This study aimed to develop a multiregional radiomics-based model using multisequence MRI to predict microvascular invasion in hepatocellular carcinoma. We enrolled 141 patients with hepatocellular carcinoma, including 61 with microvascular invasion, who were diagnosed between March 2017 and July 2022. Clinical data were compared using the Wilcoxon rank-sum test or χ 2 test. Patients were randomly divided into training (n=112, 80%) and test (n=29, 20%) data sets. Four MRI sequences-including T2-weighted imaging, T2-weighted imaging with fat suppression, arterial phase-contrast enhancement, and portal venous phase contrast enhancement-were used to build the radiomics model. The tumor volumes of interest were manually delineated, and the expand-5mm and expand-10mm volumes of interest were automatically generated. A total of 1409 radiomic features were extracted from each volume of interest. Feature selection was performed using the least absolute shrinkage and selection operator and Spearman correlation analysis. Three logistic regression models (Tumor, Tumor-Expand5, and Tumor-Expand10) were established based on the radiomic features. Model performance was assessed using receiver operating characteristic analysis and Delong's test. Maximum tumor diameter, hepatitis B virus DNA, and aspartate aminotransferase levels were significantly different between the groups. The Tumor-Expand5mm model exhibited the best performance among the 3 models, with areas under the curve of 0.90 and 0.84 in the training and test data sets. The Tumor-Expand5 model based on multisequence MRI shows great potential for predicting microvascular invasion in patients with hepatocellular carcinoma, and may further contribute to personal clinical decision-making.

Atypical cardiac myxoma usually presents as an isolated mass attached to the atrial septum on imaging, with no movement and a wider attachment base. It is difficult to distinguish it from cardiac thrombus through conventional echocardiography or computed tomography (CT). The purpose of this study is to evaluate the value of CT coronary angiography imaging features in distinguishing atypical cardiac myxoma from cardiac thrombus. This retrospective study included patients with atypical myxoma of the heart confirmed by histopathology (n = 18) and with thrombus disappearance after anticoagulation treatment (n = 23). All patients underwent a third-generation dual-source CT coronary angiography. We compared the clinical features and CT coronary angiography image characteristics of the 2 groups and used maximum-intensity projection and multiplanar reconstruction to show neovascularization of atypical cardiac myxoma. There are significant differences in the origin, surface, and enhancement patterns between atypical cardiac myxoma and thrombus ( P < 0.05, respectively). Specifically, supplied vessels were observed in the atypical cardiac myxoma group, while no neovascularization was detected in the thrombus group (83.33% vs. 0%, P < 0.001). Noninvasive CT coronary angiography can help distinguish atypical cardiac myxoma and cardiac thrombus through imaging features, especially by detecting the supplying vessels. However, supplementary examinations such as cardiac magnetic resonance imaging are still needed to identify different cardiac tumors.

This study evaluated the performance of a deep learning-based vertebral compression fracture (VCF) detection tool in patients with incidental VCF. The purpose of this study was to validate this tool across multiple sites and multiple vendors. This was a retrospective, multicenter, multinational blinded study using anonymized chest and abdominal CT scans performed for indications other than VCF in patients ≥50 years old. Images were obtained from 2 teleradiology companies in France and United States and were processed by CINA-VCF v1.0, a deep learning algorithm designed for VCF detection. Ground truth was established by majority consensus across 3 board-certified radiologists. Overall performance of CINA-VCF was evaluated, as well as subset analyses based on imaging acquisition parameters, baseline patient characteristics, and VCF severity. A subgroup was also analyzed and compared with available clinical radiology reports. Four hundred seventy-four CT scans were included in this study, comprising 166 (35.0%) positive and 308 (65.0%) negative VCF cases. CINA-VCF demonstrated an area under the curve (AUC) of 0.97 (95% CI: 0.96-0.99), accuracy of 93.7% (95% CI: 91.1%-95.7%), sensitivity of 95.2% (95% CI: 90.7%-97.9%), and specificity of 92.9% (95% CI: 89.4%-96.5%). Subset analysis based on VCF severity resulted in a specificity of 94.2% (95% CI: 90.9%-96.6%) for grade 0 negative cases and a specificity of 64.3% (95% CI: 35.1%-87.2%) for grade 1 negative cases. For grades 2 and 3 positive cases, sensitivity was 89.7% (95% CI: 79.9%-95.8%) and 99.0% (95% CI: 94.4%-100.0%), respectively. CINA-VCF successfully detected incidental VCF and even outperformed clinical reports. The performance was consistent among all subgroups analyzed. Limitations of the tool included various confounding pathologies such as Schmorl's nodes and borderline cases. Despite these limitations, this study validates the applicability and generalizability of the tool in the clinical setting.

With the widespread use of lumbar pedicle screws for internal fixation, the morphology of the screws and the surrounding tissues should be evaluated. The metal artifact reduction (MAR) technique can reduce the artifacts caused by pedicle screws, improve the quality of computed tomography (CT) images after pedicle fixation, and provide more imaging information to the clinic. To explore whether the MAR + method, a projection-based algorithm for correcting metal artifacts through multiple iterative operations, can reduce metal artifacts and have an impact on the structure of the surrounding metal. A total of 57 patients who underwent lumbar spine CT examination after lumbar internal fixation from January to December 2023 in our hospital were retrospectively enrolled. The CT images were reconstructed using MAR + and non-MAR + techniques and were subdivided into MAR + and non-MAR + groups. The CT number (in Hounsfield units) and the SD noise values of the spinal canal, vertebral body, psoas major muscle, and adjacent fat were measured in the 2 groups of CT images, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were calculated. The subjective score was evaluated by two diagnostic radiologists using a double-blind method for image quality evaluation of the MAR + group and the non-MAR + group, and the image quality was classified on a 5-point scale. The rank-sum test was utilized to compare the subjective and objective scores of the 2 groups. The SD values of the spinal canal ( Z =-4.12, P <0.01), vertebral body ( Z =-3.81, P <0.01), and psoas major muscle ( Z =-3.87, P <0.01) in the MAR + group were significantly lower than those in the non-MAR + group ( P <0.05). However, the SD values of the adjacent fat ( Z =-2.03, P =0.42) in the MAR + group, although smaller than those in the non-MAR + group, were not statistically significant. The CNR values of vertebral canal ( Z =-2.67, P =0.008) and fat ( Z =-2.60, P =0.009) were higher in the MAR + group than in the non-MAR + group, whereas the CNR values of the vertebral body ( Z =-6.74, P <0.01) in the MAR + group were smaller than those in the non-MAR + group, and the difference of all of them was statistically significant ( P <0.05). Furthermore, for both CT and SNR values, the MAR group's values were all less than those of the non-MAR group and were statistically significant ( P <0.05). The subjective scores of the measurement points were all higher in the MAR + group than in the non-MAR + group. The MAR + technique has a noise reduction effect on different tissues and artifacts are significantly reduced. Although the artifacts caused by metal screws were not completely eliminated, the MAR + technique was able to reduce the interference of artifacts in the diagnosis of CT images, thus improving their diagnostic quality.

Patient positioning during clinical practice can be challenging, and mispositioning leads to a change in CT number. CT number fluctuation was assessed in single-energy (SE) EID, dual-energy (DE) EID, and deep silicon photon-counting detector (PCD) CT over water-equivalent diameter (WED) with different mispositions. A phantom containing five clinically relevant inserts (Mercury Phantom, Gammex) was scanned on a clinical EID CT and a deep silicon PCD CT prototype at vertical positions of 0, 4, 8, and 12 cm. EID scans used 120 kV and rapid kV-switching DE techniques. CT number was calculated for air, water, polystyrene, iodine 10 mg/mL, and bone. Ideal CT numbers were calculated using the NIST XCOM database toolkit. Comparison of measured to ideal CT number utilized relative root mean square error (RMSE). Trends in CT number versus WED were compared using linear regression and statistical comparisons to test for differences in slope. No statistical difference of CT number with mispositioning was seen between acquisition modes. CT number fluctuation was larger due to WED than mispositioning for all material inserts. Water, iodine, and bone, for deep silicon PCD CT had statistically significant ( P < 0.05) smaller slopes compared to EIDof CT number over WED for all tested mispositions. The accuracy of deep silicon PCD CT was higher than either SE or DE EID CT for all materials at all mispositions except for polystyrene. WED (ie, patient size) contributes to CT number fluctuation more than mispositioning. The change in CT number was significantly smaller, and CT number accuracy was higher for deep silicon PCD CT in this phantom study.

This study aimed to assess the effectiveness of a dual digital alert system and automatic radiologist reassignment in improving the efficiency of quantitative imaging report delivery for clinical trials. Assessing tumor metrics is critical to oncologic disease management, informed treatment planning, and for monitoring therapeutic response and even more so in cancer clinical research trials. A collaborative effort with the Institutional Research Information Systems division led to developing a web-based system with a Java backend, tested using Agile methodology to improve patient care with improved turnaround time (TAT) of quantitative reports. The system sent dual digital alerts including a page and an email notification to the radiologist based on the last submitted date and time for each QIAC report and autoreassigned radiologists till report finalization. Data was extracted from the Quantitative Imaging Analysis Core database for comparing the TAT, calculated as time difference between the submission of preliminary reports by Imaging Research Specialists and the finalization by radiologists before and after the digital alert system implementation. Implementing the dual digital alert system significantly increased the number of cases finalized within 6 hours to 50%. For nonexpedited cases, the mean TAT decreased by 57.2% from 85.9 hours to 36.8 hours ( P < 0.001). Expedited cases saw a reduction in mean TAT by 63.7% from 44.9 hours to 16.3 hours ( P = 0.022). Baseline and follow-up cases also showed significantly reduced mean and median TAT after deployment ( P < 0.001). The dual digital alert system and automatic radiologist reassignment significantly improved the TAT for quantitative imaging reports in clinical trials. This enhancement in report delivery efficiency led to better therapeutic decision making and increased patient satisfaction in clinical settings.

This study aimed to enhance the predictability of local tumor progression (LTP) postthermal ablation in patients with colorectal cancer liver metastases (CRLMs). A sophisticated approach integrating magnetic resonance imaging (MRI) Δ-radiomics and clinical feature-based modeling was employed. In this retrospective study, 37 patients with CRLM were included, encompassing a total of 57 tumors. Radiomics features were derived by delineating the images of lesions pretreatment and images of the ablation zones posttreatment. The change in these features, termed Δ-radiomics, was calculated by subtracting preprocedure values from postprocedure values. Three models were developed using the least absolute shrinkage and selection operators (LASSO) and logistic regression: the preoperative lesion model, the postoperative ablation area model, and the Δ model. Additionally, a composite model incorporating identified clinical features predictive of early treatment success was created to assess its prognostic utility for LTP. LTP was observed in 20 out of the 57 lesions (35%). The clinical model identified, tumor size ( P = 0.010), and ΔCEA ( P = 0.044) as factors significantly associated with increased LTP risk postsurgery. Among the three models, the Δ model demonstrated the highest AUC value (T2WI AUC in training, 0.856; Delay AUC, 0.909; T2WI AUC in testing, 0.812; Delay AUC, 0.875), whereas the combined model yielded optimal performance (T2WI AUC in training, 0.911; Delay AUC, 0.954; T2WI AUC in testing, 0.847; Delay AUC, 0.917). Despite its superior AUC values, no significant difference was noted when comparing the performance of the combined model across the two sequences ( P = 0.6087). Combined models incorporating clinical data and Δ-radiomics features serve as valuable indicators for predicting LTP following thermal ablation in patients with CRLM.

This review aims to provide valuable insights into how energy consumption in magnetic resonance imaging (MRI) and computed tomography (CT) scanners can be effectively monitored, managed, and reduced, thereby contributing to more sustainable medical imaging practices. Demand for advanced imaging technologies such as MRI and CT scanners continues to increase, and understanding the resultant impact on greenhouse gas emissions requires a thorough evaluation of their energy consumption. This review examines the energy monitoring and consumption characteristics of MRI and CT scanners, highlighting potential approaches for energy savings. An overview of MRI and CT principles, hardware components, and their associated energy consumption is provided. After addressing the technical aspects, the hardware and software requirements essential for accurate energy metering are detailed. Baseline measurements of energy consumption data are then provided as a foundation to understand current usage patterns and identify areas for improvement. Ongoing efforts to reduce energy consumption are categorized into 3 main strategies: operations, scanner design enhancements, and active scanning techniques, including accelerated MRI protocols. Ultimately, we emphasize that achieving sustainability in medical imaging requires collaboration across disciplines. By incorporating eco-friendly design in new imaging equipment, we can reduce the environmental impact, promote sustainability, and set a health care industry standard for a healthier planet.

Detection of fat content in thymic lesions is essential to differentiate thymic hyperplasia from thymic tumors. This study assesses the reliability and efficacy of "iterative decomposition of water and fat with echo asymmetry and least-squares estimation quantization" IDEAL-IQ magnetic resonance sequence in distinguishing thymic hyperplasia from low-risk thymoma and thymic lymphoma in adulthood. Thirty patients with thymic hyperplasia, 28 low-risk thymomas, and 13 thymic lymphomas were respectively enrolled. All subjects underwent conventional thorax magnetic resonance imaging and IDEAL-IQ sequence. The fat fraction (FF mean and FF total ), signal intensity index, and R2* values of the lesions were compared for differences among 3 groups by the Mann-Whitney U and Kruskal-Wallis tests. Receiver operating characteristic curve analysis was performed to determine the differentiating efficacy. Both FF mean and FF total values in patients with thymic hyperplasia are significantly higher than those in patients with low-risk thymoma and thymic lymphoma (FF mean : 26.41% vs 1.78% and 1.93%, FF total : 27.67% vs 2.21% and 2.44%; both P < 0.001), whereas there was no significant difference in these values between low-risk thymomas and thymic lymphomas (both P > 0.05). Similarly, signal intensity index and R2* values of thymic hyperplasia were significantly higher than those of patients with low-risk thymoma and thymic lymphoma ( P < 0.001). Receiver operating characteristic curve analysis showed that FF mean had an area under the curve of 0.998, with a cutoff of 4.78% yielding 95.12% sensitivity and 100% specificity, and FF total had an area under the curve of 0.994, with a cutoff of 8.57% yielding 97.56% sensitivity and 96.67% specificity in distinguishing thymic hyperplasia from tumors. IDEAL-IQ sequence provides accurate fat quantitative parameters and can differentiate thymic hyperplasia from thymic neoplasms with robust efficacy and reliability.

The aim of the study is to investigate the efficacy of amide proton transfer-weighted (APT) imaging combined with serum squamous cell carcinoma antigen (SCC-Ag) in grading cervical cancer. Sixty-three patients with surgically confirmed cervical SCC were enrolled and categorized into 3 groups: highly differentiated (G1), moderately differentiated (G2), and poorly differentiated (G3). The diagnostic efficacies of APT imaging and serum SCC-Ag, alone or in combination, for grading cervical SCC were compared. The APT values measured by the 2 observers were in excellent agreement (intraclass correlation coefficient >0.75). Mean (± standard deviation) APT values for the high, moderate, and poor differentiation groups were 2.542 ± 0.215% (95% confidence interval [CI]: 2.423-2.677), 2.784 ± 0.175% (95% CI: 2.701-2.856), and 3.120 ± 0.221% (95% CI: 2.950-3.250), respectively. APT values for groups G2 and G3 were significantly higher than those for G1 ( P < 0.05). APT values for identifying cervical SCC in groups G1 and G2, G2 and G3, and G1 and G3, had areas under the receiver operating characteristic curve, sensitivities, and specificities of 0.815 (95% confidence interval [CI]: 0.674-0.914), 82.1%, and 72.2%, 0.882 (95% CI: 0.751-0.959), 70.6%, and 92.7%, and 0.961 (95% CI: 0.835-0.998), 94.1%, and 94.4%, respectively. APT values were significantly and positively correlated with the histological grade of cervical SCC (Spearman's correlation [ rs ] = 0.731, P < 0.01). Serum SCC-Ag levels for the high, moderate, and poor differentiation groups were 1.60 (0.88-4.63) ng/mL, 4.10 (1.85-6.98) ng/mL, and 26.10 (9.65-70.00) ng/mL, respectively. The differences were statistically significant only between groups G1 and G3 and G2 and G3 ( P < 0.05), whereas the differences between groups G1 and G2 were not statistically significant ( P > 0.05). Spearman's analysis revealed a positive correlation between SCC-Ag levels and the histological grade of cervical SCC ( rs = 0.573, P < 0.01). The diagnostic efficacy of APT imaging for the histological grading of cervical SCC was better than that of serum SCC-Ag, and the discriminatory efficacy of the combination of the 2 parameters was better than that of either alone. The diagnostic efficacy of APT imaging was better than that of serum SCC-Ag, and the combined diagnostic utility of APT and SCC-Ag was better than that of the individual parameters.