Current Medical Imaging Reviews最新文献

{"title":"A Systematic Review and Meta-Analysis of Survival Prediction in Glioblastoma Patients Using Advanced MRI Techniques.","authors":"Zayd Osama Jastaniah, Mohammed Ahmed Alsubhi, Yasser Noorelahi, Rakan Nahedh H Almutairi, Saud Saeed N Alasmari, Sarah Hamed Talebi, Leen Yahya Alqahtany, Bedoor Obidallah Alghanmi, Muaath Hamdan AlJehani, Rana Anas Beser, Abdulsalam Mohammed Aleid","doi":"10.2174/0115734056396670251114101647","DOIUrl":"https://doi.org/10.2174/0115734056396670251114101647","url":null,"abstract":"<p><strong>Introduction: </strong>Glioblastoma (GBM) is an aggressive brain tumor with a dismal prognosis. Recent advances in radiomics and machine learning (ML) applied to magnetic resonance imaging (MRI) have demonstrated promising potential in enhancing clinical decision-making and prognostic accuracy. This systematic review and meta-analysis aimed to evaluate the predictive performance of radiomics and ML techniques applied to pre-treatment MRI data in glioblastoma prognosis.</p><p><strong>Methods: </strong>A comprehensive literature search was conducted across MEDLINE, EMBASE, and the Cochrane Central Register of Controlled Trials up to March 2024 for studies using radiomics or ML techniques applied to pre-treatment MRI scans to predict progression-free survival (PFS) and overall survival (OS) in glioblastoma patients. The primary outcome was the area under the receiver operating characteristic curve (AUC). Study quality was assessed using the QUADAS-2 tool, meta-analysis employed a random-effects model, and heterogeneity was evaluated using the I2 statistic.</p><p><strong>Results: </strong>Sixteen studies comprising a total of 2,342 patients were included. MRI-based machine learning models demonstrated high predictive performance for glioblastoma prognosis (AUC: 0.71-0.92), with a tendency to outperform radiomics-based approaches (AUC: 0.68-0.88). A meta-analysis of 12 studies yielded a pooled AUC of 0.78 (95% CI: 0.74-0.82; P < 0.001) for PFS prediction with moderate heterogeneity (I2 = 59%). Four studies focused on OS prediction, showing no heterogeneity (I2 = 0%) and a pooled AUC of 0.81 (95% CI: 0.77-0.85; P < 0.001). Subgroup analysis revealed that ML models (AUC: 0.83 [95% CI: 0.78-0.87]) statistically outperformed radiomics-based models (AUC: 0.76 [95% CI: 0.71-0.80]) for PFS prediction (P = 0.02).</p><p><strong>Conclusion: </strong>Radiomics and ML approaches based on pre-treatment MRI are promising tools for predicting survival outcomes in glioblastoma patients, with ML models demonstrating a slight edge over radiomics for PFS prediction. Standardized protocols and larger multi-center studies are warranted to facilitate clinical adoption.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845692","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-Resolution Imaging Analysis of CT Severity Index in COVID-19 Patients: Impact of Age and Sex.","authors":"Mahesh Chandra Bhatt, Arun Upmanyu, Subhash Chand Bansal, Ayush Dogra","doi":"10.2174/0115734056369445251205082519","DOIUrl":"https://doi.org/10.2174/0115734056369445251205082519","url":null,"abstract":"<p><strong>Introduction: </strong>COVID-19 is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). HRCT chest imaging has been widely used during the COVID-19 pandemic. The CT severity score is helpful in assessing disease severity, which may accelerate the diagnostic workflow in COVID-19 patients. The aim of this study is to correlate the chest CT severity score of pulmonary pneumonia in COVID-19 patients with different age groups and sexes.</p><p><strong>Methods: </strong>This retrospective study investigated the thoracic high-resolution computed tomography (HRCT) findings of 229 laboratory-confirmed COVID-19 patients. The cohort comprised 168 (73.4%) males and 61 (26.6%) females, with ages ranging from 18 to 88 years (median age 47 years). Patients were stratified into four age groups: 18-30, 31-45, 46-60, and >60 years. All patients underwent HRCT of the chest using a Canon Alexion 16- slice CT scanner with a low-dose protocol. Two independent radiologists evaluated the HRCT scans, and a CT severity score was calculated for each patient based on the extent of pulmonary involvement within each lung lobe. Scores were then compared across different age groups.</p><p><strong>Results: </strong>HRCT chest findings in coronavirus infection included small patchy opacities; ground-glass opacity and consolidation were observed in 83% of patients. The present study indicates a positive correlation between higher CT severity scores and older age groups, as well as male gender, compared with younger and female patients.</p><p><strong>Discussion: </strong>The study showed that 73.4% of patients were male and 26.6% were female, and that more severe CT lung infection (higher CT severity scores) was significantly associated with male gender. More severe pulmonary infection was also more common in patients above 60 years of age. These findings are in agreement with other studies reporting that COVID-19 infection affects males more severely than females.</p><p><strong>Conclusion: </strong>HRCT chest imaging provides valuable diagnostic information regarding disease severity, percentage of lung involvement, and extent of disease, which is useful for guiding treatment and prognosis.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845928","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":"CT Radiomics for the Early Identification of Fungal Co-infection in Immunocompromised Patients with Viral Pneumonia.","authors":"Le Zhou, Renjun Huang, Xinbing Zheng, Jie Xu, Qinghua Gu, Xiaoping Huang, Yonggang Li","doi":"10.2174/0115734056443124260427113549","DOIUrl":"https://doi.org/10.2174/0115734056443124260427113549","url":null,"abstract":"<p><strong>Introduction: </strong>This study aimed to establish and validate CT-based radiomics models combined with clinical data to identify Fungal Co-Infections (FCI) in immunocompromised patients with Viral Pneumonia (VP).</p><p><strong>Materials and methods: </strong>A total of 406 patients (VP: 283; FCI: 123) from two hospitals were retrospectively enrolled and divided into training (n = 218), testing (n = 96), and external validation (n = 92) cohorts. Radiomics features were extracted from chest CT images. Feature selection was performed using the Least Absolute Shrinkage And Selection Operator (LASSO), and logistic regression models were built with clinical, radiomics, and combined inputs. Model performance was assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration, and Decision Curve Analysis (DCA).</p><p><strong>Results: </strong>The combined model achieved AUCs of 0.981 (95% CI: 0.959 - 0.992), 0.845 (95% CI: 0.762 - 0.950), and 0.835 (95% CI: 0.715 - 0.937) in the training, testing, and external validation cohorts, respectively, and consistently outperformed clinical-only and radiomics-only models.</p><p><strong>Discussion: </strong>The model identified characteristic clinical and imaging differences between VP and FCI, including higher neutrophil counts, lower lymphocyte counts, and imaging markers such as reversed halo sign and solid nodules in FCI. These findings support the potential of radiomics as a noninvasive tool for early detection and risk stratification.</p><p><strong>Conclusion: </strong>CT-based radiomics provides an effective approach for differentiating VP and FCI in immunocompromised patients, with potential to improve diagnosis and clinical management.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845775","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":"Web of Science Bibliometrics Analysis of Magnetic Resonance Imaging Research Advances in Multiple Sclerosis.","authors":"Xiaoxing Li, Dingbang Peng, Xiao Liang, Yao Wang, Chen Yang, Lin Wu, Fuqing Zhou","doi":"10.2174/0115734056439145260413194039","DOIUrl":"https://doi.org/10.2174/0115734056439145260413194039","url":null,"abstract":"<p><strong>Introduction: </strong>Comprehensive bibliometric analysis of magnetic resonance imaging applications in multiple sclerosis research remains scarce despite exponential growth. This study maps 25-year global MS-MRI trends (2000-2024) to identify transformative shifts.</p><p><strong>Methods: </strong>We analyzed 8,038 publications from the Web of Science Core Collection using VOSviewer, Bibliometrix, and CiteSpace. Machine learning clustering quantified collaboration networks, while dual-map overlays and burst detection quantified interdisciplinary bridges and paradigm shifts.</p><p><strong>Results: </strong>Publication growth showed three phases: steady (2005-2011, +6.2%/year), accelerated (2011-2021, peak 480 publications), and stabilization (2022-2024), with recent decline linked to diagnostic criteria simplification and artificial intelligence-driven consolidation. The USA dominated total output (24.2%), while the UK led international collaboration (44.2% multi-country publications). China's unique focus on psychoneuroimmunology contrasts with Western clinical-translational priorities. The strongest interdisciplinary link connected Neurology/Sports/Ophthalmology and Molecular/Biology/Genetics fields (Z-score = 5.3). Artificial intelligence drove paradigm shifts, with deep learning showing the highest keyword burst strength (413.27). Central authors (e.g., Massimo Filippi, Frederik Barkhof) bridged magnetic resonance imaging biomarkers and therapeutic innovation.</p><p><strong>Discussion: </strong>MS-MRI research is evolving from descriptive observations to AI-driven precision medicine. Future success relies on a closed-loop paradigm integrating ultra-high-field MRI and multi-omics.</p><p><strong>Conclusion: </strong>This analysis reveals: (1) Magnetic resonance imaging-artificial intelligence-biomarker integration resolves clinical-radiological paradoxes, enabling dynamic patient stratification; (2) ultra-high-field magnetic resonance imaging and multi-omics provide a roadmap for precision neurology in therapy personalization; (3) global collaboration synergies may democratize advanced multiple sclerosis care.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845895","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":"Analysis of Risk Factors Related to Carotid Artery in Patients with Acute Ischemic Stroke.","authors":"Dandan Xiao, Hongjun Nie, Yaocheng Xiao, Liyun Chen, Yanfen Zhang","doi":"10.2174/0115734056320499260424094133","DOIUrl":"https://doi.org/10.2174/0115734056320499260424094133","url":null,"abstract":"<p><strong>Objective: </strong>Studying independent risk factors for carotid artery-related Acute Ischemic Stroke (AIS) in affected patients can help guide the clinical prevention and prognosis of AIS.</p><p><strong>Methods: </strong>In this retrospective study, 81 patients who were admitted to our center for routine carotid ultrasound and contrast-enhanced ultrasound examinations were enrolled. The patients were assigned to the study and control groups based on whether they had AIS symptoms. Multivariate logistic regression was used to analyze the correlation between risk factors and carotid artery-related AIS.</p><p><strong>Results: </strong>Significant differences in Intraplaque Neovascularization (IPN) grade, vascular stenosis, different age stages, plaque length and diameter, and hypertension were observed between the two groups (P < 0.05). Two sonographers were satisfactorily consistent in IPN grading diagnosis (Kappa = 0.763). According to the multivariate logistic regression analysis, the IPN grade and hypertension were independent risk factors for carotid artery-associated AIS (P < 0.05). Receiver Operating Characteristic (ROC) analysis showed that IPN grading demonstrated better discriminative performance for AIS than lumen stenosis, with an Area Under the Curve (AUC) of 0.74 versus 0.65.</p><p><strong>Discussion: </strong>In the study group, the carotid plaques of AIS patients were mostly of IPN grade III-IV. The number of patients with IPN > II was significantly higher in the study group than in the control group (33.3% (27/81) vs 7.4% (6/81); P < 0.05). The accuracy, sensitivity, specificity, and positive and negative predictive values of carotid canal cavity stenosis were approximately 65.43%, 64.29%, 66.67%, 67.50%, and 63.41%, respectively. For patients with IPN > II, the values for the aforementioned parameters were 76.54%, 81.81%, 72.92%, 85.36%, and 67.50%, respectively. Statistically significant differences in sensitivity and negative predictive value were observed between the two groups (P < 0.05).</p><p><strong>Conclusion: </strong>IPN grading demonstrates a stronger association and higher discriminative ability for AIS than for carotid stenosis. It may provide valuable information for early clinical identification, risk stratification, and prevention of carotid artery-related AIS.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845714","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":"Automatic Recognition Algorithm for Renal Tumors and Cysts in CT Images using Mamba and YOLO11.","authors":"Jiang Jiali, Chen Zhaoxue","doi":"10.2174/0115734056450311260425112727","DOIUrl":"https://doi.org/10.2174/0115734056450311260425112727","url":null,"abstract":"<p><strong>Introduction: </strong>Renal tumors pose a serious threat to patient health and survival, highlighting the importance of early detection and accurate diagnosis. In clinical practice, differentiating renal tumors from cysts in CT images remains challenging due to similar imaging characteristics and complex anatomical structures. The aim of this study is to develop an improved detection method for renal tumors and cysts based on an enhanced YOLO11 framework.</p><p><strong>Methods: </strong>An improved YOLO11-based detection model incorporating a Mamba-inspired architecture is proposed. A gated state-space modeling module is introduced into the backbone network to enhance the modeling capability of spatial and channel information and effectively focus on key regional features. A Dynamic Upsampling module (DySample) is then adopted in the neck network to improve multi-scale feature fusion. In addition, a Multi-Dimensional Local Channel Attention (MLCA) mechanism is integrated before the detection head to jointly refine spatial and channel features, thereby enhancing the localization capability for lesion areas.</p><p><strong>Results: </strong>Experimental results demonstrate that the proposed method achieves a precision of 0.837, a recall of 0.636, mAP@0.5 of 0.732, and mAP@0.5:0.95 of 0.505. Compared with the YOLO11 model, these metrics are improved by 3.1%, 0.1%, 2.1%, and 2.5%, respectively, indicating an overall enhancement in detection performance.</p><p><strong>Discussion: </strong>YOLO11-Mamba has achieved improvements in detection accuracy and localization performance, but there are still some potential limitations. Among these, the introduction of state space models and attention mechanisms has increased the model's parameter count and computational complexity to some extent, which may pose challenges for clinical deployment, pointing the way for future research.</p><p><strong>Conclusion: </strong>The proposed method demonstrates effective performance in the detection of renal tumors and cysts from CT images. The results show fewer missed detections and improved lesion localization accuracy, suggesting the proposed model is a promising tool for renal lesion detection and clinical imaging.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845681","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":"Contrast-Enhanced Ultrasound Features of Renal Hemangiomas: A Retrospective Descriptive Study.","authors":"Chunhong Yan, Siying Zhang, Mengna He, Jie Qiu, Jianjian Xiang, Tianan Jiang","doi":"10.2174/0115734056426319260403204532","DOIUrl":"https://doi.org/10.2174/0115734056426319260403204532","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to investigate the contrast-enhanced ultrasound (CEUS) imaging features of renal hemangiomas and to evaluate their potential role in improving preoperative diagnosis and differential diagnosis.</p><p><strong>Methods: </strong>In this retrospective study, clinical and ultrasound data from 20 patients with surgically confirmed renal hemangiomas (22 lesions) were analyzed. All patients underwent preoperative conventional ultrasound. Among them, 6 patients (7 lesions) additionally underwent CEUS examination within one month before surgery. Standardized ultrasound techniques and equipment were employed, with focused analysis on the enhancement patterns and hemodynamic characteristics observed on CEUS.</p><p><strong>Results: </strong>The cohort comprised 10 men and 10 women (mean age 50 years). Most lesions (13/22) were located in the renal medulla. On conventional ultrasound, lesions typically appeared as well-defined, round, hypoechoic nodules, with most showing no significant internal flow on color Doppler imaging. In the 6 patients who underwent CEUS, a characteristic pattern of peripheral nodular enhancement in the arterial phase, followed by progressive centripetal filling, was observed. Peak enhancement intensity was generally comparable to that of the surrounding renal parenchyma. Pathologically, anastomosing hemangioma and capillary hemangioma were the most common subtypes (9 cases each), with immunohistochemical profiles (CD31/CD34 positive, low Ki-67) consistent with benign behavior.</p><p><strong>Conclusion: </strong>The combination of conventional ultrasound and CEUS may enhance the preoperative evaluation of renal hemangiomas. CEUS demonstrates distinctive enhancement patterns that can aid in differentiating these rare benign tumors from other renal malignancies. However, these findings are preliminary and require validation in larger-scale studies.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789119","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":"Intrahepatic Splenosis Mimicking Hepatocellular Carcinoma: A Case Series of Eleven Patients.","authors":"Suzhen Li, Tianhao Zou, Zhiying Wang, Sisi Chen, Weimin Wang, Xing Zhou, Yang Gao, Guoliang Wang, Chen Zhang, Qichang Zheng, Shaobo Hu, Jianjun Xu","doi":"10.2174/0115734056443530260206213844","DOIUrl":"https://doi.org/10.2174/0115734056443530260206213844","url":null,"abstract":"<p><strong>Introduction: </strong>Intrahepatic splenosis is an extremely rare intrahepatic mass, which is easily misdiagnosed and mistreated. There are a few reports in the literature that intrahepatic splenosis mimicking hepatocellular carcinoma in a patient with elevated AFP. This study aims to analyze the diagnosis and treatment strategies of intrahepatic splenosis.</p><p><strong>Materials and methods: </strong>The clinical data of eleven patients with intrahepatic splenosis, diagnosed and treated at Wuhan Asia General Hospital and Union Hospital (Wuhan, China) between March 2012 and November 2024, were retrospectively analyzed. Enhanced CT imaging and enhanced MRI were used for the screening and diagnosis of liver lesions.</p><p><strong>Results: </strong>Of the eleven patients with intrahepatic splenosis, six cases were pure intrahepatic splenosis, and five cases included extrahepatic splenosis. Enhanced CT imaging or enhanced MRI showed intrahepatic splenosis lesions with uneven enhancement in the form of fast in and fast out. Two patients were misdiagnosed with hepatocellular carcinoma due to elevated AFP, but biopsy revealed intrahepatic splenosis, thus avoiding unnecessary resection. The size of the intrahepatic splenosis lesions ranged from 1.0 to 4.2 cm. None of the nine patients who underwent surgical resection had splenosis recurrences, and the patients with intrahepatic splenosis confirmed by liver biopsy did not show lesion progression during the active examination period.</p><p><strong>Discussion: </strong>Splenosis refers to the autotransplantation of viable splenic tissue into different anatomic compartments following splenic injury. The enhanced CT or MRI features of intrahepatic splenosis are similar to those of HCC. Selective hepatic arteriography may help differentiate intrahepatic splenosis from HCC. Percutaneous liver biopsy helps diagnose intrahepatic splenosis.</p><p><strong>Conclusion: </strong>In patients who have previously undergone splenectomy due to splenic trauma, it is important to consider the potential occurrence of intrahepatic splenosis upon the identification of intrahepatic lesions, and percutaneous liver biopsy is recommended. For individuals without clinical symptoms following a confirmed diagnosis of intrahepatic splenosis, no specific treatment is required.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789104","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":"The Predictive Value of Radiomics for Esophagotracheal Fistula after Radiotherapy in Esophageal Cancer.","authors":"Huiyao Chen, Yanglong Wu, Congcong Wu","doi":"10.2174/0115734056452685260414044447","DOIUrl":"https://doi.org/10.2174/0115734056452685260414044447","url":null,"abstract":"<p><strong>Introduction: </strong>Esophagotracheal Fistula (ETF) is a serious complication following radiotherapy for esophageal cancer, with treatment outcomes significantly worse than expected.</p><p><strong>Methods: </strong>Pre-radiotherapy CT images and clinical data from patients with esophageal malignancies treated at the Second Affiliated Hospital of Wenzhou Medical University between January 2015 and December 2023 were retrospectively analyzed. Tumor contours were manually delineated using 3D Slicer, and radiomic features were extracted using PyRadiomics. Features associated with ETF development (p<0.05) were identified via the Mann-Whitney U test and further refined using Least Absolute Shrinkage and Selection Operator (LASSO) regression to determine the final radiomic signature. Subsequently, univariate and multivariate binary logistic regression analyses were performed.</p><p><strong>Results: </strong>The study included 77 patients, 30 of whom developed ETF. Of the initial 845 radiomic features, 10 were significantly associated with ETF. Among clinical factors, the type of radiation therapy was an independent predictor for ETF. In the training cohort, the radiomics model achieved an AUC of 0.866 (95% CI: 0.7907-0.9402), with a sensitivity of 0.831 and specificity of 0.792. The combined model (radiomics + clinical features) achieved an AUC of 0.892 (95% CI: 0.8238-0.9601), sensitivity of 0.823, and specificity of 0.912. In the validation cohort, the radiomics model had an AUC of 0.736 (95% CI: 0.5781-0.8947), sensitivity of 0.833, and specificity of 0.621. The combined model achieved an AUC of 0.791 (95% CI: 0.6461-0.9354), sensitivity of 0.822, and specificity of 0.797.</p><p><strong>Discussion: </strong>The combination of radiomic and clinical features achieves excellent AUC performance and shows potential for the non-invasive prediction of ETF following radiotherapy in esophageal cancer patients.</p><p><strong>Conclusion: </strong>The model, combined with radiomic and clinical features, has great predictive value.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147789112","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":"Enhancing the Visibility of Multiple Sclerosis Lesions using Fused Images of Fluid Attenuated Inversion Recovery (FLAIR) and white matter Attenuated Inversion Recovery (WAIR) MRI Sequences.","authors":"Khalid Alanazi, Abdullah Abujamea, Muhammad Fahim Amjad, Almutairi Fahad, Ali Abdu","doi":"10.2174/0115734056437290260309135652","DOIUrl":"https://doi.org/10.2174/0115734056437290260309135652","url":null,"abstract":"<p><strong>Introduction: </strong>Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system in which accurate lesion detection is essential for diagnosis and follow-up. Although the Fluid Attenuated Inversion Recovery (FLAIR) MRI sequence is routinely used, it may underestimate lesions in certain brain regions. This study evaluated whether fused images generated using minimum pixel value extraction (MinPE) from FLAIR and white matter-attenuated inversion recovery (WAIR) sequences improve lesion detection.</p><p><strong>Materials and methods: </strong>This retrospective single-center study analyzed brain MRI examinations from 65 patients with suspected or confirmed MS. Imaging protocols included conventional FLAIR and MinPE FLAIR/WAIR images. Two experienced neuroradiologists, blinded to clinical data, independently identified and classified lesions. Lesion detection rates were compared using chi-square analysis, and interobserver agreement was assessed with Cohen's kappa.</p><p><strong>Results: </strong>MinPE FLAIR/WAIR images showed improved lesion conspicuity and significantly higher detection rates compared with conventional FLAIR, particularly in the brainstem. Detection was markedly increased in the midbrain, pons, and medulla (p ≤ 0.0004), with additional improvement observed in the cerebellar hemispheres (p < 0.05).</p><p><strong>Discussion: </strong>While advanced sequences such as Double Inversion Recovery (DIR) and Phase-Sensitive Inversion-Recovery (PSIR) enhance lesion detection, their longer acquisition times limit routine use. The MinPE FLAIR/WAIR technique improves lesion visibility in challenging regions with minimal impact on scan time, allowing a more accurate estimation of disease burden.</p><p><strong>Conclusion: </strong>MinPE FLAIR/WAIR post-processing enhances MS lesion detection and may represent a practical addition to routine MRI protocols.</p>","PeriodicalId":54215,"journal":{"name":"Current Medical Imaging Reviews","volume":" ","pages":""},"PeriodicalIF":1.1,"publicationDate":"2026-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147678746","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}