Diagnostic and interventional radiology最新文献

{"title":"The role of T1 hyperintensity in differentiating granulomatous prostatitis from prostate cancer: a retrospective analysis of 31 lesions.","authors":"Selahattin Durmaz, Mert Kılıç, Bilgen Coşkun, Sergin Akpek, Barış Bakır, Tarık Esen, Metin Vural, Emre Altınmakas","doi":"10.4274/dir.2025.253242","DOIUrl":"https://doi.org/10.4274/dir.2025.253242","url":null,"abstract":"<p><strong>Purpose: </strong>To investigate the multiparametric magnetic resonance imaging (mpMRI) characteristics of granulomatous prostatitis (GP) and share our experience with 31 pathologically confirmed GP lesions in 19 patients.</p><p><strong>Methods: </strong>This two-center retrospective study reviewed the pathological and imaging data of 856 patients who underwent prostate biopsy between January 2012 and April 2024. Of these, 19 patients with available prebiopsy mpMRI and a pathologically confirmed diagnosis of GP were included. Additionally, 280 biopsy-naïve patients diagnosed with clinically significant prostate cancer (csPCa) were included as a control group for comparative analysis. Prebiopsy mpMR images of patients with GP were assessed by consensus between two of three radiologists (M.V., B.C., S.D.), evaluating lesion location, size, shape, multifocality, extraprostatic extension (EPE), signal characteristics on T1-, T2-, and diffusion-weighted imaging (DWI), the mean apparent diffusion coefficient (ADC<sub>mean</sub>) value, enhancement patterns, and prostate imaging reporting and data system (PI-RADS) scores. Statistical analyses were conducted using SPSS version 30.0.</p><p><strong>Results: </strong>In 19 patients, 31 pathologically confirmed GP lesions were identified on prebiopsy mpMRI. Twenty-six lesions were located in the peripheral zone and five in the transitional zone. Multifocal involvement was observed in nine patients (47.3%). Thirty of 31 lesions were hypointense on T2-WI, and seven showed capsular bulging and/or irregularity, suggesting EPE. DWI revealed markedly impeded diffusion in all lesions. The median ADC<sub>mean</sub> value was 825 × 10<sup>-3</sup> mm<sup>2</sup>/s (IQR: 230 × 10<sup>-3</sup> mm<sup>2</sup>/s). On dynamic contrast-enhanced sequences, 25 lesions showed early enhancement, five showed prolonged enhancement, and one showed prolonged ring enhancement. Based on mpMRI findings, 17 lesions were assigned a PI-RADS score of 4, and 13 lesions were assigned a PI-RADS score of 5. Notably, 22 lesions (71%) in 14 patients with GP (73.7%) exhibited hyperintensity on T1-WI despite no prior prostate biopsy history. Statistical analysis comparing the GP and csPCa groups revealed that hyperintensity on T1-WI was significantly more frequent in GP, both on a per-patient basis (73.7% vs. 3.2%) and a per-lesion basis (71.0% vs. 3.1%) (<i>P</i> < 0.0001 for both).</p><p><strong>Conclusion: </strong>GP shares overlapping imaging features with prostate cancer on mpMRI. However, hyperintensity on T1-WI may serve as a distinguishing feature, potentially reducing unnecessary prostate interventions. Radiologists should consider GP in PI-RADS ≥4 lesions exhibiting T1-WI hyperintensity. Furthermore, given the high incidence of GP following intravesical Bacillus Calmette-Guérin (BCG) therapy, a thorough history of BCG treatment should be obtained.</p><p><strong>Clinical significance: </strong>GP is recognized for its tenden","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144110166","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":"New lens on breast health: harnessing high-b-value synthetic diffusion-weighted imaging for breast lesion characterization.","authors":"Serap Karabiyik, Saime Ramadan, Emil Settarzade, Ali Ilker Filiz, Hatice Ozturkmen Akay","doi":"10.4274/dir.2025.253190","DOIUrl":"https://doi.org/10.4274/dir.2025.253190","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to evaluate the diagnostic efficacy of synthetic diffusion-weighted imaging (sDWI) at various high b-values in distinguishing malignant from benign breast lesions and to compare its performance with that of conventional DWI (cDWI).</p><p><strong>Methods: </strong>After the exclusion of 22 lesions, 63 women (age range, 24-99 years; mean age, 53.7 ± 15.1 years) with 68 suspicious breast lesions on ultrasound who underwent multiparametric breast magnetic resonance imaging before biopsy between January 2021 and April 2023 were included in this retrospective study. According to the pathological results, lesions were classified as malignant or benign. Volumetric mask images were defined. The lesion signal/normal breast signal ratio [relative signal intensity (rSI)] was measured on different diffusion-weighted images (cDWI at b = 800 and 1500 s/mm²; sDWI at b = 1500-5000 s/mm²), and lesion SI on apparent diffusion coefficient (ADC) 0-800 and ADC0-1500 maps (mADC) was calculated. The diagnostic performances of these parameters were evaluated using a receiver operating characteristic curve analysis and the DeLong test in both the mass and non-mass lesion groups.</p><p><strong>Results: </strong>A total of 32 (47.06%) benign and 36 (52.94%) malignant lesions were identified. Malignant lesions exhibited significantly higher rSI values on cDWI800, cDWI1500, sDWI1500, sDWI2000, and sDWI3000 (<i>P</i> values: <0.001, <0.001, <0.001, <0.001, <0.001, 0.03) and lower mADC800 and mADC1500 values (<i>P</i> values: 0.01 and 0.03). In mass lesions, synthetic b1500 and conventional b1500 demonstrated diagnostic accuracy comparable with that of routine mADC800 and mADC1500. However, in non-mass lesions, high-b-value DWI maps (b ≥ 2000 s/mm²) significantly outperformed mADC and cDWI in differentiating malignant from benign lesions. The highest diagnostic accuracy in non-mass lesions was observed with rSIC4000 [area under the curve (AUC) = 0.87], whereas in mass lesions, rSIC1500 exhibited the highest diagnostic performance (AUC = 0.79).</p><p><strong>Conclusion: </strong>The optimal b-value for DWI differs between mass and non-mass breast lesions, emphasizing the need for separate evaluation protocols. Although high-b-value sDWI provides limited added diagnostic value in mass lesions, it significantly improves malignancy detection in non-mass lesions, outperforming cDWI and ADC mapping.</p><p><strong>Clinical significance: </strong>This study underscores the need for a tailored DWI protocol for optimal breast lesion characterization, particularly for non-mass lesions, where high-b-value synthetic imaging enhances diagnostic accuracy.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144110121","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":"Evaluating the reference accuracy of large language models in radiology: a comparative study across subspecialties.","authors":"Yasin Celal Güneş, Turay Cesur, Eren Çamur","doi":"10.4274/dir.2025.253101","DOIUrl":"https://doi.org/10.4274/dir.2025.253101","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to compare six large language models (LLMs) [Chat Generative Pre-trained Transformer (ChatGPT)o1-preview, ChatGPT-4o, ChatGPT-4o with canvas, Google Gemini 1.5 Pro, Claude 3.5 Sonnet, and Claude 3 Opus] in generating radiology references, assessing accuracy, fabrication, and bibliographic completeness.</p><p><strong>Methods: </strong>In this cross-sectional observational study, 120 open-ended questions were administered across eight radiology subspecialties (neuroradiology, abdominal, musculoskeletal, thoracic, pediatric, cardiac, head and neck, and interventional radiology), with 15 questions per subspecialty. Each question prompted the LLMs to provide responses containing four references with in-text citations and complete bibliographic details (authors, title, journal, publication year/month, volume, issue, page numbers, and PubMed Identifier). References were verified using Medline, Google Scholar, the Directory of Open Access Journals, and web searches. Each bibliographic element was scored for correctness, and a composite final score [(FS): 0-36] was calculated by summing the correct elements and multiplying this by a 5-point verification score for content relevance. The FS values were then categorized into a 5-point Likert scale reference accuracy score (RAS: 0 = fabricated; 4 = fully accurate). Non-parametric tests (Kruskal-Wallis, Tamhane's T2, Wilcoxon signed-rank test with Bonferroni correction) were used for statistical comparisons.</p><p><strong>Results: </strong>Claude 3.5 Sonnet demonstrated the highest reference accuracy, with 80.8% fully accurate references (RAS 4) and a fabrication rate of 3.1%, significantly outperforming all other models (<i>P</i> < 0.001). Claude 3 Opus ranked second, achieving 59.6% fully accurate references and a fabrication rate of 18.3% (<i>P</i> < 0.001). ChatGPT-based models (ChatGPT-4o, ChatGPT-4o with canvas, and ChatGPT o1-preview) exhibited moderate accuracy, with fabrication rates ranging from 27.7% to 52.9% and <8% fully accurate references. Google Gemini 1.5 Pro had the lowest performance, achieving only 2.7% fully accurate references and the highest fabrication rate of 60.6% (<i>P</i> < 0.001). Reference accuracy also varied by subspecialty, with neuroradiology and cardiac radiology outperforming pediatric and head and neck radiology.</p><p><strong>Conclusion: </strong>Claude 3.5 Sonnet significantly outperformed all other models in generating verifiable radiology references, and Claude 3 Opus showed moderate performance. In contrast, ChatGPT models and Google Gemini 1.5 Pro delivered substantially lower accuracy with higher rates of fabricated references, highlighting current limitations in automated academic citation generation.</p><p><strong>Clinical significance: </strong>The high accuracy of Claude 3.5 Sonnet can improve radiology literature reviews, research, and education with dependable references. The poor performance of other models, with h","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143987792","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":"Comparison of the novel simultaneous biplane versus in-plane imaging technique in ultrasound-guided biopsy: a prospective randomized multi-operator cross-over phantom study.","authors":"Baki Akca, Florian Vafai-Tabrizi, Michel Bielecki, Georg-Christian Funk","doi":"10.4274/dir.2025.253191","DOIUrl":"https://doi.org/10.4274/dir.2025.253191","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate and compare the in-plane and novel biplane imaging techniques in ultrasound-guided biopsies (USBx). USBx are effective for obtaining tissue samples in suspected malignancy or infection. The in-plane technique is the gold standard, offering continuous needle visualization. The biplane technique enables simultaneous in-plane and out-of-plane visualization, potentially improving biopsy outcomes. A study was conducted using gel phantoms to simulate USBx, with the goal of determining whether one technique offers distinct advantages over the other.</p><p><strong>Methods: </strong>A total of 30 participants (mean age: 30 ± 7 years; 20 men) were recruited, primarily consisting of physicians in training with varying levels of experience. Each participant performed biopsies on gel phantoms using both the in-plane and biplane techniques in a randomized order after watching a standardized tutorial video. Procedure-related parameters were analyzed, and post-intervention questionnaires, including the NASA task load index (NASA-TLX), were collected to assess cognitive workload and personal preferences.</p><p><strong>Results: </strong>All participants achieved successful biopsies with both techniques. The first-puncture success rate was significantly higher with the biplane technique (83% vs. 63%; <i>P</i> = 0.01). The biplane technique required significantly fewer biopsy attempts than the in-plane approach (37 vs. 43; <i>P</i> = 0.03). Although the biplane technique had a longer \"mean time to first successful biopsy\" (120 seconds vs. 72 seconds), this difference was not statistically significant (<i>P</i> = 0.09), likely due to high variability. No significant differences were found in safety-related parameters, including the number of skin punctures, needle retractions, percentage of time the needle tip was visible, and the number of biopsy attempts without needle tip visualization. The NASA-TLX indicated higher mental demand with the biplane technique (<i>P</i> = 0.013), but other dimensions showed no significant differences. Overall, 83% of participants, including 88% of more experienced operators, preferred the biplane technique, citing enhanced visualization and perceived safety.</p><p><strong>Conclusion: </strong>In this study, the biplane technique in USBx was substantially superior in terms of total biopsy attempts and first-puncture success rate compared with the in-plane approach. It may offer safety and efficiency advantages, particularly for less-experienced operators. Further studies with larger sample sizes and experienced operators, especially in clinical settings, are needed to determine clear superiority.</p><p><strong>Clinical significance: </strong>These findings suggest that biplane imaging may be especially beneficial for training less-experienced operators and in cases with elevated complication risk.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143962513","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":"Pix2Pix generative-adversarial network in improving the quality of T2-weighted prostate magnetic resonance imaging: a multi-reader study.","authors":"Yeliz Başar, Mustafa Said Kartal, Mustafa Ege Seker, Deniz Alis, Delal Seker, Müjgan Orman, Sabri Şirolu, Serpil Kurtcan, Aydan Arslan, Nurper Denizoğlu, İlkay Öksüz, Ercan Karaarslan","doi":"10.4274/dir.2025.243102","DOIUrl":"https://doi.org/10.4274/dir.2025.243102","url":null,"abstract":"<p><strong>Purpose: </strong>To assess the performance and feasibility of generative deep learning in enhancing the image quality of T2-weighted (T2W) prostate magnetic resonance imaging (MRI).</p><p><strong>Methods: </strong>Axial T2W images from the prostate imaging: cancer artificial intelligence dataset (n = 1,476, biologically males; n = 1,500 scans) were used, partitioned into training (n = 1300), validation (n = 100), and testing (n = 100) sets. A Pix2Pix model was trained on original and synthetically degraded images, generated using operations such as motion, Gaussian noise, blur, ghosting, spikes, and bias field inhomogeneities to enhance image quality. The efficacy of the model was evaluated by seven radiologists using the prostate imaging quality criteria to assess original, degraded, and improved images. The evaluation also included tests to determine whether the images were original or synthetically improved. Additionally, the model's performance was tested on the in-house external testing dataset of 33 patients. The statistical significance was assessed using the Wilcoxon signedrank test.</p><p><strong>Results: </strong>Results showed that synthetically improved images [median score (interquartile range) 4.71 (1)] were of higher quality than degraded images [3.36 (3), <i>P</i> = 0.0001], with no significant difference from original images [5 (1.14), <i>P</i> > 0.05]. Observers equally identified original and synthetically improved images as original (52% and 53%), proving the model's ability to retain realistic attributes. External testing on a dataset of 33 patients confirmed a significant improvement (<i>P</i> = 0.001) in image quality, from a median score of 4 (2.286)-4.71 (1.715).</p><p><strong>Conclusion: </strong>The Pix2Pix model, trained on synthetically degraded data, effectively improved prostate MRI image quality while maintaining realism and demonstrating both applicability to real data and generalizability across various datasets.</p><p><strong>Clinical significance: </strong>This study critically assesses the efficacy of the Pix2Pix generative-adversarial network in enhancing T2W prostate MRI quality, demonstrating its potential to produce high-quality, realistic images indistinguishable from originals, thereby potentially advancing radiology practice by improving diagnostic accuracy and image reliability.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143977174","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":"Reporting checklists: from a tool after publication to a tool before submission.","authors":"Jingyu Zhong","doi":"10.4274/dir.2025.253314","DOIUrl":"https://doi.org/10.4274/dir.2025.253314","url":null,"abstract":"","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143981993","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":"Quality of reporting of studies on artificial intelligence in radiology: what is the current state of the field?","authors":"Ahmed Maiter","doi":"10.4274/dir.2025.253315","DOIUrl":"https://doi.org/10.4274/dir.2025.253315","url":null,"abstract":"","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143959172","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":"Detection of common bile duct dilatation on magnetic resonance cholangiopancreatography by deep learning.","authors":"Hilal Er Ulubaba, Rukiye Çiftçi, İpek Atik, Osman Furkan Karakuş","doi":"10.4274/dir.2025.253218","DOIUrl":"https://doi.org/10.4274/dir.2025.253218","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to detect common bile duct (CBD) dilatation using deep learning methods from artificial intelligence algorithms.</p><p><strong>Methods: </strong>To create a convolutional neural network (CNN) model, 77 magnetic resonance cholangiopancreatography (MRCP) images without CBD dilatation and 70 MRCP images with CBD dilatation were used. The system was developed using coronal maximum intensity projection reformatted 3D-MRCP images. The ResNet50, DenseNet121, and visual geometry group models were selected for training, and detailed training was performed on each model.</p><p><strong>Results: </strong>In the study, the DenseNet121 model showed the best performance, with a 97% accuracy rate. The ResNet50 model ranked second, with a 96% accuracy rate.</p><p><strong>Conclusion: </strong>CBD dilatation was detected with high performance using the DenseNet CNN model. Once validated in multicenter studies with larger datasets, this method may help in diagnosis and treatment decision-making.</p><p><strong>Clinical significance: </strong>Deep learning algorithms can aid clinicians and radiologists in the diagnostic process once technical, ethical, and financial limitations are addressed. Fast and accurate diagnosis is crucial for accelerating treatment, reducing complications, and shortening hospital stays.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143981989","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":"Possible use of digital variance angiography in uterine fibroid embolization: a retrospective observational study.","authors":"Viktor Bérczi, Szuzina Fazekas, István Góg, Marcell Gyánó, Ambrus Tóth, Ákos Bérczi, Osama Habeeballah, Krzysztof Pyra, Zoltán Harmat, Dat Tin Nguyen","doi":"10.4274/dir.2025.243042","DOIUrl":"https://doi.org/10.4274/dir.2025.243042","url":null,"abstract":"<p><strong>Purpose: </strong>Digital variance angiography (DVA), a recently developed image processing technology, provides a higher contrast-to-noise ratio (CNR) and better image quality during lower limb interventions than digital subtraction angiography (DSA). Our aim was to investigate whether the quality reserve of DVA can also be observed in uterine fibroid embolization (UFE).</p><p><strong>Methods: </strong>In this retrospective observational study, the CNR and image quality of DSA and DVA images from 56 patients (mean ± standard deviation age: 44.2 ± 5.3 years) who underwent UFE at our institution were assessed. For the visual evaluation of the same image pairs, the visibility of large vessels, small vessels, tissue blush, and background noise was compared by three experienced readers using a four-grade Likert scale. Data were analyzed using the Wilcoxon signed-rank test or the one-sample Wilcoxon test.</p><p><strong>Results: </strong>DVA provided significantly higher CNR than DSA (the median CNR_DVA/CNR_DSA was 1.96). In the visual comparison of DVA and DSA images, Likert scores did not significantly differ from zero (equal quality level) in any evaluated categories. The median (interquartile range) values were 0.00 (1.00) for large vessels, -0.33 (1.33) for small vessels, 0.00 (0.67) for tissue blush, and 0.00 (0.75) for background noise.</p><p><strong>Conclusion: </strong>Although the visual image quality of DSA and DVA was identical, DVA provided a twofold CNR in UFE, indicating a significant quality advantage for this technology.</p><p><strong>Clinical significance: </strong>The observed quality reserve may allow for dose management (reduction of applied radiation dose and/or contrast media), enhancing the safety of UFE for both patients and personnel.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143988981","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":"Comparing ultrasound-guided intra-articular injection and medial branch block for lumbar facet joint pain: a clinical study.","authors":"Hui Zhao, Yun-Long Hou, Le-Hang Guo, Qiao Wang, Tian He, Guang-Fei Gu, Li-Ping Sun, Feng-Shan Jin","doi":"10.4274/dir.2024.242765","DOIUrl":"10.4274/dir.2024.242765","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to compare the effectiveness of ultrasound-guided intra-articular (IA) injections with medial branch nerve blocks in treating lumbar facet joint pain.</p><p><strong>Methods: </strong>This retrospective study enrolled 94 patients clinically diagnosed with lumbar facet joint pain. Diagnostic blocks confirmed the diagnosis in 82 patients, evidenced by a pain visual analog score (VAS) reduction of at least 50% immediately following the injection. Of these, 42 were treated with ultrasound-guided IA injections (group 1), and 40 received ultrasound-guided medial branch blocks (group 2). Effective pain relief was defined as a VAS reduction of at least 50%.</p><p><strong>Results: </strong>Group 1 showed significantly higher pain relief rates compared with group 2 at both 1-month (54.76% versus 2.5%, <i>P</i> < 0.001) and 3-month (26.19% versus 5%, <i>P</i> = 0.014) follow-ups. Within group 1, patients aged 21-50 years experienced higher relief rates (81.25% at 1-month and 56.25% at 3-month follow-ups) compared with those over 50 (38.46% at 1-month and 7.69% at 3-month follow-ups), which was statistically significant (<i>P</i> = 0.007 at 1-month and <i>P</i> = 0.001 at 3-month follow-ups). Furthermore, in group 1, patients with sedentary jobs reported significantly greater pain relief (90.91% at 1-month and 81.82% at 3-month follow-ups) compared with those with non-sedentary jobs (41.94% at 1-month and 6.45% at 3-month follow-ups) (<i>P</i> = 0.005 at 1-month and <i>P</i> < 0.001 at 3-month follow-ups).</p><p><strong>Conclusion: </strong>Ultrasound-guided IA injection provides better pain relief compared with medial branch nerve blocks. This method serves as a viable alternative for patients, especially younger and middle-aged patients with lumbar facet pain due to sedentary lifestyles.</p><p><strong>Clinical significance: </strong>This study compared and analyzed the therapeutic effects of two different ultrasound intervention blockade methods on patients with lumbar facet joint pain, demonstrating that IA injection has a better pain relief effect and can be used as a pain relief method for patients who refuse radiofrequency therapy.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"259-263"},"PeriodicalIF":1.4,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12057530/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142726979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}