Japanese Journal of Radiology最新文献

{"title":"Structured clinical reasoning prompt enhances LLM's diagnostic capabilities in diagnosis please quiz cases.","authors":"Yuki Sonoda, Ryo Kurokawa, Akifumi Hagiwara, Yusuke Asari, Takahiro Fukushima, Jun Kanzawa, Wataru Gonoi, Osamu Abe","doi":"10.1007/s11604-024-01712-2","DOIUrl":"10.1007/s11604-024-01712-2","url":null,"abstract":"<p><strong>Purpose: </strong>Large Language Models (LLMs) show promise in medical diagnosis, but their performance varies with prompting. Recent studies suggest that modifying prompts may enhance diagnostic capabilities. This study aimed to test whether a prompting approach that aligns with general clinical reasoning methodology-specifically, using a standardized template to first organize clinical information into predefined categories (patient information, history, symptoms, examinations, etc.) before making diagnoses, instead of one-step processing-can enhance the LLM's medical diagnostic capabilities.</p><p><strong>Materials and methods: </strong>Three hundred twenty two quiz questions from Radiology's Diagnosis Please cases (1998-2023) were used. We employed Claude 3.5 Sonnet, a state-of-the-art LLM, to compare three approaches: (1) Baseline: conventional zero-shot chain-of-thought prompt, (2) two-step approach: structured two-step approach: first, the LLM systematically organizes clinical information into two distinct categories (patient history and imaging findings), then separately analyzes this organized information to provide diagnoses, and (3) Summary-only approach: using only the LLM-generated summary for diagnoses.</p><p><strong>Results: </strong>The two-step approach significantly outperformed the both baseline and summary-only approaches in diagnostic accuracy, as determined by McNemar's test. Primary diagnostic accuracy was 60.6% for the two-step approach, compared to 56.5% for baseline (p = 0.042) and 56.3% for summary-only (p = 0.035). For the top three diagnoses, accuracy was 70.5, 66.5, and 65.5% respectively (p = 0.005 for baseline, p = 0.008 for summary-only). No significant differences were observed between the baseline and summary-only approaches.</p><p><strong>Conclusion: </strong>Our results indicate that a structured clinical reasoning approach enhances LLM's diagnostic accuracy. This method shows potential as a valuable tool for deriving diagnoses from free-text clinical information. The approach aligns well with established clinical reasoning processes, suggesting its potential applicability in real-world clinical settings.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"586-592"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953165/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769017","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in fracture detection on radiographs: a literature review.","authors":"Antonio Lo Mastro, Enrico Grassi, Daniela Berritto, Anna Russo, Alfonso Reginelli, Egidio Guerra, Francesca Grassi, Francesco Boccia","doi":"10.1007/s11604-024-01702-4","DOIUrl":"10.1007/s11604-024-01702-4","url":null,"abstract":"<p><p>Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for patients, and artificial intelligence that uses algorithms to imitate human intelligence to aid or enhance human performs is a promising solution to address this issue. In the last few years, numerous commercially available algorithms have been developed to enhance radiology practice and a large number of studies apply artificial intelligence to fracture detection. Recent contributions in literature have described numerous advantages showing how artificial intelligence performs better than doctors who have less experience in interpreting musculoskeletal X-rays, and assisting radiologists increases diagnostic accuracy and sensitivity, improves efficiency, and reduces interpretation time. Furthermore, algorithms perform better when they are trained with big data on a wide range of fracture patterns and variants and can provide standardized fracture identification across different radiologist, thanks to the structured report. In this review article, we discuss the use of artificial intelligence in fracture identification and its benefits and disadvantages. We also discuss its current potential impact on the field of radiology and radiomics.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"551-585"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142620891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Association between patient position-induced breast shape changes on prone and supine MRI and mammographic breast density or thickness.","authors":"Maki Amano, Yasuo Amano, Naoya Ishibashi, Takeshi Yamaguchi, Mitsuhiro Watanabe","doi":"10.1007/s11604-024-01708-y","DOIUrl":"10.1007/s11604-024-01708-y","url":null,"abstract":"<p><strong>Purpose: </strong>The breast shape differs between the prone position in breast magnetic resonance imaging (MRI) and the supine position on an operating table. We sought to determine the relationship between patient position-induced changes on prone and supine MRI in breast shape and mammographic breast density or thickness.</p><p><strong>Materials and methods: </strong>We evaluated data from 68 women with 69 breast cancers in this retrospective observational study. The difference in the minimal distance from the nipple to the pectoralis major (DNPp-s) or the internal thoracic artery between the prone and supine MRI (DNIs-p) was defined as the breast shape changes. Mammographic breast density was assessed by conventional 4-level classification and automated and manual quantification using a dedicated mammography viewer. The compressed breast thickness was recorded during mammography (MMG). We determined the association between patient position-induced breast shape changes on MRI and mammographic breast density or compressed breast thickness on MMG.</p><p><strong>Results: </strong>On the conventional 4-level qualification, one breast appeared fatty, 39 appeared with scattered density, 23 appeared heterogeneously dense, and 6 breasts appeared extremely dense. Both automated and manual quantification of mammographic breast density differed between the 4 levels (p < 0.01 for both) and correlated with the 4 levels (p < 0.001 for both, r = 0.654 and 0.693, respectively). The manual quantification inversely correlated with DNPp-s and DNIs-p (p < 0.01 and < 0.05, r =  - 0.330 and - 0.273, respectively). The compressed breast thickness significantly correlated with DNPp-s and DNIs-p (p < 0.01 for both, r = 0.648 and 0.467, respectively).</p><p><strong>Conclusion: </strong>Compressed breast thickness during MMG can predict the degree of patient position-induced changes in breast shape on MRI. The manual quantification of the mammographic breast density, which may reflect the biomechanical properties of the breast tissues, also correlates to the breast shape changes.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"641-648"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of visceral adipose tissue on the accuracy of tumor T-staging of gastric cancer in preoperative CT.","authors":"Danping Wu, Linjie Bian, Zhongjuan Wang, Jianming Ni, Yigang Chen, Lei Zhang, Xulei Chen","doi":"10.1007/s11604-024-01711-3","DOIUrl":"10.1007/s11604-024-01711-3","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate the impact of the visceral adipose tissue (VAT) area and density on the accuracy of tumor T-staging of gastric cancer in preoperative computed tomography (CT).</p><p><strong>Methods: </strong>This study included 136 patients with gastric cancer in our research center from January 2021 to June 2022. The patients were divided into two groups based on their postoperative pathological results: accurate-staging (matched T-staging evaluated by preoperative CT and postoperative pathology) and inaccurate-staging (unmatched T-staging evaluated by preoperative CT and postoperative pathology) groups. Preoperative CT was performed to assess the VAT area and density, and logistic regression was employed to evaluate the effect of VAT on the accuracy of preoperative-CT-evaluated T-staging of patients with gastric cancer.</p><p><strong>Results: </strong>The accurate-staging group had a significantly higher VAT area (134.64 ± 70.55 cm² vs 95.44 ± 66.18 cm², P = 0.003) and significantly lower VAT density (-95.05 ± 12.28 Hounsfield Units [HU] vs - 89.68 ± 13.26 HU, P = 0.027) than the inaccurate-staging group. A low VAT area (P = 0.002) and tumor located in the upper stomach (P = 0.019) were significantly associated with and were independent risk factors for the error of CT-evaluated T-staging. Compared to a VAT area ≥ 81.04 cm², which was used as a reference, the odds ratio (OR) of a VAT area < 81.04 cm² for the probability of T-staging mis-assessment was 4.455 (95% confidence interval [CI]: 1.728-11.485).</p><p><strong>Conclusions: </strong>A low VAT area in patients with gastric cancer had adverse effects on preoperative CT-evaluated T-staging.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"656-665"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142739505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supradiaphragmatic origin of the right renal artery.","authors":"Alexandra Koberlein, Christine M G Schammel, Aron Michael Devane","doi":"10.1007/s11604-024-01706-0","DOIUrl":"10.1007/s11604-024-01706-0","url":null,"abstract":"","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"713-715"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142828515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High-precision MRI of liver and hepatic lesions on gadoxetic acid-enhanced hepatobiliary phase using a deep learning technique.","authors":"Haruka Kiyoyama, Masahiro Tanabe, Keiko Hideura, Yosuke Kawano, Keisuke Miyoshi, Naohiko Kamamura, Mayumi Higashi, Katsuyoshi Ito","doi":"10.1007/s11604-024-01693-2","DOIUrl":"10.1007/s11604-024-01693-2","url":null,"abstract":"<p><strong>Purpose: </strong>The purpose of this study was to investigate whether the high-precision magnetic resonance (MR) sequence using modified Fast 3D mode wheel and Precise IQ Engine (PIQE), that was collected in a wheel shape with sequential data filling in the k-space in the phase encode-slice encode plane, is feasible for breath-hold (BH) three-dimensional (3D) T1-weighted imaging of the hepatobiliary phase (HBP) of gadoxetic acid-enhanced MRI in comparison to the compressed sensing (CS) sequence using Advanced Intelligent Clear-IQ Engine (AiCE).</p><p><strong>Methods: </strong>This retrospective study included 54 patients with focal hepatic lesions who underwent dynamic contrast-enhanced MRI. Both standard HBP images using CS with AiCE and high-precision HBP images using modified Fast 3D mode wheel and PIQE were obtained. Image quality, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were evaluated using the Wilcoxon signed-rank test. p values of < 0.05 were considered to be statistically significant.</p><p><strong>Results: </strong>Scores for image noise, conspicuity of liver contours and intrahepatic structures, and overall image quality in high-precision HBP imaging using modified Fast 3D mode wheel and PIQE were significantly higher than those in HBP imaging using CS and AiCE (all p < 0.001). There was no significant difference in the presence of artifact and motion-related blurring. There were no significant differences between the sequences in SNR (p = 0.341) or CNR (p = 0.077). The detection rate of focal hepatic lesions was 71.4-85.3% in CS with AiCE, and 82.2-95.8% in modified Fast 3D mode wheel and PIQE.</p><p><strong>Conclusion: </strong>A high-precision MR sequence using a modified Fast 3D mode wheel and PIQE is applicable for the HBP of BH 3D T1-weighted imaging.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"649-655"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142620897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative analysis of image quality and diagnostic performance among SS-EPI, MS-EPI, and rFOV DWI in bladder cancer.","authors":"Mitsuru Takeuchi, Atsushi Higaki, Yuichi Kojima, Kentaro Ono, Takuma Maruhisa, Takatoshi Yokoyama, Hiroyuki Watanabe, Akira Yamamoto, Tsutomu Tamada","doi":"10.1007/s11604-024-01694-1","DOIUrl":"10.1007/s11604-024-01694-1","url":null,"abstract":"<p><strong>Purpose: </strong>To compare image quality and diagnostic performance among SS-EPI diffusion weighted imaging (DWI), multi-shot (MS) EPI DWI, and reduced field-of-view (rFOV) DWI for muscle-invasive bladder cancer (MIBC).</p><p><strong>Materials and methods: </strong>This retrospective study included 73 patients with bladder cancer who underwent multiparametric MRI in our referral center between August 2020 and February 2023. Qualitative image assessment was performed in 73; and quantitative assessment was performed in 66 patients with maximum lesion diameter > 10 mm. The diagnostic performance of the imaging finding of muscle invasion was evaluated in 47 patients with pathological confirmation of MIBC. T2-weighted imaging, SS-EPI DWI, MS-EPI DWI, rFOV DWI, and dynamic contrast-enhanced imaging were acquired with 3 T-MRI. Qualitative image assessment was performed by three readers who rated anatomical distortion, clarity of bladder wall, and lesion conspicuity using a four-point scale. Quantitative assessment included calculation of SNR and CNR, and grading of the presence of muscle layer invasion according to the VI-RADS diagnostic criteria. Wilcoxon matched pairs signed rank test was used to compare qualitative and quantitative image quality. McNemar test and receiver-operating characteristic analysis were used to compare diagnostic performance.</p><p><strong>Results: </strong>Anatomical distortion was less in MS-EPI DWI, rFOV DWI, and SS-EPI DWI, in that order with significant difference. Clarity of bladder wall was greater for MS-EPI DWI, SS-EPI DWI, and rFOV DWI, in that order. There were significant differences between any two combinations of the three DWI types, except between SS-EPI DWI and MS-EPI in Reader 1. Lesion conspicuity, diagnostic performance, SNR and CNR were not significantly different among the three DWI types.</p><p><strong>Conclusions: </strong>Among the three DWI sequences evaluated, MS-EPI DWI showed the least anatomical distortion and superior bladder wall delineation but no improvement in diagnostic performance for MIBC. MS-EPI DWI may be considered for additional imaging if SS-EPI DWI is of poor quality.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"666-675"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953214/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142638982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The added value of including thyroid nodule features into large language models for automatic ACR TI-RADS classification based on ultrasound reports.","authors":"Pilar López-Úbeda, Teodoro Martín-Noguerol, Alba Ruiz-Vinuesa, Antonio Luna","doi":"10.1007/s11604-024-01707-z","DOIUrl":"10.1007/s11604-024-01707-z","url":null,"abstract":"<p><strong>Objective: </strong>The ACR Thyroid Imaging, Reporting, and Data System (TI-RADS) uses a score based on ultrasound (US) imaging to stratify the risk of nodule malignancy and recommend appropriate follow-up. This study aims to analyze US reports and explore how Natural Language Processing (NLP) leveraging Transformers models can classify ACR TI-RADS from text reports using the description of thyroid nodule features.</p><p><strong>Materials and methods: </strong>This retrospective study evaluated 16,847 thyroid-free text reports from our institution. An automated system, followed by manual review by a radiologist, established baseline annotations by assigning ACR TI-RADS categories from 1 to 5. Two types of systems were evaluated and compared in the dataset. The first by performing a multiclass classification to detect the associated ACR TI-RADS, and the second by extracting thyroid nodule features from the textual reports and incorporating them into the classifier.</p><p><strong>Results: </strong>Our study showed that models enhanced with specific features systematically outperformed those without. Particularly, the BERTIN model, to which additional features were added, achieved the highest level of accuracy, with a score of 0.8426. Moreover, we found a correlation between the presence of punctate echogenic foci, a feature often linked to malignant thyroid lesions, and increased ACR TI-RADS scores.</p><p><strong>Conclusions: </strong>The features of the thyroid nodules described in thyroid US reports, such as composition, echogenicity, shape, margin or echogenic foci, help the NLP classifier to predict the associated ACR TI-RADS most accurately.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"593-602"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Be familiar with benign pediatric head and neck lesions! Image interpretation guides to overcome your weakness.","authors":"Motoo Nakagawa, Wenya Zhao, Kumiko Nozawa, Noriko Aida, Norio Shiraki, Yuki Yasuda, Akio Hiwatashi","doi":"10.1007/s11604-024-01697-y","DOIUrl":"10.1007/s11604-024-01697-y","url":null,"abstract":"<p><p>Pediatric head and neck lesions include three main categories: congenital, inflammatory, and neoplastic. It is important for management to understand the imaging features. The purpose of this pictorial review is to demonstrate the imaging features of benign head and neck lesions of pediatric patients. To get tips on overcoming anxiety about this area, this article also presents pitfalls related to each of these diseases.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"542-550"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142769005","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generation of short-term follow-up chest CT images using a latent diffusion model in COVID-19.","authors":"Naoko Kawata, Yuma Iwao, Yukiko Matsuura, Takashi Higashide, Takayuki Okamoto, Yuki Sekiguchi, Masaru Nagayoshi, Yasuo Takiguchi, Takuji Suzuki, Hideaki Haneishi","doi":"10.1007/s11604-024-01699-w","DOIUrl":"10.1007/s11604-024-01699-w","url":null,"abstract":"<p><strong>Purpose: </strong>Despite a global decrease in the number of COVID-19 patients, early prediction of the clinical course for optimal patient care remains challenging. Recently, the usefulness of image generation for medical images has been investigated. This study aimed to generate short-term follow-up chest CT images using a latent diffusion model in patients with COVID-19.</p><p><strong>Materials and methods: </strong>We retrospectively enrolled 505 patients with COVID-19 for whom the clinical parameters (patient background, clinical symptoms, and blood test results) upon admission were available and chest CT imaging was performed. Subject datasets (n = 505) were allocated for training (n = 403), and the remaining (n = 102) were reserved for evaluation. The image underwent variational autoencoder (VAE) encoding, resulting in latent vectors. The information consisting of initial clinical parameters and radiomic features were formatted as a table data encoder. Initial and follow-up latent vectors and the initial table data encoders were utilized for training the diffusion model. The evaluation data were used to generate prognostic images. Then, similarity of the prognostic images (generated images) and the follow-up images (real images) was evaluated by zero-mean normalized cross-correlation (ZNCC), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM). Visual assessment was also performed using a numerical rating scale.</p><p><strong>Results: </strong>Prognostic chest CT images were generated using the diffusion model. Image similarity showed reasonable values of 0.973 ± 0.028 for the ZNCC, 24.48 ± 3.46 for the PSNR, and 0.844 ± 0.075 for the SSIM. Visual evaluation of the images by two pulmonologists and one radiologist yielded a reasonable mean score.</p><p><strong>Conclusions: </strong>The similarity and validity of generated predictive images for the course of COVID-19-associated pneumonia using a diffusion model were reasonable. The generation of prognostic images may suggest potential utility for early prediction of the clinical course in COVID-19-associated pneumonia and other respiratory diseases.</p>","PeriodicalId":14691,"journal":{"name":"Japanese Journal of Radiology","volume":" ","pages":"622-633"},"PeriodicalIF":2.1,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11953082/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142710105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}