurate dosimetry predictions while meeting clinical objectives, indicating significant potential for enhancing prostate cancer treatment planning.</div></div>","PeriodicalId":49837,"journal":{"name":"Medical Dosimetry","volume":"51 2","pages":"Pages 174-180"},"PeriodicalIF":1.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145775553","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Total body irradiation using volumetric modulated arc therapy: a single-scan phantom study","authors":"M. Rejab MMedPhys ,&nbsp;N.F. Khairuman MSc ,&nbsp;S. Jagabattuni MMedPhys ,&nbsp;D.A. Hizam MMedSc ,&nbsp;N. Mohd Shariff MMed, FRCR ,&nbsp;D.D.W. Lee FRCR ,&nbsp;N.M. Ung PhD ,&nbsp;Z. Jamalludin PhD ,&nbsp;A.H. Ng PhD","doi":"10.1016/j.meddos.2025.11.001","DOIUrl":"10.1016/j.meddos.2025.11.001","url":null,"abstract":"","PeriodicalId":49837,"journal":{"name":"Medical Dosimetry","volume":"51 2","pages":"Pages 169-173"},"PeriodicalIF":1.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145726965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Directed Journal Reading (DJR) Program","authors":"","doi":"10.1016/j.meddos.2026.07.001","DOIUrl":"10.1016/j.meddos.2026.07.001","url":null,"abstract":"","PeriodicalId":49837,"journal":{"name":"Medical Dosimetry","volume":"51 3","pages":"Page 387"},"PeriodicalIF":1.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148660722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"New Members List & Corporate Members List","authors":"","doi":"10.1016/j.meddos.2026.07.002","DOIUrl":"10.1016/j.meddos.2026.07.002","url":null,"abstract":"","PeriodicalId":49837,"journal":{"name":"Medical Dosimetry","volume":"51 3","pages":"Page 388"},"PeriodicalIF":1.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148660720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A case study of radiotherapy as a treatment option for multifocal pseudomyogenic hemangioendothelioma of the lower extremity","authors":"Brenda Montemayor B.S., R.T.(T),&nbsp;Amber Labbe B.S., R.T.(R)(T),&nbsp;Laura Laskorski M.H.A., R.T.(R)(T)(M),&nbsp;Nishele Lenards Ph.D., CMD, R.T.(R)(T), FAAMD,&nbsp;Ashley Hunzeker M.S., CMD,&nbsp;Machele Michels B.S., CMD, R.T.(R)(T)(CT)","doi":"10.1016/j.meddos.2026.03.010","DOIUrl":"10.1016/j.meddos.2026.03.010","url":null,"abstract":"<div><div>Pseudomyogenic hemangioendothelioma (PMHE) is a rare vascular neoplasm of intermediate malignancy with limited literature to guide radiation therapy planning, particularly in multifocal presentations. Researchers in this case study describe the clinical application of palliative radiation therapy for diffuse PMHE of the lower extremity and evaluate treatment effectiveness and treatment-related toxicity. A 24-year-old male with multifocal PMHE of the left foot and lower leg involving both soft tissue and bony structures was treated using 3D conformal radiation therapy with parallel-opposed oblique fields and circumferential 1.0 cm bolus. A total dose of 39 Gy in 13 fractions was selected to balance palliation with soft tissue sarcoma-based radiobiologic principles. The treatment achieved full target coverage and symptomatic relief. However, the absence of a skin-sparing strip, combined with preexisting infection and delayed wound healing, likely contributed to the development of post-treatment lymphedema. This case demonstrates the feasibility of using sarcoma-aligned palliative radiation dosing for multifocal PMHE but also highlights the risk of lymphedema when circumferential fields are used without lymphatic preservation techniques. Further case reporting is needed to refine field design and dose selection strategies for this rare entity.</div></div>","PeriodicalId":49837,"journal":{"name":"Medical Dosimetry","volume":"51 3","pages":"Pages 375-378"},"PeriodicalIF":1.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148661151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mitigating dosimetric perturbations from large metal implants utilizing spot-scanning proton arc therapy","authors":"Xiaoda Cong MS,&nbsp;Derek A. Mumaw MD,&nbsp;Weili Zheng PhD,&nbsp;Peilin Liu MD,&nbsp;Xiaoqiang Li PhD,&nbsp;Thomas J. Quinn MD,&nbsp;Xuanfeng Ding PhD,&nbsp;Rohan Deraniyagala MD","doi":"10.1016/j.meddos.2026.01.006","DOIUrl":"10.1016/j.meddos.2026.01.006","url":null,"abstract":"<div><div>Conventional intensity modulated proton therapy (IMPT) techniques are exquisitely sensitive to setup and range uncertainties in the presence of high-density metal implants. Herein we introduce a spot-scanning proton arc (SPArc) approach to improve the nominal plan quality near metal implants by increasing the degrees of freedom. 4-field IMPT and single-arc SPArc plans were generated for 5 patients with target volumes circumscribed or traversed by metal implants. Robust optimization was used (parameters: ± 3.5% range, ± 3/5 mm setup uncertainties). Twenty-six perturbation scenarios per patient were generated to assess robustness. Nominal plan quality was evaluated by dose volume histogram (DVH) target coverage and hotspot metrics. Target volume homogeneity was assessed via standard deviation (SD), interquartile range (IQR), range width, Levene’s variance test, and homogeneity index (HI). Robustness was quantified via Levene and mean DVH 95% confidence interval (95% CI) width. Compared to IMPT, SPArc significantly improved target coverage at all tested volume and dose levels. Hotspots were significantly reduced at V_105%. Generalized equivalent uniform dose (gEUD) was significantly greater with SPArc. DVH analysis revealed greater volumes at V84.7% to V100.2% (mean 8.5% volume advantage) and volume reduction from V_104.6% to V_107.5% (mean 4.6% volume reduction). SPArc plans were more homogeneous by SD (2.3 <em>vs</em> 6.2%), IQR (1.9 <em>vs</em> 4.1%), range (22.7 <em>vs</em> 56.2%), HI (mean: 0.07 <em>vs</em> 0.21, <em>p</em> = 0.03), and by Levene (statistic: 65, <em>p</em> &lt; 1 × 10−14). SPArc was more robust to setup uncertainties as measured by Levene (mean statistic: 1.1 <em>vs</em> 1.6) and mean DVH 95% CI width (mean: 1.82 <em>vs</em> 2.52%, <em>p</em> = 0.052). In comparison to IMPT, SPArc therapy achieved substantially better homogeneity, robustness, and target coverage in the immediate presence of large metal artifacts. This provides a novel solution for a challenging clinical scenario.</div></div>","PeriodicalId":49837,"journal":{"name":"Medical Dosimetry","volume":"51 3","pages":"Pages 257-263"},"PeriodicalIF":1.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147318862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cauchy Lotus Optimization-based feature selection and ResNet 101 based XceptionNet architecture for radiation pneumonitis prediction in lung cancer patients","authors":"Haewon Byeon Ph.D,&nbsp;Madeshwari Ezhilan Ph.D,&nbsp;Preeti Pandurang Kale-Thombre Ph.D,&nbsp;Jinsha Lawrence M.E,&nbsp;Alexis Lazha Ph.D,&nbsp;Sunil Johnson Ph.D","doi":"10.1016/j.meddos.2026.03.008","DOIUrl":"10.1016/j.meddos.2026.03.008","url":null,"abstract":"<div><div>Thoracic radiotherapy for lung cancer patients followed by radiation pneumonitis (RP) has significant clinical side effects. Risk-adaptive treatment planning can be supported by accurate early RP prediction. Using thoracic CT scans, this study suggests an efficient deep learning algorithm for RP prediction. An analysis was conducted on a retrospective cohort of 548 patients with lung cancer who received thoracic radiotherapy between 2010 and 2021. According to established toxicity criteria, clinically significant RP was classified as Grade ≥ 2 and evaluated during post-treatment follow-up. Clinically accessible radiation outlines were used to separate bilateral lung regions, and an improved ResNet101-based XceptionNet architecture was used to extract deep features from CT images. Cauchy Lotus Optimization (CLO) was used for feature selection in order to minimize redundancy after an autoencoder was used for compact feature representation. At the patient level, the dataset was divided into cohorts for independent training (80%) and testing (20%). To avoid information leaking, only the training data was used for feature selection and model training. Precision, specificity, sensitivity, accuracy, and ROC-AUC were used to assess performance on the independent test set. Emperor Penguins Colony Algorithm (EPCA), Sea Lion Optimization (SLO), Spotted Hyena Optimization (SHO), Marine Predator Optimization (MPO), and other optimization-based techniques were compared to representative deep learning baselines. On the independent test set, the suggested framework demonstrated excellent predictive performance with high precision, accuracy, sensitivity, and specificity. Strong discriminative ability was shown by ROC analysis, and the suggested approach produced the highest AUC when compared with competing techniques. While maintaining discriminative power, the optimized feature selection technique significantly decreased feature dimensionality.</div></div>","PeriodicalId":49837,"journal":{"name":"Medical Dosimetry","volume":"51 3","pages":"Pages 355-369"},"PeriodicalIF":1.7,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147787421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of variable RBE in prostate cancer IMPT planning on normal tissue complications","authors":"Tae Kyu Lee PhD ,&nbsp;Wen C. Hsi PhD","doi":"10.1016/j.meddos.2025.11.004","DOIUrl":"10.1016/j.meddos.2025.11.004","url":null,"abstract":"<div><div>This study aimed to investigate the variable relative biological effectiveness with respect to the fixed RBE (1.1) on the prostate intensity-modulated-proton-therapy (IMPT), specifically focusing on normal tissue complication probabilities (NTCPs) in organs-at-risk (OARs) surrounding prostate tumors. The primary objective was to compare NTCP values between plans with fixed RBE (1.1) and plans with variable RBEs, considering the potential implications on treatment outcomes and normal tissue toxicity. Twelve prostate cancer patients undergoing simultaneous-integrated-boost treatments on a focal intraprostatic tumor (IPT) were studied. A linear function of the dose-averaged linear-energy-transfer (LET_d) was utilized to generate variable RBEs. IMPT plans with fixed RBE (1.1) and variable RBEs were generated with the same beam and spot parameters. Dosimetric evaluations, including dose-volume histograms (DVHs) with RBE weighted doses, were performed to assess the impact of RBE variations on dose distribution. NTCP calculations were conducted using the Lyman-Kutcher-Burman (LKB) Probit model to quantify the risk of normal tissue complications in the bladder wall and rectal wall. The analysis revealed notable differences in NTCP values between plans with fixed and variable RBEs. The bladder wall and rectal wall showed increased NTCP values in plans with variable RBEs compared to fixed RBE plans. The average NTCP differences were 5.7% for the bladder wall and 4.6% for the rectal wall, with maximum differences reaching 9.5%. These variations were attributed to the doses lying in the steep region, where even slight dose increases resulted in significant NTCP elevations. Incorporating variable RBEs in proton therapy treatment planning leads to higher NTCP values in OARs, indicating an increased risk of normal tissue complications. This highlights the importance of accurately accounting for RBE variations to optimize treatment outcomes and minimize the potential for radiation-induced toxicities in surrounding organs.</div></div>","PeriodicalId":49837,"journal":{"name":"Medical Dosimetry","volume":"51 2","pages":"Pages 189-196"},"PeriodicalIF":1.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145806233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-depth understanding of the multimodal deformable image registration tool of a 0.35 T MR-linac","authors":"Pierre Bugnon MSc ,&nbsp;Igor Bessieres PhD ,&nbsp;Alain Lalande PhD ,&nbsp;Aurélie Petitfils PhD ,&nbsp;Louis Marage PhD ,&nbsp;Léone Aubignac PhD ,&nbsp;Jérémy Baude MD ,&nbsp;Ilyas Achag MSc ,&nbsp;Julien Boudet MSc","doi":"10.1016/j.meddos.2025.11.005","DOIUrl":"10.1016/j.meddos.2025.11.005","url":null,"abstract":"<div><div>Linear accelerators (linac) with embedded magnetic resonance (MR) imaging involves a specific workflow that includes systematically a deformable image registration (DIR) with computed tomography images for the management of electron densities. Poor quality registration outputs could cause suboptimal dose calculations and planning treatments. The treatment planning system of the 0.35 T MR-linac MRIdian (ViewRay Inc., Oakwood Village) includes such a multimodal DIR tool with several adjustable parameters. To assist Viewray’s users, this work investigates the influence of each parameter from the DIR tool. The default set of DIR parameters has been questioned through body locations (Thorax, Abdomen, Pelvis) and two quantitative metrics (the mutual information and mean absolute error). While the <em>iterations number</em> could be higher defined by default, the <em>pyramidal number</em> parameters (or <em>control grid</em> with a low impact) should be more carefully escalated. The default regularization parameters are consistent; <em>both ways</em> ensure optimal results, and the <em>stiffness</em> default value should be considered as a safeguard against unrealistic deformations. Depending on the location considered, the same variation in a specific parameter can have different effects on the registration results. Although the quantitative metrics are not directly correlated with the quality of the registrations, this work should help users manage and optimize the DIR step.</div></div>","PeriodicalId":49837,"journal":{"name":"Medical Dosimetry","volume":"51 2","pages":"Pages 197-205"},"PeriodicalIF":1.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145913681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of general and specialized knowledge-based planning models for prostate cancer with bowel proximity","authors":"Chihiro Takahama BSc ,&nbsp;Motoharu Sasaki PhD ,&nbsp;Takeshi Kamomae PhD ,&nbsp;Wataru Sugimoto MSc ,&nbsp;Atsushi Itami MSc ,&nbsp;Akira Tsuzuki PhD ,&nbsp;Yuto Yamaji BSc ,&nbsp;Akimi Kajino MSc ,&nbsp;Yura Sakuka BSc ,&nbsp;Hana Kawabata BSc ,&nbsp;Hitoshi Ikushima MD, PhD","doi":"10.1016/j.meddos.2025.12.002","DOIUrl":"10.1016/j.meddos.2025.12.002","url":null,"abstract":"<div><div>This study evaluated knowledge-based planning (KBP) models for prostate cancer, focusing on cases where the bowel lies close to the planning target volume excluding the rectum (PTV-R). Such proximity increases toxicity risk, raising the question of whether a Special Model (Model-S) is required. Although General Models are widely applicable, their performance in bowel proximity cases remains uncertain. A General Model (Model-G) was trained with 30 standard cases and a Model-S with 30 bowel proximity cases. Thirteen additional proximity cases were used for independent evaluation. RapidPlan-generated plans (Plan-G and Plan-S) were compared with clinically used manual plans (Plan-M). Plan quality was evaluated using D98% and D2% for targets, dose-volume indices for the rectum and bladder, and D0.1/D1 cc for the bowel. Statistical analyses employed the Mann–Whitney U and paired t-tests (<em>p</em> &lt; 0.05). In the independent evaluation, Plan-M achieved significantly lower D2% than Plan-G (<em>p</em> &lt; 0.001), while Plan-S showed no significant difference (<em>P</em> = 0.069). Organ-at-risk sparing was comparable across all plans (<em>p</em> &gt; 0.05). Among the 13 evaluation cases, one Plan-G slightly exceeded the bowel D1 cc constraint of 60 Gy, indicating that in such bowel-descended cases, manual re-optimization or use of the Special Model may be required, defining the boundary where General Models remain reliable. Both models yielded clinically acceptable plans, and no clear dosimetric advantage was observed for the Special Model. These findings demonstrate the robustness of KBP against anatomical variability and highlight its potential as a foundation for future AI-assisted adaptive radiotherapy frameworks.</div></div>","PeriodicalId":49837,"journal":{"name":"Medical Dosimetry","volume":"51 2","pages":"Pages 218-224"},"PeriodicalIF":1.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145918985","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}