Diagnostic and interventional radiology最新文献

{"title":"The role of the Kaiser score system in uncertain malignant potential (B3) breast lesions: a pilot study.","authors":"Fatma Çelik Yabul, Hafize Otçu Temur, Bahar Atasoy, Serdar Balsak, Alpay Alkan, Şeyma Yıldız","doi":"10.4274/dir.2025.242401","DOIUrl":"https://doi.org/10.4274/dir.2025.242401","url":null,"abstract":"<p><strong>Purpose: </strong>This study aims to evaluate the effectiveness of the Kaiser score (KS) system in assessing breast lesions with uncertain malignant potential (B3).</p><p><strong>Methods: </strong>Breast magnetic resonance imaging (MRI) scans from a total of 76 patients with histologically proven B3 lesions were included in this study. The KS was recorded for each MRI scan. The patients were classified based on biopsy results, and upgraded lesions were identified. Statistical analysis was conducted to evaluate the association between high KS values and upgraded lesions.</p><p><strong>Results: </strong>The mean age of the 76 patients was calculated as 49.6 ± 10.1. A significant association was observed between the KS system and the prediction of malignancy upgrade (<i>P</i> < 0.001). Furthermore, among the descriptors, spiculation, margin, and upgrading prediction demonstrated a statistically significant difference (<i>P</i> < 0.001). Additionally, the specificity improved when the accepted KS cut-off value was set at seven instead of five. A significant association was also observed between the KS system and the papilloma upgrade rate within the B3 lesion subgroups (<i>P</i> < 0.001).</p><p><strong>Conclusion: </strong>Breast radiology plays a crucial role in the diagnosis of B3 lesions. Our findings suggest that the KS system holds promise as a tool for predicting the upgrade potential of B3 lesions.</p><p><strong>Clinical significance: </strong>This study demonstrated that the KS system may assist in predicting the upgrade potential of B3 breast lesions. It also demonstrated that spiculation and margin descriptors within the KS system possess a high positive predictive value for upgrade prediction. Additionally, we believe that the KS system can help prevent unnecessary surgeries in patients with B3 lesions.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143709206","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":"Efficacy of endovascular circulating false lumen occlusion in chronic aneurysmal descending aortic dissections.","authors":"Emeric Gremen, Mathieu Finas, Eliott Mathieu, Frédéric Thony, Mathieu Rodiere, Julien Ghelfi","doi":"10.4274/dir.2025.242986","DOIUrl":"https://doi.org/10.4274/dir.2025.242986","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the efficacy of endovascular circulating false lumen occlusion (CFLO) in inducing positive aortic remodeling in chronic aneurysmal descending aortic dissection (AD).</p><p><strong>Methods: </strong>This retrospective monocentric study included patients treated by CFLO between 2003 and 2022 in the context of chronic AD with progressive descending aneurysmal evolution and persistent circulating false lumen (FL). The procedure was achieved with coils, plugs, and/or glue at the entry tear or in the FL and/or with covered stenting in the supra-aortic trunk. The primary endpoint evaluated the positive aortic remodeling, defined as stabilization or a decrease in the aortic diameter on a computed tomography scan at the 1-year follow-up after the procedure. The FL circulating status, safety, and occurrence of aneurysm events during follow-up were also evaluated.</p><p><strong>Results: </strong>Twenty patients [median age: 65.4 years, interquartile range (IQR): 58.4-69.9; 13 men] were included, with a median duration from an acute AD of 32.5 months (IQR: 8.8-76.5). Twelve patients (60%) achieved complete FL thrombosis after CFLO, whereas 8/20 patients (40.0%) experienced partial thrombosis. Additionally, positive aortic remodeling was observed in 13 patients (65%). Following the procedure, the aneurysmal aortic diameter decreased in 8/20 patients (40.0%) and remained stable in 5/20 patients (25.0%). Two patients (10%) had complications related to the procedure. Two patients (10%) had secondary aneurysm events during follow-up.</p><p><strong>Conclusion: </strong>CFLO is a feasible and efficient method to induce FL thrombosis and reduce aneurysmal progression in chronic AD.</p><p><strong>Clinical significance: </strong>The positive outcomes observed highlight the potential of this technique to improve patient management in complex aortic pathologies. This approach offers a valuable option in the management of chronic AD and emphasizes the importance of endovascular interventions in enhancing patient outcomes.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647683","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 bone age assessment: a Turkish population study.","authors":"Samet Öztürk, Murat Yüce, Gül Gizem Pamuk, Candan Varlık, Ahmet Tan Cimilli, Musa Atay","doi":"10.4274/dir.2025.242999","DOIUrl":"https://doi.org/10.4274/dir.2025.242999","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Purpose: &lt;/strong&gt;Established methods for bone age assessment (BAA), such as the Greulich and Pyle atlas, suffer from variability due to population differences and observer discrepancies. Although automated BAA offers speed and consistency, limited research exists on its performance across different populations using deep learning. This study examines deep learning algorithms on the Turkish population to enhance bone age models by understanding demographic influences.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We analyzed reports from Bağcılar Hospital's Health Information Management System between April 2012 and September 2023 using \"bone age\" as a keyword. Patient images were re-evaluated by an experienced radiologist and anonymized. A total of 2,730 hand radiographs from Bağcılar Hospital (Turkish population), 12,572 from the Radiological Society of North America (RSNA), and 6,185 from the Radiological Hand Pose Estimation (RHPE) public datasets were collected, along with corresponding bone ages and gender information. A random set of 546 radiographs (273 from Bağcılar, 273 from public datasets) was initially randomly split for an internal test set with bone age stratification; the remaining data were used for training and validation. BAAs were generated using a modified InceptionV3 model on 500 × 500-pixel images, selecting the model with the lowest mean absolute error (MAE) on the validation set.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Three models were trained and tested based on dataset origin: Bağcılar (Turkish), public (RSNA-RHPE), and a Combined model. Internal test set predictions of the Combined model estimated bone age within less than 6, 12, 18, and 24 months at rates of 44%, 73%, 87%, and 94%, respectively. The MAE was 9.2 months in the overall internal test set, 7 months on the public test set, and 11.5 months on the Bağcılar internal test data. The Bağcılar-only model had an MAE of 12.7 months on the Bağcılar internal test data. Despite less training data, there was no significant difference between the combined and Bağcılar models on the Bağcılar dataset (&lt;i&gt;P&lt;/i&gt; &gt; 0.05). The public model showed an MAE of 16.5 months on the Bağcılar dataset, significantly worse than the other models (&lt;i&gt;P&lt;/i&gt; &lt; 0.05).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusion: &lt;/strong&gt;We developed an automatic BAA model including the Turkish population, one of the few such studies using deep learning. Despite challenges from population differences and data heterogeneity, these models can be effectively used in various clinical settings. Model accuracy can improve over time with cumulative data, and publicly available datasets may further refine them. Our approach enables more accurate and efficient BAAs, supporting healthcare professionals where traditional methods are time-consuming and variable.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Clinical significance: &lt;/strong&gt;The developed automated BAA model for the Turkish population offers a reliable and efficient alternative to traditional methods. By utilizing de","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143647669","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":"Grading portal vein stenosis following partial hepatectomy by high-frequency ultrasonography: an <i>in vivo</i> study of rats.","authors":"Lin Ma, Chihan Peng, Lulu Yang, Xiaoxia Zhu, Hongxia Fan, Jiali Yang, Hong Wang, Yan Luo","doi":"10.4274/dir.2024.242912","DOIUrl":"10.4274/dir.2024.242912","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the diagnostic value of ultrasound in grading portal vein stenosis (PVS) in a rat model of 70% partial hepatectomy (PH).</p><p><strong>Methods: </strong>A total of 96 Sprague-Dawley rats were randomly divided into a PH group and PVS groups with mild, moderate, and severe PVS following PH. Hemodynamic parameters were measured using high-frequency ultrasound (5-12 MHz high-frequency linear transducer), including pre-stenotic, stenotic, and post-stenotic portal vein diameters (PVD_pre, PVD_s, PVDpost); pre-stenotic and stenotic portal vein velocity (PVVpre, PVVs); hepatic artery peak systolic velocity (PSV); end-diastolic velocity; and resistive index. The portal vein diameter ratio (PVDR) and portal vein velocity ratio (PVVR) were calculated using the following formulas: PVDR=PVD_pre/PVD_s and PVVR=PVVs/PVVpre. The value of these parameters in grading PVS was assessed.</p><p><strong>Results: </strong>Portal vein hemodynamics showed gradient changes as PVS aggravated. For identifying >50% PVS, PVD_s and PVDR were the best parameters, with areas under the curve (AUC) of 0.85 and 0.86, respectively. For identifying >65% PVS, PVD_s, PVDR, and PVVR were relatively better, with AUCs of 0.94, 0.85, and 0.88, respectively. The AUC of hepatic artery PSV for identifying >65% PVS was 0.733.</p><p><strong>Conclusion: </strong>High-frequency ultrasonography can be used to grade PVS in rats, with PVD_s, PVDR, and PVVR being particularly useful. Hepatic artery PSV may help in predicting >65% PVS. These findings provide valuable information for PVS rat model research and offer an experimental basis for further studies on PVS evaluation in living-donor liver transplantation (LDLT).</p><p><strong>Clinical significance: </strong>Ultrasonography serves as a first-line technology for diagnosing PVS following LDLT. However, the grading criteria for PVS severity remain unclear. Investigating the use of ultrasonic hemodynamics in the early diagnosis of PVS and grading stenosis severity is important for early postoperative intervention and improving recipient survival rates.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"68-74"},"PeriodicalIF":1.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11880861/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142709178","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}

{"title":"Artificial intelligence in musculoskeletal applications: a primer for radiologists.","authors":"Michelle W Tong, Jiamin Zhou, Zehra Akkaya, Sharmila Majumdar, Rupsa Bhattacharjee","doi":"10.4274/dir.2024.242830","DOIUrl":"10.4274/dir.2024.242830","url":null,"abstract":"<p><p>As an umbrella term, artificial intelligence (AI) covers machine learning and deep learning. This review aimed to elaborate on these terms to act as a primer for radiologists to learn more about the algorithms commonly used in musculoskeletal radiology. It also aimed to familiarize them with the common practices and issues in the use of AI in this domain.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"89-101"},"PeriodicalIF":1.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11880867/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141999601","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}

{"title":"Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model.","authors":"Siyun Lin, Zhuangxuan Ma, Yuanshan Yao, Hou Huang, Wufei Chen, Dongfang Tang, Wen Gao","doi":"10.4274/dir.2024.242972","DOIUrl":"10.4274/dir.2024.242972","url":null,"abstract":"<p><strong>Purpose: </strong>Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC.</p><p><strong>Methods: </strong>A total of 63 eligible participants were included and randomized into training and validation groups. Radiomics features were extracted from the volumes of interest of the tumor circled in the preprocessed computed tomography (CT) images. Golden feature, clinical, radiomics, and fusion models were generated using a combination of various algorithms through autoML. The models were evaluated using a multi-class receiver operating characteristic curve.</p><p><strong>Results: </strong>In total, 1,219 radiomics features were extracted from regions of interest. The ensemble algorithm demonstrated superior performance in model construction. In the training cohort, the fusion model exhibited the highest accuracy at 0.84, with an area under the curve (AUC) of 0.89-0.98. In the validation cohort, the radiomics model had the highest accuracy at 0.89, with an AUC of 0.98-1.00; its prediction performance in the partial response subgroup outperformed that in both the clinical and radiomics models. Patients with low rad scores achieved improved progression-free survival (PFS); (median PFS 16.2 vs. 13.4, <i>P</i> = 0.009).</p><p><strong>Conclusion: </strong>autoML accurately and robustly predicted the short-term outcomes of patients with inoperable NSCLC treated with immune checkpoint inhibitor immunotherapy by constructing CT-based radiomics models, confirming it as a powerful tool to assist in the individualized management of patients with advanced NSCLC.</p><p><strong>Clinical significance: </strong>This article highlights that autoML promotes the accuracy and efficiency of feature selection and model construction. The radiomics model generated by autoML predicted the efficacy of immunotherapy in patients with advanced NSCLC effectively. This may provide a rapid and non-invasive method for making personalized clinical decisions.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"130-140"},"PeriodicalIF":1.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11880869/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143001883","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}

{"title":"Evaluating text and visual diagnostic capabilities of large language models on questions related to the Breast Imaging Reporting and Data System Atlas 5^th edition.","authors":"Yasin Celal Güneş, Turay Cesur, Eren Çamur, Leman Günbey Karabekmez","doi":"10.4274/dir.2024.242876","DOIUrl":"10.4274/dir.2024.242876","url":null,"abstract":"<p><strong>Purpose: </strong>This study aimed to evaluate the performance of large language models (LLMs) and multimodal LLMs in interpreting the Breast Imaging Reporting and Data System (BI-RADS) categories and providing clinical management recommendations for breast radiology in text-based and visual questions.</p><p><strong>Methods: </strong>This cross-sectional observational study involved two steps. In the first step, we compared ten LLMs (namely ChatGPT 4o, ChatGPT 4, ChatGPT 3.5, Google Gemini 1.5 Pro, Google Gemini 1.0, Microsoft Copilot, Perplexity, Claude 3.5 Sonnet, Claude 3 Opus, and Claude 3 Opus 200K), general radiologists, and a breast radiologist using 100 text-based multiple-choice questions (MCQs) related to the BI-RADS Atlas 5^th edition. In the second step, we assessed the performance of five multimodal LLMs (ChatGPT 4o, ChatGPT 4V, Claude 3.5 Sonnet, Claude 3 Opus, and Google Gemini 1.5 Pro) in assigning BI-RADS categories and providing clinical management recommendations on 100 breast ultrasound images. The comparison of correct answers and accuracy by question types was analyzed using McNemar's and chi-squared tests. Management scores were analyzed using the Kruskal- Wallis and Wilcoxon tests.</p><p><strong>Results: </strong>Claude 3.5 Sonnet achieved the highest accuracy in text-based MCQs (90%), followed by ChatGPT 4o (89%), outperforming all other LLMs and general radiologists (78% and 76%) (<i>P</i> < 0.05), except for the Claude 3 Opus models and the breast radiologist (82%) (<i>P</i> > 0.05). Lower-performing LLMs included Google Gemini 1.0 (61%) and ChatGPT 3.5 (60%). Performance across different categories of showed no significant variation among LLMs or radiologists (<i>P</i> > 0.05). For breast ultrasound images, Claude 3.5 Sonnet achieved 59% accuracy, significantly higher than other multimodal LLMs (<i>P</i> < 0.05). Management recommendations were evaluated using a 3-point Likert scale, with Claude 3.5 Sonnet scoring the highest (mean: 2.12 ± 0.97) (<i>P</i> < 0.05). Accuracy varied significantly across BI-RADS categories, except Claude 3 Opus (<i>P</i> < 0.05). Gemini 1.5 Pro failed to answer any BI-RADS 5 questions correctly. Similarly, ChatGPT 4V failed to answer any BI-RADS 1 questions correctly, making them the least accurate in these categories (<i>P</i> < 0.05).</p><p><strong>Conclusion: </strong>Although LLMs such as Claude 3.5 Sonnet and ChatGPT 4o show promise in text-based BI-RADS assessments, their limitations in visual diagnostics suggest they should be used cautiously and under radiologists' supervision to avoid misdiagnoses.</p><p><strong>Clinical significance: </strong>This study demonstrates that while LLMs exhibit strong capabilities in text-based BI-RADS assessments, their visual diagnostic abilities are currently limited, necessitating further development and cautious application in clinical practice.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"111-129"},"PeriodicalIF":1.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11880873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142153440","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}

{"title":"Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects.","authors":"Burak Koçak, Andrea Ponsiglione, Arnaldo Stanzione, Christian Bluethgen, João Santinha, Lorenzo Ugga, Merel Huisman, Michail E Klontzas, Roberto Cannella, Renato Cuocolo","doi":"10.4274/dir.2024.242854","DOIUrl":"10.4274/dir.2024.242854","url":null,"abstract":"<p><p>Although artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into medical practice may present a double-edged sword due to bias (i.e., systematic errors). AI algorithms have the potential to mitigate cognitive biases in human interpretation, but extensive research has highlighted the tendency of AI systems to internalize biases within their model. This fact, whether intentional or not, may ultimately lead to unintentional consequences in the clinical setting, potentially compromising patient outcomes. This concern is particularly important in medical imaging, where AI has been more progressively and widely embraced than any other medical field. A comprehensive understanding of bias at each stage of the AI pipeline is therefore essential to contribute to developing AI solutions that are not only less biased but also widely applicable. This international collaborative review effort aims to increase awareness within the medical imaging community about the importance of proactively identifying and addressing AI bias to prevent its negative consequences from being realized later. The authors began with the fundamentals of bias by explaining its different definitions and delineating various potential sources. Strategies for detecting and identifying bias were then outlined, followed by a review of techniques for its avoidance and mitigation. Moreover, ethical dimensions, challenges encountered, and prospects were discussed.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"75-88"},"PeriodicalIF":1.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11880872/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141491311","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}

{"title":"Single-center 10-year retrospective analysis of Amplatzer Vascular Plug 4 embolization for pulmonary arteriovenous malformations with feeding arteries of <6 mm","authors":"Jung Guen Cha, Jongmin Park, Byunggeon Park, Seo Young Park, So Mi Lee, Jihoon Hong","doi":"10.4274/dir.2024.242732","DOIUrl":"10.4274/dir.2024.242732","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the efficacy and safety of Amplatzer Vascular Plug 4 (AVP4) embolization in pulmonary arteriovenous malformations (PAVMs) with small- to medium-sized feeding arteries (<6 mm) and to identify factors affecting persistence and the main persistence patterns after embolization.</p><p><strong>Methods: </strong>Between June 2013 and February 2023, we retrospectively reviewed 100 patients with 217 treated PAVMs. We included PAVMs with feeding arteries <6 mm, treated with AVP4 embolization, and followed adequately with computed tomography (CT). Technical success was defined as flow cessation observed on angiography. Persistence was defined as less than a 70% reduction of the venous sac on CT. We evaluated adverse events for each embolization session. Patterns of persistence were assessed using follow-up angiography. Univariate and multivariate analyses were performed to evaluate factors affecting persistence based on the 70% CT criteria.</p><p><strong>Results: </strong>Fifty-one patients (48 women, 3 men; mean age: 50.8 years; age range: 16-71 years) with 103 PAVMs met the inclusion criteria. The technical success rate was 100%. The persistence rate was 9.7% (10/103), and the overall adverse event rate was 2.9% (3/103) during a mean follow-up of 556 days (range: 181-3,542 days). In two cases, the persistence pattern confirmed by follow-up angiography involved reperfusion via adjacent pulmonary artery collaterals. The location of embolization relative to the last normal branch of the pulmonary artery was the only factor substantially affecting persistence.</p><p><strong>Conclusion: </strong>Embolization with AVP4 appears to be safe and effective for small- to medium-sized PAVMs. The location of the embolization relative to the last normal branch of the pulmonary artery was found to be the main determinant of persistence.</p><p><strong>Clinical significance: </strong>Given the increasing demand for the treatment of small PAVMs, AVP4 embolization could be considered a viable and effective option for managing PAVMs with feeding arteries <6 mm.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"152-160"},"PeriodicalIF":1.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11880865/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141247684","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}

{"title":"Hepatic arterial infusion chemotherapy combined with toripalimab and surufatinib for the treatment of advanced intrahepatic cholangiocarcinoma.","authors":"Songlin Song, Yiming Liu, Yanqiao Ren, Chuansheng Zheng, Bin Liang","doi":"10.4274/dir.2024.242673","DOIUrl":"10.4274/dir.2024.242673","url":null,"abstract":"<p><strong>Purpose: </strong>The aim of the present study is to report the clinical results of patients with advanced intrahepatic cholangiocarcinoma (ICC) who received combination therapy of hepatic arterial infusion chemotherapy (HAIC), toripalimab and surufatinib.</p><p><strong>Methods: </strong>The study cohort consisted of 28 patients with advanced ICC who were treated with HAIC (mFOLFOX6 regimen, Q3W) in combination with intravenous toripalimab (240 mg, Q3W) and oral surufatinib (150 mg, once daily). The cohort had 14 male and 14 female patients. The baseline characteristics of the study cohort were obtained. The tumor response and drug-associated toxicity were assessed and reported.</p><p><strong>Results: </strong>During the follow-up period (median follow-up time: 11.3 months; range: 4-19 months), four patients died of tumor progression. The objective response rate and disease control rate were 58% and 79%, respectively. The mPFS was 9.5 months, and the overall survival rate was 83.3%. The most frequent adverse events were nausea and vomiting (100%) and abdominal pain (85.7%). Serious complications related to death were not observed.</p><p><strong>Conclusion: </strong>The combination treatment schedule for advanced ICC demonstrated positive efficacy and safety profiles.</p><p><strong>Clinical significance: </strong>This study provides promising clinical guidance for the treatment of advanced cholangiocarcinoma and is expected to modify the treatment strategy for this disease.</p>","PeriodicalId":11341,"journal":{"name":"Diagnostic and interventional radiology","volume":" ","pages":"145-151"},"PeriodicalIF":1.4,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11880864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141247614","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}