جستجوی مقالات مرتبط با کلیدواژه « spectral ct » در نشریات گروه « پزشکی »

The goal of this study was to create a prediction model for brain metastasis (BrMs) in patients with lung cancer using unenhanced spectral computed tomography (CT) and radiomics.

This study comprised 162 patients with lung cancer who underwent spectral CT from 2019–2021. Patients were split into training and test sets and into BrMs and BrMs-free groups. Spectral and radiomics parameters were obtained from the spectral CT images before pathological confirmation. Prediction models in the training and test sets were created using logistic regression. The receiver operating characteristic curve was used to evaluate each quantitative parameter for predicting BrMs. The diagnostic effectiveness of several parameters was analyzed and compared using the area under the curve (AUC) calculation. The final model was obtained using the Delong test.

There were statistically significant differences in the iodine concentrations and the slope of the energy spectrum attenuation curve of the two groups <(p0.05). The AUC of the combined radiomics model was greater than that of the 70 keV and 120 keV sequence models. The joint parameters of radiomics and spectral CT constructed an integrated model. In the training set, test set, and overall set, the AUCs of the integrated model were 0.875, 0.879, and 0.724, respectively. In the training and overall sets, the prediction performance of the integrated model outperformed the spectral and radiomics models (p<0.05).

This integrated model may predict the BrMs in lung cancer patients.

While various algorithms are applied in acquiring diagnostic information during computed tomography, such algorithms may affect image quality. The present study aimed to investigate the changes in image quality according to the application of the metal reduction algorithm and monoenergetic image in standard imaging. Spectral computed tomography was used to acquire images with the application of standard, metal artifact reduction, monoenergetic, and monoenergetic+metal artifact reduction under the same conditions according to without or with of metal in ACR phantom. ImageJ program was used to measure the HU, noise, and SNR of polyethylene, bone, and acrylic located inside the ACR phantom using the same-sized ROIs. HU measurement results showed changes in all materials, except acrylic with metal artifacts in the images. Moreover, the results showed a decrease in HU in images with the application of monoenergetic. Noise measurement results also showed changes in all materials, except acrylic with metal artifacts in the images. Moreover, the results showed a decrease in noise in images with the application of monoenergetic. For SNR measured relative to standard images, the results showed degradation of image quality due to a decrease of 36.5–77.7% in SNR and an increase in error value in all materials except acrylic. Whereas, acrylic showed an increase of 3.2–4.1% and a decrease in error values, resulting in improved image quality. Therefore, it is believed that the accuracy of reading could be increased by considering the changes in image quality and characteristics when applying algorithms for acquiring clinical information from CT.

Pulmonary mass-like lesions are one of the common manifestations of respiratory disorders. Differential diagnosis of these lesions is a major challenge in imaging studies.

This study aimed to explore the efficacy of spectral computed tomography (CT) features combined with conventional CT features in the differential diagnosis of pulmonary mass-like lesions.

This case-control study was performed on a malignant group consisting of patients and a benign group consisting of controls. The imaging characteristics and spectral CT parameters were evaluated in 77 patients who met the inclusion criteria. A multivariate logistic regression analysis was performed to determine independent predictors of malignant pulmonary lump-like lesions. Three models were established, including a radiomic feature model, a spectral CT model, and a combined model. A receiver operating characteristic (ROC) curve was also plotted to evaluate the diagnostic efficiency of the models.

Some CT features were significantly different between the malignant and benign groups, including the long-axis diameter (44.86 ± 18.42 in the malignant group vs. 55.59 ± 22.57 in the benign group; P = 0.07), mediastinal lymphadenopathy (25.00% in the benign group vs. 62.26% in the malignant group; P = 0.02), and mediastinal lymph node confluence (4.17% in the benign group vs. 41.51% in the malignant group; P = 0.01). The CT values at 40 keV (157.25 ± 79.23 vs. 148.46 ± 25.36, P = 0.047) and K40 - 70 keV (2.76 ± 2.05 vs. 2.52 ± 0.60, P = 0.04) were significantly higher in the benign group compared to the malignant group in the arterial phase (AP). Besides, the iodine concentration (IC) (14.73 ± 10.65 vs. 13.44 ± 3.24, P = 0.039; 17.52 ± 5.29 vs. 13.87 ± 5.81, P = 0.035), normalized iodine concentration (NIC) (0.15 ± 0.06 vs. 0.11 ± 0.05, P = 0.015; 0.41 ± 0.11 vs. 0.35 ± 0.10, P = 0.017), and Zeff value (8.46 ± 0.63 vs. 8.43 ± 0.28, P = 0.034; 8.60 ± 0.29 vs. 8.39 ± 0.33, P = 0.035) were significantly higher in the benign group compared to the malignant group, both in the AP and venous phase (VP). The logistic regression model, integrating CT features and spectral CT parameters, showed the highest diagnostic efficacy (area under the curve [AUC], 0.956; sensitivity, 87.5%; specificity, 90.6%).

The quantitative spectral CT parameters, combined with conventional CT features, could help distinguish benign and malignant pulmonary mass-like lesions, providing an essential basis for developing treatment plans.

The objective of this study was to retrospectively analyze the application of dual-energy spectral computerized tomography (DECT) to accurately diagnose breast cancer and lymph node metastasis.

Between May 2018 and December 2019, 37 patients (22 with breast cancer and 15 with normal breast cancer) who underwent spectral CT imaging were analyzed. Metastatic lymph nodes were identified in 14 patients with breast cancer. Twelve patients who underwent traditional CT were included randomly as the control group to compare the radiation dose with spectral CT. Monochromatic levels with an optimal contrast-to-noise ratio for normal breast tissue were obtained. Quantitative parameters of spectral CT were compared between normal breast and breast cancer patients. The spectral curve, histogram, and scatter plot features of metastatic lymph nodes and primary lesions were analyzed.

The monochromatic level with the optimal contrast-to-noise ratio of the breast was approximately 65keV. All quantitative parameters, including values at 40keV–140keV, the concentrations of iodine, spectral curve slope (λHU), and relative iodine concentration were increased in breast cancer compared to those in healthy breasts. Metastatic lymph nodes were more consistent with primary breast cancer lesions in the spectral curve, histogram, and scatter plot, especially in the venous phase. Additionally, the radiation of spectral CT was decreased compared to that of traditional CT.

Spectral CT can be used to identify breast cancer and metastatic lymph nodes.

The contrast medium (CM) in CT pulmonary angiography may induce adverse effects to patients, and higher CM is associated with higher rates of contrast-induced-nephropathy and mortality. Reduction of CM dosage through improvement of examination techniques may help reduce the occurrence of CM-induced adverse effects and healthcare costs.

To determine the optimal monochromatic energy levels in dual-energy spectral CT pulmonary angiography (CTPA) with low contrast dosage. Patients and

Thirty patients with suspected pulmonary embolism (PE) underwent dual-energy spectral CTPA with low radiation and low contrast doses with scanning protocol of GSI-36 with 260 mA, and 25 mL contrast (350 mgI/mL) with 4.0ml/s injection speed. The monochromatic images from 60 - 80 keV (interval 5 keV) were reconstructed using a 50% adaptive statistical iterative reconstruction (50% ASiR) algorithm at 1.25 mm slice thickness. The CT attenuation and standard deviation (SD) values of the main, right, left, right lower and left lower pulmonary arteries and the back muscle at the same level were measured on 60 – 80 keV images, and signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated and analyzed. The subjective image quality was evaluated by two experienced radiologists using a 5-level scoring method independently. Measurements were analyzed using IBM SPSS Statistics for Windows, version 25.0. (Armonk, NY: IBM Corp.).

CT attenuation values of the pulmonary arteries decreased with the increase of energy level in five-energy groups, with values greater than 300 HU at 60 keV - 70 keV energies. The 65 keV image had the highest SNR, CNR and lowest SD, with significant differences compared with those of other image sets (P < 0.05). The subjective quality scores for the 65 keV image was judged to be the highest by the two radiologists, but it was not significantly different from 60 keV and 70 keV (all P > 0.05).

The 65 keV monochromatic images provided the highest SNR, CNR and subjective scores with the lowest image noise in dual-energy spectral CTPA with low contrast dosage.