Current Medical Imaging Reviews最新文献

{"title":"A Comparative Study of CT-Guided Radiofrequency Ablation and Targeted Therapy: Intervention Efficacy and Survival Rates in Lung Cancer Patients.","authors":"Tianyu Zhao, Chunjing Zhang, Hang Dai, Jingyu Li, Liguo Hao, Yanan Liu","doi":"10.2174/0115734056311827241211092432","DOIUrl":"https://doi.org/10.2174/0115734056311827241211092432","url":null,"abstract":"<p><strong>Objective: </strong>The study aims to evaluate the clinical efficacy of CT-guided radiofrequency ablation in conjunction with targeted therapy in lung cancer patients.</p><p><strong>Method: </strong>We retrospectively analyzed 80 lung cancer patients. They were stratified into the Observation Group (OG, n=40, treated with CT-guided radiofrequency ablation in conjunction with targeted therapy) and the Control Group (CG, n=40, treated solely with targeted therapy).</p><p><strong>Results: </strong>The OG group reported 4 cases of Complete Response (CR), 24 cases of Partial Response (PR), 10 cases of Stable Disease (SD), and 2 cases of Progressive Disease (PD). The Overall Response Rate (ORR) was 70.00% (28/40), and the Disease Control Rate (DCR) was 95.00% (38/40). In contrast, the CG group exhibited 3 cases of CR, 20 cases of PR, 12 cases of SD, and 5 cases of PD. The ORR was 57.50% (23/40), and the DCR was 87.50% (35/40). The ORR and DCR in the OG group were significantly higher than those in the CG group. After 6 weeks of treatment, the levels of SCC, CEA, and CA125 in the OG group were significantly lower than those in the CG group; The CD4+ levels in the OG group were significantly higher and the CD8+ levels significantly lower than those in the CG group. A 24-month follow-up showed that the survival rate of the OG group was 47.50% (19/40), which was significantly higher than that of the CG group at 27.50% (11/40).</p><p><strong>Conclusion: </strong>CT-guided radiofrequency ablation and targeted therapy have been proven effective in treating lung cancer.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933557","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":"Machine-Learning Based Computed Tomography Radiomics Nomgram For Predicting Perineural Invasion In Gastric Cancer.","authors":"Pei Huang, Sheng Li, Zhikang Deng, Fangfang Hu, Di Jin, Situ Xiong, Bing Fan","doi":"10.2174/0115734056323323250102073559","DOIUrl":"10.2174/0115734056323323250102073559","url":null,"abstract":"<p><strong>Objective: </strong>The aim of this study was to develop and validate predictive models for perineural invasion (PNI) in gastric cancer (GC) using clinical factors and radiomics features derived from contrast-enhanced computed tomography (CE-CT) scans and to compare the performance of these models.</p><p><strong>Methods: </strong>This study included 205 GC patients, who were randomly divided into a training set (n=143) and a validation set (n=62) in a 7:3 ratio. Optimal radiomics features were selected using the least absolute shrinkage and selection operator (LASSO) algorithm. A radiomics model was constructed utilizing the optimal among five machine learning filters, and a radiomics score (rad-score) was computed for each participant. A clinical model was built based on clinical factors identified through multivariate logistic regression. Independent clinical factors were combined with the radscore to create a combined radiomics nomogram. The discrimination ability of the models was evaluated by receiver operating characteristic (ROC) curves and the DeLong test.</p><p><strong>Results: </strong>Independent predictive factors of the clinical model included tumor T stage, N stage, and tumor differentiation, with AUC values of 0.777 and 0.809 in the training and validation sets. The radiomics model was constructed using the support vector machine (SVM) classifier with the best AUC (0.875 in the training set and 0.826 in the validation set). The combined radiomics nomogram, which combines independent clinical predictors and the rad-score, demonstrated better predictive performance (AUC=0.889 in the training set; AUC=0.885 in the validation set).</p><p><strong>Conclusion: </strong>The nomogram integrating independent clinical predictors and CE-CT radiomics was constructed to predict PNI in GC. This model demonstrated favorable performance and could potentially assist in prognosis evaluation and clinical decision-making for GC patients.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":"e15734056323323"},"PeriodicalIF":1.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985586","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":"Integration of Three-dimensional Visualization Reconstruction Technology with Problem-Based Learning in the Clinical Training of Resident Physicians Specialized in Pheochromocytoma.","authors":"Dong Wang","doi":"10.2174/0115734056327236250101052226","DOIUrl":"10.2174/0115734056327236250101052226","url":null,"abstract":"<p><strong>Objective: </strong>We examined the effectiveness of integrating three-dimensional (3D) visualization reconstruction technology with Problem-Based Learning (PBL) in the clinical teaching of resident physicians focusing on pheochromocytoma.</p><p><strong>Methods: </strong>Fifty resident physicians specializing in urology at Peking Union Medical College Hospital were randomly divided into two groups over the period spanning January 2022 to January 2024: an experimental group and a control group. The experimental group underwent instruction utilizing a pedagogical approach that integrated 3D visualization reconstruction technology with PBL, while the control group used a traditional teaching model. A comparative analysis of examination performance and teaching satisfaction between both groups of resident physicians was conducted to assess the efficacy of the integrated 3D visualization and PBL teaching methods in clinical instruction.</p><p><strong>Results: </strong>The experimental group demonstrated superior performance in both theoretical assessment and clinical skills evaluation, along with heightened levels of teaching satisfaction compared to the control group, with statistically significant differences (p < 0.05). Additionally, the experimental group exhibited markedly higher scores in both theoretical examinations and practical assessments compared to their counterparts in the control group (p < 0.05). The results of theoretical examinations for the experimental group and the control group were 92.15±3.22 and 81.09±4.46, respectively (< 0.0001). The results of practical examinations for the experimental group and the control group were 90.17±3.48 and 70.75±5.11, respectively (< 0.0001).</p><p><strong>Conclusion: </strong>In the clinical teaching of training resident physicians specializing in urology for the management of pheochromocytoma, the integration of 3D visualization reconstruction technology with the PBL method significantly enhanced the teaching efficacy, improving both the quality of instruction and the level of satisfaction among participants.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":"e15734056327236"},"PeriodicalIF":1.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143588084","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":"Intracranial Structural Malformations in Children in Tibet: CT and MRI Findings in a Single Tertiary Center.","authors":"Xuan Yin, Dawa Ciren, Ciren Guojie, Guofu Zhang, Jimei Wang, He Zhang","doi":"10.2174/0115734056321642241213103658","DOIUrl":"10.2174/0115734056321642241213103658","url":null,"abstract":"<p><strong>Objectives: </strong>The objective of this study was to summarize the findings of children's intracranial congenital or developmental malformations found during imaging procedures in the Tibetan plateau.</p><p><strong>Methods: </strong>We retrospectively reviewed the imaging data of the suspected patients who presented with the central nervous system (CNS) malformations and were enrolled either through the clinic or after ultrasound examinations between June 2019 and June 2023 in our institution. All imaging data were interpreted by two experienced radiologists through consensus reading.</p><p><strong>Results: </strong>In this study, we recruited 36 patients, including two neonates, 17 infants and 17 children. Seven cases underwent an MRI examination, while the others had a CT scan. Polygyria and pachygyria malformation were the most common type of congenital neurological malformations (7 cases, 31.8%), followed by cystic changes of the cerebral parenchyma (3 cases, 13.6%). Cerebral atrophy was the most common type of secondary CNS abnormality(8 cases, 57.1%), followed by communicative hydrocephalus (3 cases, 21.4%). Five patients in the congenital group and 4 patients in the secondary group had complex malformations. In the current study group, there were 8 deaths, 12 cases with neurological sequelae, 1 case with normal development, and 15 cases lost to follow-up. There were no significant differences between the primary and secondary CNS groups in terms of the outcome for both the infants and children groups.</p><p><strong>Conclusions: </strong>CNS malformations in the Tibetan Plateau are associated with high mortality and morbidity rates. Better utilization of imaging modalities could help design tailored treatments as early as possible.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":"e15734056321642"},"PeriodicalIF":1.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142933650","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":"Application of Tuning-ensemble N-Best in Auto-Sklearn for Mammographic Radiomic Analysis for Breast Cancer Prediction.","authors":"Faikah Awang Ismail, Muhammad Khalis Abdul Karim, Siti Izzatul Akma Zaidon, Kaltham Abdulwahid Noor","doi":"10.2174/0115734056400080250722024127","DOIUrl":"10.2174/0115734056400080250722024127","url":null,"abstract":"<p><strong>Introduction: </strong>Breast cancer is a major cause of mortality among women globally. While mammography remains the gold standard for detection, its interpretation is often limited by radiologist variability and the challenge of differentiating benign and malignant lesions. The study explores the use of Auto- Sklearn, an automated machine learning (AutoML) framework, for breast tumor classification based on mammographic radiomic features.</p><p><strong>Methods: </strong>244 mammographic images were enhanced using Contrast Limited Adaptive Histogram Equalization (CLAHE) and segmented with Active Contour Method (ACM). Thirty-seven radiomic features, including first-order statistics, Gray-Level Co-occurance Matrix (GLCM) texture and shape features were extracted and standardized. Auto-Sklearn was employed to automate model selection, hyperparameter tuning and ensemble construction. The dataset was divided into 80% training and 20% testing set.</p><p><strong>Results: </strong>The initial Auto-Sklearn model achieved an 88.71% accuracy on the training set and 55.10% on the testing sets. After the resampling strategy was applied, the accuracy for the training set and testing set increased to 95.26% and 76.16%, respectively. The Receiver Operating Curve and Area Under Curve (ROC-AUC) for the standard and resampling strategy of Auto-Sklearn were 0.660 and 0.840, outperforming conventional models, demonstrating its efficiency in automating radiomic classification tasks.</p><p><strong>Discussion: </strong>The findings underscore Auto-Sklearn's ability to automate and enhance tumor classification performance using handcrafted radiomic features. Limitations include dataset size and absence of clinical metadata.</p><p><strong>Conclusion: </strong>This study highlights the application of Auto-Sklearn as a scalable, automated and clinically relevant tool for breast cancer classification using mammographic radiomics.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":"e15734056400080"},"PeriodicalIF":1.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144776916","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":"A Case Report on the Dramatic Response of ¹⁷⁷Lu-PSMA Therapy for Metastatic Prostate Cancer.","authors":"Aysenur Sinem Erdogan, Haluk Sayan, Bedri Seven, Berna Okudan","doi":"10.2174/0115734056362468250709045212","DOIUrl":"10.2174/0115734056362468250709045212","url":null,"abstract":"<p><strong>Introduction: </strong>In nuclear medicine, Prostate-specific Membrane Antigen (PSMA) is a potential target for theranostics. Offering superior diagnostic accuracy to conventional imaging in prostate cancer (PCa), Gallium-68 labeled PSMA (⁶⁸Ga-PSMA) positron emission tomography/computed tomography (PET/CT) is considered the new standard of care in PCa management. Tumor cells identified as PSMA-avid on PET/CT imaging can be targeted and eliminated with PSMA-labeled Lutetium-177 (¹⁷⁷Lu-PSMA) therapy.</p><p><strong>Case presentation: </strong>A sixty-eight years old patient who had metastatic castration-resistant PCa was reported in this study. Prior to receiving ¹⁷⁷Lu-PSMA therapy, the patient's PSA level was 358 ng/ml, and experienced extensive bone discomfort. Following ten cycles of ¹⁷⁷Lu-PSMA therapy, exceptional results were observed.</p><p><strong>Conclusion: </strong>¹⁷⁷Lu-PSMA therapy is likely to result in significantly better outcomes if first- or second-line treatments preserve the patient's bone marrow reserve or if the therapy is administered at earlier stages of the disease.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":"e15734056362468"},"PeriodicalIF":1.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144683543","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":"Challenges and Advances in Classifying Brain Tumors: An Overview of Machine, Deep Learning, and Hybrid Approaches with Future Perspectives in Medical Imaging.","authors":"Faisal Alshomrani","doi":"10.2174/0115734056365191250602124819","DOIUrl":"10.2174/0115734056365191250602124819","url":null,"abstract":"<p><p>Accurate brain tumor classification is essential in neuro-oncology, as it directly informs treatment strategies and influences patient outcomes. This review comprehensively explores machine learning (ML) and deep learning (DL) models that enhance the accuracy and efficiency of brain tumor classification using medical imaging data, particularly Magnetic Resonance Imaging (MRI). As a noninvasive imaging technique, MRI plays a central role in detecting, segmenting, and characterizing brain tumors by providing detailed anatomical views that help distinguish various tumor types, including gliomas, meningiomas, and metastatic brain lesions. The review presents a detailed analysis of diverse ML approaches, from classical algorithms such as Support Vector Machines (SVM) and Decision Trees to advanced DL models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and hybrid architectures that combine multiple techniques for improved performance. Through comparative analysis of recent studies across various datasets, the review evaluates these methods using metrics such as accuracy, sensitivity, specificity, and AUC-ROC, offering insights into their effectiveness and limitations. Significant challenges in the field are examined, including the scarcity of annotated datasets, computational complexity requirements, model interpretability issues, and barriers to clinical integration. The review proposes future directions to address these challenges, highlighting the potential of multi-modal imaging that combines MRI with other imaging modalities, explainable AI frameworks for enhanced model transparency, and privacy-preserving techniques for securing sensitive patient data. This comprehensive analysis demonstrates the transformative potential of ML and DL in advancing brain tumor diagnosis while emphasizing the necessity for continued research and innovation to overcome current limitations and ensure successful clinical implementation for improved patient care.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":"e15734056365191"},"PeriodicalIF":1.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287124","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":"Clinical Efficacy of Ultrasound Guidance in Brachial Plexus Nerve Conduction Study: A Comparative Analysis.","authors":"Zheyuan Zhang, Xiuli Li, Guangju Qi, Huabin Zhang, Xinhong Feng, Zhiyong Bai","doi":"10.2174/0115734056377599250717101905","DOIUrl":"10.2174/0115734056377599250717101905","url":null,"abstract":"<p><strong>Introduction: </strong>Brachial plexopathy is a diagnostically challenging condition that requires a comprehensive evaluation, including physical examination, imaging, and Electrodiagnostic (EDx).</p><p><strong>Testing: </strong>Ultrasound guidance may improve the efficiency and precision of nerve conduction studies by addressing the limitations of blind techniques, such as discomfort and inaccurate localization.</p><p><strong>Methods: </strong>We prospectively enrolled 30 patients undergoing electrodiagnostic testing. The left upper limb was examined with ultrasound guidance (Group A), while the right upper limb underwent the blind method (Group B). The examined nerves included the median, ulnar, radial, medial and lateral antebrachial cutaneous, axillary, musculocutaneous, suprascapular, and long thoracic nerves. Stimulation duration, number of stimulation attempts, average current, and total examination time were recorded. The differences in data between the two groups were compared and analyzed.</p><p><strong>Results: </strong>Group A demonstrated significantly lower stimulation duration (156.70±50.13 vs. 260.17±53.19 s), fewer stimulation attempts (17.73±3.94 vs. 25.80±5.23), and lower average current [32.45 (30.28, 40.13) vs. 42.75 (37.78,50.68) mA] compared to Group B (all P < 0.001). No significant difference was observed in total examination time (387.40 ± 33.72 vs. 372.00 ± 47.01 s; P = 0.150).</p><p><strong>Discussion: </strong>Ultrasound guidance improves procedural precision and reduces the need for repeated stimulations and higher electrical intensities. These benefits are achieved without extending the total examination time, making it a feasible and patient-friendly approach for routine use in clinical neurophysiology.</p><p><strong>Conclusion: </strong>Ultrasound-guided nerve conduction studies of the brachial plexus enhance procedural efficiency and patient comfort compared to the blind method. Further large-scale studies are recommended to validate these findings and assess broader clinical applications.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":"e15734056377599"},"PeriodicalIF":1.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144745959","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":"Clinical Value of Nomogram Model based on Multimodality Ultrasound Image Characteristics Differentiating Benign and Malignant Breast Masses.","authors":"Jiaxin Yan, Jianting Zheng, Shurong Chen, Jiahua Zhao, Yangfan Han, Bo Liang","doi":"10.2174/0115734056378619250722034152","DOIUrl":"10.2174/0115734056378619250722034152","url":null,"abstract":"<p><strong>Introduction: </strong>Finding a convenient, accurate, and non-invasive method to differentiate between benign and malignant breast masses is especially important for clinical practice, and this study aimed to explore the clinical value of Nomogram model based on multimodality ultrasound image characteristics and clinical baseline data for detecting benign and malignant breast masses.</p><p><strong>Methods: </strong>A retrospective analysis of the clinical data and ultrasound imaging characteristics of 132 patients with breast masses. Data were randomly divided into a training set (92 cases) and a validation set (40 cases) in a ratio of 7:3. Logistic regression was applied to the training set data to analyze risk factors related to malignant breast masses and to construct a Nomogram model. Clinical applicability of the model was evaluated and validated.</p><p><strong>Results: </strong>In training set, ROC cure analysis results showed that AUC of Nomogram model constructed with CA15-3, CA125, E_max, E_sd, Ratio of Elastic Moduli, Elasticity Scoring, blurry boundaries, irregular shape, penetrating vessels, and stiff rim sign was 1.00 (95%CI: 0.99-1.00), Hosmer- Lemeshow goodness-of-fit test result showed predicted curve closely aligns with ideal curve, and DCA showed that Nomogram model exhibited high net benefits across multiple thresholds. The clinical applicability of the Nomogram model was also confirmed with consistent results in the validation set.</p><p><strong>Discussion: </strong>In this study, we constructed a Nomogram model using risk factors associated with malignant breast masses, and the model showed good clinical applicability in distinguishing benign and malignant breast masses. However, this study is a single-center study, and the sample size of the dataset is relatively small, which, to some extent, limits the breadth and depth of validation.</p><p><strong>Conclusion: </strong>The Nomogram model built on multimodal ultrasound imaging features and clinical data demonstrates a strong discriminative ability for malignant breast masses, allowing patients to achieve a significant net benefit.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":"e15734056378619"},"PeriodicalIF":1.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144790690","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":"Diagnostic Performance of SWE and Predictive Models Based on SWE for Post- Hepatectomy Liver Failure: A Systematic Review and Meta-analysis.","authors":"Jiaxu Liang, Fukun Shi, Lan Zhang, Suo Yin, Yong Chen","doi":"10.2174/0115734056379123250626163120","DOIUrl":"10.2174/0115734056379123250626163120","url":null,"abstract":"<p><strong>Background: </strong>Post-hepatic resection liver failure (PHLF) remains one of the most serious complications after hepatic resection, with an overall morbidity rate as high as 32% and an approximate 5% mortality. Previous studies demonstrate the potential of shear wave elastography (SWE) to predict PHLF. This meta-analysis aimed to evaluate the diagnostic accuracy of SWE in identifying liver failure after hepatectomy.</p><p><strong>Methods: </strong>A comprehensive search was performed across PubMed/Medline, Embase, and Web of Science to identify studies assessing the diagnostic accuracy of SWE for predicting PHLF. The combined sensitivity, specificity, and the hierarchical summary receiver operating characteristic curve (HSROC) for SWE in detecting PHLF in liver resection patients. The Quality Assessment of Diagnostic Accuracy Studies tool was used to evaluate the quality of the studies included in the analysis. Heterogeneity was explored through sensitivity analysis, univariable meta-regression and subgroup analysis.</p><p><strong>Results: </strong>This meta-analysis included a total of 13 studies involving 2985 patients. For quantitative analysis. The combined sensitivities and specificities of SWE for detecting post-hepatectomy liver failure were 0.81 and 0.68, respectively. The HSROC value for SWE was 0.82. Significant heterogeneity (I² = 80.22) was observed in pooled specificity. Meta-regression and subgroup analyses suggest that differences in the proportion of patients with HCC and in the diagnostic criteria for PHLF may account for the observed heterogeneity. For the qualitative analysis, six predictive models based on SWE were included, and their AUCs were 0.80-0.915.</p><p><strong>Conclusion: </strong>Both SWE alone and SWE-based prediction models appear to accurately detect PHLF and help to categorize patients into high- and low-risk groups. It may also assist surgeons in identifying the best candidates for liver resection and enhancing perioperative management.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":"e15734056379123"},"PeriodicalIF":1.1,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561958","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}