Physics and Imaging in Radiation Oncology最新文献

{"title":"Uncertainty estimation for reliable neural network-based radiotherapy dose modelling using Monte Carlo dropout, mean variance estimation and deep ensemble","authors":"Moritz Schneider,&nbsp;Tabea Eberhardt,&nbsp;Cihan Gani,&nbsp;Paul Fischer,&nbsp;Christian F. Baumgartner,&nbsp;Daniela Thorwarth","doi":"10.1016/j.phro.2026.101072","DOIUrl":"10.1016/j.phro.2026.101072","url":null,"abstract":"<div><h3>Background and Purpose</h3><div>Neural networks promise fast dose modelling with high accuracy for challenging situations like magnetic resonance imaging (MRI)-guided radiotherapy. As they are data-driven, failure can occur and early identification of erroneous dose calculations is required.</div><div>In this study, we implemented and evaluated three uncertainty estimation techniques to assess whether they can indicate increased prediction error.</div></div><div><h3>Materials and methods</h3><div>Using a dataset of 6713 radiotherapy segments from 130 1.5 T MRI linear accelerator plans and a 3D UNet for dose modelling, three techniques were implemented to assess uncertainty: Monte Carlo dropout (MCD), mean variance estimation (MVE) and a deep ensemble (DE).</div><div>All methods were evaluated regarding calibration using the expected normalized calibration error (ENCE) and correlation to mean absolute error (MAE).</div><div>Cumulative failure rates were calculated across uncertainty thresholds, with a 3 mm/3% gamma passing rate &lt; 95% defining failure.</div></div><div><h3>Results</h3><div>After calibration, all three methods showed ENCE values of 0.14/0.05/0.10 for MCD/MVE/DE. A strong relationship between mean uncertainty and MAE was observed, reflected by high Spearman correlation coefficients (MCD: ρ = 0.64, MVE: ρ = 0.76, DE: ρ = 0.67).</div><div>Failure rates increased with mean uncertainty across all methods, enabling thresholds targeting a 10% failure rate. On independent evaluation, 44%–77% of segments were accepted, with observed failure rates among accepted segments of 7.2%–9.5%.</div></div><div><h3>Conclusions</h3><div>All methods provided meaningful uncertainty estimates clearly associated with prediction error. MVE showed the lowest ENCE and strongest correlation with MAE while requiring the least computation. DE most closely matched the target failure rate while flagging the fewest segments.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"41 ","pages":"Article 101072"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148937968","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 standardized measurement-based approach to estimate modelled relative biological effectiveness for auditing carbon ion beams","authors":"Shannon Hartzell,&nbsp;Fada Guan,&nbsp;Paige Taylor,&nbsp;Phillip J. Taddei,&nbsp;Alfredo Mirandola,&nbsp;Giuseppe Magro,&nbsp;Mario Ciocca,&nbsp;Christine B. Peterson,&nbsp;Stephen Kry","doi":"10.1016/j.phro.2026.101066","DOIUrl":"10.1016/j.phro.2026.101066","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Differences in dose delivered during carbon radiotherapy are due largely to variations in relative biological effectiveness (RBE), which can be calculated by several models. While RBE can theoretically be calculated using Microdosimetric Kinetic Model (MKM) and measured microdosimetric quantities, clinical implementation relies on lookup tables. There exist no means of measuring RBE by MKM or other common models, including Repair Misrepair Fixation (RMF) and Local Effect Model-I (LEM). This study investigated estimating RBE using a single microdosimetric measurement for each model, allowing measurement-based validation and inter-model comparison.</div></div><div><h3>Materials and methods</h3><div>Monte Carlo simulations were performed at 260 positions across nine carbon beams, from which microdosimetric quantities (<span><math><msup><mi>y</mi><mo>∗</mo></msup></math></span>) and linear (<span><math><mi>α</mi></math></span>) and quadratic (<span><math><mi>β</mi></math></span>) RBE parameters were derived using each model definition. To estimate RBE from microdosimetric input, <span><math><mi>α</mi></math></span> and <span><math><mi>β</mi></math></span> were fit as functions of <span><math><msup><mi>y</mi><mo>∗</mo></msup></math></span>for two test beams. The fits were applied to microdosimetric data from the remaining simulated beams and experimental measurements to validate resulting RBE predictions against full Monte Carlo-calculated RBE.</div></div><div><h3>Results</h3><div>Modelled RBE calculated using simulated input parameters versus estimated with the measurement-based fit were compared and found to be within ±10% accuracy in 96% (MKM), 100% (LEM), and 98% (RMF) of points. Average estimation uncertainty was 2.6% (MKM), 1.7% (LEM), and 4.3% (RMF).</div></div><div><h3>Conclusions</h3><div>While modelled RBE has extensive associated uncertainty, this study demonstrated that it could be estimated with reasonable accuracy using a standardized measurement-based framework. Results suggested that microdosimetry can be used as a quality assurance tool to characterize carbon fields and verify correct implementation of RBE models.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"41 ","pages":"Article 101066"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148937974","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":"Critical methodological concerns regarding the validation of NanOx model in helium SOBP irradiation","authors":"Ehsan Khodamoradi","doi":"10.1016/j.phro.2026.101090","DOIUrl":"10.1016/j.phro.2026.101090","url":null,"abstract":"","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"41 ","pages":"Article 101090"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149141427","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":"Real-time tumor tracking for magnetic resonance-guided radiotherapy using label-efficient foundation model adaptation","authors":"Amparo S. Betancourt Tarifa,&nbsp;Marcel Verheij,&nbsp;René Monshouwer,&nbsp;John J. Hermans,&nbsp;Peter J. Koopmans,&nbsp;Erik van der Bijl","doi":"10.1016/j.phro.2026.101078","DOIUrl":"10.1016/j.phro.2026.101078","url":null,"abstract":"<div><h3>Background and Purpose:</h3><div>Real-time tumor tracking on cine magnetic resonance imaging (cine-MRI) enables intra-fraction motion management in magnetic resonance-guided radiotherapy. Pipelines often rely on deformable registration or template matching to propagate contours, which can degrade under non-rigid motion and cine-MRI artifacts. This study evaluated two MedSAM2-based adaptations for real-time tracking with limited labels.</div></div><div><h3>Materials and Methods:</h3><div>The TrackRAD2025 dataset includes sagittal cine-MRI sequences from six institutions acquired on 0.35<!--> <!-->T and 1.5<!--> <!-->T MRI-linear accelerators, with 50 labeled and 477 unlabeled training cases and 50 test cases. Tracking was formulated as frame-wise target segmentation from the first-frame ground-truth mask. Method A fine-tuned MedSAM2 on 40 labeled cases and used rank-weighted checkpoint averaging. Method B combined two MedSAM2 models trained on different labeled splits augmented with pseudo-labels, and merged their predictions during inference.</div></div><div><h3>Results:</h3><div>On the hidden test set, Method A ranked highest on Dice similarity coefficient (DSC), center distance (CD), 95th-percentile Hausdorff distance (HD95), and mean average surface distance (MASD), with DSC = 0.891, CD = 1.47 mm, HD95 = 4.22 mm, MASD = 1.66 mm, relative dose to 98% of the target volume (<span><math><msub><mrow><mi>D</mi></mrow><mrow><mn>98</mn><mtext>%</mtext></mrow></msub></math></span>) = 0.936, and runtime of 0.04 s per frame. Method B achieved similar geometric performance and numerically higher relative <span><math><msub><mrow><mi>D</mi></mrow><mrow><mn>98</mn><mtext>%</mtext></mrow></msub></math></span> (0.953), at 0.10 s per frame. Paired Wilcoxon signed-rank testing showed no significant differences after correction for multiple testing.</div></div><div><h3>Conclusions:</h3><div>MedSAM2 adaptation enabled real-time cine-MRI tumor tracking with limited labels. The single-model approach provided faster inference and simpler deployment, while semi-supervised ensembling showed no significant improvement at higher computational cost.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"41 ","pages":"Article 101078"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148937985","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":"Corrigendum to “Cone beam computed tomography image guidance within a magnetic resonance imaging-only planning workflow” [Phys. Imaging Radiat. Oncol. 27 (2023) 100472]","authors":"Laura M. O'Connor,&nbsp;Alesha Quinn,&nbsp;Samuel Denley,&nbsp;Lucy Leigh,&nbsp;Jarad Martin,&nbsp;Jason A. Dowling,&nbsp;Kate Skehan,&nbsp;Helen Warren-Forward,&nbsp;Peter B. Greer","doi":"10.1016/j.phro.2026.101073","DOIUrl":"10.1016/j.phro.2026.101073","url":null,"abstract":"","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"41 ","pages":"Article 101073"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148937986","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 interim analysis of acute treatment adverse events and patient reported quality of life for functional imaging guided salivary gland sparing radiation therapy using diffusion-weighted magnetic resonance imaging","authors":"Katelyn Cahill,&nbsp;Catriona Hargrave,&nbsp;Kane Miller,&nbsp;Alexandria Leppien,&nbsp;Claudia Chapple,&nbsp;Jasmine Meaghan,&nbsp;Michelle Phan,&nbsp;Mark Denham,&nbsp;Patrick O'Connor,&nbsp;Alan Ho,&nbsp;David Hogan,&nbsp;Joanne Castelli,&nbsp;Zack Y. Shan,&nbsp;Myo Min","doi":"10.1016/j.phro.2026.101095","DOIUrl":"10.1016/j.phro.2026.101095","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Evolving evidence suggests that regional sparing of the parotid gland during radiation therapy could reduce radiation-induced xerostomia in head and neck cancer. This interim analysis investigates the functional relationship between the diffusion-weighted magnetic resonance imaging (DW-MRI) derived parotid gland biological at-risk volume (BRV) dose and acute adverse events, including patient-reported quality of life (QoL).</div></div><div><h3>Materials and methods</h3><div>Twenty-four head and neck cancer patients underwent MRI-simulation to delineate parotid gland BRVs using DW-MRI derived apparent diffusion coefficient measurements. Dose was optimised, sparing the parotid gland BRV. Clinician-reported acute adverse events (xerostomia, dysphagia, oral pain) were recorded and patient-reported QoL was assessed using University of Washington QoL Questionnaire v4.1 with Radiation Therapy Oncology Group modification. Adverse events were quantified using area under the curve and time to side effect peaks were calculated and analysed.</div></div><div><h3>Results</h3><div>A moderate positive correlation trend was observed between contralateral BRV mean dose and xerostomia area under the curve, although this did not reach statistical significance <em>(r</em> = 0.34<em>, p</em> = 0.13). There was a strong relationship between the contralateral BRV sector and days to peak dysphagia (<em>r</em> = 0.56, <em>p = 0.005</em>). Low- and high-dose grouped analysis revealed a trend toward significance for time to peak xerostomia (<em>p = 0.059</em>). There were no significant associations with the ipsilateral parotid gland BRVs.</div></div><div><h3>Conclusion</h3><div>Findings suggest a potential functional-dose relationship between the contralateral BRV mean dose and acute xerostomia. Ongoing follow-up will determine whether the suggestive trends observed in the acute phase strengthen with long-term recovery time.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"41 ","pages":"Article 101095"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149141425","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":"Time, gravity, and the relativity of tumor position in free-breathing lung stereotactic body radiation therapy","authors":"Paul Retif,&nbsp;Barbara Knäusl,&nbsp;William H. Nailon","doi":"10.1016/j.phro.2026.101099","DOIUrl":"10.1016/j.phro.2026.101099","url":null,"abstract":"","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"41 ","pages":"Article 101099"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149141428","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 of ultra-low-field MRI in an ion beam treatment room for online MR-guided particle therapy","authors":"Julia Pfitzer,&nbsp;Hermann Scharfetter,&nbsp;Panagiotis Antypas,&nbsp;Dietmar Georg,&nbsp;Hermann Fuchs","doi":"10.1016/j.phro.2026.101092","DOIUrl":"10.1016/j.phro.2026.101092","url":null,"abstract":"<div><h3>Background and purpose:</h3><div>Online MR-guided particle therapy may improve beam delivery precision and treatment efficacy. Ultra-low-field (ULF) MRI may enable image guidance with minimal modification of existing facilities. The feasibility of operating a ULF magnetic resonance (MR) scanner for online MR-guided particle therapy in an unmodified ion-beam therapy environment was investigated.</div></div><div><h3>Material and methods:</h3><div>A portable 49 mT ULF-MRI system (OSI² ONE MR scanner) was placed at the isocenter of a research room at an ion-beam therapy facility without radio-frequency shielding. Image quality was assessed using a grid phantom under static and dynamic irradiation conditions for protons and carbon ions. Electromagnetic interference (EMI) was characterized and mitigated via grounding and local shielding. Beam deflections were quantified using a scintillation detector and compared to numerical simulations. The impact of ongoing proton and carbon ion irradiation on the imaging process was assessed. Dosimetric measurements were performed using a Semiflex ionization chamber in a PMMA phantom with and without simultaneous MR imaging.</div></div><div><h3>Results:</h3><div>Stable image quality was achieved across proton and carbon ion irradiation with no relevant irradiation-induced artifacts. Initially substantial EMI was reduced to near-baseline levels by internal shielding. Proton beam deflections ranged from 0.9–1.7 mm at isocenter, consistent with simulations. Dosimetric deviations were below 0.05% for 2D fields and 0.02% for spread-out Bragg peaks (SOBP).</div></div><div><h3>Conclusion:</h3><div>ULF-MRI operation in an ion-beam therapy environment without a dedicated shielded room is feasible, without compromising imaging, beam delivery, or dosimetry. This approach supports future MR-guided particle therapy research with reduced infrastructural requirements.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"41 ","pages":"Article 101092"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149141568","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 interactive virtual dosimetrist for adjusting radiotherapy dose quality","authors":"Skylar S. Gay,&nbsp;Tucker J. Netherton,&nbsp;Barbara Marquez,&nbsp;Raymond Mumme,&nbsp;Mary Gronberg,&nbsp;Brent Parker,&nbsp;Chelsea Pinnix,&nbsp;Sanjay Shete,&nbsp;Carlos E. Cardenas,&nbsp;Laurence E. Court","doi":"10.1016/j.phro.2026.101080","DOIUrl":"10.1016/j.phro.2026.101080","url":null,"abstract":"<div><h3>Background and Purpose:</h3><div>Effective education in radiotherapy plan quality review requires exposure to treatment plans of varying quality and opportunities for plan improvements. However, the current clinic-based paradigm does not fully support these needs. Trainees often review limited numbers of diverse treatment plans and are not fully exposed to the planning decisions essential for developing strong evaluation skills.</div></div><div><h3>Materials and Methods:</h3><div>To address these challenges, we developed “Virtual Dosimetrist” models that generate and modify radiotherapy dose distributions through language conditioning, similar to communicating with a planner during plan review. This enables creation of diverse suboptimal examples and real-time, language-guided refinement.</div></div><div><h3>Results:</h3><div>The models modify dose distributions following language directives, allowing the generation of lower-quality examples and subsequent improvement. Dose predictions were produced quickly and without reliance on a planning system, enabling interactive adjustment that mirrors the clinical review-edit cycle. For the 11 patients in the test set, good control over the extent and magnitude of dose changes across 17 OOIs, together with robustness to variations in the language directives, was observed. When comparing to dose originating from a TPS, median RMSE was 52 cGy across all dose-level comparisons and 98% of predictions met the prespecified equivalence criterion of RMSE <span><math><mo>&lt;</mo></math></span> 200 cGy.</div></div><div><h3>Conclusions:</h3><div>This work is the first to combine deep learning-based dose prediction with natural language processing specifically designed to support the language-conditioned generation of suboptimal dose distributions and their improvement. The framework could be used as a foundation for educational applications, such as replicating plan quality review in an interactive environment.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"41 ","pages":"Article 101080"},"PeriodicalIF":3.2,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13639743/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115915","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 of predicting free-breathing body contours from biplanar CT scout images for surface-guided DIBH radiotherapy","authors":"Yoonsuk Huh,&nbsp;Seonghee Kang,&nbsp;Jaewon Yang,&nbsp;Jung-in Kim","doi":"10.1016/j.phro.2026.101023","DOIUrl":"10.1016/j.phro.2026.101023","url":null,"abstract":"<div><h3>Background and purpose</h3><div>Surface-guided deep inspiration breath-hold radiotherapy uses deep inspiration breath-hold CT for treatment planning, whereas a free-breathing body contour is needed for baseline surface registration. This requires an additional free-breathing CT acquisition. This study investigated the feasibility of generating a three-dimensional free-breathing body contour directly from routine biplanar CT scout images using deep learning.</div></div><div><h3>Material and methods</h3><div>A total of 173 thoracic CT-simulation studies with paired coronal and sagittal CT scout images and corresponding free-breathing CT images were retrospectively collected. The CT-derived body mask was generated using an automated threshold- and morphology-based procedure and used as the reference contour. The dataset was divided into training, validation, and test cohorts of 121, 26, and 26 studies, respectively. Geometric performance was evaluated using the Dice coefficient, 95th percentile Hausdorff distance, and mean surface distance.</div></div><div><h3>Results</h3><div>In the test cohort, the predicted contours demonstrated high geometric agreement with the reference contours, with a Dice coefficient of 0.979 ± 0.017, 95th percentile Hausdorff distance of 4.23 ± 2.87 mm, and mean surface distance of 1.05 ± 0.73 mm. Slice-wise analysis showed similar performance. Larger and more symmetric, geometrically regular torso shapes were associated with better performance.</div></div><div><h3>Conclusions</h3><div>A three-dimensional free-breathing body contour was generated from routine biplanar CT scout images with high geometric agreement to the CT-derived reference contour. This approach may reduce the need for additional free-breathing CT acquisition for patient setup, but prospective validation in clinical setup, registration, and gating workflows is required.</div></div>","PeriodicalId":36850,"journal":{"name":"Physics and Imaging in Radiation Oncology","volume":"40 ","pages":"Article 101023"},"PeriodicalIF":3.2,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13324509/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148377570","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}