Quantitative Imaging in Medicine and Surgery最新文献

{"title":"CT-guided needle insertion with an optical navigation robot-assisted puncture system: <i>ex vivo</i> and <i>in vivo</i> experimental studies in the liver and kidneys.","authors":"Wei Cui, Yi Deng, Jingjing Chen, Yanqing Le, Huaying Shi, Suyi Ye, Bingding Huang, Xiaoming Chen, Jing Li, Rongde Xu","doi":"10.21037/qims-24-2100","DOIUrl":"10.21037/qims-24-2100","url":null,"abstract":"<p><strong>Background: </strong>Robotic technologies have promising applications in computed tomography (CT)-guided puncture. However, nodule-surrogate models can be difficult to develop for relevant studies, and thus the accuracy of optical navigation robot-assisted puncture remains unclear. This study aims to evaluate a starch mixture (the starch group) and a copper particle nodule-surrogate model (the particle group) and to compare the accuracy of optical navigation robot-assisted puncture (the robot group) with traditional CT-guided manual puncture (the manual group) using swine liver and kidneys.</p><p><strong>Methods: </strong>The study was approved by the institutional animal care and use committee. <i>Ex vivo</i> and <i>in vivo</i> studies of three swine liver and kidney samples using nodule surrogates were imaged by CT scan to assess the accuracy of the starch and particle groups. In an <i>in vivo</i> study of six swine, 24 punctures made by the robot and manual groups were performed using copper particle nodule-surrogate targets in the liver and kidneys under CT guidance. The accuracy of insertion was evaluated with a 5.0-mm margin. The needle insertion time, level of radiation exposure, and complications were evaluated.</p><p><strong>Results: </strong>In the first experiment, all nodule surrogates were easily visible on the CT images. However, other aspects of the starch group (one starch overflow, one starch dispersion event, and one air embolism) were inferior to those of the particle group. In experiment 2, the accuracy of needle insertion in the robot group (3.71±1.34 mm) was higher than in the manual group (11.89±9.59 mm) (P<0.001). The needle insertion time and level of radiation exposure were superior in the robot group compared to the manual group. Complications were similar between the two groups.</p><p><strong>Conclusions: </strong>The particle method may be superior to the starch method. The robot group was more accurate than the manual group, and the occurrence of complications was similar.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5114-5125"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209681/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555793","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":"Deep learning for automated grading of radiographic sacroiliitis.","authors":"Xinyi Meng, Yongku Du, Rongrong Jia, Qing Zhou, Yuwei Xia, Feng Shi, Fanhui Zhao, Yanjun Gao","doi":"10.21037/qims-2024-2742","DOIUrl":"10.21037/qims-2024-2742","url":null,"abstract":"<p><strong>Background: </strong>Grading assessment of sacroiliitis via X-ray is widely used in clinical evaluation. The aim of this study was to develop and validate an artificial intelligence (AI) system to help physicians in assessing and diagnosing sacroiliitis from standard X-ray images.</p><p><strong>Methods: </strong>In this retrospective study, a deep learning model for the automated grading assessment of radiographic sacroiliitis was developed using pelvic X-ray images from a training set of 465 individuals (930 single sacroiliac joints) and a validation set of 195 individuals (390 single sacroiliac joints). The algorithm was tested using an external test set of 223 individuals (446 single sacroiliac joints). The receiver operating characteristic (ROC) curve was used to calculate the area under the curve (AUC), sensitivity, and specificity to assess the model's performance. The findings of the model were used as a reference to determine its utility in aiding radiologists in the diagnosis and grading assessment of sacroiliitis.</p><p><strong>Results: </strong>The neural network model demonstrated proficiency in assessing grading of sacroiliitis. In the external test set, the model achieved a grading accuracy rate of 63.90% for radiographic sacroiliitis, and its diagnostic accuracy for determining the presence of radiographic sacroiliitis reached 90.13%. With the assistance of the model, the diagnostic accuracy of radiological sacroiliac arthritis by two junior imaging physicians improved significantly, increasing from 92.45% and 91.10% to 97.17% and 95.29%, respectively. Furthermore, the accuracy of image grading (grades 0 to 4) also showed notable improvement, rising from 75.00% and 74.08% to 88.89% and 80.90%, respectively.</p><p><strong>Conclusions: </strong>The AI model demonstrated high diagnostic accuracy and can greatly enhance the precision of radiographic sacroiliitis grading.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5137-5150"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209610/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555794","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":"Skeletal muscle fatty deposition in young and middle-aged adults with metabolic dysfunction-associated fatty liver disease: a magnetic resonance proton density fat fraction study.","authors":"Qun Wen, Jiaoyan Wang, Jie Yuan, Zhigang Gong, Yanwen Huang, Songhua Zhan, Guang Tan, Mengxiao Liu, Wenli Tan","doi":"10.21037/qims-24-1696","DOIUrl":"10.21037/qims-24-1696","url":null,"abstract":"<p><strong>Background: </strong>Metabolic dysfunction-associated fatty liver disease (MAFLD)-a major global health concern-is known to influence skeletal muscle. Therefore, in this study, we investigated whether the changes fat in skeletal muscle are associated with the degree of fatty liver in patients with MAFLD.</p><p><strong>Methods: </strong>We evaluated 398 patients who underwent abdominal magnetic resonance imaging (MRI) between January 2020 and August 2023 in a tertiary university hospital. Using MRI proton density fat fraction (PDFF), two radiologists manually measured the hepatic fat fraction (HFF), psoas major fat fraction (PMFF), paraspinal muscle fat fraction (PAMFF), and subcutaneous adipose tissue thickness (SATT). The severity of hepatic fatty deposition was classified as follows: G0, HFF <5%; G1, 5%≤ HFF <10%; G2, 10%≤ HFF <25%; and G3, HFF ≥25%).</p><p><strong>Results: </strong>PMFF and PAMFF increased in a stepwise manner as the severity of hepatic steatosis increased. There were no significant differences in PMFF or PAMFF between the G2 and G3 groups (P=0.058), while PMFF and PAMFF differed significantly between the other groups (P<0.05). The Pearson analysis showed that HFF was positively correlated with PMFF (r=0.475; P<0.001) and PAMFF (r=0.343; P<0.001). After adjustments were made for the levels of alanine aminotransferase (ALT), aspartate aminotransferase (AST), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglyceride (TG), and albumin (ALB), these correlations remained significant (PMFF: r=0.332, P<0.001; PAMFF: r=0.392, P<0.001). PMFF was positively correlated with age (r=0.155; P=0.002), ALT (r=0.169; P=0.003), AST (r=0.186; P=0.001), and blood glucose levels (r=0.177; P=0.003). PAMFF was positively correlated with age (r=0.107; P=0.033), ALT (r=0.118; P=0.040), AST (r=0.169; P=0.004), and blood glucose level (r=0.138; P=0.020) but negatively correlated with ALB level (r=-0.168; P=0.004). SATT was negatively correlated with age (r=-0.301; P=0.000), TG (r=-0.171; P=0.003), and ALB (r=-0.145; P=0.013). HFF was positively correlated with blood glucose level (r=0.144; P=0.015), and blood glucose level partly mediated the relationship between HFF and PAMFF (indirect effect =0.0046; 95 % CI: 0.0004-0.0130).</p><p><strong>Conclusions: </strong>Skeletal muscle fat content is significantly associated with the severity of hepatic steatosis. Accurate and quantitative body composition measurement and degree of hepatic steatosis can be noninvasively performed using PDFF.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5463-5473"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209667/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555832","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 narrative review of foundation models for medical image segmentation: zero-shot performance evaluation on diverse modalities.","authors":"Seungha Noh, Byoung-Dai Lee","doi":"10.21037/qims-2024-2826","DOIUrl":"10.21037/qims-2024-2826","url":null,"abstract":"<p><strong>Background and objective: </strong>Foundation models are deep learning models pretrained on extensive datasets, equipped with the ability to adapt to a variety of downstream tasks. Recently, they have gained prominence across various domains, including medical imaging. These models exhibit remarkable contextual understanding and generalization capabilities, spurring active research in healthcare to develop versatile artificial intelligence solutions for real-world clinical environments. Inspired by this, this study offers a comprehensive review of foundation models in medical image segmentation (MIS), evaluates their zero-shot performance on diverse datasets, and assesses their practical applicability in clinical settings.</p><p><strong>Methods: </strong>A total of 63 studies on foundation models for MIS were systematically reviewed, utilizing platforms such as arXiv, ResearchGate, Google Scholar, Semantic Scholar, and PubMed. Additionally, we curated 31 unseen medical image datasets from The Cancer Imaging Archive (TCIA), Kaggle, Zenodo, Institute of Electrical and Electronics Engineers (IEEE) DataPort, and Grand Challenge to evaluate the zero-shot performance of six foundation models. Performance analysis was conducted from various perspectives, including modality and anatomical structure.</p><p><strong>Key content and findings: </strong>Foundation models were categorized based on a taxonomy that incorporates criteria such as data dimensions, modality coverage, prompt type, and training strategy. Furthermore, the zero-shot evaluation revealed key insights into their strengths and limitations across diverse imaging modalities. This analysis underscores the potential of these models in MIS while highlighting areas for improvement to optimize real-world applications.</p><p><strong>Conclusions: </strong>Our findings provide a valuable resource for understanding the role of foundation models in MIS. By identifying their capabilities and limitations, this review lays the groundwork for advancing their practical deployment in clinical environments, supporting further innovation in medical image analysis.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5825-5858"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209621/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555755","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 novel computed tomography-based multi-parameter decision tree algorithm model for preoperatively predicting the risk of lymph node metastasis in surgically resectable synchronous multiple primary lung cancer.","authors":"Wenbiao Zhang, Huiyun Ma, Ying Zhu, Wenjing Gou, Baocong Liu, Qiong Li, Shuangjiang Li","doi":"10.21037/qims-24-2440","DOIUrl":"10.21037/qims-24-2440","url":null,"abstract":"<p><strong>Background: </strong>Chest thin-section computed tomography (TS-CT) has the potential to provide evidence for the prediction of lymph node metastasis (LNM) in synchronous multiple primary lung cancer (SMPLC). The present study aims to develop and validate a new CT-based multi-parametric decision tree algorithm (CT-DTA) model capable of accurate risk evaluation for LNM in SMPLC preoperatively.</p><p><strong>Methods: </strong>A total of 235 patients with surgically resected SMPLC from Sun Yat-Sen University Cancer Center (SYSUCC), the First Affiliated Hospital of Sun Yat-Sen University (FAH-SYSU) and Sichuan Provincial People's Hospital (SPPH) were finally included. We initially retrieved all the CT-derived quantitative signs in the training cohort (139 cases from SYSUCC) and selected those with statistical significance to build a DTA model. The discriminative power of CT-DTA model for the occurrence of LNM was further externally validated among the validation cohort (96 patients from FAH-SYSU and SPPH). In addition, the performance of CT-DTA model was also assessed across different subgroups of the entire cohort.</p><p><strong>Results: </strong>Five key quantitative covariables measured on chest TS-CT constituted a CT-DTA model with seven leaf nodes, and long-axis diameter of the solid portion was the most dominant risk contributor of LNM. This CT-DTA model gained a satisfactory predictive accuracy, revealed by an area under the curve >0.80 in both the training cohort (0.905; P<0.001) and the validation cohort (0.812; P<0.001). Moreover, our CT-DTA model was also exhaustively demonstrated to perform as an independent predictor for risk stratification of LNM in both the training cohort (odds ratio: 12.01; P=0.003) and the validation cohort (odds ratio: 8.11; P=0.033). Its potent performance for risk prediction still remained stable across nearly all of the subgroups stratified by clinicopathological characteristics.</p><p><strong>Conclusions: </strong>This CT-DTA model could serve as a noninvasive, user-friendly and practicable risk prediction tool to aid treatment decision-making in surgically resectable SMPLC.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"4972-4994"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555756","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 three-classification machine learning model for non-invasive prediction of molecular subtypes in diffuse glioma: a two-center study.","authors":"Meilin Zhu, Weishu Hou, Jiahao Gao, Fang Han, Shanshan Huang, Xiaohu Li, Longlin Yin, Jiawen Zhang","doi":"10.21037/qims-24-2461","DOIUrl":"10.21037/qims-24-2461","url":null,"abstract":"<p><strong>Background: </strong>Determining the molecular status of gliomas is crucial for evaluating treatment efficacy and prognosis. However, this process currently requires the invasive and cumbersome method of histological analysis. We aimed to develop and validate a non-invasive three-classification machine learning (ML) model to predict the three molecular subtypes of adult-type diffuse gliomas according to the 2021 World Health Organization classification of tumors of the central nervous system 5^th edition (WHO CNS 5).</p><p><strong>Methods: </strong>This retrospective study included a total of 306 glioma patients, among whom 258 were from Center 1 (Huashan Hospital; 180 for the training and 78 for the internal validation set) and 48 were from Center 2 (The First Affiliated Hospital of Anhui Medical University; external validation set). Conventional magnetic resonance imaging (MRI) features of tumors were assessed, and the radiomics and Swin Transformer-based deep learning (RSTD) features were respectively extracted from tumor segmentation on axial three-dimensional contrast-enhanced T1-weighted (3D T1C) and T2-fluid-attenuated inversion recovery (T2-FLAIR) sequences. Three types of prediction models: conventional MRI (CM) model, RSTD model, and combined model were respectively trained using six ML classifiers [k-nearest neighbor (kNN), light gradient-boosting machine (LightGBM), random forest (RF), support vector machine (SVM), stochastic gradient descent (SGD), and extreme gradient boosting (XGBoost)] to identify the three major molecular subtypes of adult-type diffuse gliomas. The performance of the models was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity, specificity, accuracy, precision, and F1-score.</p><p><strong>Results: </strong>XGBoost classifier was chosen as our algorithm for model construction due to its superior performance in the training and internal validation cohorts. The combined model, which incorporates CM features, RSTD features, as well as demographic features, achieved best performance in the internal [micro-AUC (0.905) and macro-AUC (0.878)] and external validation sets [micro-AUC (0.911) and macro-AUC (0.891)]. The SHapley Additive explanations (SHAP) and gradient-weighted class activation mapping (Grad-CAM) were used to explain the model.</p><p><strong>Conclusions: </strong>Our study constructed a three-classification ML model that combined CM features, RSTD features, and demographic characteristics, achieved promising performance in predicting molecular subtypes of diffuse glioma. The combined model provided a non-invasive, timely, and accurate diagnostic approach prior to patient treatment to assist clinical decision-making.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5752-5768"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209611/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555759","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":"Heart-aorta-angle correlates left atrial low voltage areas formation in hypertensive atrial fibrillation patients.","authors":"Guanqi Fan, Yuhang Yang, Tongshuai Chen, Peili Bu","doi":"10.21037/qims-2025-80","DOIUrl":"10.21037/qims-2025-80","url":null,"abstract":"<p><strong>Background: </strong>The mechanism of low voltage areas (LVAs) formation in hypertensive atrial fibrillation (AF) patients is not clear. This observational study aimed to investigate the characteristics of atrial substrate in hypertensive AF patients and potential mechanism for abnormal LVAs formation related to heart-aorta-angle (HAA).</p><p><strong>Methods: </strong>It was an observational cohort study. From June 2022 to September 2023, AF patients who underwent coronary computed tomography angiography (CCTA) and catheter ablation were included and assigned into hypertensive or normotensive group. The distribution of LVAs mapping in left atrium, CCTA measured HAA, and dimension of aortic sinus were brought into analysis.</p><p><strong>Results: </strong>Forty-Eight of 93 patients (51.6%) (mean age, 62.63±10.01 years; 54 men) had long-standing hypertension. CCTA scan analysis showed hypertensive group (<i>vs.</i> normotensive group) had smaller ascending aorta-left atrium (AAo-LA) angle [mean ± standard deviation (SD), 29.11°±2.87° <i>vs.</i> 31.83°±2.04°, P<0.001], greater ascending aorta-left ventricular (AAo-LV) angle [median (interquartile range), 132.22° (129.80°-134.59°) <i>vs.</i> 129.33° (127.38°-131.87°), P<0.001], larger non-coronary cusp (NCC) diameter [20.80 (19.37, 21.71) <i>vs.</i> 19.11 (17.66, 19.69) mm, P<0.001], and larger NCC-commissure distance (34.85±2.57 <i>vs.</i> 33.54±2.14 mm, P=0.009). LVAs mapping results showed a larger total LVAs area in left atrium [19.11 (16.36, 20.13) <i>vs.</i> 15.63 (14.35, 18.04) cm2, P<0.001], especially in anterior wall (AW) in hypertensive group [5.11 (3.03, 5.75) <i>vs.</i> 3.42 (2.35, 4.42) cm2, P<0.001]. The AAo-LV angle (r=0.233, P=0.024), NCC diameter (r=0.324, P=0.002), and NCC-commissure distance (r=0.274, P=0.008) were positively related with AW-LVAs, and AAo-LA angle correlated negatively with AW-LVAs (r=-0.358, P<0.001). During a follow-up of 12 months, AF recurred in 16 patients (33.3%) in hypertensive group and in 7 patients (15.6%) in normotensive group (P=0.041).</p><p><strong>Conclusions: </strong>In AF patients with hypertension, smaller AAo-LA angle is common. The closer AAo-LA interaction relationship may increase the mechanical contact, which relates to LVAs formation in left atrium and contribute to the atrial fibrosis in hypertensive AF patients.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5781-5795"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209654/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555764","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":"Magnetic resonance imaging findings, prognosis, and treatment of fetal ovarian cysts.","authors":"Yi Zhang, Rui Yan, Le Liu","doi":"10.21037/qims-2024-2493","DOIUrl":"10.21037/qims-2024-2493","url":null,"abstract":"<p><strong>Background: </strong>With the widespread use and promotion of prenatal diagnosis, the detection of fetal ovarian cysts (FOCs) has become prevalent. However, there is limited research on the magnetic resonance imaging (MRI) findings of these cysts. The aim of this study was to analyze the MRI features of FOCs to enhance diagnostic accuracy, and discuss their prognosis and treatment options.</p><p><strong>Methods: </strong>A total of 22 cases of FOCs were retrospectively collected in our hospital from January 2016 to June 2024. The MRI findings, prognosis, and treatment were analyzed.</p><p><strong>Results: </strong>In the included cases, the gestational age of initial diagnosis of cysts ranged from 30 to 37 weeks. Most (21/22, 95%) cases involved unilateral cysts, whereas 1 case had bilateral cysts. Among them, there were 14 simple cysts and 8 complex cysts with maximum diameter ranging from 20 to 96 mm. All cysts showed hypointensity on T1-weighted imaging (T1WI). Simple cysts showed uniform hyperintensity on T2-weighted imaging (T2WI). Of the 8 cases of complex cysts, 5 showed mixed signal intensity on T2WI and 3 had fluid-fluid level. There was no statistical difference in the maximum diameter of the cysts and the number of non-operative cases between two groups. However, a significant difference in cyst diameter was observed between the operation group and the non-operation group, with a critical threshold diameter of 5.8 cm.</p><p><strong>Conclusions: </strong>MRI findings of FOCs are varied, especially in complex cysts. The prognosis and treatment of FOCs mainly depends on the size and dynamic changes of the cyst. If the cyst is smaller than 5 cm, it will disappear spontaneously. Otherwise, it should be operated on in time to preserve the ovarian tissue maximumly and avoid autoamputation of the ovary.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5276-5283"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209642/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555768","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":"Prenatal diagnosis of umbilical cord true knot using high-definition flow (HD-flow) render mode and spatio-temporal image correlation: a case series.","authors":"Ping-An Qi, Tian-Gang Li, Qing-Yun Zhou","doi":"10.21037/qims-24-2421","DOIUrl":"10.21037/qims-24-2421","url":null,"abstract":"","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5933-5939"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209646/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555786","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":"Comparison of fast and standard segmented techniques for detection of late gadolinium enhancement in acute myocardial infarction: a prospective clinical cardiovascular magnetic resonance trial.","authors":"Jinshui Li, Huihui Kong, Zhaozhao Wang, Ying Yuan, Jing An, Yi He","doi":"10.21037/qims-24-2308","DOIUrl":"10.21037/qims-24-2308","url":null,"abstract":"<p><strong>Background: </strong>Segmented phase-sensitive inversion recovery (PSIR) turbo fast low-angle shot (FLASH) has become the reference standard sequence for late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) imaging. However, it has a long scanning time, requires multiple breath holds, and is prone to motion artifacts. This study aimed to compare the accuracy of two fast LGE sequences with FLASH PSIR in acute myocardial infarction (AMI) detection and quantification of LGE.</p><p><strong>Methods: </strong>We prospectively recruited consecutive AMI patients who underwent clinical contrast-enhanced CMR with three different LGE sequences at Beijing Friendship Hospital. The overall image quality (IQ) score and contrast-to-noise ratio (CNR) were used to comprehensively evaluate IQ. LGE and microvascular obstruction (MVO) were qualitatively and quantitatively assessed.</p><p><strong>Results: </strong>A total of 110 AMI patients (90 males, 58.61±10.9 years) were included in our analyses. Of these, 100 patients (84 males, 58.6±10.9 years) presented LGE (+), and 60 patients developed MVO. Participants were divided into three groups according to the LGE results, namely LGE (-), LGE (+) without MVO, and LGE (+) with MVO. The overall IQ score and CNR for the two fast sequences [single-shot true fast imaging with steady-state precession (TrueFISP PSIR), PSIR motion-corrected, free-breathing single-shot balanced steady-state free precession (moco bSSFP)] were significantly higher than those for the FLASH PSIR (P<0.001). On visual assessment, the number of layers (P=0.20 and 0.22, respectively) and segments (P=0.09 and 0.32, respectively) for LGE displayed no difference and showed excellent matching with those of FLASH PSIR. There were no significant differences in LGE mass (P=0.61 and 0.83, respectively) and MVO mass (P=0.15 and 0.55, respectively) between the FLASH PSIR and the two fast sequences.</p><p><strong>Conclusions: </strong>In clinical practice, these two rapid sequences can achieve good IQ, as well as accurate localization and quantification of LGE when acquired during a single breath hold or in a free-breathing state. We recommend them as the preferred LGE CMR sequence for AMI patients.</p>","PeriodicalId":54267,"journal":{"name":"Quantitative Imaging in Medicine and Surgery","volume":"15 6","pages":"5769-5780"},"PeriodicalIF":2.9,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209636/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144555791","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}