Image Feature Analysis Research Articles

Masked Face Emotion Recognition Based on Facial Landmarks and Deep Learning Approaches for Visually Impaired People.

Current artificial intelligence systems for determining a person's emotions rely heavily on lip and mouth movement and other facial features such as eyebrows, eyes, and the forehead. Furthermore, low-light images are typically classified incorrectly because of the dark region around the eyes and eyebrows. In this work, we propose a facial emotion recognition method for masked facial images using low-light image enhancement and feature analysis of the upper features of the face with a convolutional neural network. The proposed approach employs the AffectNet image dataset, which includes eight types of facial expressions and 420,299 images. Initially, the facial input image's lower parts are covered behind a synthetic mask. Boundary and regional representation methods are used to indicate the head and upper features of the face. Secondly, we effectively adopt a facial landmark detection method-based feature extraction strategy using the partially covered masked face's features. Finally, the features, the coordinates of the landmarks that have been identified, and the histograms of the oriented gradients are then incorporated into the classification procedure using a convolutional neural network. An experimental evaluation shows that the proposed method surpasses others by achieving an accuracy of 69.3% on the AffectNet dataset.

Retracted: Analysis of Clinical Manifestations, Imaging Features, and Gene Mutation Characteristics of 6 Children with Cystic Fibrosis in China.

[This retracts the article DOI: 10.1155/2021/7254391.].

Analysis of chest imaging features of novel coronavirus pneumonia, bacterial pneumonia and viral pneumonia

To investigate and summarize the chest CT imaging features of patients with novel coronavirus pneumonia (COVID-19), bacterial pneumonia and other viral pneumonia. Chest CT data of 102 patients with pulmonary infection due to different etiologies were retrospectively analyzed, including 36 patients with COVID-19 admitted to Hainan Provincial People's Hospital and the Second Affiliated Hospital of Hainan Medical University from December 2019 to March 2020, 16 patients with other viral pneumonia admitted to Hainan Provincial People's Hospital from January 2018 to February 2020, and 50 patients with bacterial pneumonia admitted to Haikou Affiliated Hospital of Central South University Xiangya School of Medicine from April 2018 to May 2020. Two senior radiologists and two senior intensive care physicians were participated to evaluated the extent of lesions involvement and imaging features of the first chest CT after the onset of the disease. Bilateral pulmonary lesions were more common in patients with COVID-19 and other viral pneumonia, and the incidence was significantly higher than that of bacterial pneumonia (91.6%, 75.0% vs. 26.0%, P < 0.05). Compared with other viral pneumonia and COVID-19, bacterial pneumonia was mainly characterized by single-lung and multi-lobed lesion (62.0% vs. 18.8%, 5.6%, P < 0.05), accompanied by pleural effusion and lymph node enlargement. The proportion of ground-glass opacity in the lung tissues of patients with COVID-19 was 97.2%, that of patients with other viral pneumonia was 56.2%, and that of patients with bacterial pneumonia was only 2.0% (P < 0.05). The incidence rate of lung tissue consolidation (25.0%, 12.5%), air bronchial sign (13.9%, 6.2%) and pleural effusion (16.7%, 37.5%) in patients with COVID-19 and other viral pneumonia were significantly lower than those in patients with bacterial pneumonia (62.0%, 32.0%, 60.0%, all P < 0.05), paving stone sign (22.2%, 37.5%), fine mesh sign (38.9%, 31.2%), halo sign (11.1%, 25.0%), ground-glass opacity with interlobular septal thickening (30.6%, 37.5%), bilateral patchy pattern/rope shadow (80.6%, 50.0%) etc. were significantly higher than those of bacterial pneumonia (2.0%, 4.0%, 2.0%, 0%, 22.0%, all P < 0.05). The incidence of local patchy shadow in patients with COVID-19 was only 8.3%, significantly lower than that in patients with other viral pneumonia and bacterial pneumonia (8.3% vs. 68.8%, 50.0%, P < 0.05). There was no significant difference in the incidence of peripheral vascular shadow thickening in patients with COVID-19, other viral pneumonia and bacterial pneumonia (27.8%, 12.5%, 30.0%, P > 0.05). The probability of ground-glass opacity, paving stone and grid shadow in chest CT of patients with COVID-19 was significantly higher than those of bacterial pneumonia, and it was more common in the lower lungs and lateral dorsal segment. In other patients with viral pneumonia, ground-glass opacity was distributed in both upper and lower lungs. Bacterial pneumonia is usually characterized by single lung consolidation, distributed in lobules or large lobes and accompanied by pleural effusion.

Comparative analysis of intraoperative and imaging features of invasive growth in pituitary adenomas

Comparative analysis of intraoperative and imaging features of invasive growth in pituitary adenomas

A FOREST FIRE IDENTIFICATION METHOD FOR UNMANNED AERIAL VEHICLE MONITORING VIDEO IMAGES

An automatic forest fire monitoring system based on UAV (unmanned aerial vehicle) acquired video images was studied in this paper. This novel method was proposed to address current problems in forest fire information monitoring practices such as poor real-time performance and low efficiency. Besides, it aims to realize the dynamic monitoring of forest fires in wild environment. In this paper, a forest fire monitoring method based on active analysis of UAV-acquired video image features is proposed to automatically detect and identify the occurrence of forest fires. The motion detection method based on dense optical flow and background modeling method were used to extract the motion regions for eliminating the influence of image background. By using wavelet energy feature and texture feature, 9 video images acquired by multi-rotor UAV on forest fire monitoring were selected as sample images (8 images for experiment and 1 image for contrast purpose). The mean values and standard deviations of the gray level co-occurrence matrix eigen values (angular second moment, entropy moment and reciprocal differential moment) were calculated as the discriminate basis for identifying forest fires. The experimental results showed that the proposed algorithm can effectively identify the forest fire, which provides a theoretical guarantee for the forest resources protection.

Retracted: Artificial Intelligence-Based Feature Analysis of Ultrasound Images of Liver Fibrosis.

[This retracts the article DOI: 10.1155/2022/2859987.].

The problem of automatic classification of pictures using an intelligent decision-making system based on the knowledge graph and fine-grained image analysis

In order to prevent the illegal export of paintings abroad, a museum examination using various methods for studying a work of art is carried out. At the same time, an analysis is also made of historical, art history, financial and other information and documents confirming the painting’s authenticity — provenance. Automation of such examination is hampered by the need to take into account numerical values of visual features, quality indicators, and verbal descriptions from provenance. In this paper, we consider the problem of automatic multi-task classification of paintings for museum expertise. A system architecture is proposed that checks provenance, implements a fine-grained image analysis (FGIA) of visual image features, and automatically classifies a painting by authorship, genre, and time of creation. Provenance is contained in a knowledge graph; for its vectorization, it is proposed to use a graph2vec type encoder with an attention mechanism. Fine-grained image analysis is proposed to be performed using searching discriminative regions (SDR) and learning discriminative regions (LDR) allocated by convolutional neural networks. To train the classifier, a generalized loss function is proposed. A data set is also proposed, including provenance and images of paintings by European and Ukrainian artists.

Biased instantaneous regional muscle activation maps: Embedded fuzzy topology and image feature analysis.

The instantaneous spatial representation of electrical propagation produced by muscle contraction may introduce bias in surface electromyographical (sEMG) activation maps. Here, we described the effect of instantaneous spatial representation (sEMG segmentation) on embedded fuzzy topological polyhedrons and image features extracted from sEMG activation maps. We analyzed 73,008 topographic sEMG activation maps from seven healthy participants (age 21.4 ± 1.5 years and body mass 74.5 ± 8.5kg) who performed submaximal isometric plantar flexions with 64 surface electrodes placed over the medial gastrocnemius muscle. Window lengths of 50, 100, 150, 250, 500, and 1,000ms and overlap of 0, 25, 50, 75, and 90% to change sEMG map generation were tested in a factorial design (grid search). The Shannon entropy and volume of global embedded tri-dimensional geometries (polyhedron projections), and the Shannon entropy, location of the center (LoC), and image moments of maps were analyzed. The polyhedron volume increased when the overlap was <25% and >75%. Entropy decreased when the overlap was <25% and >75% and when the window length was <100ms and >500ms. The LoC in the x-axis, entropy, and the histogram moments of maps showed effects for overlap (p < 0.001), while the LoC in the y-axis and entropy showed effects for both overlap and window length (p < 0.001). In conclusion, the instantaneous sEMG maps are first affected by outer parameters of the overlap, followed by the length of the window. Thus, choosing the window length and overlap parameters can introduce bias in sEMG activation maps, resulting in distorted regional muscle activation.

Intelligent classification model of land resource use using deep learning in remote sensing images

Accurate and effective remote sensing image classification and analysis methods can provide reliable support for the sustainable development of land resources, but the current methods can't support the precise processing of complex images. To solve this problem, this paper proposes a new classification and analysis method of remote sensing image land resources based on deep learning. Firstly, this method first uses ENVI software to realize image preprocessing on the collected data set, and realizes reliable data set; At the same time, based on the sparse auto-encoder network model, the sample features of the image data set are deeply mined to make a better reference for category determination; And based on ELM classifier network, convolution layer and sub sampling layer are alternately connected to extract the depth feature of remote sensing image, which can greatly reduce the number of network parameters and improve the computational. Finally, the actual collected data set is used for simulation verification, and the experimental results show that the accuracy of complex image analysis reaches 98.15%, which shows excellent performance of image feature classification and analysis.

Microvascular invasion risk scores affect the estimation of early recurrence after resection in patients with hepatocellular carcinoma: a retrospective study

BackgroundMicrovascular invasion (MVI) is a histological factor that is closely related to the early recurrence of hepatocellular carcinoma (HCC) after resection. To investigate whether a noninvasive risk score system based on MVI status can be established to estimate early recurrence of HCC after resection.MethodsBetween January 2018 to March 2021, a total of 108 patients with surgically treated single HCC was retrospectively included in our study. Fifty-one patients were pathologically confirmed with MVI and 57 patients were absent of MVI. Univariate and multivariate logistic regression analysis of preoperative laboratory and magnetic resonance imaging (MRI) features were used to screen noninvasive risk factors in association with MVI in HCC. Risk scores based on the odds ratio (OR) values of MVI-related risk factors were calculated to estimate the early recurrence after resection of HCC.ResultsIn multivariate logistic regression analysis, tumor size > 2 cm (P = 0.024, OR 3.05, 95% CI 1.19–11.13), Prothrombin induced by vitamin K absence-II > 32 mAU/ml (P = 0.001, OR 4.13, 95% CI 1.23–11.38), irregular tumor margin (P = 0.018, OR 3.10, 95% CI 1.16–8.31) and apparent diffusion coefficient value < 1007 × 10− 3mm2/s (P = 0.035, OR 2.27, 95% CI 1.14–7.71) were independent risk factors correlated to MVI in HCC. Risk scores of patients were calculated and were then categorized into high or low-risk levels. In multivariate cox survival analysis, only high-risk score of MVI was the independent risk factor of early recurrence (P = 0.009, OR 2.11, 95% CI 1.20–3.69), with a sensitivity and specificity of 0.52, 0.88, respectively.ConclusionA risk score system based on MVI status can help stratify patients in high-risk of early recurrence after resection of HCC.

Analysis of CT and MR imaging features of the brain in patients with hydrogen sulfide poisoning based on clinical symptom grading

ObjectiveTo retrospectively analyze CT and MR imaging features of the brain in patients with hydrogen sulfide poisoning based on clinical symptom grading and to investigate their correlations with clinical symptoms and patients’ prognosis.MethodsA retrospective analysis was performed of CT and MR imaging data of the brain in 40 patients with hydrogen sulfide poisoning in our hospital. There were four main imaging manifestations. Patients were clinically graded according to the central nervous system symptom scores of the Poisoning Severity Score (PSS) and staged according to the gas inhalation time segment. Based on clinical symptom grading, the frequencies and proportions of four imaging signs that occurred in each group were counted, their development trends were analyzed, and the correlations of imaging features with clinical grading and prognosis were calculated.ResultsForty patients were divided into minor, moderate and severe clinical grades and classified into four stages. In patients with minor and moderate clinical grading, only one patient suffered from generalized brain edema at stage 1, with a good prognosis. Patients with severe clinical grade showed the highest probability of presenting with the four imaging signs. The imaging signs were correlated with the severe clinical grade and a poor prognosis (P = 0.000, R = 0.828; P = 0.000, R = 0.858).ConclusionIn patients with the severe clinical grade, generalized brain edema and symmetrical hypodensity/abnormal signals in the bilateral basal ganglia and around the lateral ventricles were the main findings and were shown to persist. The presence of imaging signs can assist in the clinically effective evaluation of clinical symptom grade.

Analysis of clinical and imaging features of 6 cases of linear scleroderma en coup de sabre with central nervous system involvement in children

Objective: To summarize the clinical and imaging features of linear scleroderma en coup de saber (LSCS) with central nervous system involvement in children. Methods: The clinical data(clinical manifestations and imaging features) of 6 children diagnosed with LSCS with central nervous system involvement who were admitted to Beijing Children's Hospital Affiliated to Capital Medical University from May 2019 to November 2021 were retrospectively analyzed. Results: The 6 patients were all female, aged 6.8 (3.3, 11.0) years at the time of diagnosis, and aged 3.0 (1.7, 4.1) years at the time of discovery of facial skin lesions. Facial skin lesions appeared before neurological symptoms in 5 cases, and neurological symptoms appeared 2 months before skin lesions in 1 case. All the patients had "sword wound" skin lesions on the forehead with alopecia. Neurological manifestations included epileptic seizures in 6 cases, focal neurological defects in 5 cases, and headaches in 2 cases. The intracranial lesions were all ipsilateral to the skin lesions. The magnetic resonance imaging (MRI) of 6 cases showed abnormal signals mainly involving white matter in 1 hemisphere, and 3 cases showed local encephalomalacia. The scattered low signal was observed in 5 cases on susceptibility weighted imaging. Localized brain parenchyma or leptomeninges enhancement was seen on Gadolinium-enhanced sequences in 5 cases. Scattered foci of calcification on the affected side were seen on cranial CT in 4 cases. Skin biopsy was performed in 2 cases. Part of the lesion of the brain was removed in 1 case, and the pathological findings suggested small vasculitis, which was consistent with skin pathological changes. All patients received symptomatic treatment with antiepileptic drugs. Oral prednisone combined with methotrexate was given in 4 cases, and 1 case was given oral prednisone only. One case was presumed to be in the resting stage of the disease due to significant cerebral atrophy in half of the brain, and only antiepileptic drugs were added. The patients were followed up for 6-36 months. The skin lesions of scleroderma and alopecia did not progress in 5 cases, and hemifacial atrophy was developed in 1 case, which was considered to be combined with Parry-Romberg syndrome. The seizures were controlled in 4 cases. One case had reduced seizure frequency but left hemiplegia. One patient still had intractable epilepsy and paroxysmal headache. Conclusions: LSCS with central nervous system involvement is more common in girls, with seizures and neurological defects as the main manifestations. Intracranial lesions are mostly ipsilateral to the skin lesions. Cerebral microbleeds, calcification, and encephalomalacia foci are common, and the pathological changes in skin and intracranial lesions are consistent with small-vessel vasculitis. Prednisone combined with methotrexate treatment has shown some efficacy, but some children remain with refractory epilepsy and neurological deficit symptoms.

An image-based approach for building fuzzy color spaces

Color is one of the most used features for image analysis. However, two uncertainty problems arise in this scope: first, color computer representation does not match with how humans understand the color concept; second, the color feature is imprecise by nature. For this reason, fuzzy colors and fuzzy color spaces were developed as a suitable way to model color categories. The most recent and best accurate approaches use crisp color prototypes to build fuzzy color spaces, considering standard and fixed set of colors prototypes (such as ISCC-NBS system) for this purpose. The use of these types of general purpose prototypes may not be flexible enough to collect particular colors of a given image (or a set of images); in addition, image colors may be context dependent in some cases (for example, red color in winery or medicine context). In order to solve these drawbacks, in this paper we propose to learn fuzzy color spaces by analyzing the relevant colors in a given set of representative images for a specific context and/or application; then, we will use them as prototypes in order to build adaptive fuzzy color spaces. Some real experiments are performed in order to illustrate the advantages of our proposal.

Artificial intelligence neural network analysis and application of CT imaging features to predict lymph node metastasis in non-small cell lung cancer.

Computed tomography (CT) is important in the diagnosing of lung cancer. The combination of CT features and artificial intelligence algorithm have been used in the diagnosis of various lung diseases. However, limited studies focused on the relationship between the combination of CT features and artificial intelligence algorithm and lymph node metastasis in non-small cell lung cancer (NSCLC). This study developed an algorithm for lung cancer CT image segmentation based on an artificial neural network model and investigated the role of a nomogram model based on CT images for predicting lymph node metastasis in lung cancer. Wiener filtering and fuzzy enhancement were first used to suppress image noise and improve image contrast. Next, texture features and fractal features were extracted. In the third step, the artificial neural network model was trained and tested according to the best parameters of the network. The area under the curve (AUC) of the constructed nomogram model on the training set and the test set were 0.859 (sensitivity, 0.810; specificity, 0.773) and 0.864 (sensitivity, 0.820; specificity, 0.753), respectively. The decision curve indicated that the model had good clinical application value. The lung cancer CT images contained 13 significant regional features of cancer. The best classification function obtained from training and testing data was Levenberg-Marquardt backpropagation. The sensitivity, specificity, and accuracy in the training stage could reach 98.4%, 100%, and 98.6%, respectively, and the corresponding indexes in the test stage reached 90.9%, 100%, and 95.1%, respectively. The image segmentation algorithm based on the artificial neural network model could extract CT lung cancer lesions efficiently and quasi-determinately, which could be used as an effective tool for radiologists to diagnose lung cancer. The nomogram model based on CT image features and related clinical indicators was an effective method for noninvasive prediction of lymph node metastasis in lung cancer.

Decoding Brain Fog in Head and Neck Cancer Survivors Using Artificial Intelligence

<h3>Purpose/Objective(s)</h3> Brain fogging, which is characterized by memory decline, word-finding difficulty, and decreased multi-tasking ability, is common in head and neck cancer survivors. The precise causes of brain fog are poorly understood, as MRIs are essentially normal in these patients. The purpose of this study was to use artificial intelligence (AI) to detect micro-architectural and biological changes in the brain that are invisible to human eyes. <h3>Materials/Methods</h3> DVH of temporal lobes and whole brain were obtained in 566 and 60 patients, respectively, who underwent IMRT or proton radiation with concurrent chemotherapy for nasopharyngeal carcinoma. EORTC QLQ-C30 was obtained from 92 patients who enrolled in two prospective clinical studies. Automated cortical region segmentation using an open-source software for processing and analyzing brain MRI images was performed in 33 patients with a minimum follow-up of 4 years. Architectural and biological MRI changes in gray matter, white matter, and subcortical brain regions were determined in 34 brain regions. Graph theory-based brain connectome was evaluated to assess the connectivity between different regions of the brain. Radiomics which employs high-throughput quantitative analysis of image features were performed on the T1- and diffusion-weighted sequences. <h3>Results</h3> Patients experienced a continuous decline of cognitive function after radiation, with a 6.6% in decrease in their first year. The brain received a large low-dose bath with a median V10-20(whole brain) (volume of whole brain that received 10-20 Gy) 170cc, V20-30(whole brain) 50.8cc, V10-20(temporal lobe)11.4cc, and V20-30(temporal lobe) 6.18cc. There were global changes in gray matter thickness, with most pronounced changes occurred in parietal lobe (-4.79%, p=0.007) and occipital lobe (-5.68%, p=0.03). Similarly, there were diffuse changes in white matter and subcortical volume. After radiation, the frontal lobe increased by 17.5% (p=0.04), lateral ventricle 41% (p=0.03), and choroid plexus 34.3% (p=0.03). After radiation, the brain exhibited diffuse disrupted connectivity as characterized by increased clustering coefficient (p=0.002) and decreased modularity (p=0.003). Corpus collusum, choroid plexus, cerebellum, hippocampus, brainstem, medialorbito- and superior-frontal, temporal pole, and inferior temporal were analyzed using radiomics. First order interquartile-range, entropy, uniformity, and GLCM features were found to be statistically significant (p< 0.01). After adjusted for age, gender, and time interval between MRI, IMRT patients developed significant more changes in white matter of the temporal lobe (p=0.03) and corpus callosum (p=0.03) when compared to proton patients. <h3>Conclusion</h3> By employing artificial intelligence and computational methods, we are able to detect global disruption in architecture, connectivity, and function in the brain of head and neck cancer survivors. IMRT affects the brain differently than proton.

An Artificial Intelligence Ultrasound Platform for Screening and Staging Thyroid C

<h3>Purpose/Objective(s)</h3> Thyroid cancer is one of the most rapidly increasing cancer in the US, largely due to increased detection. BRAF mutation (V600E) is common in thyroid cancer and is a druggable mutation. The purpose of this study was to establish a multimodal artificial intelligence (AI) ultrasound platform that consists of radiomics, topological data analysis (TDA), machine learning (ML)TI-RADS features, and deep learning (DL) to predict malignancy, pathological, and genomic outcome in patients with thyroid cancer. <h3>Materials/Methods</h3> Between 2010 and 2021, 1,346 thyroid nodule images through routine diagnostic ultrasound from 784 patients. The ultrasound images were divided into two datasets, one for internal training and validation, and one for external validation. Malignancy was confirmed with samples obtained from fine needle biopsy. Pathological staging and mutational status were confirmed with operative reports and genomic sequencing, respectively. To achieve maximum malignancy prediction, a quad-model AI method was performed – (1) radiomics which employs high-throughput quantitative analysis of image features, (2) TDA which analyzes geometric relationships between data points in images, (3) machine learning (ML) which algorithm that utilizes TI-RADS-defined ultrasound properties as ML features, (ML)TI-RADS, (4) deep learning (DL) which employed convoluted neural network algorithms to achieve prediction. For prediction of tumor (T) stage, nodal (N) stage, extrathyroidal extension, and BRAF, a tri-model that consists of radiomics, TDA, and (ML)TI-RADS was used. One hundred and four quantitative radiomics features, 476 topological algebraic-geometric features, and 5 TI-RADS features were obtained for all nodules. Linear discriminant analysis (LDA) and the Pearson correlation coefficient (0.85 threshold) were employed for feature extraction. Support vector machine (SVM) was chosen as the machine learning classifier. <h3>Results</h3> For malignancy prediction, radiomics, TDA, (ML)TI-RADS, and DL individual model achieves an accuracy of 89% (0.87 AUC), 81% (0.81 AUC), 80% (0.76 AUC), and 87% (0.92 AUC), respectively. Our quad-model achieves a 98.7% accuracy, 0.98 AUC, 96% precision, 98% sensitivity and specificity for malignancy prediction, which is significantly improved when compared to individual model (radiomics, TDA, (ML)TI-RADS: p<0.001, deep learning: p=0.002). Using the external validation dataset, the quad-model achieves a 93% accuracy (0.94 AUC) for malignancy prediction. Our tri-model yields an accuracy of 93% accuracy (0.93 AUC), 89% accuracy (0.88 AUC), 98% (0.96 AUC), and 96% (0.97 AUC) for prediction of T, N, extrathyroidal extension, and BRAF mutation, respectively. <h3>Conclusion</h3> Using routine clinical ultrasonography, we have established a robust AI platform that allows prediction of malignancy, pathological stage, and BRAF mutation for thyroid cancer with high accuracy.

Positive predictive value of LI-RADS US-3 observations: multivariable analysis of clinical and imaging features.

To determine how clinical and imaging features affect the positive predictive values (PPV) of US-3 observations. In this retrospective study, 10,546 adult patients who were high risk for hepatocellular carcinoma (HCC) from 2017 to 2021 underwent ultrasound screening/surveillance. Of these, 225 adult patients (100 women, 125 men) with an US-3 observation underwent diagnostic characterization with multiphasic CT (93; 41%), MRI (130; 58%), or contrast-enhanced ultrasound (2; 1%). US-3 observations included focal observations ≥ 10mm in 216 patients and new venous thrombi in 9 patients. PPV with 95% confidence intervals were calculated using diagnostic characterization as the reference standard. Multivariable analysis of clinical and imaging features was performed to determine the strongest associations with cancer. Overall PPV for an US-3 observationwas 33% (27-39%) for at least intermediate probability of cancer (≥ LR-3) and 15% (10-20%) for at least probable cancer (≥ LR-4). At multivariable analysis, cirrhosis had the strongest effect size for at least probable cancer (p < 0.001; odds ratio OR 20.4), followed by observation size (p < 0.001; OR 2.65) and age (p = 0.004; OR 1.05). Alpha-fetoprotein, visualization score, and observation echogenicity were not statistically significant associations. Modality (MRI versus CT) did not affect PPV. Due to the large effect of cirrhosis, PPV was then stratified by the presence (n = 116; 52%) or absence (n = 109; 48%) of cirrhosis. For at least probable cancer (≥ LR-4), PPV increased from 4% (0-7%; non-cirrhotic) to 26% (18-34%; p < 0.001; cirrhosis). Cirrhosis most strongly affects PPV of US-3 observations for at least probable cancer at diagnostic characterization among high-risk patients, increasing to 1 in 4 among cirrhotic patients from 1 in 25 among non-cirrhotic patients.

Multimodality imaging differentiation of pancreatic neuroendocrine tumors and solid pseudopapillary tumors with a nomogram model: A large single-center study.

BackgroundPancreatic neuroendocrine tumors (pNETs) and solid pseudopapillary tumors (SPTs) are two of the most common pancreatic neoplasms with different treatment procedures. However, the broad heterogeneity of pNETs and SPTs in clinical manifestations and radiological features often confuse the presurgical discrimination in clinical practice, and the clinical and molecular differentiation of the two tumors remains elusive to date. We presume that a large and comprehensive study into the multimodality features of pNETs and SPTs is necessary for precise clinical management.MethodsWe collected and analyzed the clinicopathological information and multimodality features of nonfunctional pNET and SPT patients, for a total of 631 cases from 2006 to 2021. Univariate analysis of imaging features, including contrast-enhanced computed tomography (CT), magnetic resonance imaging, endoscopic ultrasound (EUS) and nuclear medicine imaging, and clinical characteristics was performed, and CT features and clinical information were integrated to establish a nomogram model.ResultsWe recruited 354 nonfunctional pNET and 277 SPT patients in our cohort. Regarding demographic information, pNET patients had a lower female percentage (55.4% vs. 72.9%), smaller tumor size (2.8 vs. 4.8 cm), and older age (53.4 vs. 35.3 years). In CT imaging and EUS, pNETs tended to appear as solid and homogenous lesions with strong enhancement intensity. Multifocal lesions, duct dilation, and lymph node (LN) enlargement were more likely to be observed in pNETs, while calcification was more common in SPT lesions. On positron emission tomography (PET)/CT, pNETs exhibited significant sensitivity to somatostatin receptor scintigraphy (SRS), with positive rates of 81.4% and 95% on 99mTc-HYNIC-TOC and 68Ga-DOTATATE PET/CT, respectively, while SPTs were all negative on SRS. Multivariate analysis identifies tumor size, age, enhancement intensity, calcification, and LN enlargement as statistically significant variables.ConclusionsCompared to SPT patients, pNET patients exhibit an older age and smaller tumor size. CT manifestations of strong intensity, LN enlargement, and no calcification could indicate a higher possibility of pNET. Meanwhile, the similarity in the immunohistochemical profile indicates that the two tumors could potentially develop from the same origin.

Investigation on Imaging Features and Clinical Significance of Cardiac CT in Comprehensive Evaluation of Aortic Valve and Root before Percutaneous Aortic Valve Replacement.

Medical imaging feature analysis is the basis of medical image processing and analysis. The solution of this problem not only directly affects the successful application of computer graphics and image technology in medicine but also has important theoretical and practical significance. In this paper, the imaging characteristics and clinical significance are discussed by studying the comprehensive evaluation of aortic valve and root before aortic valve replacement. In recent years, preoperative comprehensive evaluation of the aortic valve and root has been gradually carried out. Compared with traditional methods, minimally invasive surgery brings more accurate diagnosis to patients, quick recovery and discharge after surgery, and less pain. This study retrospectively includes patients with severe aortic stenosis who underwent TAVR with routine computed tomography. Based on CT images, the determination and grouping of bicuspid aortic valve and tricuspid aortic valve were completed. Thirteen cross-sectional levels of the aorta-iliac-femoral vascular access were completed. The results showed that 3 people had stroke (17.6%) and 5 people had myocardial infarction (29.4%) during the follow-up period. Atrial fibrillation occurred in 5 patients (29.4%), permanent pacemaker implantation was performed in 1 patient (5.9%), and acute kidney injury occurred in 7 patients (41.2%). No patient died due to surgery-related causes, and the analysis of imaging features and clinical significance in the preoperative comprehensive evaluation of the aortic valve and root played a crucial role. In the training stage, the principal component analysis method was used to train the shape, and the model of the shape intensity of the aortic valve and the shape change of each principal component was obtained. The most probable aortic valve region in the target image was obtained by matching the similarity of all atlases, and the correct aortic valve segmentation was obtained by using the first level set of shape intensity. The experimental part verified the accuracy of the algorithm.

Diagnostic value of dual-energy CT and clinicopathological and imaging feature analysis of mixed endometrial stromal and smooth muscle tumors with intracardiac extension.

ObjectiveTo describe the clinicopathological and imaging features of mixed endometrial stromal and smooth muscle tumors with intracardiac extension and to explore the diagnostic value of dual-energy computed tomography (DECT) for this rare entity.Materials and methodsThis retrospective study analyzed the clinicopathological data and images of a 41-year-old female patient with pathologically documented mixed endometrial stromal and smooth muscle tumors with intracardiac extension who had undergone DECT examination. Seven virtual monoenergetic images (VMIs) in 10-kiloelectron volt (keV) intervals (range = 40–100 keV), iodine density (ID) maps, and Z effective (Zeff) maps were reconstructed, and lesion conspicuity was assessed. Tumor homology was analyzed using quantitative DECT parameters and energy spectrum attenuation curve.ResultsThe patient complained of a 10-day history of bilateral lower extremity edema. Computed tomography showed a hypoattenuating filling defect located within the paracervical vein that extended into the right atrium to the ventricle through the right iliac veins and inferior vena cava (IVC). Intracardiac and intravenous lesions mainly demonstrated moderate progressive enhancement, with localized non-enhancing necrotic areas on contrast-enhanced CT. Multiple nodules showing progressive enhancement (long-T1 signal, long-T2 signal) were observed at the fundus of the uterus on dynamic contrast-enhanced magnetic resonance imaging (MRI), which were deemed the primary lesions of the tumor. Overall, the tumor was characterized by a small primary lesion with extensive vascular extension. In addition, the 40 keV VMIs reconstructions were found to provide best visualization for the early detection of tumors.ConclusionAlthough a definitive diagnosis of MESSMT with intracardiac extension requires confirmation by histopathological examination, imaging examination can be used to characterize the extent of the lesion. The dual-energy dataset facilitates tumor visualization and homology evaluation.