Insights into Imaging最新文献

{"title":"The value of restriction spectrum imaging in predicting lymph node metastases in rectal cancer: a comparative study with diffusion-weighted imaging and diffusion kurtosis imaging.","authors":"Huijia Yin, Wenling Liu, Qin Xue, Chen Song, Jipeng Ren, Ziqiang Li, Dongdong Wang, Kaiyu Wang, Dongming Han, Ruifang Yan","doi":"10.1186/s13244-024-01852-z","DOIUrl":"https://doi.org/10.1186/s13244-024-01852-z","url":null,"abstract":"<p><strong>Background: </strong>To investigate the efficacy of three-compartment restriction spectrum imaging (RSI), diffusion kurtosis imaging (DKI), and diffusion-weighted imaging (DWI) in the assessment of lymph node metastases (LNM) in rectal cancer.</p><p><strong>Methods: </strong>A total of 77 patients with rectal cancer who underwent pelvic MRI were enrolled. RSI-derived parameters (f₁, f₂, and f₃), DKI-derived parameters (D_app and K_app), and the DWI-derived parameter (ADC) were calculated and compared using a Mann-Whitney U test or independent samples t-test. Logistic regression (LR) analysis was used to identify independent predictors of LNM status. Area under the receiver operating characteristic curve (AUC) and Delong analysis were performed to assess the diagnostic performance of each parameter.</p><p><strong>Results: </strong>The LNM-positive group exhibited significantly higher f₁ and K_app levels and significantly lower f₃, D_app, and ADC levels compared to the LNM-negative group (p < 0.05). There was no difference in f₂ levels between the two groups (p = 0.783). LR analysis showed that D_app and K_app were independent predictors of a positive LNM status. AUC and Delong analysis showed that DKI (D_app + K_app) exhibited significantly higher diagnostic efficacy (AUC = 0.908; sensitivity = 87.10%; specificity = 86.96%) than RSI (f₁ + f₃) and DWI (ADC), with AUCs were 0.842 and 0.771 (Z = 2.113, 3.453; p = 0.035, < 0.001, respectively). The AUC performance between RSI and DWI was also statistically significant (Z = 1.972, p = 0.049).</p><p><strong>Conclusion: </strong>The RSI model is superior to conventional DWI but inferior to DKI in differentiation between LNM-positive and LNM-negative rectal cancers. Further study is needed before it could serve as a promising biomarker for guiding effective treatment strategies.</p><p><strong>Critical relevance statement: </strong>The three-compartment restriction spectrum imaging was able to differentiate between LNM-positive and LNM-negative rectal cancers with high accuracy, which has the potential to serve as a promising biomarker that could guide treatment strategies.</p><p><strong>Key points: </strong>Three-compartment restriction spectrum imaging could differentiate lymph node metastases in rectal cancer. Diffusion kurtosis imaging and diffusion-weighted were associated with lymph node metastases in rectal cancer. The combination of different parameters has the potential to serve as a promising biomarker.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"302"},"PeriodicalIF":4.1,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142853323","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":"How can effective communication help radiographers meet the expectations of patients-COMMUNICATION-a joint statement by the ESR & EFRS.","authors":"Charlotte Beardmore, Andrew England, Cheryl Cruwys, Dominique Carrié","doi":"10.1186/s13244-024-01868-5","DOIUrl":"https://doi.org/10.1186/s13244-024-01868-5","url":null,"abstract":"<p><p>The Patient Advisory Group (PAG) of the European Society of Radiology, in collaboration with the European Federation of Radiographer Societies (EFRS), aims to highlight, in this short paper, the important role that communication plays when trying to meet patients' expectations throughout their imaging journey in a radiology department. The interactions with radiography professionals carrying out diagnostic or interventional procedures are critical in supporting high-quality patient care and patients' expectations. The key areas of consideration have been summarised in an easy-to-remember mnemonic: COMMUNICATION. There are different healthcare systems and medical imaging services across Europe, and healthcare providers should be mindful, when setting up new operational procedures, of the need for processes and systems to support the delivery of patient-centred care. At times when new or improved technology is being introduced, such as artificial intelligence applications, telemedicine, robotisation of interventional procedures, and digitised records, the impact on patient-radiographer communication and interactions should be considered. CRITICAL RELEVANCE STATEMENT: Effective communication helps radiographers meet patients' expectations by ensuring clear explanations, reducing anxiety, fostering trust, and improving cooperation during procedures. This enhances patient satisfaction, safety, and the overall quality of care, aligning with professional standards and patient-centred healthcare. KEY POINTS: Patient-centred imaging services are key to meeting patients' demands. Radiography professionals in radiology departments and medical imaging services should always communicate effectively with patients. This ESR-Patient Advisory Group publication attempts to summarise the key areas that should be embedded in patient communication. The 'COMMUNICATION' statement can be used as a reminder to all radiography professionals to work to improve patient-radiographer interactions and provide patient-focused services.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"300"},"PeriodicalIF":4.1,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142852917","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":"Deep learning for opportunistic, end-to-end automated assessment of epicardial adipose tissue in pre-interventional, ECG-gated spiral computed tomography.","authors":"Maike Theis, Laura Garajová, Babak Salam, Sebastian Nowak, Wolfgang Block, Ulrike I Attenberger, Daniel Kütting, Julian A Luetkens, Alois M Sprinkart","doi":"10.1186/s13244-024-01875-6","DOIUrl":"https://doi.org/10.1186/s13244-024-01875-6","url":null,"abstract":"<p><strong>Objectives: </strong>Recently, epicardial adipose tissue (EAT) assessed by CT was identified as an independent mortality predictor in patients with various cardiac diseases. Our goal was to develop a deep learning pipeline for robust automatic EAT assessment in CT.</p><p><strong>Methods: </strong>Contrast-enhanced ECG-gated cardiac and thoraco-abdominal spiral CT imaging from 1502 patients undergoing transcatheter aortic valve replacement (TAVR) was included. Slice selection at aortic valve (AV)-level and EAT segmentation were performed manually as ground truth. For slice extraction, two approaches were compared: A regression model with a 2D convolutional neural network (CNN) and a 3D CNN utilizing reinforcement learning (RL). Performance evaluation was based on mean absolute z-deviation to the manually selected AV-level (Δz). For tissue segmentation, a 2D U-Net was trained on single-slice images at AV-level and compared to the open-source body and organ analysis (BOA) framework using Dice score. Superior methods were selected for end-to-end evaluation, where mean absolute difference (MAD) of EAT area and tissue density were compared. 95% confidence intervals (CI) were assessed for all metrics.</p><p><strong>Results: </strong>Slice extraction using RL was slightly more precise (Δz: RL 1.8 mm (95% CI: [1.6, 2.0]), 2D CNN 2.0 mm (95% CI: [1.8, 2.3])). For EAT segmentation at AV-level, the 2D U-Net outperformed BOA significantly (Dice score: 2D U-Net 91.3% (95% CI: [90.7, 91.8]), BOA 85.6% (95% CI: [84.7, 86.5])). The end-to-end evaluation revealed high agreement between automatic and manual measurements of EAT (MAD area: 1.1 cm² (95% CI: [1.0, 1.3]), MAD density: 2.2 Hounsfield units (95% CI: [2.0, 2.5])).</p><p><strong>Conclusions: </strong>We propose a method for robust automatic EAT assessment in spiral CT scans enabling opportunistic evaluation in clinical routine.</p><p><strong>Critical relevance statement: </strong>Since inflammatory changes in epicardial adipose tissue (EAT) are associated with an increased risk of cardiac diseases, automated evaluation can serve as a basis for developing automated cardiac risk assessment tools, which are essential for efficient, large-scale assessment in opportunistic settings.</p><p><strong>Key points: </strong>Deep learning methods for automatic assessment of epicardial adipose tissue (EAT) have great potential. A 2-step approach with slice extraction and tissue segmentation enables robust automated evaluation of EAT. End-to-end automation enables large-scale research on the value of EAT for outcome analysis.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"301"},"PeriodicalIF":4.1,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142854175","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":"Fully automated MRI-based convolutional neural network for noninvasive diagnosis of cirrhosis.","authors":"Tianying Zheng, Yajing Zhu, Yidi Chen, Shengshi Mai, Lixin Xu, Hanyu Jiang, Ting Duan, Yuanan Wu, Yali Qu, Yinan Chen, Bin Song","doi":"10.1186/s13244-024-01872-9","DOIUrl":"10.1186/s13244-024-01872-9","url":null,"abstract":"<p><strong>Objectives: </strong>To develop and externally validate a fully automated diagnostic convolutional neural network (CNN) model for cirrhosis based on liver MRI and serum biomarkers.</p><p><strong>Methods: </strong>This multicenter retrospective study included consecutive patients receiving pathological evaluation of liver fibrosis stage and contrast-enhanced liver MRI between March 2010 and January 2024. On the training dataset, an MRI-based CNN model was constructed for cirrhosis against pathology, and then a combined model was developed integrating the CNN model and serum biomarkers. On the testing datasets, the area under the receiver operating characteristic curve (AUC) was computed to compare the diagnostic performance of the combined model with that of aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), and radiologists. The influence of potential confounders on the diagnostic performance was evaluated by subgroup analyses.</p><p><strong>Results: </strong>A total of 1315 patients (median age, 54 years; 1065 men; training, n = 840) were included, 855 (65%) with pathological cirrhosis. The CNN model was constructed on pre-contrast T1- and T2-weighted imaging, and the combined model was developed integrating the CNN model, age, and eight serum biomarkers. On the external testing dataset, the combined model achieved an AUC of 0.86, which outperformed FIB-4, APRI and two radiologists (AUC: 0.67 to 0.73, all p < 0.05). Subgroup analyses revealed comparable diagnostic performances of the combined model in patients with different sizes of focal liver lesions.</p><p><strong>Conclusion: </strong>Based on pre-contrast T1- and T2-weighted imaging, age, and serum biomarkers, the combined model allowed diagnosis of cirrhosis with moderate accuracy, independent of the size of focal liver lesions.</p><p><strong>Critical relevance statement: </strong>The fully automated convolutional neural network model utilizing pre-contrast MR imaging, age and serum biomarkers demonstrated moderate accuracy, outperforming FIB-4, APRI, and radiologists, independent of size of focal liver lesions, potentially facilitating noninvasive diagnosis of cirrhosis pending further validation.</p><p><strong>Key points: </strong>This fully automated convolutional neural network (CNN) model, using pre-contrast MRI, age, and serum biomarkers, diagnoses cirrhosis. The CNN model demonstrated an external testing dataset AUC of 0.86, independent of the size of focal liver lesions. The CNN model outperformed aminotransferase-to-platelet ratio index, fibrosis-4 index, and radiologists, potentially facilitating noninvasive diagnosis of cirrhosis.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"298"},"PeriodicalIF":4.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638457/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142812932","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":"CT imaging findings of invasive pulmonary fungal infections in hemato-oncologic children.","authors":"Leonor Alamo, Francesco Ceppi, Estelle Tenisch, Catherine Beigelman-Aubry","doi":"10.1186/s13244-024-01871-w","DOIUrl":"10.1186/s13244-024-01871-w","url":null,"abstract":"<p><p>Hemato-oncologic children form a heterogeneous group with a wide spectrum of ages, malignancy types, and immunosuppression grades during the different phases of their treatment. Immunosuppression is caused by multiple factors, including the malignancy itself, bone marrow suppression secondary to therapy, and wide use of steroids and antibiotics, among others. At the same time, the risk of infections in these patients remains high because of prolonged hospitalizations or the need for long-timing implanted devices between other features. In this context, a pulmonary fungal infection can rapidly turn into a life-threatening condition that requires early diagnosis and appropriate management. This pictorial essay illustrates the main imaging findings detected in chest computed tomography examinations performed in pediatric hemato-oncologic patients with proven pulmonary invasive fungal infections caused by Candida, Aspergillus, or Mucor. In addition, it describes useful clues for limiting differential diagnoses, reviews the literature on pediatric patients, and compares imaging findings in adults and children. CRITICAL RELEVANCE STATEMENT: The main fungal pathogens causing invasive fungal infections (IFI) in hemato-oncologic children are Candida, Aspergillus, and Mucor. This review describes the most frequently affected organs and the most common imaging findings detected in chest CT exams in children with pulmonary IFI. KEY POINTS: To review the main computed tomography imaging findings suggesting pulmonary invasive fungal infection (IFI) in hemato-oncologic children. To describe differences between pediatric and adult patients with proven pulmonary IFI. To provide useful clues for limiting the differential diagnosis of pulmonary IFI in pediatric patients.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"296"},"PeriodicalIF":4.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142812928","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":"The top 100 most-cited articles on artificial intelligence in breast radiology: a bibliometric analysis.","authors":"Sneha Singh, Nuala A Healy","doi":"10.1186/s13244-024-01869-4","DOIUrl":"10.1186/s13244-024-01869-4","url":null,"abstract":"<p><strong>Introduction: </strong>Artificial intelligence (AI) in radiology is a rapidly evolving field. In breast imaging, AI has already been applied in a real-world setting and multiple studies have been conducted in the area. The aim of this analysis is to identify the most influential publications on the topic of artificial intelligence in breast imaging.</p><p><strong>Methods: </strong>A retrospective bibliometric analysis was conducted on artificial intelligence in breast radiology using the Web of Science database. The search strategy involved searching for the keywords 'breast radiology' or 'breast imaging' and the various keywords associated with AI such as 'deep learning', 'machine learning,' and 'neural networks'.</p><p><strong>Results: </strong>From the top 100 list, the number of citations per article ranged from 30 to 346 (average 85). The highest cited article titled 'Artificial Neural Networks In Mammography-Application To Decision-Making In The Diagnosis Of Breast-Cancer' was published in Radiology in 1993. Eighty-three of the articles were published in the last 10 years. The journal with the greatest number of articles was Radiology (n = 22). The most common country of origin was the United States (n = 51). Commonly occurring topics published were the use of deep learning models for breast cancer detection in mammography or ultrasound, radiomics in breast cancer, and the use of AI for breast cancer risk prediction.</p><p><strong>Conclusion: </strong>This study provides a comprehensive analysis of the top 100 most-cited papers on the subject of artificial intelligence in breast radiology and discusses the current most influential papers in the field.</p><p><strong>Clinical relevance statement: </strong>This article provides a concise summary of the top 100 most-cited articles in the field of artificial intelligence in breast radiology. It discusses the most impactful articles and explores the recent trends and topics of research in the field.</p><p><strong>Key points: </strong>Multiple studies have been conducted on AI in breast radiology. The most-cited article was published in the journal Radiology in 1993. This study highlights influential articles and topics on AI in breast radiology.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"297"},"PeriodicalIF":4.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142812947","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":"Pediatric menisci: normal aspects, anatomical variants, lesions, tears, and postsurgical findings.","authors":"Flávia Ferreira Araújo, Júlio Brandão Guimarães, Isabela Azevedo Nicodemos da Cruz, Leticia Dos Reis Morimoto, Alípio Gomes Ormond Filho, Marcelo Astolfi Caetano Nico","doi":"10.1186/s13244-024-01867-6","DOIUrl":"10.1186/s13244-024-01867-6","url":null,"abstract":"<p><p>The reported incidence of meniscal tears in the pediatric age group has increased because of increased sports participation and more widespread use of MRI. Meniscal injury is one of the most commonly reported internal derangements in skeletally immature knees and can be associated with early degenerative joint disease leading to disability. The pediatric meniscus has particularities, and knowledge of normal anatomy, anatomical variations, and the patterns of meniscal injury in the pediatric age group is essential to provide a correct diagnosis. CRITICAL RELEVANCE STATEMENT: Accurate MRI interpretation of pediatric meniscal injuries is crucial. Understanding age-specific anatomy, vascularity, and variations can improve diagnostic precision, guiding targeted treatments to prevent early joint degeneration and disability. KEY POINTS: Meniscal lesions are common injuries in skeletally immature knees. Awareness of anatomical meniscus variants, patterns of injury, and associated injuries is essential. Meniscal tears in pediatric patients should be repaired if possible.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"295"},"PeriodicalIF":4.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638453/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142812939","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":"Preoperative detection of extraprostatic tumor extension in patients with primary prostate cancer utilizing [⁶⁸Ga]Ga-PSMA-11 PET/MRI.","authors":"Clemens P Spielvogel, Jing Ning, Kilian Kluge, David Haberl, Gabriel Wasinger, Josef Yu, Holger Einspieler, Laszlo Papp, Bernhard Grubmüller, Shahrokh F Shariat, Pascal A T Baltzer, Paola Clauser, Markus Hartenbach, Lukas Kenner, Marcus Hacker, Alexander R Haug, Sazan Rasul","doi":"10.1186/s13244-024-01876-5","DOIUrl":"10.1186/s13244-024-01876-5","url":null,"abstract":"<p><strong>Objectives: </strong>Radical prostatectomy (RP) is a common intervention in patients with localized prostate cancer (PCa), with nerve-sparing RP recommended to reduce adverse effects on patient quality of life. Accurate pre-operative detection of extraprostatic extension (EPE) remains challenging, often leading to the application of suboptimal treatment. The aim of this study was to enhance pre-operative EPE detection through multimodal data integration using explainable machine learning (ML).</p><p><strong>Methods: </strong>Patients with newly diagnosed PCa who underwent [⁶⁸Ga]Ga-PSMA-11 PET/MRI and subsequent RP were recruited retrospectively from two time ranges for training, cross-validation, and independent validation. The presence of EPE was measured from post-surgical histopathology and predicted using ML and pre-operative parameters, including PET/MRI-derived features, blood-based markers, histology-derived parameters, and demographic parameters. ML models were subsequently compared with conventional PET/MRI-based image readings.</p><p><strong>Results: </strong>The study involved 107 patients, 59 (55%) of whom were affected by EPE according to postoperative findings for the initial training and cross-validation. The ML models demonstrated superior diagnostic performance over conventional PET/MRI image readings, with the explainable boosting machine model achieving an AUC of 0.88 (95% CI 0.87-0.89) during cross-validation and an AUC of 0.88 (95% CI 0.75-0.97) during independent validation. The ML approach integrating invasive features demonstrated better predictive capabilities for EPE compared to visual clinical read-outs (Cross-validation AUC 0.88 versus 0.71, p = 0.02).</p><p><strong>Conclusion: </strong>ML based on routinely acquired clinical data can significantly improve the pre-operative detection of EPE in PCa patients, potentially enabling more accurate clinical staging and decision-making, thereby improving patient outcomes.</p><p><strong>Critical relevance statement: </strong>This study demonstrates that integrating multimodal data with machine learning significantly improves the pre-operative detection of extraprostatic extension in prostate cancer patients, outperforming conventional imaging methods and potentially leading to more accurate clinical staging and better treatment decisions.</p><p><strong>Key points: </strong>Extraprostatic extension is an important indicator guiding treatment approaches. Current assessment of extraprostatic extension is difficult and lacks accuracy. Machine learning improves detection of extraprostatic extension using PSMA-PET/MRI and histopathology.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"299"},"PeriodicalIF":4.1,"publicationDate":"2024-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11638435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142812943","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":"Assessment of vascular invasion of pancreatic ductal adenocarcinoma based on CE-boost black blood CT technique.","authors":"Yue Lin, Tongxi Liu, Yingying Hu, Yinghao Xu, Jian Wang, Sijia Guo, Sheng Xie, Hongliang Sun","doi":"10.1186/s13244-024-01870-x","DOIUrl":"10.1186/s13244-024-01870-x","url":null,"abstract":"<p><strong>Objectives: </strong>To explore the diagnostic efficacy of advanced intelligent clear-IQ engine (AiCE) and adaptive iterative dose reduction 3D (AIDR 3D), combination with and without the black blood CT technique (BBCT), for detecting vascular invasion in patients diagnosed with nonmetastatic pancreatic ductal adenocarcinoma (PDAC).</p><p><strong>Methods: </strong>A total of 35 consecutive patients diagnosed with PDAC, proceeding with contrast-enhanced abdominal CT scans, were enrolled in this study. The arterial and portal venous phase images were reconstructed using AiCE and AIDR 3D. The corresponding BBCT images were established as AiCE-BBCT and AIDR 3D-BBCT, respectively. Two observers scored the image quality independently. Cohen's kappa (k) value or intraclass correlation coefficient (ICC) was used to analyze consistency. The diagnostic performance of four algorithms in detecting vascular invasion in PDAC patients was assessed using the area under the curve (AUC).</p><p><strong>Results: </strong>The AiCE and AiCE-BBCT groups demonstrated superior image noise and diagnostic acceptability compared with AIDR 3D and AIDR 3D-BBCT groups (all p < 0.001), and the k value was 0.861-0.967 for both reviewers. In terms of diagnostic capability for vascular invasion in PDAC, the AiCE-BBCT group exhibited higher specificity (95.0%) and sensitivity (93.3%) compared to the AIDR 3D and AIDR 3D-BBCT groups, with an AUC of 0.942 (95% CI: 0.849-1.000, p < 0.05). Furthermore, all vascular evaluations conducted using AiCE-BBCT demonstrated better consistency (ICC: 0.847-0.935).</p><p><strong>Conclusion: </strong>The BBCT technique in conjunction with AiCE could lead to notable enhancements in both the image quality of PDAC images and the diagnostic performance for tumor vascular invasion.</p><p><strong>Critical relevance statement: </strong>Better diagnostic accuracy of vascular invasion of PDAC based on BBCT in combination with an AiCE is a critical factor in determining treatment strategies and patient outcomes.</p><p><strong>Key points: </strong>Identifying vascular invasion of PDAC is important for prognostication. Combined images provide improved image quality and higher diagnostic accuracy. Combined images can excellently display the vascular wall and invasion.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"293"},"PeriodicalIF":4.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621291/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785479","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":"MRI analysis of relative tumor enhancement in liver metastases and correlation with immunohistochemical features.","authors":"Felix Barajas Ordonez, Sebastian Gottschling, Kai Ina Eger, Jan Borggrefe, Dörthe Jechorek, Alexey Surov","doi":"10.1186/s13244-024-01866-7","DOIUrl":"10.1186/s13244-024-01866-7","url":null,"abstract":"<p><strong>Objective: </strong>Investigate the association between the relative tumor enhancement (RTE) of gadoxetic acid across various MRI phases and immunohistochemical (IHC) features in patients with liver metastases (LM) from colorectal cancer (CRC), breast cancer (BC), and pancreatic cancer (PC).</p><p><strong>Methods: </strong>A retrospective analysis was conducted on 68 patients with LM who underwent 1.5-T MRI scans. Non-contrast and contrast-enhanced T1-weighted (T1-w) gradient echo (GRE) sequences were acquired before LM biopsy. RTE values among LM groups were compared by cancer type using analysis of variance. The relationships between RTE and IHC features tumor stroma ratio, cell count, Ki67 proliferation index, and CD45 expression were evaluated using Spearman's rank correlation coefficients.</p><p><strong>Results: </strong>Significant differences in RTE were observed across different MRI phases among patients with BCLM, CRCLM, and PCLM: arterial phase (0.75 ± 0.42, 0.37 ± 0.36, and 0.44 ± 0.19), portal venous phase (1.09 ± 0.41, 0.59 ± 0.44, and 0.53 ± 0.24), and venous phase (1.11 ± 0.45, 0.65 ± 0.61, and 0.50 ± 0.20). In CRCLM, RTE inversely correlated with mean Ki67 (r = -0.50, p = 0.01) in the hepatobiliary phase. Negative correlations between RTE and CD45 expression were found in PCLM and CRCLM in the portal venous phase (r = -0.69, p = 0.01 and r = -0.41, p = 0.04) and the venous phase (r = -0.65, p = 0.01 and r = -0.44, p = 0.02).</p><p><strong>Conclusion: </strong>Significant variations in RTE were identified among different types of LM, with correlations between RTE values and IHC markers such as CD45 and Ki67 suggesting that RTE may serve as a non-invasive biomarker for predicting IHC features in LM.</p><p><strong>Critical relevance statement: </strong>RTE values serve as a predictive biomarker for IHC features in liver metastasis, potentially enhancing non-invasive patient assessment, disease monitoring, and treatment planning.</p><p><strong>Key points: </strong>Few studies link gadoxetic acid-enhanced MRI with immunohistochemistry in LM. RTE varies by liver metastasis type and correlates with CD45 and Ki67. RTE reflects IHC features in LM, aiding non-invasive assessment.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"294"},"PeriodicalIF":4.1,"publicationDate":"2024-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11621246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142785489","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}