Radiologia Medica最新文献

{"title":"Quantification of abdominal aortic calcification using photon-counting CT angiography: an imaging biomarker for high-risk cardiovascular patients.","authors":"Takashi Ota, Atsushi Nakamoto, Masatoshi Hori, Hideyuki Fukui, Hiromitsu Onishi, Mitsuaki Tatsumi, Noriyuki Tomiyama","doi":"10.1007/s11547-025-01978-0","DOIUrl":"https://doi.org/10.1007/s11547-025-01978-0","url":null,"abstract":"<p><strong>Objectives: </strong>To evaluate abdominal aortic calcification parameters derived from 3D volumetric analysis using photon-counting CT (PCCT) angiography-based virtual non-calcium (VNCa) algorithm as an imaging biomarker for high-risk cardiovascular disease (CVD) patients.</p><p><strong>Methods: </strong>This retrospective study included patients who underwent abdominal PCCT angiography and non-contrast-enhanced chest CT (nCE-CCT, including CT scanners other than PCCT) between March 2023 and June 2024. Abdominal aortic calcification maps were generated by subtracting VNCa from the corresponding CTA images to calculate the abdominal calcification volume (ACV) and aortic wall volume (AWV). Percentage calcification volume (PCV) was calculated as ACV/AWV. Agatston scores from nCE-CCT classified patients into low- (≤ 100) and high-risk (> 100) CVD groups. Correlations between Agatston score, ACV, and PCV were analyzed using Spearman's rank correlation, and receiver operating characteristic analysis was used to determine the performance and cutoff values of ACV and PCV, with McNemar's test comparing sensitivities and specificities.</p><p><strong>Results: </strong>The study included 200 patients, 163 low- and 37 high-risk patients. Agatston score correlations with ACV and PCV were 0.75 and 0.78, respectively (p < 0.0001). PCV showed a superior AUC (0.94) than ACV (0.90, p = 0.0002). Cutoff values were 5.74 mL for ACV (75.7% sensitivity, 89.0% specificity) and 14.81% for PCV (73.0% sensitivity, 99.4% specificity), and PCV specificity was significantly higher than ACV specificity (p < 0.0001).</p><p><strong>Conclusion: </strong>PCV > 14.81% indicates an increased CVD risk, suggesting that PCV is a potential imaging biomarker for high-risk patients with CVD. Abdominal CTA alone may identify high-risk patients with CVD, warranting further cardiovascular screening.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification models for arthropathy grades of multiple joints based on hierarchical continual learning.","authors":"Bong Kyung Jang, Shiwon Kim, Jae Yong Yu, JaeSeong Hong, Hee Woo Cho, Hong Seon Lee, Jiwoo Park, Jeesoo Woo, Young Han Lee, Yu Rang Park","doi":"10.1007/s11547-025-01974-4","DOIUrl":"https://doi.org/10.1007/s11547-025-01974-4","url":null,"abstract":"<p><strong>Purpose: </strong>To develop a hierarchical continual arthropathy classification model for multiple joints that can be updated continuously for large-scale studies of various anatomical structures.</p><p><strong>Materials and methods: </strong>This study included a total of 1371 radiographs of knee, elbow, ankle, shoulder, and hip joints from three tertiary hospitals. For model development, 934 radiographs of the knee, elbow, ankle, and shoulder were gathered from Sinchon Severance Hospital between July 1 and December 31, 2022. For external validation, 125 hip radiographs were collected from Yongin Severance Hospital between January 1 and December 31, 2022, and 312 knee cases were gathered from Gangnam Severance Hospital between January 1 and June 31, 2023. The Hierarchical Dynamically Expandable Representation (Hi-DER) model was trained stepwise on four joints using five-fold cross-validation. Arthropathy classification was evaluated at three hierarchical levels: abnormal classification (L1), low-grade or high-grade classification (L2), and specific grade classification (L3). The model's performance was compared with the grading predictions of two other AI models and three radiologists. For model explainability, gradient-weighted class activation mapping (Grad-CAM) and progressive erasing plus progressive restoration (PEPPR) were employed.</p><p><strong>Results: </strong>The model achieved a weighted average AUC of 0.994 (95% CI: 0.985, 0.999) for L1, 0.980 (95% CI: 0.958, 0.996) for L2, and 0.973 (95% CI: 0.943, 0.993) for L3. The model maintained an AUC above 0.800 with 70% of the input regions erased. During external validation on hip joints, the model demonstrated a weighted average AUC of 0.978 (95% CI: 0.952, 0.996) for L1, 0.977 (95% CI: 0.946, 0.996) for L2, and 0.971 (95% CI: 0.934, 0.996) for L3. For external knee data, the model yielded a weighted average AUC of 0.934 (95%: CI 0.904, 0.958), 0.929 (95% CI: 0.900, 0.954), and 0.857 (95% CI: 0.816, 0.894) for L1, L2, and L3, respectively.</p><p><strong>Conclusion: </strong>The Hi-DER may enhance the efficiency of arthropathy diagnosis through accurate classification of arthropathy grades across multiple joints, potentially enabling early treatment.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701242","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to the letter to the editor titled \"perspectives on MRI sequences and clustering techniques in predicting osteosarcoma treatment response\".","authors":"Giovanni Benvenuti, Simona Marzi, Antonello Vidiri, Vincenzo Anelli","doi":"10.1007/s11547-025-02000-3","DOIUrl":"https://doi.org/10.1007/s11547-025-02000-3","url":null,"abstract":"","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification and classification of lumbar disc herniation on axial magnetic resonance images using deep learning models.","authors":"Elzat Elham-Yilizati Yilihamu, Jun Shang, Zhi-Hai Su, Jin-Tao Yang, Kun Zhao, Hai Zhong, Shi-Qing Feng","doi":"10.1007/s11547-025-01996-y","DOIUrl":"https://doi.org/10.1007/s11547-025-01996-y","url":null,"abstract":"<p><strong>Purpose: </strong>Application of a deep learning model visualization plugin for rapid and accurate automatic quantification and classification of lumbar disc herniation (LDH) types on axial T2-weighted MRIs.</p><p><strong>Methods: </strong>Retrospective analysis of 2500 patients, with the training set comprising data from 2120 patients (25,554 images), an internal test set covering data from 80 patients (784 images), and an external test set including data from 300 patients (3285 images). To enhance implementation, this study categorized normal and bulging discs as a grade without significant abnormalities, defining the region and severity grades of LDH based on the relationship between the disc and the spinal canal. The automated detection training and validation process employed the YOLOv8 object detection model for target area localization, the YOLOv8-seg segmentation model for disc recognition, and the YOLOv8-pose keypoint detection model for positioning. Finally, the stability of the detection results was verified using metrics such as Intersection over Union (IoU), mean error (ME), precision (P), F1 score (F1), Kappa coefficient (kappa), and 95% confidence interval (95%CI).</p><p><strong>Results: </strong>The segmentation model achieved an mAP50:95 of 98.12% and an IoU of 98.36% in the training set, while the keypoint detection model achieved an mAP50:95 of 93.58% with a mean error (ME) of 0.208 mm. For the internal and external test sets, the segmentation model's IoU was 97.58 and 97.49%, respectively, while the keypoint model's ME was 0.219 mm and 0.221 mm, respectively. In the quantification validation of the extent of LDH, P, F1, and kappa were measured. For LDH classification (18 categories), the internal and external test sets showed P = 81.21% and 74.50%, F1 = 81.26% and 74.42%, and kappa = 0.75 (95%CI 0.68, 0.82, p = 0.00) and 0.69 (95%CI 0.65, 0.73, p = 0.00), respectively. For the severity grades of LDH (four categories), the internal and external test sets showed P = 92.51% and 90.07%, F1 = 92.36% and 89.66%, and kappa = 0.88 (95%CI 0.80, 0.96, p = 0.00) and 0.85 (95%CI 0.81, 0.89, p = 0.00), respectively. For the regions of LDH (eight categories), the internal and external test sets showed P = 83.34% and 77.87%, F1 = 83.85% and 78.21%, and kappa = 0.77 (95%CI 0.70, 0.85, p = 0.00) and 0.71 (95%CI 0.67, 0.75, p = 0.00), respectively.</p><p><strong>Conclusion: </strong>The automated aided diagnostic model achieved high performance in detecting and classifying LDH and demonstrated substantial consistency with expert classification.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The role of contrast-enhanced ultrasound in the radiological classification of liver observations identified by CT and MRI.","authors":"Hui Huang, Dan-Ni He, Rui-Fang Lu, Wen-Juan Tong, Ying Wang, Si Qin, Rong Wen, Shao-Hong Wu, Si-Min Ruan, Guang-Jian Liu, Ming-De Lu, Ming Kuang, Wei Wang, Mei-Qing Cheng, Hong Yang, Li-Da Chen","doi":"10.1007/s11547-025-01995-z","DOIUrl":"https://doi.org/10.1007/s11547-025-01995-z","url":null,"abstract":"<p><strong>Background & aims: </strong>Timely and accurate diagnosis of hepatocellular carcinoma (HCC) is essential for improving patient outcomes and guiding treatment. This multicenter study aimed to optimize the diagnostic workflow for HCC through a step-wise combination of CT/MRI and contrast-enhanced ultrasound (CEUS) Liver Imaging Reporting and Data System (LI-RADS).</p><p><strong>Methods: </strong>This was a multicenter, retrospective analysis of prospectively recruited high-risk HCC participants with liver observations from 4 institutions, between January 2017 and December 2021. These participants initially underwent CT/MRI followed by CEUS, with observations categorized according to CT/MRI/CEUS LI-RADS. Three step-wise diagnostic strategies were evaluated, starting with CT/MRI and followed by CEUS, and compared to CT/MRI LI-RADS alone. Performance metrics included AUC, accuracy, sensitivity, specificity, PPV, and NPV, using pathology or over one year of follow-up as standards. The impact on clinical decisions was measured by false-negative, false-positive, and biopsy rates.</p><p><strong>Results: </strong>Of 1264 participants, 874 (69%) were confirmed as HCC. The step-wise strategies outperformed CT/MRI LI-RADS. Strategy-3, which involved subsequent CEUS for CT/MRI LR-3/4 observations, significantly improved sensitivity (88.8% vs. 79.9%, P < 0.001) while maintaining comparable specificity (88.2% vs. 91.3%, P > 0.05). Strategy-3 reduced biopsy rate (31.5-22.4%, P = 0.028) and decreased false-negative rate (20.1-11.2%, P < 0.001). Additionally, 96% (55/57) of CT/MRI LR-3 and 97% (77/79) of CT/MRI LR-4 observations were accurately diagnosed and treated as HCC, with 61% (74/121) of CT/MRI LR-4 observations avoiding biopsy with CEUS-assisted.</p><p><strong>Conclusion: </strong>A step-wise approach using CT/MRI followed by CEUS for LR-3/4 observations improved the diagnostic performance and further refined clinical decision-making in HCC.</p><p><strong>Trial registration: </strong>Clinical Trial Registration Number: ChiCTR-DDD-16010089.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Personalized screening based on risk and density: prevalence data from the RIBBS study.","authors":"Francesca Caumo, Gisella Gennaro, Alessandra Ravaioli, Enrica Baldan, Elisabetta Bezzon, Silvia Bottin, Paolo Carlevaris, Lina Ciampani, Alessandro Coran, Chiara Dal Bosco, Sara Del Genio, Alessia Dalla Pietà, Fabio Falcini, Federico Maggetto, Giuseppe Manco, Tiziana Masiero, Maria Petrioli, Ilaria Polico, Tiziana Pisapia, Martina Zemella, Manuel Zorzi, Stefania Zovato, Lauro Bucchi","doi":"10.1007/s11547-025-01981-5","DOIUrl":"https://doi.org/10.1007/s11547-025-01981-5","url":null,"abstract":"<p><strong>Purpose: </strong>To present the prevalence screening results of the RIsk-Based Breast Screening (RIBBS) study (ClinicalTrials.gov NCT05675085), a quasi-experimental population-based study evaluating a personalized screening model for women aged 45-49. This model uses digital breast tomosynthesis (DBT) and stratifies participants by risk and breast density, incorporating tailored screening intervals with or without supplemental imaging (ultrasound, US, and breast MRI), with the goal of reducing advanced breast cancer (BC) incidence compared to annual digital mammography (DM).</p><p><strong>Materials and methods: </strong>An interventional cohort of 10,269 women aged 45 was enrolled (January 2020-December 2021. Participants underwent DBT and completed a BC risk questionnaire. Volumetric breast density and lifetime risk were used to assign five subgroups to tailored screening regimens: low-risk low-density (LR-LD), low-risk high-density (LR-HD), intermediate-risk low-density (IR-LD), intermediate-risk high-density (IR-HD), and high-risk (HR). Screening performance was compared with an observational control cohort of 43,838 women undergoing annual DM.</p><p><strong>Results: </strong>Compared to LR-LD, intermediate-risk groups showed a 4.9- (IR-LD) and 4.6-fold (IR-HD) higher prevalence of BC, driven by a 7.1- and 7.1-fold higher prevalence of pT1c tumors. The interventional cohort had lower recall rate (rate ratio, 0.5), higher surgery rate (1.9) and increased prevalence of DCIS (2.9), pT1c (2.3) and grade 3 tumors (2.4), compared to controls.</p><p><strong>Conclusion: </strong>The prevalence screening demonstrated the feasibility of using DBT and -in high-density subgroups- supplemental US. The stratification criteria effectively identified subpopulations with different BC prevalence. Increasing the detection rate of pT1c tumors is not sufficient but necessary to achieve a reduction in advanced BC incidence.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of radiomics in predicting early disease recurrence in locally advanced breast cancer patients: integration of radiomic features and RECIST criteria.","authors":"Charlotte Trombadori, Edda Boccia, Elena Huong Tran, Antonio Franco, Armando Orlandi, Gianluca Franceschini, Luisa Carbognin, Alba Di Leone, Valeria Masiello, Fabio Marazzi, Antonella Palazzo, Ida Paris, Roberta Dattoli, Antonino Mulè, Nikola Dino Capocchiano, Diana Giannarelli, Riccardo Masetti, Paolo Belli, Luca Boldrini, Anna D'Angelo, Alessandra Fabi","doi":"10.1007/s11547-025-01984-2","DOIUrl":"https://doi.org/10.1007/s11547-025-01984-2","url":null,"abstract":"<p><strong>Background: </strong>Breast cancer (BC) is a major global health issue with significant heterogeneity among its subtypes. Neoadjuvant treatment (NAT) has been extended to include early BC patients, particularly those with HER2 + and triple-negative subtypes, to achieve pathological complete response and improve long-term outcomes. However, disease recurrence remains a challenge, highlighting the need for predictive biomarkers. This study evaluates the role of radiomics from pre-treatment breast MRI, integrated with clinical and radiological variables, in predicting early disease recurrence (EDR) after NAT.</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 238 BC patients treated with NAT and assessed using pre- and post-treatment breast MRI. Radiomic features were extracted and combined with clinical and radiological data to develop predictive models for EDR. Models were evaluated using AUC, accuracy, sensitivity, and specificity metrics.</p><p><strong>Results: </strong>The radiological-radiomic model, which integrated pre-treatment MRI radiomics with RECIST response data, demonstrated the highest predictive performance for EDR (AUC 0.77, sensitivity 0.85). Internal validation confirmed the robustness of the model.</p><p><strong>Conclusion: </strong>Combining radiomic features from pre-NAT MRI with RECIST response evaluation from post-NAT MRI enhances the prediction of EDR in BC patients, supporting precision medicine in treatment strategies and follow-up planning. Further validation on larger cohorts is needed to confirm these findings.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bowel wall iodine concentration measurement at dual-energy CT in non-occlusive mesenteric ischemia: a call for standardization.","authors":"Paul Calame, Gabriel Simon, Gael Piton","doi":"10.1007/s11547-025-01998-w","DOIUrl":"https://doi.org/10.1007/s11547-025-01998-w","url":null,"abstract":"","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multimodal prediction of major adverse cardiovascular events in hypertensive patients with coronary artery disease: integrating pericoronary fat radiomics, CT-FFR, and clinicoradiological features.","authors":"Qing Zou, Taichun Qiu, Chunxiao Liang, Fang Wang, Yongji Zheng, Jie Li, Xingchen Li, Yudan Li, Zhongyan Lu, Bing Ming","doi":"10.1007/s11547-025-01991-3","DOIUrl":"https://doi.org/10.1007/s11547-025-01991-3","url":null,"abstract":"<p><strong>Purpose: </strong>People with both hypertension and coronary artery disease (CAD) are at a significantly increased risk of major adverse cardiovascular events (MACEs). This study aimed to develop and validate a combination model that integrates radiomics features of pericoronary adipose tissue (PCAT), CT-derived fractional flow reserve (CT-FFR), and clinicoradiological features, which improves MACE prediction within two years.</p><p><strong>Materials and methods: </strong>Coronary-computed tomography angiography data were gathered from 237 patients diagnosed with hypertension and CAD. These patients were randomly categorized into training and testing cohorts at a 7:3 ratio (165:72). The least absolute shrinkage and selection operator logistic regression and linear discriminant analysis method were used to select optimal radiomics characteristics. The predictive performance of the combination model was assessed through receiver operating characteristic curve analysis and validated via calibration, decision, and clinical impact curves.</p><p><strong>Results: </strong>The results reveal that the combination model (Radiomics.</p><p><strong>Clinical: </strong>Imaging) improves the discriminatory ability for predicting MACE. Its predictive efficacy is comparable to that of the Radiomics.Imaging model in both the training (0.886 vs. 0.872) and testing cohorts (0.786 vs. 0.815), but the combination model exhibits significantly improved specificity, accuracy, and precision. Decision and clinical impact curves further confirm the use of the combination prediction model in clinical practice.</p><p><strong>Conclusions: </strong>The combination prediction model, which incorporates clinicoradiological features, CT-FFR, and radiomics features of PCAT, is a potential biomarker for predicting MACE in people with hypertension and CAD.</p>","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Perspectives on MRI sequences and clustering techniques in predicting osteosarcoma treatment response.","authors":"Yu Mori, Naoko Mori","doi":"10.1007/s11547-025-01999-9","DOIUrl":"https://doi.org/10.1007/s11547-025-01999-9","url":null,"abstract":"","PeriodicalId":20817,"journal":{"name":"Radiologia Medica","volume":" ","pages":""},"PeriodicalIF":9.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143674319","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}