Clinical radiology最新文献

{"title":"Challenges in the imaging and diagnosis of chronic non-bacterial osteomyelitis (CNO)","authors":"Z. Sheikh ,&nbsp;D. Bhatt ,&nbsp;M. Chowdhury ,&nbsp;K. Johnson","doi":"10.1016/j.crad.2025.106905","DOIUrl":"10.1016/j.crad.2025.106905","url":null,"abstract":"<div><div>Chronic non-bacterial osteomyelitis (CNO) is a benign skeletal disorder of childhood and adolescence characterised by sterile bone inflammation that results in chronic insidious bone pain. Although it is increasingly recognised, many challenges remain in its diagnosis with patients waiting 15–24 months for diagnosis from the time of symptom onset. Prompt identification and treatment are key to reducing morbidity from CNO, which affects school attendance and can result in chronic limb or spine deformities. Identifying its imaging features, particularly its characteristic lesion distribution on whole-body MRI, remains key to diagnosis. Image-guided biopsy is also required in many cases, particularly those with clinical uncertainty around the diagnosis. We present a review of the challenges encountered in the diagnosis of CNO from a critical appraisal of the literature and our experience as a national paediatric sarcoma, rheumatology and orthopaedic centre. This review hopes to inform radiology colleagues involved in the evaluation of potential CNO patients by discussing its imaging appearances, typical MRI phenotypes, potential mimics and the role of image-guided biopsy.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"85 ","pages":"Article 106905"},"PeriodicalIF":2.1,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable PET-based intratumoral and peritumoral machine learning model for predicting visceral pleural invasion in clinical-stage IA non-small cell lung cancer: A two-center study","authors":"B. Xue ,&nbsp;J. Lan ,&nbsp;S. Chen ,&nbsp;L. Wang ,&nbsp;E. Xin ,&nbsp;J. Xie ,&nbsp;X. Zheng ,&nbsp;L.g Wang ,&nbsp;K. Tang","doi":"10.1016/j.crad.2025.106903","DOIUrl":"10.1016/j.crad.2025.106903","url":null,"abstract":"<div><h3>AIM</h3><div>The aim of this study was to develop a PET-based machine learning model for predicting visceral pleural invasion (VPI) in patients with clinical stage IA non-small cell lung cancer.</div></div><div><h3>MATERIALS AND METHODS</h3><div>A total of 294 patients and 69 patients from two institutions who underwent the ¹⁸F-FDG-PET scan were retrospectively analyzed. We extracted PET-based radiomics features from the gross tumor volume (GTV) and gross tumor volume incorporating peritumoral 4, 8 and 12 mm regions (GPTV4, GPTV8, GPTV12), respectively. Then four models were respectively established by using machine learning algorithms. The performance of the models was assessed by the receiver operating characteristic (ROC) curve and decision curve analyses (DCA). Shapley additive explanation (SHAP) was employed to explain the machine learning (ML) models and visualize variable predictions.</div></div><div><h3>RESULTS</h3><div>Compared with GTV, GPTV4, and GPTV12 radiomics models, the radiomics model based on GPTV8 using random forest (RF) among the 10 features demonstrated better prediction performance, with the AUC of 0.879, 0.846, and 0.745 in the training, internal validation, and external validation sets, respectively. The results of the SHAP method showed that the GLRLM_ShortRunLowGreyLevel Emphasis features were the most important factors in VPI. At the patient level, SHAP force plots provided a deep understanding for predicting VPI.</div></div><div><h3>Conclusion</h3><div>The PET-based intratumoral and peritumoral model based on machine learning offers an innovative tool for preoperative prediction of VPI in patients with lung adenocarcinoma. By employing the SHAP method, clinicians may gain a clearer insight into the factors contributing to VPI, which could enhance clinical decision-making of prognosis assessment.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"85 ","pages":"Article 106903"},"PeriodicalIF":2.1,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the diagnostic accuracy and prognostic utility of artificial intelligence detection and grading of coronary artery calcification on nongated computed tomography (CT) thorax","authors":"B. Shear ,&nbsp;J. Graby ,&nbsp;D. Murphy ,&nbsp;K. Strong ,&nbsp;A. Khavandi ,&nbsp;T.A. Burnett ,&nbsp;P.F.P. Charters ,&nbsp;J.C.L. Rodrigues","doi":"10.1016/j.crad.2025.106909","DOIUrl":"10.1016/j.crad.2025.106909","url":null,"abstract":"<div><h3>Aims</h3><div>This study assessed the diagnostic accuracy and prognostic implications of an artificial intelligence (AI) tool for coronary artery calcification (CAC) assessment on nongated, noncontrast thoracic computed tomography (CT).</div></div><div><h3>Materials and Methods</h3><div>A single-centre retrospective analysis of 75 consecutive patients per age group (&lt;40, 40-49, 50-59, 60-69, 70-79, 80-89, and ≥90 years) undergoing non-gated, non-contrast CT (January-December 2015) was conducted. AI analysis reported CAC presence and generated an Agatston score, and the performance was compared with baseline CT reports and a dedicated radiologist re-review. Interobserver variability between AI and radiologist assessments was measured using Cohen's κ. All-cause mortality was recorded, and its association with AI-detected CAC was tested.</div></div><div><h3>Results</h3><div>A total of 291 patients (mean age: 64 ± 19, 51% female) were included, with 80% (234/291) of AI reports passing radiologist quality assessment. CAC was reported on 7% (17/234) of initial clinical reports, 58% (135/234) on radiologist re-review, and 57% (134/234) by AI analysis. After manual quality assurance (QA) assessment, the AI tool demonstrated high sensitivity (96%), specificity (96%), positive predictive value (95%), and negative predictive value (97%) for CAC detection compared with radiologist re-review. Interobserver agreement was strong for CAC prevalence (κ = 0.92) and moderate for severity grading (κ = 0.60). AI-detected CAC presence and severity predicted all-cause mortality (<em>p</em> &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>The AI tool exhibited feasible analysis potential for non-contrast, non-gated thoracic CTs, offering prognostic insights if integrated into routine practice. Nonetheless, manual quality assessment remains essential.</div></div><div><h3>Advances in knowledge</h3><div>This AI tool represents a potential enhancement to CAC detection and reporting on routine noncardiac chest CT.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"85 ","pages":"Article 106909"},"PeriodicalIF":2.1,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143879106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comment on “Magnetic resonance imaging–based nomograms predict high-risk cytogenetic abnormalities in multiple myeloma: a two-centre study”","authors":"S. Kumar,&nbsp;R. Mehta,&nbsp;R. Sah","doi":"10.1016/j.crad.2025.106907","DOIUrl":"10.1016/j.crad.2025.106907","url":null,"abstract":"","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"85 ","pages":"Article 106907"},"PeriodicalIF":2.1,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143869999","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding Erdheim–Chester Disease: a Pictorial Essay of the Radiologic and Pathologic Findings, and its Main Differential Diagnoses","authors":"I. Dixe de Oliveira Santo ,&nbsp;G. Lerner ,&nbsp;A.N. Rubinowitz ,&nbsp;S.A. Fadel ,&nbsp;M.-K Chen ,&nbsp;H.R. Mojibian ,&nbsp;M. Mathur","doi":"10.1016/j.crad.2025.106873","DOIUrl":"10.1016/j.crad.2025.106873","url":null,"abstract":"<div><div>Erdheim–Chester disease (ECD) is a rare non–Langerhans cell histiocytic neoplasm that primarily affects adults in their fifth to seventh decades of life. It is characterized by multisystem infiltration of CD68-positive, CD1a-negative foamy histiocytes and is driven by mutations in the MAPK and PI3K-AKT pathways, classifying it as a clonal myeloid neoplasm. ECD manifests with a wide spectrum of clinical features, including skeletal, cardiovascular, central nervous system, retroperitoneal, pulmonary, and endocrine involvement, frequently causing delays in diagnosis due to its nonspecific presentation. Targeted therapies, particularly BRAF and MEK inhibitors, have transformed the management of ECD, leading to significant improvements in patient outcomes.</div><div>Imaging plays a pivotal role in raising diagnostic suspicion, evaluating disease extent, and monitoring treatment response. Early recognition and accurate diagnosis rely on a multidisciplinary approach, integrating clinical evaluation, histopathology, molecular mutation analysis, and radiological findings. This pictorial essay aims to enhance radiologists’ familiarity with the key imaging findings of ECD across affected organ systems, highlighting characteristic patterns, potential diagnostic pitfalls, and important differentiating features from other mimicking conditions. We also provide an overview of the disease’s pathogenesis and modern treatment strategies. By increasing awareness of this challenging and often under-recognised condition, we aim to facilitate earlier diagnosis, more accurate imaging interpretation, and improved patient care.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"85 ","pages":"Article 106873"},"PeriodicalIF":2.1,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143807871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review and reflections on live AI mammographic screen reading in a large UK NHS breast screening unit","authors":"S. Puri,&nbsp;M. Bagnall,&nbsp;G. Erdelyi","doi":"10.1016/j.crad.2025.106872","DOIUrl":"10.1016/j.crad.2025.106872","url":null,"abstract":"<div><div>The Radiology team from a large Breast Screening Unit in the UK with a screening population of over 135,000 took part in a service evaluation project using artificial intelligence (AI) for reading breast screening mammograms.</div></div><div><h3>AIM</h3><div>To evaluate the clinical benefit AI may provide when implemented as a silent reader in a double reading breast screening programme and to evaluate feasibility and the operational impact of deploying AI into the breast screening programme.</div><div>The service was one of 14 breast screening sites in the UK to take part in this project and we present our local experience with AI in breast screening.</div></div><div><h3>MATERIALS AND METHODS</h3><div>A commercially available AI platform was deployed and worked in real time as a ‘silent third reader’ so as not to impact standard workflows and patient care. All cases flagged by AI but not recalled by standard double reading (positive discordant cases) were reviewed along with all cases recalled by human readers but not flagged by AI (negative discordant cases).</div></div><div><h3>RESULTS</h3><div>9,547 cases were included in the evaluation. 1,135 positive discordant cases were reviewed, and one woman was recalled from the reviews who was not found to have cancer on further assessment in the breast assessment clinic. 139 negative discordant cases were reviewed, and eight cancer cases (8.79% of total cancers detected in this period) recalled by human readers were not detected by AI. No additional cancers were detected by AI during the study.</div></div><div><h3>CONCLUSION</h3><div>Performance of AI was inferior to human readers in our unit. Having missed a significant number of cancers makes it unreliable and not safe to be used in clinical practice.</div><div>AI is not currently of sufficient accuracy to be considered in the NHS Breast Screening Programme.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"85 ","pages":"Article 106872"},"PeriodicalIF":2.1,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820287","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cone beam computed tomography (CT)–assisted radiologically inserted gastrostomy. Technical considerations and impact on radiation dose","authors":"J. Finnegan,&nbsp;J. O'Mahony,&nbsp;C. Reid,&nbsp;L. Selvarajah,&nbsp;E. Boylan,&nbsp;M. Sheehan,&nbsp;A. Keeling,&nbsp;A. McGrath,&nbsp;M. Given,&nbsp;D. Mulholland","doi":"10.1016/j.crad.2025.106871","DOIUrl":"10.1016/j.crad.2025.106871","url":null,"abstract":"<div><h3>AIM</h3><div>Intraprocedural cone beam computed tomography (CT) can provide anatomic evaluation during retrograde radiologically inserted gastrostomy (RIG). This study explores the technical and radiation dose implications of cone beam CT-assisted RIG, compared with conventional RIG technique using fluoroscopy alone.</div></div><div><h3>MATERIALS AND METHODS</h3><div>One hundred seven retrograde RIG procedures were included in the analysis, 48 of which were performed with cone beam CT assistance and 59 using conventional fluoroscopy alone.</div></div><div><h3>RESULTS</h3><div>Forty six of the 48 (95.8%) cone beam CT-assisted RIG procedures and 47 of the 59 (79.6%) procedures using conventional RIG technique were successful. One complication (tube malplacement) was encountered with the conventional RIG technique. Cone beam CT-assisted RIG was associated with significantly higher average dose area product (DAP) of 8.5 Gy.cm² compared with conventional technique’s DAP of 3.4 Gy.cm². The average procedure and fluoroscopic screening time for cone beam CT-assisted RIG was significantly lower.</div></div><div><h3>CONCLUSION</h3><div>Cone beam CT-assisted RIG is a safe and effective technique. Whilst there is an increase in radiation dose for the patient, it is counter balanced by definitive anatomical evaluation, higher success rates and lack of major complication with reduced fluoroscopic screening exposure for interventional staff.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"85 ","pages":"Article 106871"},"PeriodicalIF":2.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143800539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Levator ani muscle assessment and its correlation with recurrence of pelvic organ prolapse: a pelvic floor MRI study","authors":"Cheng Zhang ,&nbsp;Luyang Ma ,&nbsp;Xiaotian Li ,&nbsp;Jiaming Qin ,&nbsp;Yumeng Zhao ,&nbsp;Yanhong Wu ,&nbsp;Qian Zhao ,&nbsp;Yujiao Zhao ,&nbsp;Wen Shen","doi":"10.1016/j.crad.2025.106870","DOIUrl":"10.1016/j.crad.2025.106870","url":null,"abstract":"<div><h3>Purpose</h3><div>The levator ani muscle (LAM) plays a vital role in pelvic floor support. Understanding its influence on pelvic organ prolapse (POP) recurrence is essential for improving surgical techniques and postoperative care. This study hypothesized that patients with impaired preoperative LAM integrity, as assessed by pelvic floor MRI, had a higher risk of POP recurrence after pelvic floor repair surgery.</div></div><div><h3>Methods</h3><div>This retrospective study enrolled 38 patients with POP who underwent pelvic floor repair surgery. The patients were categorized into recurrence and non-recurrence groups based on gynecological examinations and Pelvic Floor Distress Inventory Questionnaire-20 (PFDI-20) scores. The structural and functional characteristics of the LAM were evaluated using preoperative static and dynamic MRI. A comparative analysis was performed between the two groups, and the Spearman correlation coefficient was used to quantitatively assess the correlation between LAM measurements and postoperative symptoms.</div></div><div><h3>Results</h3><div>The comparative analysis showed that the recurrence group had significantly more LAM injury, thinner puborectalis and iliococcygeus muscles, longer H-lines and M-lines, and larger levator hiatus compared to the non-recurrence group (<em>p</em>&lt;0.05). Furthermore, significant correlations were found between LAM thickness and injury and PFDI-20 scores, with thinner and more severely injured LAM associated with more severe postoperative symptoms (<em>p</em>&lt;0.05).</div></div><div><h3>Conclusions</h3><div>Incorporating pelvic floor MRI assessment of LAM into preoperative evaluation might help identify patients at higher risk for POP recurrence, allowing for optimized patient management and care.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"85 ","pages":"Article 106870"},"PeriodicalIF":2.1,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143790958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Determinants of clinical success and complications in fluoroscopy-guided self-expandable metal stent for treatment of malignant rectal obstruction","authors":"Y. Wan ,&nbsp;M.-S. Zou ,&nbsp;D. Li ,&nbsp;H.-H. Wu ,&nbsp;B. Zhang","doi":"10.1016/j.crad.2025.106869","DOIUrl":"10.1016/j.crad.2025.106869","url":null,"abstract":"<div><h3>Aim</h3><div>To evaluate the safety and efficacy of self-expanding metal stents (SEMS) in the management of patients with malignant rectal obstruction (MRO), as well as to identify the factors contributing to clinical failure and associated complications.</div></div><div><h3>MATERIALS AND METHODS</h3><div>Cases of MRO from September 2014 to July 2024 were retrospectively collected and reviewed. Patient data were analyzed to identify determinants influencing clinical success rates and short-term complications. Subsequently, the log-rank test and Cox proportional hazards model were employed to investigate factors affecting stent patency in palliative treatment.</div></div><div><h3>RESULTS</h3><div>A cohort of 66 MRO patients was included in the analysis. The technical and clinical success rates were observed to be 98.5% and 95.5%, respectively. Multivariate analysis identified the time of obstruction as a significant predictor for clinical success rate (OR = 1.081; 95% CI = 1.081 to 1.148; p=0.010) and the incidence of short-term complications (OR = 1.061; 95% CI = 1.008 to 1.116; p=0.022). In a cohort of 38 patients undergoing palliative treatment, Cox regression analysis identified postoperative chemotherapy as the significant determinant influencing stent patency duration (OR = 0.25; 95% CI = 0.082 to 0.762; p=0.015).</div></div><div><h3>CONCLUSION</h3><div>For MRO patients, SEMS constitutes an efficacious and successful therapeutic approach. Prompt alleviation of the obstruction is associated with a high rate of clinical success and a reduced incidence of short-term complications. Furthermore, in patients undergoing palliative treatment, the administration of postoperative chemotherapy has been shown to extend the duration of stent patency.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"84 ","pages":"Article 106869"},"PeriodicalIF":2.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776992","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic accuracy of TVUS and MRI in the preoperative evaluation of myometrial infiltration in patients with endometrial cancer: A meta-analysis","authors":"Y. Jin,&nbsp;C. Zhou","doi":"10.1016/j.crad.2025.106868","DOIUrl":"10.1016/j.crad.2025.106868","url":null,"abstract":"<div><h3>AIM</h3><div>The incidence of endometrial cancer is on the rise worldwide. Accurate preoperative evaluation of myometrial infiltration is crucial for developing treatment strategies. This study compares the diagnostic accuracy of transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI) for myometrial infiltration (MI) in endometrial cancer (EC).</div></div><div><h3>MATERIALS AND METHODS</h3><div>We performed a systematic review and meta-analysis of observational studies, identified by screening Web of Science, SCOPUS, MEDLINE, PubMed, Google Scholar, and EMBASE databases. Studies published between January 1964 and June 2024 comparing the diagnostic accuracy of TVUS and MRI for MI were included. The data analysis focused on sensitivity, specificity, and overall diagnostic accuracy.</div></div><div><h3>RESULTS</h3><div>Twenty-two studies in EC patients were included. The diagnostic odds ratio (OR) for TVUS and for MRI was 18 (95% CI: 22-26) and 20 (95% CI: 14-28), respectively. TVUS was associated with a sensitivity and specificity of 76% (95% CI: 72-82%) and 84% (95% CI: 79-88%), respectively, while MRI had a sensitivity and specificity of 79% (95% CI: 73-84%) and 84% (95% CI: 80-88%), respectively. The area under the receiver operating characteristic curve (AUCROC) was 0.88 for TVUS and 0.89 for MRI. No significant publication bias was detected.</div></div><div><h3>CONCLUSIONS</h3><div>Both TVUS and MRI demonstrated comparable diagnostic accuracy for the preoperative evaluation of MI in EC.</div></div>","PeriodicalId":10695,"journal":{"name":"Clinical radiology","volume":"85 ","pages":"Article 106868"},"PeriodicalIF":2.1,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}