Physics and Imaging in Radiation Oncology最新文献

{"title":"Clinical implementation of a commercial synthetic computed tomography solution for radiotherapy treatment of glioblastoma","authors":"Sevgi Emin ,&nbsp;Elia Rossi ,&nbsp;Elisabeth Myrvold Rooth ,&nbsp;Torsten Dorniok ,&nbsp;Mattias Hedman ,&nbsp;Giovanna Gagliardi ,&nbsp;Fernanda Villegas","doi":"10.1016/j.phro.2024.100589","DOIUrl":"10.1016/j.phro.2024.100589","url":null,"abstract":"<div><h3>Background and Purpose</h3><p>Magnetic resonance (MR)-only radiotherapy (RT) workflow eliminates uncertainties due to computed tomography (CT)-MR image registration, by using synthetic CT (sCT) images generated from MR. This study describes the clinical implementation process, from retrospective commissioning to prospective validation stage of a commercial artificial intelligence (AI)-based sCT product. Evaluation of the dosimetric performance of the sCT is presented, with emphasis on the impact of voxel size differences between image modalities.</p></div><div><h3>Materials and methods</h3><p>sCT performance was assessed in glioblastoma RT planning. Dose differences for 30 patients in both commissioning and validation cohorts were calculated at various dose-volume-histogram (DVH) points for target and organs-at-risk (OAR). A gamma analysis was conducted on regridded image plans. Quality assurance (QA) guidelines were established based on commissioning phase results.</p></div><div><h3>Results</h3><p>Mean dose difference to target structures was found to be within ± 0.7 % regardless of image resolution and cohort. OARs’ mean dose differences were within ± 1.3 % for plans calculated on regridded images for both cohorts, while differences were higher for plans with original voxel size, reaching up to −4.2 % for chiasma D2% in the commissioning cohort. Gamma passing rates for the brain structure using the criteria 1 %/1mm, 2 %/2mm and 3 %/3mm were 93.6 %/99.8 %/100 % and 96.6 %/99.9 %/100 % for commissioning and validation cohorts, respectively.</p></div><div><h3>Conclusions</h3><p>Dosimetric outcomes in both commissioning and validation stages confirmed sCT’s equivalence to CT. The large patient cohort in this study aided in establishing a robust QA program for the MR-only workflow, now applied in glioblastoma RT at our center.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"30 ","pages":"Article 100589"},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000599/pdfft?md5=a8a0010d377763cc77048052e12a32c8&pid=1-s2.0-S2405631624000599-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141034422","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":"Comparing different boost concepts and beam configurations for proton therapy of pancreatic cancer","authors":"Taiki Takaoka ,&nbsp;Takeshi Yanagi ,&nbsp;Shinsei Takahashi ,&nbsp;Yuta Shibamoto ,&nbsp;Yuto Imai ,&nbsp;Dai Okazaki ,&nbsp;Masanari Niwa ,&nbsp;Akira Torii ,&nbsp;Nozomi Kita ,&nbsp;Seiya Takano ,&nbsp;Natsuo Tomita ,&nbsp;Akio Hiwatashi","doi":"10.1016/j.phro.2024.100583","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100583","url":null,"abstract":"<div><h3>Background and Purpose</h3><p>Interfractional geometrical and anatomical variations impact the accuracy of proton therapy for pancreatic cancer. This study investigated field-in-field (FIF) and simultaneous integrated boost (SIB) concepts for scanned proton therapy treatment with different beam configurations.</p></div><div><h3>Materials and Methods</h3><p>Robustly optimized treatment plans for fifteen patients were generated using FIF and SIB techniques with two, three, and four beams. The prescribed dose in 20 fractions was 60 Gy(RBE) for the internal gross tumor volume (IGTV) and 46 Gy(RBE) for the internal clinical target volume. Verification computed tomography (vCT) scans was performed on treatment days 1, 7, and 16. Initial treatment plans were recalculated on the rigidly registered vCTs. V_100% and D_95% for targets and D_2cm³ for the stomach and duodenum were evaluated. Robustness evaluations (range uncertainty of 3.5 %) were performed to evaluate the stomach and duodenum dose-volume parameters.</p></div><div><h3>Results</h3><p>For all techniques, IGTV V_100% and D_95% decreased significantly when recalculating the dose on vCTs (p &lt; 0.001). The median IGTV V_100% and D_95% over all vCTs ranged from 74.2 % to 90.2 % and 58.8 Gy(RBE) to 59.4 Gy(RBE), respectively. The FIF with two and three beams, and SIB with two beams maintained the highest IGTV V_100% and D_95%. In robustness evaluations, the ΔD_2cm³ of stomach was highest in two beams plans, while the ΔD_2cm³ of duodenum was highest in four beams plans, for both concepts.</p></div><div><h3>Conclusion</h3><p>Target coverage decreased when recalculating on CTs at different time for both concepts. The FIF with three beams maintained the highest IGTV coverage while sparing normal organs the most.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"30 ","pages":"Article 100583"},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000538/pdfft?md5=ffe03b8cff6b4534dcb55deeb2392584&pid=1-s2.0-S2405631624000538-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140818431","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":"Relating pre-treatment non-Gaussian intravoxel incoherent motion diffusion-weighted imaging to human papillomavirus status and response in oropharyngeal carcinoma","authors":"Nienke D. Sijtsema ,&nbsp;Iris Lauwers ,&nbsp;Gerda M. Verduijn ,&nbsp;Mischa S. Hoogeman ,&nbsp;Dirk H.J. Poot ,&nbsp;Juan A. Hernandez-Tamames ,&nbsp;Aad van der Lugt ,&nbsp;Marta E. Capala ,&nbsp;Steven F. Petit","doi":"10.1016/j.phro.2024.100574","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100574","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Diffusion-weighted imaging (DWI) is a promising technique for response assessment in head-and-neck cancer. Recently, we optimized Non-Gaussian Intravoxel Incoherent Motion Imaging (NG-IVIM), an extension of the conventional apparent diffusion coefficient (<em>ADC</em>) model, for the head and neck. In the current study, we describe the first application in a group of patients with human papillomavirus (HPV)-positive and HPV-negative oropharyngeal squamous cell carcinoma. The aim of this study was to relate <em>ADC</em> and NG-IVIM DWI parameters to HPV status and clinical treatment response.</p></div><div><h3>Materials and methods</h3><p>Thirty-six patients (18 HPV-positive, 18 HPV-negative) were prospectively included. Presence of progressive disease was scored within one year. The mean pre-treatment <em>ADC</em> and NG-IVIM parameters in the gross tumor volume were compared between HPV-positive and HPV-negative patients. In HPV-negative patients, <em>ADC</em> and NG-IVIM parameters were compared between patients with and without progressive disease.</p></div><div><h3>Results</h3><p><em>ADC</em>, the NG-IVIM diffusion coefficient <em>D</em>, and perfusion fraction <em>f</em> were significantly higher, while pseudo-diffusion coefficient <em>D*</em> and kurtosis <em>K</em> were significantly lower in the HPV-negative compared to HPV-positive patients. In the HPV-negative group, a significantly lower <em>D</em> was found for patients with progressive disease compared to complete responders. No relation with <em>ADC</em> was observed.</p></div><div><h3>Conclusion</h3><p>The results of our single-center study suggest that <em>ADC</em> is related to HPV status, but not an independent response predictor. The NG-IVIM parameter <em>D,</em> however, was independently associated to response in the HPV-negative group. Noteworthy in the opposite direction as previously thought based on <em>ADC</em>.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"30 ","pages":"Article 100574"},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000447/pdfft?md5=21e7ad76975caaf833ab8dd40588c057&pid=1-s2.0-S2405631624000447-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140548132","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":"Explicitly encoding the cyclic nature of breathing signal allows for accurate breathing motion prediction in radiotherapy with minimal training data","authors":"Andreas Renner ,&nbsp;Ingo Gulyas ,&nbsp;Martin Buschmann ,&nbsp;Gerd Heilemann ,&nbsp;Barbara Knäusl ,&nbsp;Martin Heilmann ,&nbsp;Joachim Widder ,&nbsp;Dietmar Georg ,&nbsp;Petra Trnková","doi":"10.1016/j.phro.2024.100594","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100594","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Active breathing motion management in radiotherapy consists of motion monitoring, quantification and mitigation. It is impacted by associated latencies of a few 100 ms. Artificial neural networks can successfully predict breathing motion and eliminate latencies. However, they require usually a large dataset for training. The objective of this work was to demonstrate that explicitly encoding the cyclic nature of the breathing signal into the training data enables significant reduction of training datasets which can be obtained from healthy volunteers.</p></div><div><h3>Material and methods</h3><p>Seventy surface scanner breathing signals from 25 healthy volunteers in anterior-posterior direction were used for training and validation (ratio 4:1) of long short-term memory models. The model performance was compared to a model using decomposition into phase, amplitude and a time-dependent baseline. Testing of the models was performed on 55 independent breathing signals in anterior-posterior direction from surface scanner (35 lung, 20 liver) of 30 patients with a mean breathing amplitude of (5.9 ± 6.7)<!--> <!-->mm.</p></div><div><h3>Results</h3><p>Using the decomposed breathing signal allowed for a reduction of the absolute root-mean square error (RMSE) from 0.34 mm to 0.12 mm during validation. Testing using patient data yielded an average absolute RMSE of the breathing signal of (0.16 ± 0.11)<!--> <!-->mm with a prediction horizon of 500 ms.</p></div><div><h3>Conclusion</h3><p>It was demonstrated that a motion prediction model can be trained with less than 100 datasets of healthy volunteers if breathing cycle parameters are considered. Applied to 55 patients, the model predicted breathing motion with a high accuracy.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"30 ","pages":"Article 100594"},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000642/pdfft?md5=11454d87d05403c0a30c0d3df553dcde&pid=1-s2.0-S2405631624000642-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141240197","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":"Effectiveness of multi-criteria optimization in combination with knowledge-based modeling in radiotherapy of left-sided breast including regional nodes","authors":"Sornjarod Oonsiri,&nbsp;Sakda Kingkaew,&nbsp;Mananchaya Vimolnoch,&nbsp;Nichakan Chatchumnan,&nbsp;Nuttha Plangpleng,&nbsp;Puntiwa Oonsiri","doi":"10.1016/j.phro.2024.100595","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100595","url":null,"abstract":"<div><p>Multi-criteria optimization (MCO) is a method that was added to treatment planning to create high-quality treatment plans. This study aimed to investigate the effectiveness of MCO in combination with knowledge-based planning (KBP) in radiotherapy for left-sided breasts, including regional nodes. Dose/volume parameters were evaluated for manual plans (MP), KBP, and KBP + MCO. Planning target volume doses of MP had better coverage while KBP + MCO plans demonstrated the lowest organ at risk doses. KBP and KBP + MCO plans had increasing complexity as expressed in the number of monitor units.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"30 ","pages":"Article 100595"},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000654/pdfft?md5=aad13395cae5b20bf3da4050293db116&pid=1-s2.0-S2405631624000654-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141240198","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":"Automatic gross tumor volume segmentation with failure detection for safe implementation in locally advanced cervical cancer","authors":"Rahimeh Rouhi ,&nbsp;Stéphane Niyoteka ,&nbsp;Alexandre Carré ,&nbsp;Samir Achkar ,&nbsp;Pierre-Antoine Laurent ,&nbsp;Mouhamadou Bachir Ba ,&nbsp;Cristina Veres ,&nbsp;Théophraste Henry ,&nbsp;Maria Vakalopoulou ,&nbsp;Roger Sun ,&nbsp;Sophie Espenel ,&nbsp;Linda Mrissa ,&nbsp;Adrien Laville ,&nbsp;Cyrus Chargari ,&nbsp;Eric Deutsch ,&nbsp;Charlotte Robert","doi":"10.1016/j.phro.2024.100578","DOIUrl":"10.1016/j.phro.2024.100578","url":null,"abstract":"<div><h3>Background and Purpose</h3><p>Automatic segmentation methods have greatly changed the RadioTherapy (RT) workflow, but still need to be extended to target volumes. In this paper, Deep Learning (DL) models were compared for Gross Tumor Volume (GTV) segmentation in locally advanced cervical cancer, and a novel investigation into failure detection was introduced by utilizing radiomic features.</p></div><div><h3>Methods and materials</h3><p>We trained eight DL models (UNet, VNet, SegResNet, SegResNetVAE) for 2D and 3D segmentation. Ensembling individually trained models during cross-validation generated the final segmentation. To detect failures, binary classifiers were trained using radiomic features extracted from segmented GTVs as inputs, aiming to classify contours based on whether their Dice Similarity Coefficient <span><math><mrow><mo>(</mo><mi>DSC</mi><mo>)</mo><mo>&lt;</mo><mi>T</mi></mrow></math></span> and <span><math><mrow><mi>DSC</mi><mo>⩾</mo><mi>T</mi></mrow></math></span>. Two distinct cohorts of T2-Weighted (T2W) pre-RT MR images captured in 2D sequences were used: one retrospective cohort consisting of 115 LACC patients from 30 scanners, and the other prospective cohort, comprising 51 patients from 7 scanners, used for testing.</p></div><div><h3>Results</h3><p>Segmentation by 2D-SegResNet achieved the best DSC, Surface DSC (<span><math><mrow><msub><mrow><mi>SDSC</mi></mrow><mrow><mn>3</mn><mi>mm</mi></mrow></msub></mrow></math></span>), and 95th Hausdorff Distance (95HD): DSC = 0.72 ± 0.16, <span><math><mrow><msub><mrow><mi>SDSC</mi></mrow><mrow><mn>3</mn><mi>mm</mi></mrow></msub></mrow></math></span>=0.66 ± 0.17, and 95HD = 14.6 ± 9.0 mm without missing segmentation (<span><math><mrow><mi>M</mi></mrow></math></span>=0) on the test cohort. Failure detection could generate precision (<span><math><mrow><mi>P</mi><mo>=</mo><mn>0.88</mn></mrow></math></span>), recall (<span><math><mrow><mi>R</mi><mo>=</mo><mn>0.75</mn></mrow></math></span>), F1-score (<span><math><mrow><mi>F</mi><mo>=</mo><mn>0.81</mn></mrow></math></span>), and accuracy (<span><math><mrow><mi>A</mi><mo>=</mo><mn>0.86</mn></mrow></math></span>) using Logistic Regression (LR) classifier on the test cohort with a threshold T = 0.67 on DSC values.</p></div><div><h3>Conclusions</h3><p>Our study revealed that segmentation accuracy varies slightly among different DL methods, with 2D networks outperforming 3D networks in 2D MRI sequences. Doctors found the time-saving aspect advantageous. The proposed failure detection could guide doctors in sensitive cases.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"30 ","pages":"Article 100578"},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000484/pdfft?md5=7b13fafc60565c0e7bc24b1dadfb8ba2&pid=1-s2.0-S2405631624000484-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140793345","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":"Feasibility and safety of contrast-enhanced magnetic resonance-guided adaptive radiotherapy for upper abdominal tumors: A preliminary exploration","authors":"Wenheng Jiang ,&nbsp;Xihua Shi ,&nbsp;Xiang Zhang ,&nbsp;Zhenjiang Li ,&nbsp;Jinbo Yue","doi":"10.1016/j.phro.2024.100582","DOIUrl":"10.1016/j.phro.2024.100582","url":null,"abstract":"<div><p>This study investigates the use of contrast-enhanced magnetic resonance (MR) in MR-guided adaptive radiotherapy (MRgART) for upper abdominal tumors. Contrast-enhanced T1-weighted MR (cT1w MR) using half doses of gadoterate was used to guide daily adaptive radiotherapy for tumors poorly visualized without contrast. The use of gadoterate was found to be feasible and safe in 5-fraction MRgART and could improve the contrast-to-noise ratio of MR images. And the use of cT1w MR could reduce the interobserver variation of adaptive tumor delineation compared to plain T1w MR (4.41 vs. 6.58, p &lt; 0.001) and T2w MR (4.41 vs. 7.42, p &lt; 0.001).</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"30 ","pages":"Article 100582"},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000526/pdfft?md5=d282df7dcb80fe0f3096017be000b790&pid=1-s2.0-S2405631624000526-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140769123","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":"Relative biological effectiveness of oxygen ion beams in the rat spinal cord: Dependence on linear energy transfer and dose and comparison with model predictions","authors":"Christin Glowa ,&nbsp;Maria Saager ,&nbsp;Lisa Hintz ,&nbsp;Rosemarie Euler-Lange ,&nbsp;Peter Peschke ,&nbsp;Stephan Brons ,&nbsp;Michael Scholz ,&nbsp;Stewart Mein ,&nbsp;Andrea Mairani ,&nbsp;Christian P. Karger","doi":"10.1016/j.phro.2024.100581","DOIUrl":"10.1016/j.phro.2024.100581","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Ion beams exhibit an increased relative biological effectiveness (RBE) with respect to photons. This study determined the RBE of oxygen ion beams as a function of linear energy transfer (LET) and dose in the rat spinal cord.</p></div><div><h3>Materials and methods</h3><p>The spinal cord of rats was irradiated at four different positions of a 6 cm spread-out Bragg-peak (LET: 26, 66, 98 and 141 keV/µm) using increasing levels of single and split oxygen ion doses. Dose-response curves were established for the endpoint paresis grade II and based on ED₅₀ (dose at 50 % effect probability), the RBE was determined and compared to model predictions.</p></div><div><h3>Results</h3><p>When LET increased from 26 to 98 keV/µm, ED₅₀ decreased from 17.2 ± 0.3 Gy to 13.5 ± 0.4 Gy for single and from 21.7 ± 0.4 Gy to 15.5 ± 0.5 Gy for split doses, however, at 141 keV/µm, ED₅₀ rose again to 15.8 ± 0.4 Gy and 17.2 ± 0.4 Gy, respectively. As a result, the RBE increased from 1.43 ± 0.05 to 1.82 ± 0.08 (single dose) and from 1.58 ± 0.04 to 2.21 ± 0.08 (split dose), respectively, before declining again to 1.56 ± 0.06 for single and 1.99 ± 0.06 for split doses at the highest LET. Deviations from RBE-predictions were model-dependent.</p></div><div><h3>Conclusion</h3><p>This study established first RBE data for the late reacting central nervous system after single and split doses of oxygen ions. The data was used to validate the RBE-dependence on LET and dose of three RBE-models. This study extends the existing data base for protons, helium and carbon ions and provides important information for future patient treatments with oxygen ions.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"30 ","pages":"Article 100581"},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000514/pdfft?md5=85eeacebc8a9bc581264509be7601574&pid=1-s2.0-S2405631624000514-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140794471","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":"Magnetic resonance imaging radiomic features stability in brain metastases: Impact of image preprocessing, image-, and feature-level harmonization","authors":"Zahra Khodabakhshi,&nbsp;Hubert Gabrys,&nbsp;Philipp Wallimann,&nbsp;Matthias Guckenberger,&nbsp;Nicolaus Andratschke,&nbsp;Stephanie Tanadini-Lang","doi":"10.1016/j.phro.2024.100585","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100585","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Magnetic resonance imaging (MRI) scans are highly sensitive to acquisition and reconstruction parameters which affect feature stability and model generalizability in radiomic research. This work aims to investigate the effect of image pre-processing and harmonization methods on the stability of brain MRI radiomic features and the prediction performance of radiomic models in patients with brain metastases (BMs).</p></div><div><h3>Materials and methods</h3><p>Two T1 contrast enhanced brain MRI data-sets were used in this study. The first contained 25 BMs patients with scans at two different time points and was used for features stability analysis. The effect of gray level discretization (GLD), intensity normalization (Z-score, Nyul, WhiteStripe, and in house-developed method named N-Peaks), and ComBat harmonization on features stability was investigated and features with intraclass correlation coefficient &gt;0.8 were considered as stable. The second data-set containing 64 BMs patients was used for a classification task to investigate the informativeness of stable features and the effects of harmonization methods on radiomic model performance.</p></div><div><h3>Results</h3><p>Applying fixed bin number (FBN) GLD, resulted in higher number of stable features compare to fixed bin size (FBS) discretization (10 ± 5.5 % higher). `Harmonization in feature domain improved the stability for non-normalized and normalized images with Z-score and WhiteStripe methods. For the classification task, keeping the stable features resulted in good performance only for normalized images with N-Peaks along with FBS discretization.</p></div><div><h3>Conclusions</h3><p>To develop a robust MRI based radiomic model we recommend using an intensity normalization method based on a reference tissue (e.g N-Peaks) and then using FBS discretization.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"30 ","pages":"Article 100585"},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000551/pdfft?md5=97d3acbd58ad3959ee1f67cf8dcbaf29&pid=1-s2.0-S2405631624000551-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140951178","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":"Dynamic anthropomorphic thorax phantom for quality assurance of motion management in radiotherapy","authors":"Sara Abdollahi ,&nbsp;Ali Asghar Mowlavi ,&nbsp;Mohammad Hadi Hadizadeh Yazdi ,&nbsp;Sofie Ceberg ,&nbsp;Marianne Camille Aznar ,&nbsp;Fatemeh Varshoee Tabrizi ,&nbsp;Roham Salek ,&nbsp;Matthias Guckenberger ,&nbsp;Stephanie Tanadini-Lang","doi":"10.1016/j.phro.2024.100587","DOIUrl":"10.1016/j.phro.2024.100587","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Motion management techniques are important to spare the healthy tissue adequately. However, they are complex and need dedicated quality assurance. The aim of this study was to create a dynamic phantom designed for quality assurance and to replicate a patient’s size, anatomy, and tissue density.</p></div><div><h3>Materials and methods</h3><p>A computed tomography (CT) scan of a cancer patient was used to create molds for the lungs, heart, ribs, and vertebral column via additive manufacturing. A pump system and software were developed to simulate respiratory dynamics. The extent of respiratory motion was quantified using a 4DCT scan. End-to-end tests were conducted to evaluate two motion management techniques for lung stereotactic body radiotherapy (SBRT).</p></div><div><h3>Results</h3><p>The chest wall moved between 4 mm and 13 mm anteriorly and 2 mm to 7 mm laterally during the breathing. The diaphragm exhibited superior-inferior movement ranging from 5 mm to 16 mm in the left lung and 10 mm to 36 mm in the right lung. The left lung tumor displaced ± 7 mm superior-inferiorly and anterior-posteriorly. The CT numbers were for lung: −716 ± 108 HU (phantom) and −713 ± 70 HU (patient); bone: 460 ± 20 HU (phantom) and 458 ± 206 HU (patient); soft tissue: 92 ± 9 HU (phantom) and 60 ± 25 HU (patient). The end-to-end testing showed an excellent agreement between the measured and the calculated dose for ion chamber and film dosimetry.</p></div><div><h3>Conclusions</h3><p>The phantom is recommended for quality assurance, evaluating the institution’s specific planning and motion management strategies either through end-to-end testing or as an external audit phantom.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"30 ","pages":"Article 100587"},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000575/pdfft?md5=47408dbd30f314b2470c02f2550b87bd&pid=1-s2.0-S2405631624000575-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141055946","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}