Diagnostic and Interventional Imaging最新文献

{"title":"Gadobenate dimeglumine-enhanced MRI: A surrogate marker of liver function recovery after auxiliary partial orthotopic liver transplantation","authors":"Marco Dioguardi Burgio , Federica Dondero , Rachida Lebtahi , Maxime Ronot","doi":"10.1016/j.diii.2024.09.010","DOIUrl":"10.1016/j.diii.2024.09.010","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 41-42"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142356417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Added value of artificial intelligence solutions for arterial stenosis detection on head and neck CT angiography: A randomized crossover multi-reader multi-case study","authors":"Kunhua Li ,&nbsp;Yang Yang ,&nbsp;Yongwei Yang ,&nbsp;Qingrun Li ,&nbsp;Lanqian Jiao ,&nbsp;Ting Chen ,&nbsp;Dajing Guo","doi":"10.1016/j.diii.2024.07.008","DOIUrl":"10.1016/j.diii.2024.07.008","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to investigate the added value of artificial intelligence (AI) solutions for the detection of arterial stenosis (AS) on head and neck CT angiography (CTA).</div></div><div><h3>Materials and methods</h3><div>Patients who underwent head and neck CTA examinations at two hospitals were retrospectively included. CTA examinations were randomized into group 1 (without AI-washout-with AI) and group 2 (with AI-washout-without AI), and six readers (two radiology residents, two non-neuroradiologists, and two neuroradiologists) independently interpreted each CTA examination without and with AI solutions. Additionally, reading time was recorded for each patient. Digital subtraction angiography was used as the standard of reference. The diagnostic performance for AS at lesion and patient levels with four AS thresholds (30 %, 50 %, 70 %, and 100 %) was assessed by calculating sensitivity, false-positive lesions index (FPLI), specificity, and accuracy.</div></div><div><h3>Results</h3><div>A total of 268 patients (169 men, 63.1 %) with a median age of 65 years (first quartile, 57; third quartile, 72; age range: 28–88 years) were included. At the lesion level, AI improved the sensitivity of all readers by 5.2 % for detecting AS ≥ 30 % (<em>P</em> &lt; 0.001). Concurrently, AI reduced the FPLI of all readers and specifically neuroradiologists for detecting non-occlusive AS (all <em>P</em> &lt; 0.05). At the patient level, AI improved the accuracy of all readers by 4.1 % (73.9 % [1189/1608] without AI vs. 78.0 % [1254/1608] with AI) (<em>P</em> &lt; 0.001). Sensitivity for AS ≥ 30 % and the specificity for AS ≥ 70 % increased for all readers with AI assistance (<em>P</em> = 0.01). The median reading time for all readers was reduced from 268 s without AI to 241 s with AI (<em>P</em> <em>&lt;</em> 0.001).</div></div><div><h3>Conclusion</h3><div>AI-assisted diagnosis improves the performance of radiologists in detecting head and neck AS, and shortens reading time.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 11-21"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomics machine learning algorithm facilitates detection of small pancreatic neuroendocrine tumors on CT","authors":"Felipe Lopez-Ramirez ,&nbsp;Sahar Soleimani ,&nbsp;Javad R. Azadi ,&nbsp;Sheila Sheth ,&nbsp;Satomi Kawamoto ,&nbsp;Ammar A. Javed ,&nbsp;Florent Tixier ,&nbsp;Ralph H. Hruban ,&nbsp;Elliot K. Fishman ,&nbsp;Linda C. Chu","doi":"10.1016/j.diii.2024.08.003","DOIUrl":"10.1016/j.diii.2024.08.003","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to develop a radiomics-based algorithm to identify small pancreatic neuroendocrine tumors (PanNETs) on CT and evaluate its robustness across manual and automated segmentations, exploring the feasibility of automated screening.</div></div><div><h3>Materials and methods</h3><div>Patients with pathologically confirmed T1 stage PanNETs and healthy controls undergoing dual-phase CT imaging were retrospectively identified. Manual segmentation of pancreas and tumors was performed, then automated pancreatic segmentations were generated using a pretrained neural network. A total of 1223 radiomics features were independently extracted from both segmentation volumes, in the arterial and venous phases separately. Ten final features were selected to train classifiers to identify PanNETs and controls. The cohort was divided into training and testing sets, and performance of classifiers was assessed using area under the receiver operator characteristic curve (AUC), specificity and sensitivity, and compared against two radiologists blinded to the diagnoses.</div></div><div><h3>Results</h3><div>A total of 135 patients with 142 PanNETs, and 135 healthy controls were included. There were 168 women and 102 men, with a mean age of 55.4 ± 11.6 (standard deviation) years (range: 20–85 years). Median PanNET size was 1.3 cm (Q1, 1.0; Q3, 1.5; range: 0.5–1.9). The arterial phase LightGBM model achieved the best performance in the test set, with 90 % sensitivity (95 % confidence interval [CI]: 80–98), 76 % specificity (95 % CI: 62–88) and an AUC of 0.87 (95 % CI: 0.79–0.94). Using features from the automated segmentations, this model achieved an AUC of 0.86 (95 % CI: 0.79–0.93). In comparison, the two radiologists achieved a mean 50 % sensitivity and 100 % specificity using arterial phase CT images.</div></div><div><h3>Conclusion</h3><div>Radiomics features identify small PanNETs, with stable performance when extracted using automated segmentations. These models demonstrate high sensitivity, complementing the high specificity of radiologists, and could serve as opportunistic screeners.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 28-40"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison between artificial intelligence solution and radiologist for the detection of pelvic, hip and extremity fractures on radiographs in adult using CT as standard of reference","authors":"Maxime Pastor ,&nbsp;Djamel Dabli ,&nbsp;Raphaël Lonjon ,&nbsp;Chris Serrand ,&nbsp;Fehmi Snene ,&nbsp;Fayssal Trad ,&nbsp;Fabien de Oliveira ,&nbsp;Jean-Paul Beregi ,&nbsp;Joël Greffier","doi":"10.1016/j.diii.2024.09.004","DOIUrl":"10.1016/j.diii.2024.09.004","url":null,"abstract":"<div><h3>Purpose</h3><div>The purpose of this study was to compare the diagnostic performance of an artificial intelligence (AI) solution for the detection of fractures of pelvic, proximal femur or extremity fractures in adults with radiologist interpretation of radiographs, using standard dose CT examination as the standard of reference.</div></div><div><h3>Materials and methods</h3><div>This retrospective study included 94 adult patients with suspected bone fractures who underwent a standard dose CT examination and radiographs of the pelvis and/or hip and extremities at our institution between January 2022 and August 2023. For all patients, an AI solution was used retrospectively on the radiographs to detect and localize bone fractures of the pelvis and/or hip and extremities. Results of the AI solution were compared to the reading of each radiograph by a radiologist using McNemar test. The results of standard dose CT examination as interpreted by a senior radiologist were used as the standard of reference.</div></div><div><h3>Result</h3><div>A total of 94 patients (63 women; mean age, 56.4 ± 22.5 [standard deviation] years) were included. Forty-seven patients had at least one fracture, and a total of 71 fractures were deemed present using the standard of reference (25 hand/wrist, 16 pelvis, 30 foot/ankle). Using the standard of reference, the analysis of radiographs by the AI solution resulted in 58 true positive, 13 false negative, 33 true negative and 15 false positive findings, yielding 82 % sensitivity (58/71; 95 % confidence interval [CI]: 71–89 %), 69 % specificity (33/48; 95 % CI: 55–80 %), and 76 % accuracy (91/119; 95 % CI: 69–84 %). Using the standard of reference, the reading of the radiologist resulted in 65 true positive, 6 false negative, 42 true negative and 6 false positive findings, yielding 92 % sensitivity (65/71; 95 % CI: 82–96 %), 88 % specificity (42/48; 95 % CI: 75–94 %), and 90 % accuracy (107/119; 95 % CI: 85–95 %). The radiologist outperformed the AI solution in terms of sensitivity (<em>P</em> = 0.045), specificity (<em>P</em> = 0.016), and accuracy (<em>P</em> &lt; 0.001).</div></div><div><h3>Conclusion</h3><div>In this study, the radiologist outperformed the AI solution for the diagnosis of pelvic, hip and extremity fractures of the using radiographs. This raises the question of whether a strong standard of reference for evaluating AI solutions should be used in future studies comparing AI and human reading in fracture detection using radiographs.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 22-27"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dural arteriovenous fistula masquerading as neuromyelitis optica spectrum disorder","authors":"Sylvain Bourdoncle ,&nbsp;Caroline Papeix ,&nbsp;Augustin Lecler","doi":"10.1016/j.diii.2024.09.003","DOIUrl":"10.1016/j.diii.2024.09.003","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 43-44"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142298958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence solutions for head and neck CT angiography: Ready for prime time?","authors":"Alexandre Bani-Sadr ,&nbsp;Augustin Lecler","doi":"10.1016/j.diii.2024.09.005","DOIUrl":"10.1016/j.diii.2024.09.005","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 1-2"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142256137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in interventional radiology: Current concepts and future trends","authors":"Armelle Lesaunier ,&nbsp;Julien Khlaut ,&nbsp;Corentin Dancette ,&nbsp;Lambros Tselikas ,&nbsp;Baptiste Bonnet ,&nbsp;Tom Boeken","doi":"10.1016/j.diii.2024.08.004","DOIUrl":"10.1016/j.diii.2024.08.004","url":null,"abstract":"<div><div>While artificial intelligence (AI) is already well established in diagnostic radiology, it is beginning to make its mark in interventional radiology. AI has the potential to dramatically change the daily practice of interventional radiology at several levels. In the preoperative setting, recent advances in deep learning models, particularly foundation models, enable effective management of multimodality and increased autonomy through their ability to function minimally without supervision. Multimodality is at the heart of patient-tailored management and in interventional radiology, this translates into the development of innovative models for patient selection and outcome prediction. In the perioperative setting, AI is manifesting itself in applications that assist radiologists in image analysis and real-time decision making, thereby improving the efficiency, accuracy, and safety of interventions. In synergy with advances in robotic technologies, AI is laying the groundwork for an increased autonomy. From a research perspective, the development of artificial health data, such as AI-based data augmentation, offers an innovative solution to this central issue and promises to stimulate research in this area. This review aims to provide the medical community with the most important current and future applications of AI in interventional radiology.</div></div>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 5-10"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142194565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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 for bone fracture detection: A promising tool but no substitute for human expertise","authors":"Daphné Guenoun ,&nbsp;Mickaël Tordjman","doi":"10.1016/j.diii.2024.10.004","DOIUrl":"10.1016/j.diii.2024.10.004","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":"106 1","pages":"Pages 3-4"},"PeriodicalIF":4.9,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142510986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time multislice MR-thermometry of the prostate: Assessment of feasibility, accuracy and sources of biases in patients.","authors":"Clément Marcelin, Amandine Crombé, Eva Jambon, Grégoire Robert, Franck Bladou, Pierre Bour, Thibaut Faller, Valéry Ozenne, Nicolas Grenier, Bruno Quesson","doi":"10.1016/j.diii.2024.11.006","DOIUrl":"https://doi.org/10.1016/j.diii.2024.11.006","url":null,"abstract":"<p><strong>Purpose: </strong>The primary purpose of this study was to evaluate the accuracy of an MR-thermometry sequence for monitoring prostate temperature. The secondary purposes were to analyze clinical and technical factors that may affect accuracy and testing the method in a realistic setting, with MR-guided Laser ablation on an ex vivo muscle sample.</p><p><strong>Materials and methods: </strong>An ex vivo muscle sample was subjected to Laser ablation while using a two-dimensional multislice segmented echo planar imaging sequence for MR thermometry. The MR thermometry measurements were compared with invasive sensor temperature readings to assess accuracy. Subsequently, 56 men with a median age of 70 years (age range: 53-84 years) who underwent prostate MRI examinations at 1.5- (n = 27) or 3 T (n = 24) were prospectively included. For each patient, the proportion of 'noisy voxels' (i.e., those with a temporal standard deviation of temperature [SD(T)] > 2 °C) in the prostate was calculated. The impact of clinical and technical factors on the proportion of noisy voxels was also examined.</p><p><strong>Results: </strong>MR-thermometry showed excellent correlation with invasive sensors during MR-guided Laser ablation on the ex vivo muscle sample. The median proportion of noisy voxels per patient in the entire cohort was 1 % (Q1, 0.2; Q3, 4.9; range: 0-90.4). No significant differences in median proportion of noisy voxels were observed between examinations performed at 1.5 T and those at 3 T (P = 0.89 before and after adjustment). No clinical or technical factors significantly influenced the proportion of noisy voxels.</p><p><strong>Conclusion: </strong>Two-dimensional real time multislice MR-thermometry is feasible and accurate for monitoring prostate temperature in patients.</p>","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142873071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Node-RADS: Finally, something new on the front of cross-sectional imaging of lymph nodes?","authors":"Olivier Rouvière, Laurence Rocher","doi":"10.1016/j.diii.2024.12.002","DOIUrl":"https://doi.org/10.1016/j.diii.2024.12.002","url":null,"abstract":"","PeriodicalId":48656,"journal":{"name":"Diagnostic and Interventional Imaging","volume":" ","pages":""},"PeriodicalIF":4.9,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142856298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}