Purpose: To investigate the clinical value of spectral computed tomography (CT) imaging combined with alpha-fetoprotein (AFP) in identifying liver cancer and hepatic focal nodular hyperplasia (FNH).
Methods: A total of 132 patients with local liver space-occupying lesions, including 68 patients with liver cancer, were randomly enrolled. All the patients underwent spectral CT imaging and AFP examinations. The corresponding specificity, sensitivity, accuracy rate, positive predictive value and negative predictive value of spectral CT imaging, AFP and combined detection were recorded, respectively, with pathological findings as the gold standards. SPSS 17.0 software was used for statistical analysis. P<0.05 suggested that the difference was statistically significant.
Results: The diagnostic rate of spectral CT imaging was 79.5% for liver cancer and 81.3% for hepatic FNH. In arterial phase and portal venous phase, the contrast-to-noise ratio (CNR) of liver cancer was remarkably lower than that of FNH, showing a statistically significant difference, and the difference was the greatest at 70-100 keV between the two kinds of lesions. The detection rate of AFP for liver cancer was 86.8%, and the exclusive diagnostic rate of AFP for hepatic FNH was 96.9%. AFP had the highest specificity (73.2%) in identifying liver cancer and hepatic FNH. The spectral CT imaging possessed the highest sensitivity (91.7%) in identifying liver cancer and hepatic FNH. Both the sensitivity (98.1%) and accuracy (89.1%) of spectral CT imaging combined with AFP were the highest in identifying liver cancer and hepatic FNH.
Conclusion: The spectral CT imaging combined with AFP is conducive to improving the efficiency of differential diagnosis of liver cancer and hepatic FNH.
Download full-text PDF |
Source |
---|
Abdom Radiol (NY)
January 2025
Department of Radiology, Affiliated Kunshan Hospital of Jiangsu University, Kunshan, China.
Objectives: To develop a nomogram based on the radiomics features of tumour and perigastric adipose tissue adjacent to the tumor in dual-layer spectral detector computed tomography (DLCT) for lymph node metastasis (LNM) prediction in gastric cancer (GC).
Methods: A retrospective analysis was conducted on 175 patients with gastric adenocarcinoma. They were divided into training cohort (n = 125) and validation cohort (n = 50).
Diabet Med
January 2025
School of Medicine, University of Galway, Galway, Ireland.
Aims: To describe the sonographic features of active Charcot neuro-osteoarthropathy (CNO) and assess the potential role of ultrasound in identifying those with active CNO.
Methods: Using a prospective case-series study design we assessed the sonographic features of 14 patients with a diagnosis of diabetes presenting with clinical signs and symptoms suspicious for active CNO. Patients had standard weight-bearing plain X-Ray and, where possible, MRI to evaluate the presence of active CNO.
Sensors (Basel)
January 2025
Department of Optical Engineering, Utsunomiya University, 7-2-1 Yoto, Utsunomiya 321-8585, Japan.
We describe the various steps of a gas imaging algorithm developed for detecting, identifying, and quantifying gas leaks using data from a snapshot infrared spectral imager. The spectral video stream delivered by the hardware allows the system to combine spatial, spectral, and temporal correlations into the gas detection algorithm, which significantly improves its measurement sensitivity in comparison to non-spectral video, and also in comparison to scanning spectral imaging. After describing the special calibration needs of the hardware, we show how to regularize the gas detection/identification for optimal performance, provide example SNR spectral images, and discuss the effects of humidity and absorption nonlinearity on detection and quantification.
View Article and Find Full Text PDFSensors (Basel)
January 2025
Institute for Energy Engineering, Universitat Politècnica de València, Camino. de Vera s/n, 46022 Valencia, Spain.
Induction motors are essential components in industry due to their efficiency and cost-effectiveness. This study presents an innovative methodology for automatic fault detection by analyzing images generated from the Fourier spectra of current signals using deep learning techniques. A new preprocessing technique incorporating a distinctive background to enhance spectral feature learning is proposed, enabling the detection of four types of faults: healthy motor coupled to a generator with a broken bar (HGB), broken rotor bar (BRB), race bearing fault (RBF), and bearing ball fault (BBF).
View Article and Find Full Text PDFSensors (Basel)
January 2025
Department of Environmental Remote Sensing and Geoinformatics, Trier University, Universitätsring 15, 54296 Trier, Germany.
Assessing vines' vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!