Physics and Imaging in Radiation Oncology最新文献

{"title":"Cone-beam CT-based estimations of prostate motion and dose distortion during radiotherapy","authors":"Karolina A Klucznik ,&nbsp;Thomas Ravkilde ,&nbsp;Simon Skouboe ,&nbsp;Ditte S Møller ,&nbsp;Steffen Hokland ,&nbsp;Paul Keall ,&nbsp;Simon Buus ,&nbsp;Lise Bentzen ,&nbsp;Per R Poulsen","doi":"10.1016/j.phro.2025.100798","DOIUrl":"10.1016/j.phro.2025.100798","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Intra-fractional prostate translational and rotational (6DoF) motion can cause dose distortions. As intra-fractional motion monitoring is often unavailable, this study compares three methods to use pre- and post-treatment cone beam CTs (CBCT) to estimate prostate positioning errors during treatment and their dosimetric impact.</div></div><div><h3>Material and Methods</h3><div>Eighteen patients received prostate radiotherapy with pre-treatment CBCT setup. For 7–10 fractions per patient (total:174), triggered kV-images were acquired every 3 s during beam-on and a CBCT was acquired post-treatment. The 6DoF prostate position error during treatment was determined from the kV-images (ground truth) and estimated from the CBCTs assuming a static position as in the pre-CBCT(Scenario1), a linear drift between pre- and post-CBCT position(Scenario2) or a static position as in the post-CBCT(Scenario3). The positioning errors and prostate dose from each scenario were compared with the ground truth.</div></div><div><h3>Results</h3><div>Scenario1 was inferior to the others with prostate position root-mean-square errors of 1.1 mm(LR), 1.7 mm(AP) and 1.8 mm(CC). Scenario2 and 3 were similarly accurate with root-mean-square errors of 0.5 mm(LR), 0.9 mm(AP) and 0.8 mm(CC) (Scenario2) and 0.6 mm(LR), 1.1 mm(AP) and 0.9 mm(CC) (Scenario3). The prostate position errors reduced the CTV D_99.5% by more than 2/3 % at 24/15 % of the fractions, respectively. The sensitivity in detecting these dose deficits was low for Scenario1 (9–16 %) and considerably higher for Scenario2 (68–76 %) and Scenario3 (86–91 %). All scenarios showed high specificity (93–99 %).</div></div><div><h3>Conclusion</h3><div>Using the post-CBCT prostate position, acquired right after treatment, performed best in detecting intra-fractional prostate position errors and CTV dose deficits. It offers a scalable and conservative estimate of motion-induced dose distortions.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"35 ","pages":"Article 100798"},"PeriodicalIF":3.4,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144502088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rectal-RadioSAM: Large model-assisted multi-parametric magnetic resonance imaging pipeline for predicting response to neoadjuvant chemoradiotherapy in rectal cancer without human intervention","authors":"Shao-Jun Xia ,&nbsp;Zhi-Nan Wang ,&nbsp;Jia-Qi Wu ,&nbsp;Qing-Yang Li ,&nbsp;Yan-Jie Shi ,&nbsp;Xiao-Ting Li ,&nbsp;Xiao-Yan Zhang ,&nbsp;Ying-Shi Sun","doi":"10.1016/j.phro.2025.100797","DOIUrl":"10.1016/j.phro.2025.100797","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Accurate evaluation of response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer is critical for guiding clinical decision-making. This study developed and validated a large model-assisted automated prediction tool to assess response to nCRT in locally advanced rectal cancer (LARC), focusing on segmentation and radiomic feature extraction.</div></div><div><h3>Material and methods</h3><div>A retrospective analysis included 378 LARC patients (756 cases: baseline and post-nCRT MRI). MRI protocols comprised T2-weighted imaging (T2WI) and logarithmic diffusion-weighted imaging (DWI, b = 1000 s/mm²). A two-stage hybrid model combined fine-tuned four-channel MedSAM networks for lesion segmentation and a coupled XGBoost model for pathologic complete response (pCR) prediction. Resilience of radiomic features was assessed by comparing automated and manual segmentations.</div></div><div><h3>Results</h3><div>In the independent testing set comprising 112 LARC patients, the large segmentation models achieved mean (± std) Dice coefficients of 0.74 (± 0.09), 0.66 (± 0.13), 0.67 (± 0.15), and 0.46 (± 0.15) for pre-nCRT T2WI, post-nCRT T2WI, pre-nCRT DWI (log[S(1000)]), and post-nCRT DWI (log[S(1000)]) images, respectively. Meanwhile, First-Order and Shape radiomic features exhibited significant correlations between the large model-assisted segmentations and manual delineations (<em>p</em> &lt; 0.01). In the prediction phase, the combined pipeline achieved a mean (± std) AUC value of 0.83 (± 0.04).</div></div><div><h3>Conclusion</h3><div>The large model-assisted multi-parametric MRI pipeline demonstrated robust performance in predicting pCR for rectal cancer, enabling fully automated radiological assessment without human intervention.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"35 ","pages":"Article 100797"},"PeriodicalIF":3.4,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tumour nuclear size heterogeneity as a biomarker for post-radiotherapy outcomes in gynecological malignancies","authors":"Yujing Zou ,&nbsp;Harry Glickman ,&nbsp;Manuela Pelmus ,&nbsp;Farhad Maleki ,&nbsp;Boris Bahoric ,&nbsp;Magali Lecavalier-Barsoum ,&nbsp;Shirin A. Enger","doi":"10.1016/j.phro.2025.100793","DOIUrl":"10.1016/j.phro.2025.100793","url":null,"abstract":"<div><h3>Background and Purpose:</h3><div>Radiotherapy targets DNA in cancer cell nuclei. Radiation dose, however, is prescribed to a macroscopic target volume assuming uniform distribution, failing to consider microscopic variations in dose absorbed by individual nuclei. This study investigated a potential link between pre-treatment tumour nuclear size distributions and post-radiotherapy outcomes in gynecological squamous cell carcinoma (SCC).</div></div><div><h3>Materials and Methods:</h3><div>Our multi-institutional cohort consisted of 191 non-metastatic gynecological SCC patients who had received radiotherapy with diagnostic whole slide images (WSIs) available. Tumour nuclear size distribution mean and standard deviation were extracted from WSIs using deep learning, and used to predict progression-free interval (PFI) and overall survival (OS) in multivariate Cox proportional hazards (CoxPH) analysis adjusted for age and clinical stage.</div></div><div><h3>Results:</h3><div>Multivariate CoxPH analysis revealed that a larger nuclear size distribution mean results in more favorable outcomes for PFI (HR = 0.45, 95% CI: 0.19 - 1.09, p = 0.084) and OS (HR = 0.55, 95% CI: 0.24 - 1.25, p = 0.16), and that a larger nuclear size standard deviation results in less favorable outcomes for PFI (HR = 7.52, 95% CI: 1.43 - 39.52, p = 0.023) and OS (HR = 4.67, 95% CI: 0.96 - 22.57, p = 0.063). The bootstrap-validated C-statistic was 0.56 for PFI and 0.57 for OS.</div></div><div><h3>Conclusion:</h3><div>Despite low accuracy, tumour nuclear size heterogeneity aided prognostication over standard clinical variables and was associated with outcomes following radiotherapy in gynecological SCC. This highlights the potential importance of personalized multiscale dosimetry and warrants further large-scale pan-cancer studies.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"35 ","pages":"Article 100793"},"PeriodicalIF":3.4,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Stability of liver position in a shuttle-based workflow for daily online magnetic resonance imaging-guided particle therapy","authors":"Friderike K. Longarino ,&nbsp;Una Maguire ,&nbsp;Cedric Beyer ,&nbsp;Rita Pestana ,&nbsp;Sebastian Regnery ,&nbsp;Jürgen Debus ,&nbsp;Sebastian Klüter ,&nbsp;Katharina Seidensaal ,&nbsp;Julia Bauer","doi":"10.1016/j.phro.2025.100795","DOIUrl":"10.1016/j.phro.2025.100795","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Online adaptive particle therapy makes it possible to consider interfractional changes during treatment, and therefore may lead to improved treatment outcomes. The advantages of online adaptive particle therapy may be realized with minimal workflow disruption by employing a shuttle-based daily quasi-online magnetic resonance imaging (MRI)-guided strategy, where the patient remains in the treatment position on a transfer table for MRI at a diagnostic device and subsequent treatment delivery. This study investigated potential liver displacement and deformation while using a shuttle-based workflow.</div></div><div><h3>Material and methods</h3><div>Fourteen healthy volunteers each underwent four MRI scans in a 1.5<!--> <!-->T MRI scanner, with intra-hospital transport simulations between MRI scans. The study proceeded in the following steps: volunteer positioning, first MRI scan (MRI<!--> <!-->1), ten-minute time control phase, second MRI scan (MRI<!--> <!-->2), short transport phase, third MRI scan (MRI<!--> <!-->3), long transport phase, and finally the last MRI scan (MRI<!--> <!-->4). In each MRI set, the liver and relevant external outline were contoured. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) were calculated to quantify consecutive shifts between image sets and accumulative shifts over the course of the study.</div></div><div><h3>Results</h3><div>Median MDA values for the liver (and for the external) contour were 0.6<!--> <!-->mm (0.4<!--> <!-->mm) for MRI<!--> <!-->1<!--> <!-->–<!--> <!-->MRI<!--> <!-->2, 0.4<!--> <!-->mm (0.5<!--> <!-->mm) for MRI<!--> <!-->2<!--> <!-->–<!--> <!-->MRI<!--> <!-->3, and 0.3<!--> <!-->mm (0.7<!--> <!-->mm) for MRI<!--> <!-->3<!--> <!-->–<!--> <!-->MRI<!--> <!-->4. All subjects exhibited MDA values of &lt;1.0<!--> <!-->mm (&lt;1.5<!--> <!-->mm) and DSC values of &gt;0.97 (&gt;0.98) during transport phases. Outliers for the accumulative shift from MRI<!--> <!-->1<!--> <!-->–<!--> <!-->MRI<!--> <!-->4 remained at &lt;2.0<!--> <!-->mm (&lt;1.8<!--> <!-->mm) after approximately 75 minutes.</div></div><div><h3>Conclusions</h3><div>The study demonstrated the high stability of the liver position in a shuttle-based workflow, a finding that can be used to enhance MRI-guided adaptive treatment strategies in radiotherapy.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"35 ","pages":"Article 100795"},"PeriodicalIF":3.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A randomized safety study of tolerance to table rotation in dynamic trajectory radiotherapy in healthy volunteers","authors":"Paul-Henry Mackeprang ,&nbsp;Jenny Bertholet ,&nbsp;Claas Wessels ,&nbsp;Jean-Benoit Rossel ,&nbsp;Andreas Limacher ,&nbsp;Daniel M. Aebersold ,&nbsp;Michael K. Fix ,&nbsp;Peter Manser","doi":"10.1016/j.phro.2025.100796","DOIUrl":"10.1016/j.phro.2025.100796","url":null,"abstract":"<div><div>This study aimed to show that table movement of dynamic trajectory radiotherapy (DTRT) does not induce more motion sickness than standard-of-care non-coplanar volumetric modulated arc therapy (ncVMAT). Forty-one healthy volunteers underwent dry-runs of DTRT and ncVMAT in four different, randomly allocated sequences. The primary outcome was the Motion Sickness Assessment Questionnaire (MSAQ) summary score. The average change in MSAQ summary score before to after dry-runs was 1.88 for DTRT and 1.62 for ncVMAT. The difference between both techniques was 0.26 [95% CI: −0.24 to 0.75]; the CI demonstrated non-inferiority of DTRT to ncVMAT for motion sickness in healthy volunteers.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"35 ","pages":"Article 100796"},"PeriodicalIF":3.4,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Magnetic resonance-only rapid on-table planning and immediate treatment for spine metastases","authors":"Jerrold E. Kielbasa,&nbsp;Logan Kimble,&nbsp;Justin Rineer,&nbsp;Cameron W. Swanick,&nbsp;Patrick Kelly,&nbsp;Amish P. Shah","doi":"10.1016/j.phro.2025.100791","DOIUrl":"10.1016/j.phro.2025.100791","url":null,"abstract":"<div><h3>Background and purpose</h3><div>This work aims to develop a magnetic resonance (MR)-only treatment planning protocol for integration into magnetic resonance simulation (MR-SIM), on-table treatment planning, and immediate treatment workflow for rapid palliation of painful spine metastases.</div></div><div><h3>Materials and methods</h3><div>Thirty-five treatment plans from healthy volunteers were generated on MR-SIM scans using a protocol including: (1) a library of 4 planning target volume (PTV) structures based on vertebral level, (2) default beam templates covering the PTVs while avoiding organs at risk (OAR), (3) bulk density assignments for dose calculations, and (4) a single set of dose optimization parameters. We transferred each plan to the patient’s prior computed tomography simulation (CT-SIM) images and compared dosimetric parameters. A time study was performed on the MR-SIM, on-table planning, and immediate treatment workflow for all 35 cases using healthy volunteers.</div></div><div><h3>Results</h3><div>All bulk density MR-SIM plans resulted in acceptable dose distributions, with 100 % deemed appropriate for treatment by two physicians. The maximum point dose was within 3.2 %, and the minimum dose to 90 % of the target volume was within 2.8 % of the prescription dose. The time study demonstrated that the proposed workflow could be completed in a mean time of 23.6 min for the 3 Gy (10 fractions) plan and 25.4 min for the 8 Gy (single fraction) plan, from patient placement to treatment completion.</div></div><div><h3>Conclusion</h3><div>These results demonstrate that safe, fast palliation of spine metastases can be achieved in under 30 min using MR-SIM, bulk density on-table planning, and immediate treatment delivery.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"35 ","pages":"Article 100791"},"PeriodicalIF":3.4,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144338764","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient-specific deep learning tracking for real-time 2D pancreas localisation in kV-guided radiotherapy","authors":"Abdella M. Ahmed ,&nbsp;Levi Madden ,&nbsp;Maegan Stewart ,&nbsp;Brian V.Y. Chow ,&nbsp;Adam Mylonas ,&nbsp;Ryan Brown ,&nbsp;Gabrielle Metz ,&nbsp;Meegan Shepherd ,&nbsp;Carlito Coronel ,&nbsp;Leigh Ambrose ,&nbsp;Alex Turk ,&nbsp;Maiko Crispin ,&nbsp;Andrew Kneebone ,&nbsp;George Hruby ,&nbsp;Paul Keall ,&nbsp;Jeremy T. Booth","doi":"10.1016/j.phro.2025.100794","DOIUrl":"10.1016/j.phro.2025.100794","url":null,"abstract":"<div><h3>Background and purpose</h3><div>In pancreatic stereotactic body radiotherapy (SBRT), accurate motion management is crucial for the safe delivery of high doses per fraction. Intra-fraction tracking with magnetic resonance imaging-guidance for gated SBRT has shown potential for improved local control. Visualisation of pancreas (and surrounding organs) remains challenging in intra-fraction kilo-voltage (kV) imaging, requiring implanted fiducials. In this study, we investigate patient-specific deep-learning approaches to track the gross-tumour-volume (GTV), pancreas-head and the whole-pancreas in intra-fraction kV images.</div></div><div><h3>Materials and methods</h3><div>Conditional-generative-adversarial-networks were trained and tested on data from 25 patients enrolled in an ethics-approved pancreatic SBRT trial for contour prediction on intra-fraction 2D kV images. Labelled digitally-reconstructed-radiographs (DRRs) were generated from contoured planning-computed-tomography (CTs) (CT-DRRs) and cone-beam-CTs (CBCT-DRRs). A population model was trained using CT-DRRs of 19 patients. Two patient-specific model types were created for six additional patients by fine-tuning the population model using CBCT-DRRs (CBCT-models) or CT-DRRs (CT-models) acquired in exhale-breath-hold. Model predictions on unseen triggered-kV images from the corresponding six patients were evaluated against projected-contours using Dice-Similarity-Coefficient (DSC), centroid-error (CE), average Hausdorff-distance (AHD), and Hausdorff-distance at 95th-percentile (HD95).</div></div><div><h3>Results</h3><div>The mean ± 1SD (standard-deviation) DSCs were 0.86 ± 0.09 (CBCT-models) and 0.78 ± 0.12 (CT-models). For AHD and CE, the CBCT-model predicted contours within 2.0 mm ≥90.3 % of the time, while HD95 was within 5.0 mm ≥90.0 % of the time, and had a prediction time of 29.2 ± 3.7 ms per contour.</div></div><div><h3>Conclusion</h3><div>The patient-specific CBCT-models outperformed the CT-models and predicted the three contours with 90th-percentile error ≤2.0 mm, indicating the potential for clinical real-time application.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"35 ","pages":"Article 100794"},"PeriodicalIF":3.4,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FAPI-positron emission tomography for radiotherapy in head and neck cancer: applications and future directions","authors":"Mischa de Ridder ,&nbsp;Remco de Bree ,&nbsp;Bart de Keizer","doi":"10.1016/j.phro.2025.100792","DOIUrl":"10.1016/j.phro.2025.100792","url":null,"abstract":"<div><div>Radiotherapy is a cornerstone in the treatment of head and neck squamous cell carcinoma (HNSCC). Imaging plays an important role in target delineation in order to achieve adequate target dose and on the other hand limit radiation-induced toxicity to surrounding healthy tissues. Advancements in imaging techniques, including functional imaging with positron emission tomography (PET), have improved accurate target delineation, allowed for adaptive radiotherapy, and improved response evaluation.</div><div>This review explores the potential role of fibroblast activation protein inhibitor (FAPI)-PET/CT in radiotherapy planning for HNSCC, comparing its efficacy to the current standard, fluorine-18 fluorodeoxyglucose (18F-FDG) PET/CT.</div><div>FAPI-PET/CT is an emerging imaging modality that for pretreatment imaging may increase lesion detection, especially near the skull base. For target delineation FAPI PET/CT may enhance the specificity of tumor delineation, particularly in distinguishing tumor tissue from normal tissue. Current evidence suggests that FAPI-PET increases the GTV volume, but whether this represents more accurate tumor delineation or overestimation remains unclear. For response monitoring after treatment, there is no evidence in head and neck cancer, but for rectal cancer FAPI-PET/CT showed promising results in detection of residual disease after neo-adjuvant radiotherapy.</div><div>For now, FAPI-PET/CT could be used as additional imaging, and should not be used as replacement for other imaging modalities. There is an urgent need for prospective validation studies to determine the clinical impact of FAPI-PET on radiotherapy planning and response monitoring.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"35 ","pages":"Article 100792"},"PeriodicalIF":3.4,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144280425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine-learning-based integration of temporal and spectral prompt gamma-ray information for proton range verification","authors":"Aaron Kieslich ,&nbsp;Sonja M. Schellhammer ,&nbsp;Alex Zwanenburg ,&nbsp;Toni Kögler ,&nbsp;Steffen Löck","doi":"10.1016/j.phro.2025.100788","DOIUrl":"10.1016/j.phro.2025.100788","url":null,"abstract":"<div><h3>Background and Purpose:</h3><div>Prompt gamma-ray timing (PGT) and prompt gamma-ray spectroscopy (PGS) are non-invasive techniques for dose delivery monitoring in proton radiotherapy. Integrating PGT and PGS into a unified data analysis framework may improve proton range verification by incorporating both temporal and spectral information from prompt gamma-ray events. This study evaluates the effectiveness of this integration for enhancing the accuracy of proton range verification using machine-learning.</div></div><div><h3>Material and Methods:</h3><div>A homogeneous phantom was irradiated with 162 and 225<!--> <!-->MeV static and scanned proton beams. Air cavities of 5, 10 and 20 mm were introduced to simulate anatomical variations. The energy and time of arrival of prompt gamma rays were measured using a PGT detector. 2-dimensional time-energy spectra were extracted for 1,440 proton spots. Different feature sets (energy-only, time-only, energy-restricted time, image) were computed. These feature sets were used by four different machine-learning models to predict range shifts. Model performance was assessed using the root mean square error (RMSE).</div></div><div><h3>Results:</h3><div>Time-only and combined time-energy feature sets exhibited good performance with RMSE values of 3 to 4 mm, consistent with previously developed models. Energy-only and image features led to poorer performance with RMSE values exceeding 5 mm. The integration of energy-only features did not improve prediction accuracy compared to exclusively using time-only features.</div></div><div><h3>Conclusion:</h3><div>While spectral information did not contribute additional value for determining proton beam range shifts in the investigated setup, the findings show that temporal information alone is sufficient to perform accurate proton range verification.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"35 ","pages":"Article 100788"},"PeriodicalIF":3.4,"publicationDate":"2025-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A method for sensitivity analysis of automatic contouring algorithms across different contrast weightings using synthetic magnetic resonance imaging","authors":"Lucas McCullum ,&nbsp;Zayne Belal ,&nbsp;Warren Floyd ,&nbsp;Alaa Mohamed Shawky Ali ,&nbsp;Natalie West ,&nbsp;Samuel Mulder ,&nbsp;Yao Ding ,&nbsp;Jiaofeng Xu ,&nbsp;Dan Thill ,&nbsp;Nicolette O’Connell ,&nbsp;Joseph Stancanello ,&nbsp;Kareem A. Wahid ,&nbsp;David T. Fuentes ,&nbsp;Ken-Pin Hwang ,&nbsp;Clifton D. Fuller","doi":"10.1016/j.phro.2025.100790","DOIUrl":"10.1016/j.phro.2025.100790","url":null,"abstract":"<div><h3>Background and purpose</h3><div>A majority of institution-specific automatic magnetic resonance imaging (MRI)-based contouring algorithms utilize one contrast-weighting (i.e., T2-weighted), however their performance within this contrast-weighting (i.e., across different repetition time, TR, and echo time, TE) is under-investigated and poorly understood. The purpose of this study was to develop a method to evaluate the robustness of automatic contouring algorithms to varying MRI contrast-weightings.</div></div><div><h3>Materials and methods</h3><div>One healthy volunteer and one patient were scanned using the multi-delay multi-echo (MDME) scan on a 3T MRI. The parotid and submandibular glands in these subjects were contoured using an automatic contouring algorithm trained on T2-weighted MRIs. Ground truth consensus contours were created by two radiation oncology residents and one pre-resident physician. A total of 216 different TR and TE combinations were simulated across T1-, T2-, and PD-weighted contrast ranges using SyMRI. Comparisons between automatic contouring algorithm contours and the ground truth were determined using the Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95) with interobserver variability used as a threshold for clinical acceptance.</div></div><div><h3>Results</h3><div>Differences in the automatic contouring model’s performance were seen across the contrast-weighted regions. The range of discrepancy in DSC and HD95 exceeded 0.2 and 3.66 mm, respectively. In the T2-weighted contrast region, 100 %, 40 %, 24 %, and 57 % for the DSC in the left parotid, right parotid, left submandibular, and right submandibular gland, respectively, exceeded interobserver variability.</div></div><div><h3>Conclusions</h3><div>This study demonstrates the variable performance of MRI-based automatic contouring algorithms across varying TR and TE combinations even within the same contrast-weighting region (i.e., T2-weighted).</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"35 ","pages":"Article 100790"},"PeriodicalIF":3.4,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144313423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}