Physics and Imaging in Radiation Oncology最新文献

{"title":"Deep learning based automatic segmentation of the Internal Pudendal Artery in definitive radiotherapy treatment planning of localized prostate cancer","authors":"Anjali Balagopal,&nbsp;Michael Dohopolski,&nbsp;Young Suk Kwon,&nbsp;Steven Montalvo,&nbsp;Howard Morgan,&nbsp;Ti Bai,&nbsp;Dan Nguyen,&nbsp;Xiao Liang,&nbsp;Xinran Zhong,&nbsp;Mu-Han Lin,&nbsp;Neil Desai,&nbsp;Steve Jiang","doi":"10.1016/j.phro.2024.100577","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100577","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Radiation-induced erectile dysfunction (RiED) commonly affects prostate cancer patients, prompting clinical trials across institutions to explore dose-sparing to internal-pudendal-arteries (IPA) for preserving sexual potency. IPA, challenging to segment, isn't conventionally considered an organ-at-risk (OAR). This study proposes a deep learning (DL) auto-segmentation model for IPA, using Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) or CT alone to accommodate varied clinical practices.</p></div><div><h3>Materials and methods</h3><p>A total of 86 patients with CT and MRI images and noisy IPA labels were recruited in this study. We split the data into 42/14/30 for model training, testing, and a clinical observer study, respectively. There were three major innovations in this model: 1) we designed an architecture with squeeze-and-excite blocks and modality attention for effective feature extraction and production of accurate segmentation, 2) a novel loss function was used for training the model effectively with noisy labels, and 3) modality dropout strategy was used for making the model capable of segmentation in the absence of MRI.</p></div><div><h3>Results</h3><p>Test dataset metrics were DSC 61.71 ± 7.7 %, ASD 2.5 ± .87 mm, and HD95 7.0 ± 2.3 mm. AI segmented contours showed dosimetric similarity to expert physician’s contours. Observer study indicated higher scores for AI contours (mean = 3.7) compared to inexperienced physicians’ contours (mean = 3.1). Inexperienced physicians improved scores to 3.7 when starting with AI contours.</p></div><div><h3>Conclusion</h3><p>The proposed model achieved good quality IPA contours to improve uniformity of segmentation and to facilitate introduction of standardized IPA segmentation into clinical trials and practice.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000472/pdfft?md5=53478e33cbd9f7767e08c384d1e0b0dd&pid=1-s2.0-S2405631624000472-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140643744","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":"Intra-fractional geometric and dose/volume metric variations of magnetic resonance imaging-guided stereotactic radiotherapy of prostate bed after radical prostatectomy","authors":"Yu Gao ,&nbsp;Stephanie Yoon ,&nbsp;Ting Martin Ma ,&nbsp;Yingli Yang ,&nbsp;Ke Sheng ,&nbsp;Daniel A. Low ,&nbsp;Leslie Ballas ,&nbsp;Michael L. Steinberg ,&nbsp;Amar U Kishan ,&nbsp;Minsong Cao","doi":"10.1016/j.phro.2024.100573","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100573","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Magnetic Resonance Imaging (MRI)-guided Stereotactic body radiotherapy (SBRT) treatment to prostate bed after radical prostatectomy has garnered growing interests. The aim of this study is to evaluate intra-fractional anatomic and dose/volume metric variations for patients receiving this treatment.</p></div><div><h3>Materials and methods</h3><p>Nineteen patients who received 30–34 Gy in 5 fractions on a 0.35T MR-Linac were included. Pre- and post-treatment MRIs were acquired for each fraction (total of 75 fractions). The Clinical Target Volume (CTV), bladder, rectum, and rectal wall were contoured on all images. Volumetric changes, Hausdorff distance, Mean Distance to Agreement (MDA), and Dice similarity coefficient (DSC) for each structure were calculated. Median value and Interquartile range (IQR) were recorded. Changes in target coverage and Organ at Risk (OAR) constraints were compared and evaluated using Wilcoxon rank sum tests at a significant level of 0.05.</p></div><div><h3>Results</h3><p>Bladder had the largest volumetric changes, with a median volume increase of 48.9 % (IQR 28.9–76.8 %) and a median MDA of 5.1 mm (IQR 3.4–7.1 mm). Intra-fractional CTV volume remained stable with a median volume change of 1.2 % (0.0–4.8 %). DSC was 0.97 (IQR 0.94–0.99). For the dose/volume metrics, there were no statistically significant changes observed except for an increase in bladder hotspot and a decrease of bladder V_32.5 Gy and mean dose. The CTV V_95% changed from 99.9 % (IQR 98.8–100 %) to 99.6 % (IQR 93.9–100 %).</p></div><div><h3>Conclusion</h3><p>Despite intra-fractional variations of OARs, CTV coverage remained stable during MRI-guided SBRT treatments for the prostate bed.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000435/pdfft?md5=e52624a49b4218e54df3bf2a95ec9ef7&pid=1-s2.0-S2405631624000435-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140309236","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":"Minimum imaging dose for deep learning-based pelvic synthetic computed tomography generation from cone beam images","authors":"Yan Chi Ivy Chan ,&nbsp;Minglun Li ,&nbsp;Adrian Thummerer ,&nbsp;Katia Parodi ,&nbsp;Claus Belka ,&nbsp;Christopher Kurz ,&nbsp;Guillaume Landry","doi":"10.1016/j.phro.2024.100569","DOIUrl":"10.1016/j.phro.2024.100569","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Daily cone-beam computed tomography (CBCT) in image-guided radiotherapy administers radiation exposure and subjects patients to secondary cancer risk. Reducing imaging dose remains challenging as image quality deteriorates. We investigated three imaging dose levels by reducing projections and correcting images using two deep learning algorithms, aiming at identifying the lowest achievable imaging dose.</p></div><div><h3>Materials and methods</h3><p>CBCTs were reconstructed with 100%, 25%, 15% and 10% projections. Models were trained (30), validated (3) and tested (8) with prostate cancer patient datasets. We optimized and compared the performance of 1) a cycle generative adversarial network (cycleGAN) with residual connection and 2) a contrastive unpaired translation network (CUT) to generate synthetic computed tomography (sCT) from reduced imaging dose CBCTs. Volumetric modulated arc therapy plans were optimized on a reference intensity-corrected full dose CBCT_cor and recalculated on sCTs. Hounsfield unit (HU) and positioning accuracy were evaluated. Bladder and rectum were manually delineated to determine anatomical fidelity.</p></div><div><h3>Results</h3><p>All sCTs achieved average mean absolute mean absolute error/structural similarity index measure/peak signal-to-noise ratio of <span><math><mrow><mo>⩽</mo></mrow></math></span>59HU/<span><math><mrow><mo>⩾</mo></mrow></math></span>0.94/<span><math><mrow><mo>⩾</mo></mrow></math></span>33 dB. All dose-volume histogram parameter differences were within 2 Gy or 2<span><math><mrow><mo>%</mo></mrow></math></span>. Positioning differences were <span><math><mrow><mo>⩽</mo></mrow></math></span>0.30 mm or 0.30°. cycleGAN with Dice similarity coefficients (DSC) for bladder/rectum of <span><math><mrow><mo>⩾</mo></mrow></math></span>0.85/<span><math><mrow><mo>⩾</mo></mrow></math></span>0.81 performed better than CUT (<span><math><mrow><mo>⩾</mo></mrow></math></span>0.83/<span><math><mrow><mo>⩾</mo></mrow></math></span>0.76). A significantly lower DSC accuracy was observed for 15<span><math><mrow><mo>%</mo></mrow></math></span> and 10<span><math><mrow><mo>%</mo></mrow></math></span> sCTs. cycleGAN performed better than CUT for contouring, however both yielded comparable outcomes in other evaluations.</p></div><div><h3>Conclusion</h3><p>sCTs based on different CBCT doses using cycleGAN and CUT were investigated. Based on segmentation accuracy, 25<span><math><mrow><mo>%</mo></mrow></math></span> is the minimum imaging dose.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000393/pdfft?md5=452ad846baea997c6e88892e1c54bf7e&pid=1-s2.0-S2405631624000393-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140273356","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":"Repeatability and reproducibility of prostate apparent diffusion coefficient values on a 1.5 T magnetic resonance linear accelerator","authors":"Nitara Fernando ,&nbsp;Tony Tadic ,&nbsp;Winnie Li ,&nbsp;Tirth Patel ,&nbsp;Jerusha Padayachee ,&nbsp;Anna T. Santiago ,&nbsp;Jennifer Dang ,&nbsp;Peter Chung ,&nbsp;Enrique Gutierrez ,&nbsp;Catherine Coolens ,&nbsp;Edward Taylor ,&nbsp;Jeff D. Winter","doi":"10.1016/j.phro.2024.100570","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100570","url":null,"abstract":"<div><h3>Background and Purpose</h3><p>Integrated magnetic resonance linear accelerator (MR-Linac) systems offer potential for biologically based adaptive radiation therapy using apparent diffusion coefficient (ADC). Accurate tracking of longitudinal ADC changes is key to establishing ADC-driven dose adaptation. Here, we report repeatability and reproducibility of intraprostatic ADC using deformable image registration (DIR) to correct for inter-fraction prostate changes.</p></div><div><h3>Materials and Methods</h3><p>The study included within-fraction repeat ADC measurements for three consecutive fractions for 20 patients with prostate cancer treated on a 1.5 T MR-Linac. We deformably registered successive fraction T₂-weighted images and applied the deformation vector field to corresponding ADC maps to align to fraction 2. We delineated gross tumour volume (GTV), peripheral zone (PZ) and prostate clinical target volume (CTV) regions-of-interest (ROIs) on T₂-weighted MRI and copied to ADC maps. We computed intraclass correlation coefficients (ICC) and percent repeatability coefficient (%RC) to determine within-fraction repeatability and between-fraction reproducibility for individual voxels, mean and 10th percentile ADC values per ROI.</p></div><div><h3>Results</h3><p>The ICC between repeats and fractions was excellent for mean and 10th percentile ADC in all ROIs (ICC &gt; 0.86), and moderate repeatability and reproducibility existed for individual voxels (ICC &gt; 0.542). Similarly, low %RC within-fraction (4.2–17.9 %) mean and 10th percentile ADC existed, with greater %RC between fractions (10.2–36.8 %). Higher %RC existed for individual voxel within-fraction (21.7–30.6 %) and between-fraction (32.1–34.5 %) ADC.</p></div><div><h3>Conclusions</h3><p>Results suggest excellent ADC repeatability and reproducibility in clinically relevant ROIs using DIR to correct between-fraction anatomical changes. We established the precision of voxel-level ADC tracking for future biologically based adaptation implementation.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S240563162400040X/pdfft?md5=0bfdc83d67412f850f6edc15aad577ee&pid=1-s2.0-S240563162400040X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140160142","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":"[18]F-fluoroethyl-l-tyrosine positron emission tomography for radiotherapy target delineation: Results from a Radiation Oncology credentialing program","authors":"Nathaniel Barry ,&nbsp;Eng-Siew Koh ,&nbsp;Martin A. Ebert ,&nbsp;Alisha Moore ,&nbsp;Roslyn J. Francis ,&nbsp;Pejman Rowshanfarzad ,&nbsp;Ghulam Mubashar Hassan ,&nbsp;Sweet P. Ng ,&nbsp;Michael Back ,&nbsp;Benjamin Chua ,&nbsp;Mark B. Pinkham ,&nbsp;Andrew Pullar ,&nbsp;Claire Phillips ,&nbsp;Joseph Sia ,&nbsp;Peter Gorayski ,&nbsp;Hien Le ,&nbsp;Suki Gill ,&nbsp;Jeremy Croker ,&nbsp;Nicholas Bucknell ,&nbsp;Catherine Bettington ,&nbsp;Andrew M. Scott","doi":"10.1016/j.phro.2024.100568","DOIUrl":"10.1016/j.phro.2024.100568","url":null,"abstract":"<div><h3>Background and purpose</h3><p>The [18]F-fluoroethyl-<span>l</span>-tyrosine (FET) PET in Glioblastoma (FIG) study is an Australian prospective, multi-centre trial evaluating FET PET for newly diagnosed glioblastoma management. The Radiation Oncology credentialing program aimed to assess the feasibility in Radiation Oncologist (RO) derivation of standard-of-care target volumes (TV_MR) and hybrid target volumes (TV_MR+FET) incorporating pre-defined FET PET biological tumour volumes (BTVs).</p></div><div><h3>Materials and methods</h3><p>Central review and analysis of TV_MR and TV_MR+FET was undertaken across three benchmarking cases. BTVs were pre-defined by a sole nuclear medicine expert. Intraclass correlation coefficient (ICC) confidence intervals (CIs) evaluated volume agreement. RO contour spatial and boundary agreement were evaluated (Dice similarity coefficient [DSC], Jaccard index [JAC], overlap volume [OV], Hausdorff distance [HD] and mean absolute surface distance [MASD]). Dose plan generation (one case per site) was assessed.</p></div><div><h3>Results</h3><p>Data from 19 ROs across 10 trial sites (54 initial submissions, 8 resubmissions requested, 4 conditional passes) was assessed with an initial pass rate of 77.8 %; all resubmissions passed. TV_MR+FET were significantly larger than TV_MR (p &lt; 0.001) for all cases. RO gross tumour volume (GTV) agreement was moderate-to-excellent for GTV_MR (ICC = 0.910; 95 % CI, 0.708–0.997) and good-to-excellent for GTV_MR+FET (ICC = 0.965; 95 % CI, 0.871–0.999). GTV_MR+FET showed greater spatial overlap and boundary agreement compared to GTV_MR. For the clinical target volume (CTV), CTV_MR+FET showed lower average boundary agreement versus CTV_MR (MASD: 1.73 mm vs. 1.61 mm, p = 0.042). All sites passed the planning exercise.</p></div><div><h3>Conclusions</h3><p>The credentialing program demonstrated feasibility in successful credentialing of 19 ROs across 10 sites, increasing national expertise in TV_MR+FET delineation.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000381/pdfft?md5=38b001d63ba09a46731b378b207e1349&pid=1-s2.0-S2405631624000381-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140283115","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":"An in-silico planning study of stereotactic body radiation therapy for polymetastatic patients with more than ten extra-cranial lesions","authors":"Federico Iori ,&nbsp;Nathan Torelli ,&nbsp;Jan Unkelbach ,&nbsp;Stephanie Tanadini-Lang ,&nbsp;Sebastian M. Christ ,&nbsp;Matthias Guckenberger","doi":"10.1016/j.phro.2024.100567","DOIUrl":"10.1016/j.phro.2024.100567","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Limited data is available about the feasibility of stereotactic body radiation therapy (SBRT) for treating more than five extra-cranial metastases, and almost no data for treating more than ten. The aim of this study was to investigate the feasibility of SBRT in this polymetatstatic setting.</p></div><div><h3>Materials and methods</h3><p>Consecutive metastatic melanoma patients with more than ten extra-cranial metastases and a maximum lesion diameter below 11 cm were selected from a single-center prospective registry for this in-silico planning study. For each patient, SBRT plans were generated to treat all metastases with a prescribed dose of 5x7Gy, and dose-limiting organs (OARs) were analyzed. A cell-kill based inverse planning approach was used to automatically determine the maximum deliverable dose to each lesion individually, while respecting all OARs constraints.</p></div><div><h3>Results</h3><p>A total of 23 polymetastatic patients with a medium of 17 metastases (range, 11–51) per patient were selected. SBRT plans with sufficient target coverage and respected OARs dose constraints were achieved in 16 out of 23 patients. In the remaining seven patients, the lungs V5Gy &lt; 80 % and the liver D700 cm³ &lt; 15Gy were most frequently the dose-limiting constraints. The cell-kill based planning approach allowed optimizing the dose administration depending on metastases total volume and location.</p></div><div><h3>Conclusion</h3><p>This retrospective planning study shows the feasibility of definitive SBRT for 70% of polymetastatic patients with more than ten extra-cranial lesions and proposes the cell-killing planning approach as an approach to individualize treatment planning in polymetastatic patients’.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S240563162400037X/pdfft?md5=1896bd3902fa12467699ba71bd9e6fea&pid=1-s2.0-S240563162400037X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140083894","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":"Treatment planning with high-resolution 3D dose maps in preclinical and translational synchrotron microbeam radiation therapy","authors":"Sarvenaz Keshmiri ,&nbsp;Gaëtan Lemaire ,&nbsp;Sylvan Brocard ,&nbsp;Camille Verry ,&nbsp;Yacine Bencheikh ,&nbsp;Samy Kefs ,&nbsp;Laura Eling ,&nbsp;Raphaël Serduc ,&nbsp;Jean-François Adam","doi":"10.1016/j.phro.2024.100565","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100565","url":null,"abstract":"<div><h3>Background and Purpose</h3><p>Microbeam Radiation Therapy (MRT) aims to deliver higher doses to the target while minimizing radiation damage to healthy tissues using synchrotron x-ray microbeams. Translational MRT research has now started, driven by promising results from preclinical studies. This study aimed to propose a first dose-outcome model by analyzing micrometric dose distributions obtained with high-resolution 3D dose calculations, accounting for the inherent physical dose distribution complexity in MRT. The feasibility of integrating penMRT, our full Monte Carlo multiscale dose calculation algorithm based on PENELOPE into translational research on veterinary patients was also investigated.</p></div><div><h3>Material and Methods</h3><p>Micrometric dose distributions were calculated in tumor-bearing rats and for a veterinary patient with penMRT, for conformal multi-directional MRT treatment plans. Absorbed dose maps were obtained with 0.005 × 0.005 × 1 mm³ voxel sizes. High-resolution dose-volume histograms were extracted and analyzed against radiobiology studies.</p></div><div><h3>Results</h3><p>The complexity of the MRT dose distribution was properly rendered at a micrometer scale on 3D dose maps, with well separated dose regions observed on the differential dose-volume histograms. The median survival time of glioma-bearing rats varied linearly with the volume fraction of the planning target volume that received doses higher than 50 Gy (R² = 0.98). The feasibility of using penMRT for treatment planning in large volumes has been shown on a veterinary patient.</p></div><div><h3>Conclusions</h3><p>This study demonstrated the significant added value of penMRT for planning and prescribing MRT treatments. It also shed light on the correlation between the high-resolution 3D dose distributions and the treatment outcome.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000356/pdfft?md5=a4d42f42c22b2625bda5f01d14a139f4&pid=1-s2.0-S2405631624000356-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140296284","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 lung segmentation of magnetic resonance images: A new approach applied to healthy volunteers undergoing enhanced Deep-Inspiration-Breath-Hold for motion-mitigated 4D proton therapy of lung tumors","authors":"John H. Missimer ,&nbsp;Frank Emert ,&nbsp;Antony J. Lomax ,&nbsp;Damien C. Weber","doi":"10.1016/j.phro.2024.100531","DOIUrl":"10.1016/j.phro.2024.100531","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Respiratory suppression techniques represent an effective motion mitigation strategy for 4D-irradiation of lung tumors with protons. A magnetic resonance imaging (MRI)-based study applied and analyzed methods for this purpose, including enhanced Deep-Inspiration-Breath-Hold (eDIBH). Twenty-one healthy volunteers (41–58 years) underwent thoracic MR scans in four imaging sessions containing two eDIBH-guided MRIs per session to simulate motion-dependent irradiation conditions. The automated MRI segmentation algorithm presented here was critical in determining the lung volumes (LVs) achieved during eDIBH.</p></div><div><h3>Materials and methods</h3><p>The study included 168 MRIs acquired under eDIBH conditions. The lung segmentation algorithm consisted of four analysis steps: (i) image preprocessing, (ii) MRI histogram analysis with thresholding, (iii) automatic segmentation, (iv) 3D-clustering. To validate the algorithm, 46 eDIBH-MRIs were manually contoured. Sørensen-Dice similarity coefficients (DSCs) and relative deviations of LVs were determined as similarity measures. Assessment of intrasessional and intersessional LV variations and their differences provided estimates of statistical and systematic errors.</p></div><div><h3>Results</h3><p>Lung segmentation time for 100 2D-MRI planes was ∼ 10 s. Compared to manual lung contouring, the median DSC was 0.94 with a lower 95 % confidence level (CL) of 0.92. The relative volume deviations yielded a median value of 0.059 and 95 % CLs of −0.013 and 0.13. Artifact-based volume errors, mainly of the trachea, were estimated. Estimated statistical and systematic errors ranged between 6 and 8 %.</p></div><div><h3>Conclusions</h3><p>The presented analytical algorithm is fast, precise, and readily available. The results are comparable to time-consuming, manual segmentations and other automatic segmentation approaches. Post-processing to remove image artifacts is under development.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000010/pdfft?md5=bc535869d6a431c218a8911f84d20dba&pid=1-s2.0-S2405631624000010-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139395954","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":"Virtual unenhanced dual-energy computed tomography for photon radiotherapy: The effect on dose distribution and cone-beam computed tomography based position verification","authors":"Maryam Afifah ,&nbsp;Marloes C. Bulthuis ,&nbsp;Karin N. Goudschaal ,&nbsp;Jolanda M. Verbeek-Spijkerman ,&nbsp;Tezontl S. Rosario ,&nbsp;Duncan den Boer ,&nbsp;Karel A. Hinnen ,&nbsp;Arjan Bel ,&nbsp;Zdenko van Kesteren","doi":"10.1016/j.phro.2024.100545","DOIUrl":"10.1016/j.phro.2024.100545","url":null,"abstract":"<div><h3>Background and Purpose</h3><p>Virtual Unenhanced images (VUE) from contrast-enhanced dual-energy computed tomography (DECT) eliminate manual suppression of contrast-enhanced structures (CES) or pre-contrast scans. CT intensity decreases in high-density structures outside the CES following VUE algorithm application. This study assesses VUE's impact on the radiotherapy workflow of gynecological tumors, comparing dose distribution and cone-beam CT-based (CBCT) position verification to contrast-enhanced CT (CECT) images.</p></div><div><h3>Materials and Methods</h3><p>A total of 14 gynecological patients with contrast-enhanced CT simulation were included. Two CT images were reconstructed: CECT and VUE. Volumetric Modulated Arc Therapy (VMAT) plans generated on CECT were recalculated on VUE using both the CECT lookup table (LUT) and a dedicated VUE LUT. Gamma analysis assessed 3D dose distributions. CECT and VUE images were retrospectively registered to daily CBCT using Chamfer matching algorithm..</p></div><div><h3>Results</h3><p>Planning target volume <strong>(</strong>PTV) dose agreement with CECT was within 0.35% for D_2%, D_mean, and D_98%. Organs at risk (OARs) D_2% agreed within 0.36%. A dedicated VUE LUT lead to smaller dose differences, achieving a 100% gamma pass rate for all subjects. VUE imaging showed similar translations and rotations to CECT, with significant but minor translation differences (&lt;0.02 cm). VUE-based registration outperformed CECT. In 24% of CBCT-CECT registrations, inadequate registration was observed due to contrast-related issues, while corresponding VUE images achieved clinically acceptable registrations.</p></div><div><h3>Conclusions</h3><p>VUE imaging in the radiotherapy workflow is feasible, showing comparable dose distributions and improved CBCT registration results compared to CECT. VUE enables automated bone registration, limiting inter-observer variation in the Image-Guided Radiation Therapy (IGRT) process.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000150/pdfft?md5=37c70830fa65a62f5a009711aa0a3200&pid=1-s2.0-S2405631624000150-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139684862","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":"Artificial intelligence-based automated segmentation and radiotherapy dose mapping for thoracic normal tissues","authors":"Jue Jiang ,&nbsp;Chloe Min Seo Choi ,&nbsp;Joseph O. Deasy ,&nbsp;Andreas Rimner ,&nbsp;Maria Thor ,&nbsp;Harini Veeraraghavan","doi":"10.1016/j.phro.2024.100542","DOIUrl":"https://doi.org/10.1016/j.phro.2024.100542","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Objective assessment of delivered radiotherapy (RT) to thoracic organs requires fast and accurate deformable dose mapping. The aim of this study was to implement and evaluate an artificial intelligence (AI) deformable image registration (DIR) and organ segmentation-based AI dose mapping (AIDA) applied to the esophagus and the heart.</p></div><div><h3>Materials and methods</h3><p>AIDA metrics were calculated for 72 locally advanced non-small cell lung cancer patients treated with concurrent chemo-RT to 60 Gy in 2 Gy fractions in an automated pipeline. The pipeline steps were: (i) automated rigid alignment and cropping of planning CT to week 1 and week 2 cone-beam CT (CBCT) field-of-views, (ii) AI segmentation on CBCTs, and (iii) AI-DIR-based dose mapping to compute dose metrics. AIDA dose metrics were compared to the planned dose and manual contour dose mapping (manual DA).</p></div><div><h3>Results</h3><p>AIDA required ∼2 min/patient. Esophagus and heart segmentations were generated with a mean Dice similarity coefficient (DSC) of 0.80±0.15 and 0.94±0.05, a Hausdorff distance at 95th percentile (HD95) of 3.9±3.4 mm and 14.1±8.3 mm, respectively. AIDA heart dose was significantly lower than the planned heart dose (p = 0.04). Larger dose deviations (&gt;=1Gy) were more frequently observed between AIDA and the planned dose (N = 26) than with manual DA (N = 6).</p></div><div><h3>Conclusions</h3><p>Rapid estimation of RT dose to thoracic tissues from CBCT is feasible with AIDA. AIDA-derived metrics and segmentations were similar to manual DA, thus motivating the use of AIDA for RT applications.</p></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2405631624000125/pdfft?md5=0978a92b274beaa5016ea68b04c41752&pid=1-s2.0-S2405631624000125-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139714508","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}