Insights into Imaging最新文献

{"title":"An ensemble machine learning model assists in the diagnosis of gastric ectopic pancreas and gastric stromal tumors.","authors":"Kui Sun, Ying Wang, Rongchao Shi, Siyu Wu, Ximing Wang","doi":"10.1186/s13244-024-01809-2","DOIUrl":"https://doi.org/10.1186/s13244-024-01809-2","url":null,"abstract":"<p><strong>Objective: </strong>To develop an ensemble machine learning (eML) model using multiphase computed tomography (MPCT) for distinguishing between gastric ectopic pancreas (GEP) and gastric stromal tumors (GIST) in lesions < 3 cm.</p><p><strong>Methods: </strong>In this study, we retrospectively collected MPCT images from 138 patients between April 2017 and June 2023 across two centers. Cohort 1 comprised 94 patients divided into a training cohort and an internal validation cohort, while the 44 patients from Cohort 2 constituted the external validation cohort. Deep learning (DL) models were constructed based on the lesion region, and radiomics features were extracted to develop radiomics models, which were later integrated into the fusion model. Model performance was assessed through the analysis of the area under the receiver operating characteristic curve (AUROC). The diagnostic efficacy of the optimal model was compared with that of a radiologist. Additionally, the radiologist with the assistance of the eML model provides a secondary diagnosis, to assess the potential clinical value of the model.</p><p><strong>Results: </strong>After evaluation using an external validation cohort, the radiomics model demonstrated the highest performance in the venous phase, achieving AUROC of 0.87. The DL model showed optimal performance in the non-contrast phase, with AUROC of 0.81. The eML achieved the best performance across all models, with AUROC of 0.90. The use of eML-assisted analysis resulted in a significant improvement in the junior radiologist's accuracy, rising from 0.77 to 0.93 (p < 0.05). However, the senior radiologist's accuracy, while improving from 0.86 to 0.95, did not exhibit a statistically significant difference.</p><p><strong>Conclusion: </strong>eML model based on MPCT can effectively distinguish between GEPs and GISTs < 3 cm.</p><p><strong>Critical relevance statement: </strong>The multiphase CT-based fusion model, incorporating radiomics and DL technology, proves effective in distinguishing between GEP and gastric stromal tumors, serving as a valuable tool to enhance diagnoses and offering references for clinical decision-making.</p><p><strong>Key points: </strong>No studies yet differentiated these tumors via radiomics or DL. Radiomics and DL methodologies unveil potentially distinct phenotypes within lesions. Quantitative analysis on CT for GIST and ectopic pancreas. Ensemble learning aids accurate diagnoses, assisting treatment decisions.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"225"},"PeriodicalIF":4.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424595/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142346025","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":"A practical risk stratification system based on ultrasonography and clinical characteristics for predicting the malignancy of soft tissue masses.","authors":"Ying-Lun Zhang, Meng-Jie Wu, Yu Hu, Xiao-Jing Peng, Qian Ma, Cui-Lian Mao, Ye Dong, Zong-Kai Wei, Ying-Qian Gao, Qi-Yu Yao, Jing Yao, Xin-Hua Ye, Ju-Ming Li, Ao Li","doi":"10.1186/s13244-024-01802-9","DOIUrl":"https://doi.org/10.1186/s13244-024-01802-9","url":null,"abstract":"<p><strong>Objective: </strong>To establish a practical risk stratification system (RSS) based on ultrasonography (US) and clinical characteristics for predicting soft tissue masses (STMs) malignancy.</p><p><strong>Methods: </strong>This retrospective multicenter study included patients with STMs who underwent US and pathological examinations between April 2018 and April 2023. Chi-square tests and multivariable logistic regression analyses were performed to assess the association of US and clinical characteristics with the malignancy of STMs in the training set. The RSS was constructed based on the scores of risk factors and validated externally.</p><p><strong>Results: </strong>The training and validation sets included 1027 STMs (mean age, 50.90 ± 16.64, 442 benign and 585 malignant) and 120 STMs (mean age, 51.93 ± 17.90, 69 benign and 51 malignant), respectively. The RSS was constructed based on three clinical characteristics (age, duration, and history of malignancy) and six US characteristics (size, shape, margin, echogenicity, bone invasion, and vascularity). STMs were assigned to six categories in the RSS, including no abnormal findings, benign, probably benign (fitted probabilities [FP] for malignancy: 0.001-0.008), low suspicion (FP: 0.008-0.365), moderate suspicion (FP: 0.189-0.911), and high suspicion (FP: 0.798-0.999) for malignancy. The RSS displayed good diagnostic performance in the training and validation sets with area under the receiver operating characteristic curve (AUC) values of 0.883 and 0.849, respectively.</p><p><strong>Conclusion: </strong>The practical RSS based on US and clinical characteristics could be useful for predicting STM malignancy, thereby providing the benefit of timely treatment strategy management to STM patients.</p><p><strong>Critical relevance statement: </strong>With the help of the RSS, better communication between radiologists and clinicians can be realized, thus facilitating tumor management.</p><p><strong>Key points: </strong>There is no recognized grading system for STM management. A stratification system based on US and clinical features was built. The system realized great communication between radiologists and clinicians in tumor management.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"226"},"PeriodicalIF":4.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424597/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142346015","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":"I thought it was a hemangioma! A pictorial essay about common and uncommon liver hemangiomas' mimickers.","authors":"Matteo Bonatti, Riccardo Valletta, Valentina Corato, Tommaso Gorgatti, Andrea Posteraro, Vincenzo Vingiani, Fabio Lombardo, Giacomo Avesani, Andrea Mega, Giulia A Zamboni","doi":"10.1186/s13244-024-01745-1","DOIUrl":"https://doi.org/10.1186/s13244-024-01745-1","url":null,"abstract":"<p><p>Focal liver lesions are frequently encountered during imaging studies, and hemangiomas represent the most common solid liver lesion. Liver hemangiomas usually show characteristic imaging features that enable characterization without the need for biopsy or follow-up. On the other hand, there are many benign and malignant liver lesions that may show one or more imaging features resembling hemangiomas that radiologists must be aware of. In this article we will review the typical imaging features of liver hemangiomas and will show a series of potential liver hemangiomas' mimickers, giving radiologists some hints for improving differential diagnoses. CRITICAL RELEVANCE STATEMENT: The knowledge of imaging features of potential liver hemangiomas mimickers is fundamental to avoid misinterpretation. KEY POINTS: Liver hemangiomas typically show imaging features that enable avoiding a biopsy. Many benign and malignant liver lesions show imaging features resembling hemangiomas. Radiologists must know the potentially misleading imaging features of hemangiomas' mimickers.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"228"},"PeriodicalIF":4.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11413281/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142286271","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":"Explainable breast cancer molecular expression prediction using multi-task deep-learning based on 3D whole breast ultrasound.","authors":"Zengan Huang, Xin Zhang, Yan Ju, Ge Zhang, Wanying Chang, Hongping Song, Yi Gao","doi":"10.1186/s13244-024-01810-9","DOIUrl":"https://doi.org/10.1186/s13244-024-01810-9","url":null,"abstract":"<p><strong>Objectives: </strong>To noninvasively estimate three breast cancer biomarkers, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) and enhance performance and interpretability via multi-task deep learning.</p><p><strong>Methods: </strong>The study included 388 breast cancer patients who received the 3D whole breast ultrasound system (3DWBUS) examinations at Xijing Hospital between October 2020 and September 2021. Two predictive models, a single-task and a multi-task, were developed; the former predicts biomarker expression, while the latter combines tumor segmentation with biomarker prediction to enhance interpretability. Performance evaluation included individual and overall prediction metrics, and Delong's test was used for performance comparison. The models' attention regions were visualized using Grad-CAM + + technology.</p><p><strong>Results: </strong>All patients were randomly split into a training set (n = 240, 62%), a validation set (n = 60, 15%), and a test set (n = 88, 23%). In the individual evaluation of ER, PR, and HER2 expression prediction, the single-task and multi-task models achieved respective AUCs of 0.809 and 0.735 for ER, 0.688 and 0.767 for PR, and 0.626 and 0.697 for HER2, as observed in the test set. In the overall evaluation, the multi-task model demonstrated superior performance in the test set, achieving a higher macro AUC of 0.733, in contrast to 0.708 for the single-task model. The Grad-CAM + + method revealed that the multi-task model exhibited a stronger focus on diseased tissue areas, improving the interpretability of how the model worked.</p><p><strong>Conclusion: </strong>Both models demonstrated impressive performance, with the multi-task model excelling in accuracy and offering improved interpretability on noninvasive 3DWBUS images using Grad-CAM + + technology.</p><p><strong>Critical relevance statement: </strong>The multi-task deep learning model exhibits effective prediction for breast cancer biomarkers, offering direct biomarker identification and improved clinical interpretability, potentially boosting the efficiency of targeted drug screening.</p><p><strong>Key points: </strong>Tumoral biomarkers are paramount for determining breast cancer treatment. The multi-task model can improve prediction performance, and improve interpretability in clinical practice. The 3D whole breast ultrasound system-based deep learning models excelled in predicting breast cancer biomarkers.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"227"},"PeriodicalIF":4.1,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424596/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142346027","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":"Accelerated 3D whole-heart non-contrast-enhanced mDIXON coronary MR angiography using deep learning-constrained compressed sensing reconstruction","authors":"Xi Wu, Xun Yue, Pengfei Peng, Xianzheng Tan, Feng Huang, Lei Cai, Lei Li, Shuai He, Xiaoyong Zhang, Peng Liu, Jiayu Sun","doi":"10.1186/s13244-024-01797-3","DOIUrl":"https://doi.org/10.1186/s13244-024-01797-3","url":null,"abstract":"To investigate the feasibility of a deep learning-constrained compressed sensing (DL-CS) method in non-contrast-enhanced modified DIXON (mDIXON) coronary magnetic resonance angiography (MRA) and compare its diagnostic accuracy using coronary CT angiography (CCTA) as a reference standard. Ninety-nine participants were prospectively recruited for this study. Thirty healthy subjects (age range: 20–65 years; 50% female) underwent three non-contrast mDIXON-based coronary MRA sequences including DL-CS, CS, and conventional sequences. The three groups were compared based on the scan time, subjective image quality score, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The remaining 69 patients suspected of coronary artery disease (CAD) (age range: 39–83 years; 51% female) underwent the DL-CS coronary MRA and its diagnostic performance was compared with that of CCTA. The scan time for the DL-CS and CS sequences was notably shorter than that of the conventional sequence (9.6 ± 3.1 min vs 10.0 ± 3.4 min vs 13.0 ± 4.9 min; p < 0.001). The DL-CS sequence obtained the highest image quality score, mean SNR, and CNR compared to CS and conventional methods (all p < 0.001). Compared to CCTA, the accuracy, sensitivity, and specificity of DL-CS mDIXON coronary MRA per patient were 84.1%, 92.0%, and 79.5%; those per vessel were 90.3%, 82.6%, and 92.5%; and those per segment were 98.0%, 85.1%, and 98.0%, respectively. The DL-CS mDIXON coronary MRA provided superior image quality and short scan time for visualizing coronary arteries in healthy individuals and demonstrated high diagnostic value compared to CCTA in CAD patients. DL-CS resulted in improved image quality with an acceptable scan time, and demonstrated excellent diagnostic performance compared to CCTA, which could be an alternative to enhance the workflow of coronary MRA. ","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"77 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259342","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":"ECR 2024 Book of Abstracts","authors":"","doi":"10.1186/s13244-024-01766-w","DOIUrl":"https://doi.org/10.1186/s13244-024-01766-w","url":null,"abstract":"&lt;p&gt;&lt;b&gt;Open Access&lt;/b&gt; This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.&lt;/p&gt;\u0000&lt;p&gt;Reprints and permissions&lt;/p&gt;&lt;img alt=\"Check for updates. Verify currency and authenticity via CrossMark\" height=\"81\" loading=\"lazy\" src=\"data:image/svg+xml;base64,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","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"80 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259343","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":"Focal cortical dysplasia lesion segmentation using multiscale transformer","authors":"Xiaodong Zhang, Yongquan Zhang, Changmiao Wang, Lin Li, Fengjun Zhu, Yang Sun, Tong Mo, Qingmao Hu, Jinping Xu, Dezhi Cao","doi":"10.1186/s13244-024-01803-8","DOIUrl":"https://doi.org/10.1186/s13244-024-01803-8","url":null,"abstract":"Accurate segmentation of focal cortical dysplasia (FCD) lesions from MR images plays an important role in surgical planning and decision but is still challenging for radiologists and clinicians. In this study, we introduce a novel transformer-based model, designed for the end-to-end segmentation of FCD lesions from multi-channel MR images. The core innovation of our proposed model is the integration of a convolutional neural network-based encoder-decoder structure with a multiscale transformer to augment the feature representation of lesions in the global field of view. Transformer pathways, composed of memory- and computation-efficient dual-self-attention modules, leverage feature maps from varying depths of the encoder to discern long-range interdependencies among feature positions and channels, thereby emphasizing areas and channels relevant to lesions. The proposed model was trained and evaluated on a public-open dataset including MR images of 85 patients using both subject-level and voxel-level metrics. Experimental results indicate that our model offers superior performance both quantitatively and qualitatively. It successfully identified lesions in 82.4% of patients, with a low false-positive lesion cluster rate of 0.176 ± 0.381 per patient. Furthermore, the model achieved an average Dice coefficient of 0.410 ± 0.288, outperforming five established methods. Integration of the transformer could enhance the feature presentation and segmentation performance of FCD lesions. The proposed model has the potential to serve as a valuable assistive tool for physicians, enabling rapid and accurate identification of FCD lesions. The source code and pre-trained model weights are available at https://github.com/zhangxd0530/MS-DSA-NET . This multiscale transformer-based model performs segmentation of focal cortical dysplasia lesions, aiming to help radiologists and clinicians make accurate and efficient preoperative evaluations of focal cortical dysplasia patients from MR images. ","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"7 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178901","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":"ESHNR 2024 Book of Abstracts","authors":"","doi":"10.1186/s13244-024-01789-3","DOIUrl":"https://doi.org/10.1186/s13244-024-01789-3","url":null,"abstract":"&lt;p&gt;&lt;b&gt;Open Access&lt;/b&gt; This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.&lt;/p&gt;\u0000&lt;p&gt;Reprints and permissions&lt;/p&gt;&lt;img alt=\"Check for updates. Verify currency and authenticity via CrossMark\" height=\"81\" loading=\"lazy\" src=\"data:image/svg+xml;base64,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","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"30 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142259344","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":"Identification of proliferative hepatocellular carcinoma using the SMARS score and implications for microwave ablation","authors":"Peng Zhou, Yan Bao, De-Hua Chang, Jun-Xiang Li, Tian-Zhi An, Ya-Ping Shen, Wen-Wu Cai, Lu Wen, Yu-Dong Xiao","doi":"10.1186/s13244-024-01792-8","DOIUrl":"https://doi.org/10.1186/s13244-024-01792-8","url":null,"abstract":"To compare therapeutic outcomes of predicted proliferative and nonproliferative hepatocellular carcinoma (HCC) after microwave ablation (MWA) using a previously developed imaging-based predictive model, the SMARS score. This multicenter retrospective study included consecutive 635 patients with unresectable HCC who underwent MWA between August 2013 and September 2020. Patients were stratified into predicted proliferative and nonproliferative phenotypes according to the SMARS score. Overall survival (OS) and recurrence-free survival (RFS) were compared between the predicted proliferative and nonproliferative HCCs before and after propensity score matching (PSM). OS and RFS were also compared between the two groups in subgroups of tumor size smaller than 30 mm and tumor size 30–50 mm. The SMARS score classified 127 and 508 patients into predicted proliferative and nonproliferative HCCs, respectively. The predicted proliferative HCCs exhibited worse RFS but equivalent OS when compared with nonproliferative HCCs before (p < 0.001 for RFS; p = 0.166 for OS) and after (p < 0.001 for RFS; p = 0.456 for OS) matching. Regarding subgroups of tumor size smaller than 30 mm (p = 0.098) and tumor size 30–50 mm (p = 0.680), the OSs were similar between the two groups. However, predicted proliferative HCCs had worse RFS compared to nonproliferative HCCs in the subgroup of tumor size 30–50 mm (p < 0.001), while the RFS did not differ in the subgroup of tumor size smaller than 30 mm (p = 0.141). Predicted proliferative HCCs have worse RFS than nonproliferative ones after MWA, especially in tumor size larger than 30 mm. However, the phenotype of the tumor may not affect the OS. Before performing microwave ablation for hepatocellular carcinoma, the tumor phenotype should be considered because it may affect the therapeutic outcome. ","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"39 1","pages":""},"PeriodicalIF":4.7,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178909","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":"Critical but commonly neglected factors that affect contrast medium administration in CT.","authors":"Michael C McDermott, Joachim E Wildberger, Kyongtae T Bae","doi":"10.1186/s13244-024-01750-4","DOIUrl":"10.1186/s13244-024-01750-4","url":null,"abstract":"<p><strong>Objective: </strong>Past decades of research into contrast media injections and optimization thereof in radiology clinics have focused on scan acquisition parameters, patient-related factors, and contrast injection protocol variables. In this review, evidence is provided that a fourth bucket of crucial variables has been missed which account for previously unexplained phenomena and higher-than-expected variability in data. We propose how these critical factors should be considered and implemented in the contrast-medium administration protocols to optimize contrast enhancement.</p><p><strong>Methods: </strong>This article leverages a combination of methodologies for uncovering and quantifying confounding variables associated with or affecting the contrast-medium injection. Engineering benchtop equipment such as Coriolis flow meters, pressure transducers, and volumetric measurement devices are combined with small, targeted systematic evaluations querying operators, equipment, and the physics and fluid dynamics that make a seemingly simple task of injecting fluid into a patient a complex and non-linear endeavor.</p><p><strong>Results: </strong>Evidence is presented around seven key factors affecting the contrast-medium injection including a new way of selecting optimal IV catheters, degraded performance from longer tubing sets, variability associated with the mechanical injection system technology, common operator errors, fluids exchanging places stealthily based on gravity and density, wasted contrast media and inefficient saline flushes, as well as variability in the injected flow rate vs. theoretical expectations.</p><p><strong>Conclusion: </strong>There remain several critical, but not commonly known, sources of error associated with contrast-medium injections. Elimination of these hidden sources of error where possible can bring immediate benefits and help to drive standardized and optimized contrast-media injections.</p><p><strong>Critical relevance statement: </strong>This review brings to light the commonly neglected/unknown factors negatively impacting contrast-medium injections and provides recommendations that can result in patient benefits, quality improvements, sustainability increases, and financial benefits by enabling otherwise unachievable optimization.</p><p><strong>Key points: </strong>How IV contrast media is administered is a rarely considered source of CT imaging variability. IV catheter selection, tubing length, injection systems, and insufficient flushing can result in unintended variability. These findings can be immediately addressed to improve standardization in contrast-enhanced CT imaging.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"15 1","pages":"219"},"PeriodicalIF":4.1,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11358578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142080161","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}