Physics and Imaging in Radiation Oncology最新文献

{"title":"Accuracy of manufacturer integrated quality control for helical radiotherapy","authors":"Frederik Crop,&nbsp;Maeva Bocquet,&nbsp;Clémence Kirie,&nbsp;Julien Laffarguette,&nbsp;Romain Cayez,&nbsp;Mohamed Tahar Ladjimi,&nbsp;Erwann Rault,&nbsp;Pauline Comte,&nbsp;Ludovic Vanquin,&nbsp;Thomas Lacornerie,&nbsp;Camille Decoene","doi":"10.1016/j.phro.2025.100750","DOIUrl":"10.1016/j.phro.2025.100750","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Manufacturer-integrated quality control (MIQC) systems are often used but not considered standard in codes-of-practice (COP), such as TG148/306 or NCS27, for helical radiotherapy. MIQC can lead to false positive results and generally lacks external validation. Energy quality control (QC) conditions are defined in COPs, manuals, or MIQC using various field sizes, phantoms, and indicators assuming equal response functions to energy changes. This study investigated the accuracy of MIQC for helical radiotherapy.</div></div><div><h3>Materials and Methods</h3><div>A clinical helical treatment unit was detuned in terms of energy, dose rate, field width, and air pressure. The reproducibility/precision and response/trueness of MIQC, conventional QC methods, and patient-specific quality assurance were evaluated. Monte Carlo calculations were performed to identify differences in responses of depth dose ratios DD10/1.5, DD20/1.5, DD20/10, Tissue-Phantom Ratio TPR20/10, and ratio to max DD10(x) for various field sizes and phantoms.</div></div><div><h3>Results</h3><div>The accuracy of MIQC for underlying causes was determined; precision was often excellent, but trueness required proportionality correction: e.g., 1 % DD10(x, 5 × 10 cm², H₂O) showed almost equal response to TPR20/10, DD20/10 and exit detector flatness in most conditions but a 2 % DD20/1.5(1x40cm²) and step-wedge MIQC response. Exit detector output constancy was not significantly sensitive to field size changes but was sensitive to energy and dose rates. A guiding table containing response functions and reproducibility coefficients was established.</div></div><div><h3>Conclusions</h3><div>The MIQC metrological accuracy assessment can be used to define action/tolerance limits for COPs as well as to easily analyze out-of-bound results in routine practice.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100750"},"PeriodicalIF":3.4,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143696040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Physics-based data augmentation for improved training of cone-beam computed tomography auto-segmentation of the female pelvis","authors":"Yvonne J.M. de Hond ,&nbsp;Paul M.A. van Haaren ,&nbsp;An-Sofie E. Verrijssen ,&nbsp;Rob H.N. Tijssen ,&nbsp;Coen W. Hurkmans","doi":"10.1016/j.phro.2025.100744","DOIUrl":"10.1016/j.phro.2025.100744","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Labeling cone-beam computed tomography (CBCT) images is challenging due to poor image quality. Training auto-segmentation models without labelled data often involves deep-learning to generate synthetic CBCTs (sCBCT) from planning CTs (pCT), which can result in anatomical mismatches and inaccurate labels. To prevent this issue, this study assesses an auto-segmentation model for female pelvic CBCT scans exclusively trained on delineated pCTs, which were transformed into sCBCT using a physics-driven approach.</div></div><div><h3>Materials and Methods</h3><div>To replicate CBCT noise and artefacts, a physics-driven sCBCT (Ph-sCBCT) was synthesized from pCT images using water-phantom CBCT scans. A 3D nn-UNet model was trained for auto-segmentation of cervical cancer CBCTs using Ph-sCBCT images with pCT contours. This study included female pelvic patients: 63 for training, 16 for validation and 20 each for testing on Ph-sCBCTs and clinical CBCTs. Auto-segmentations of bladder, rectum and clinical target volume (CTV) were evaluated using Dice Similarity Coefficient (DSC) and 95th percentile Hausdorff Distance (HD95). Initial evaluation occurred on Ph-sCBCTs before testing generalizability on clinical CBCTs.</div></div><div><h3>Results</h3><div>The model auto-segmentation performed well on Ph-sCBCT images and generalized well to clinical CBCTs, yielding median DSC’s of 0.96 and 0.94 for the bladder, 0.88 and 0.81 for the rectum, and 0.89 and 0.82 for the CTV on Ph-sCBCT and clinical CBCT, respectively. Median HD95′s for the CTV were 5 mm on Ph-sCBCT and 7 mm on clinical CBCT.</div></div><div><h3>Conclusions</h3><div>This study demonstrates the successful training of auto-segmentation model for female pelvic CBCT images, without necessarily delineating CBCTs manually.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100744"},"PeriodicalIF":3.4,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143609812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel moving phantom insert for image quality assessment in magnetic resonance imaging","authors":"Frédérique P.D. van Gameren ,&nbsp;Pim T.S. Borman ,&nbsp;Cornelis A.T. van den Berg ,&nbsp;Mike Cole ,&nbsp;Grant R. Koenig ,&nbsp;Martin F. Fast ,&nbsp;Astrid L.H.M.W. van Lier","doi":"10.1016/j.phro.2025.100742","DOIUrl":"10.1016/j.phro.2025.100742","url":null,"abstract":"<div><div>Dedicated motion compensated Magnetic Resonance Imaging (MRI) for radiotherapy treatment planning promises to mitigate motion effects on imaging. We demonstrate a novel insert for an MRI safe motion phantom, which enables quality assurance of these image strategies. The capability to analyse apparent slice thickness, positional accuracy and motion blur is demonstrated for scenarios with and without motion. A respiratory-compensated scan with a 4 mm trigger-window and 16 mm peak-to-peak (p2p) motion showed a +5.0% deviation from the nominal 2 mm slice thickness. In contrast, a non-compensated scan with 4 mm p2p motion showed a +77.5% deviation, illustrating the effectiveness of motion compensation.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100742"},"PeriodicalIF":3.4,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143642908","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Interfractional body surface monitoring using daily cone-beam computed tomography imaging for pediatric adaptive proton therapy","authors":"Ozgur Ates,&nbsp;Hoyeon Lee,&nbsp;Jinsoo Uh,&nbsp;Matthew J. Krasin,&nbsp;Thomas E. Merchant,&nbsp;Chia-ho Hua","doi":"10.1016/j.phro.2025.100746","DOIUrl":"10.1016/j.phro.2025.100746","url":null,"abstract":"<div><h3>Background and purpose</h3><div>A novel method was developed to detect body surface changes on daily cone-beam computed tomography (CBCT) and estimate the impact on proton plan quality for pediatric patients.</div></div><div><h3>Materials and methods</h3><div>Simulation CT, daily CBCT, and repeat CT images were collected for 21 pediatric non-central nervous system (CNS) patients. Changes in the body surface in the proton beam path (ΔSurface_CBCT) were calculated for each spot by comparing simulation CT with daily CBCT. Subsequently, changes in water equivalent path length (WEPL) (ΔWEPL_{Synthetic CT}) were calculated for each spot by comparing the simulation CT with the synthetic CT converted from daily CBCT. The ground truth surface (ΔSurface_{Repeat CT}) and WEPL changes (ΔWEPL_{Repeat CT}) were calculated by comparing the simulation CT with the repeat CT taken on the same day as the CBCT.</div></div><div><h3>Results</h3><div>The root-mean-square (RMS) error between the ΔSurface_CBCT and ΔSurface_{Repeat CT} was 1.3 mm, while the RMS error between ΔWEPL_{Synthetic CT} and ΔWEPL_{Repeat CT} was 1.6 mm. A strong linear correlation was determined between ΔSurface_CBCT and ΔWEPL_{Synthetic CT} (R² = 0.97). The non-linear regression analysis of the dose volume parameters indicated that a 5 % decrease in clinical target volume (CTV) D_min and D_99% was caused by 3.9 mm and 6.3 mm of ΔSurface_CBCT, and 4.0 mm and 6.6 mm of ΔWEPL_{Synthetic CT}, respectively.</div></div><div><h3>Conclusions</h3><div>The findings revealed that a 5 mm change in body surface can lead to a significant degradation of plan quality, reducing CTV D_min by 11.7 % and underscoring the need for adapting treatment plan.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100746"},"PeriodicalIF":3.4,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143591948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dosimetric comparison of helical tomotherapy treatment delivery with real-time adaption and no motion correction","authors":"Jonathan Hindmarsh ,&nbsp;Scott Crowe ,&nbsp;Julia Johnson ,&nbsp;Chandrima Sengupta ,&nbsp;Jemma Walsh ,&nbsp;Sonja Dieterich ,&nbsp;Jeremy Booth ,&nbsp;Paul Keall","doi":"10.1016/j.phro.2025.100741","DOIUrl":"10.1016/j.phro.2025.100741","url":null,"abstract":"<div><div>This study assesses the ability of a helical tomotherapy system equipped with kV imaging and optical surface guidance to adapt to motion traces in real-time. To assess the delivery accuracy with motion, a unified testing framework was used. The average 2 %/2 mm γ-fail rates across all lung traces were 0.1 % for motion adapted and 17.4 % for no motion correction. Average 2 %/2 mm γ-fail rates across all prostate traces were 0.4 % for motion adapted and 12.2 % for no motion correction. Real-time motion adaption was shown to improve the accuracy of dose delivered to a moving phantom compared with no motion adaption.</div><div><strong>MeSH Keywords:</strong> Radiotherapy, image-guided; Radiation therapy, targeted.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100741"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning-based prediction of Monte Carlo dose distribution for heavy ion therapy","authors":"Rui He ,&nbsp;Hui Zhang ,&nbsp;Jian Wang ,&nbsp;Guosheng Shen ,&nbsp;Ying Luo ,&nbsp;Xinyang Zhang ,&nbsp;Yuanyuan Ma ,&nbsp;Xinguo Liu ,&nbsp;Yazhou Li ,&nbsp;Haibo Peng ,&nbsp;Pengbo He ,&nbsp;Qiang Li","doi":"10.1016/j.phro.2025.100735","DOIUrl":"10.1016/j.phro.2025.100735","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Current methods, like treatment planning system algorithms (TPSDose), lack accuracy, whereas Monte Carlo dose distribution (MCDose) is accurate but computationally intensive. We proposed a deep learning (DL) model for rapid prediction of Monte Carlo simulated dose distribution (MCDose) in heavy ion therapy (HIT).</div></div><div><h3>Materials and methods</h3><div>We developed a DL model − the Cascade Hierarchically Densely 3D U-Net (CHD U-Net) − to predict MCDose using computed tomography images and TPSDose of 67 head-and-neck patients and 30 thorax-and-abdomen patients. We also compared the results with other proton dose DL models and TPSDose.</div></div><div><h3>Results</h3><div>Compared to TPSDose, the gamma passing rate (GPR) improved by 16 % (1 %/1 mm). Notably, the model achieved 99 % and 97 % accuracy under clinically relevant criteria (3 %/3 mm) across the whole dose distribution in patients. For head-and-neck patients, the GPRs of the C3D and HD U-Net models in the PTV region were 97 % and 85 %, and in the body were 98 % and 97 %, respectively. For thorax-and-abdomen patients, the GPR of the C3D and HD U-Net models in the PTV region were 71 % and 51 %, and in the body were 95 % and 90 %, respectively.</div></div><div><h3>Conclusions</h3><div>The proposed CHD U-Net model can predict MCDose in a few seconds and outperforms two alternative DL models. The predicted dose can replace TPSDose in HIT clinical process due to its MC simulation accuracy, thus improving the accuracy of dose calculation and providing a valuable reference for quality assurance.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100735"},"PeriodicalIF":3.4,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143580381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Respiratory navigator-guided multi-slice free-breathing cardiac T1 mapping on a magnetic resonance-guided linear accelerator","authors":"Beau P. Pontré ,&nbsp;Stefano Mandija ,&nbsp;Manon M.N. Aubert ,&nbsp;Tim Schakel ,&nbsp;Osman Akdag ,&nbsp;Katrinus Keijnemans ,&nbsp;Pim T.S. Borman ,&nbsp;Astrid L.H.M.W. van Lier ,&nbsp;Cornelis A.T. van den Berg ,&nbsp;Martin F. Fast","doi":"10.1016/j.phro.2025.100739","DOIUrl":"10.1016/j.phro.2025.100739","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Image-guided cardiac radioablation on a magnetic resonance-guided linear accelerator (MR-linac) is emerging as a non-invasive treatment alternative for patients with cardiac arrhythmia. Precise target identification is required for such treatments. However, owing to concerns with the use of gadolinium-based contrast agents during treatment with high-energy radiation, non-contrast alternatives must be considered. Native T₁ mapping is a promising technique to delineate myocardial scar which can serve as a surrogate for the treatment target. Further, the likely presence of an implantable cardioverter defibrillator (ICD) in arrhythmia patients necessitates approaches that are robust to metal-related artefacts.</div></div><div><h3>Materials and Methods</h3><div>We implemented an electrocardiogram (ECG)-triggered free-breathing cardiac T₁ mapping approach on an MR-linac, making use of a respiratory navigator to account for respiratory motion. The technique was validated in a motion phantom and tested in healthy volunteers. We also compared the use of different readout schemes to evaluate performance in the presence of an ICD.</div></div><div><h3>Results</h3><div>The free-breathing cardiac T₁ mapping approach agreed within 5% compared with ground truth T₁ in a motion phantom. In healthy volunteers, an average difference in T₁ of −3.5% was seen between the free-breathing and breath-hold approaches, but T₁ quantification was impacted by data discarded by the respiratory navigator. Compared to balanced SSFP, the spoiled gradient echo readout was much less susceptible to artefacts caused by an ICD, but the lower signal adversely affected T₁ quantification.</div></div><div><h3>Conclusions</h3><div>Free-breathing cardiac T₁ mapping is feasible on an MR-linac. Further optimisation is required to reduce scan times and improve accuracy.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100739"},"PeriodicalIF":3.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143686520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"External validation of an algorithm to detect vertebral level mislabeling and autocontouring errors","authors":"Tucker J. Netherton ,&nbsp;Didier Duprez ,&nbsp;Tina Patel ,&nbsp;Gizem Cifter ,&nbsp;Laurence E. Court ,&nbsp;Christoph Trauernicht ,&nbsp;Ajay Aggarwal","doi":"10.1016/j.phro.2025.100738","DOIUrl":"10.1016/j.phro.2025.100738","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>This work performs external validation of a previously developed vertebral body autocontouring tool and investigates a post-processing method to increase performance to clinically acceptable levels.</div></div><div><h3>Materials and Methods</h3><div>Vertebral bodies within CT scans from two separate institutions (40 from institution A and 41 from institution B) were automatically 1) localized and enumerated, 2) contoured, and 3) screened as a means of quality assurance (QA) for errors. Identification rate, contour acceptability rate, and QA accuracy were calculated to assess the tool’s performance. These metrics were compared to those calculated on CTs from the model’s original training dataset, and a post-processing technique was developed to increase the tool’s accuracy.</div></div><div><h3>Results</h3><div>When testing the model without post-processing on external datasets A and B, accurate identification rates of 83 % and 92 % were achieved for vertebral bodies (C1-L5). Identification rate, contour acceptability rate and QA accuracy were reduced on both datasets compared to accuracies and rates measured on the model’s orginal testing dataset. After algorithm adjustment, identification rate across all vertebrae increased on average by 4 % (p &lt; 0.01) for dataset A and also 4 % on the dataset B (p = 0.01).</div></div><div><h3>Conclusions</h3><div>A post-processing adjustment within the machine learning pipeline increased performance of vertebral body localization accuracy to acceptable levels for clinical use. External validation of machine learning and deep learning tools is essential to perform before deployment to different insitutions.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"34 ","pages":"Article 100738"},"PeriodicalIF":3.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of artificial intelligence-based autosegmentation for a high-performance cone-beam computed tomography imaging system in the pelvic region","authors":"Judith H. Sluijter,&nbsp;Agustinus J.A.J. van de Schoot,&nbsp;Abdelmounaim el Yaakoubi,&nbsp;Maartje de Jong,&nbsp;Martine S. van der Knaap - van Dongen,&nbsp;Britt Kunnen,&nbsp;Nienke D. Sijtsema,&nbsp;Joan J. Penninkhof,&nbsp;Kim C. de Vries,&nbsp;Steven F. Petit,&nbsp;Maarten L.P. Dirkx","doi":"10.1016/j.phro.2024.100687","DOIUrl":"10.1016/j.phro.2024.100687","url":null,"abstract":"<div><h3>Background and purpose</h3><div>A novel ring-gantry cone-beam computed tomography (CBCT) imaging system shows improved image quality compared to its conventional version, but its effect on autosegmentation is unknown. This study evaluates the impact of this high-performance CBCT on autosegmentation performance, inter-observer variability, contour correction times and delineation confidence, compared to the conventional CBCT.</div></div><div><h3>Materials and methods</h3><div>Twenty prostate cancer patients were enrolled in this prospective clinical study. Per patient, one pair of high-performance CBCT and conventional CBCT scans was included. Three observers manually corrected contours generated by the artificial intelligence (AI) model for prostate, seminal vesicles, bladder, rectum and bowel. Differences between AI-based and manual corrected contours were quantified using Dice Similarity Coefficient (DSC) and 95th percentile of Hausdorff distance (HD95). Autosegmentation performance and interobserver variation were compared using a random effects model; correction times and confidence scores using a paired <em>t</em>-test and Wilcoxon signed-rank test, respectively.</div></div><div><h3>Results</h3><div>Autosegmentation performance showed small, but statistically insignificant differences. Interobserver variability, assessed by the intraclass correlation coefficient, was significantly different across most organs, but these were considered clinically irrelevant (maximum difference = 0.08). Mean contour correction times were similar for both CBCT systems (11:03 versus 11:12 min; p = 0.66). Delineation confidence scores were significantly higher with the high-performance CBCT scans for prostate, seminal vesicles and rectum (4.5 versus 3.5, 4.3 versus 3.5, 4.8 versus 4.3; all p &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>The high-performance CBCT did not (clinically) improve autosegmentation performance, inter-observer variability or contour correction time compared to conventional CBCT. However, it clearly enhanced user confidence in organ delineation for prostate, seminal vesicles and rectum.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100687"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11721864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive radiotherapy in locally advanced head and neck cancer: The importance of reduced margins","authors":"Hedda Enocson ,&nbsp;André Haraldsson ,&nbsp;Per Engström ,&nbsp;Sofie Ceberg ,&nbsp;Maria Gebre-Medhin ,&nbsp;Gabriel Adrian ,&nbsp;Per Munck af Rosenschöld","doi":"10.1016/j.phro.2025.100696","DOIUrl":"10.1016/j.phro.2025.100696","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Adaptive radiotherapy (ART) involves treatment re-planning based on anatomical changes, which may improve target coverage and sparing of organs-at-risk (OARs). This study retrospectively assessed the technical feasibility and potential benefits of daily ART in combination with reduced planning target volume (PTV) margins for head and neck squamous cell carcinoma (HNSCC).</div></div><div><h3>Materials and Methods</h3><div>Thirty-one patients, encompassing 902 treatment fractions, treated with radiotherapy to 60.0–68.0 Gy in 2 Gy/fraction were studied. Synthetic CTs (sCT) from daily kVCT images were created and contours propagated using deformable image registration (DIR). Target contours were reviewed and corrected. On the sCT, non-adapted delivered doses and ART-plans with 5 mm (clinical standard) and 2 mm PTV-margin were evaluated. All daily dose distributions were then accumulated.</div></div><div><h3>Results</h3><div>Target contours required correction in 48 % of the fractions. Daily non-adapted D_98%,CTV was &gt; 95 % in 890 (5 mm) and 825 (2 mm) out of 902 fractions. All adapted plans achieved D_98%,CTV &gt; 95 %. Significant reductions in mean doses to OARs were observed for PTV = 2 mm ART-plans: 4.1 Gy for parotid, 2.6 Gy for submandibular, 3.3 Gy for oral cavity, 4.0 Gy for esophagus, and 3.8 Gy for larynx.</div></div><div><h3>Conclusion</h3><div>ART-planning on sCT and DIR propagated contours was feasible and promising for further clinical testing. To obtain a potential clinical benefit of ART, a synchronous reduction of the PTV-margin was warranted. Daily ART can be used to maintain adequate target dosimetry for every fraction, though for the accumulated treatment, insufficient target coverage without ART is unlikely to occur.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"33 ","pages":"Article 100696"},"PeriodicalIF":3.4,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11787698/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143081205","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}