Advances in the Study of Bioactive Nanoparticles for the Treatment of HCC and Its Postoperative Residual Cancer.

Primary hepatocellular carcinoma (HCC, hepatocellular carcinoma) is the third leading cause of tumor death in the world and the second leading cause in China. The high recurrence rate at 5 years after surgery also seriously affects the long-term survival of HCC patients. For reasons such as poor liver function, large tumors, or vascular invasion, only relatively limited palliative treatment is available. Therefore, effective diagnostic and therapeutic strategies are needed to improve the complex microenvironment and block the mechanism of tumor development in order to treat the tumor and prevent recurrence. A variety of bioactive nanoparticles have been shown to have therapeutic effects on hepatocellular carcinoma and have the advantages of improving drug solubility, reducing drug side effects, preventing degradation in the blood, increasing drug exposure time, and reducing drug resistance. The development of bioactive nanoparticles is expected to complete the current clinical therapeutic approach. In this review, we discuss the therapeutic advances of different nanoparticles for hepatocellular carcinoma and discuss their potential for postoperative applications with respect to possible mechanisms of hepatocellular carcinoma recurrence. We further discuss the limitations regarding the application of NPs and the safety of NPs.

Abdominal virtual non-contrast images derived from energy spectrum CT to evaluate chemotherapy-related fatty liver disease.

Background: Chemotherapy-related fatty liver disease (CRFLD) is an important evaluation in patients undergoing computed tomography (CT) for cancer follow-up. This study set out to explore the feasibility of using abdominal virtual non-contrast (VNC) images derived from energy spectrum CT to evaluate CRFLD and reduce the radiation dose. Methods: A total of 160 eligible consecutive patients who underwent energy spectrum CT at Lanzhou University Second Hospital between June 2020 and July 2021 were retrospectively enrolled. The average CT attenuation values of the liver and spleen and the liver-to-spleen ratio (LSR) were measured by two independent blinded radiologists on true non-contrast (TNC) images and three types of VNC image. The diagnostic performance of the LSR for CRFLD, image quality, and diagnostic confidence were compared between the two types of imaging. Results: The average CT attenuation values of the liver and spleen were significantly lower on VNC images than on TNC images (P<0.05), whereas the LSR showed good agreement between the two (P>0.05). The average CT attenuation values of the liver and the LSR measured on the TNC and three types of VNC image were significantly lower in patients with CRFLD than in those without CRFLD (P<0.001). The area under the curve (AUC) values of the LSR for the diagnosis of CRFLD calculated on TNC and three types of VNC image were 0.870 (95% CI: 0.808-0.918), 0.852 (95% CI: 0.787-0.903), 0.819 (95% CI: 0.750-0.875), and 0.851 (95% CI: 0.786-0.902), respectively. The DeLong test confirmed the consistency of TNC and VNC images of diagnostic efficacy (P>0.05). There were no significant differences in image quality or diagnostic confidence between the TNC and three types of VNC image (P>0.05). When VNC imaging was applied, the radiation dose was reduced by approximately 25.0%. Conclusions: VNC imaging could become a reliable alternative to TNC imaging for the clinical evaluation of patients with CRFLD and could reduce the radiation dose by up to 25%.

Deep learning image reconstruction algorithm for carotid dual-energy computed tomography angiography: evaluation of image quality and diagnostic performance.

OBJECTIVES: To evaluate image quality and diagnostic performance of carotid dual-energy computed tomography angiography (DECTA) using deep learning image reconstruction (DLIR) compared with images using adaptive statistical iterative reconstruction-Veo (ASIR-V). METHODS: Carotid DECTA datasets of 28 consecutive patients were reconstructed at 50 keV using DLIR at low, medium, and high levels (DLIR-L, DLIR-M, and DLIR-H) and 80% ASIR-V algorithms. Mean attenuation, image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) at different levels of arteries were measured and calculated. Image quality for noise and texture, depiction of arteries, and diagnostic performance toward carotid plaques were assessed subjectively by two radiologists. Quantitative and qualitative parameters were compared between the ASIR-V, DLIR-L, DLIR-M, and DLIR-H groups. RESULTS: The image noise at aorta and common carotid artery, SNR, and CNR at all level arteries of DLIR-H images were significantly higher than those of ASIR-V images (p = 0.000-0.040). The quantitative analysis of DLIR-L and DLIR-M showed comparable denoise capability with ASIR-V. The overall image quality (p = 0.000) and image noise (p = 0.000-0.014) were significantly better in the DLIR-M and DLIR-H images. The image texture was improved by DLR at all level compared to ASIR-V images (p = 0.000-0.008). Depictions of head and neck arteries and diagnostic performance were comparable between four groups (p > 0.05). CONCLUSIONS: Compared with 80% ASIR-V, we recommend DLIR-H for clinical carotid DECTA reconstruction, which can significantly improve the image quality of carotid DECTA at 50 keV but maintain a desirable diagnostic performance and arterial depiction.

The Value of Deep Learning Image Reconstruction in Improving the Quality of Low-Dose Chest CT Images.

This study aimed to evaluate the value of the deep learning image reconstruction (DLIR) algorithm (GE Healthcare's TrueFidelity™) in improving the image quality of low-dose computed tomography (LDCT) of the chest. First, we retrospectively extracted raw data of chest LDCT from 50 patients and reconstructed them by using model-based adaptive statistical iterative reconstruction-Veo at 50% (ASIR-V 50%) and DLIR at medium and high strengths (DLIR-M and DLIR-H). Three sets of images were obtained. Next, two radiographers measured the mean CT value/image signal and standard deviation (SD) in Hounsfield units at the region of interest (ROI) and calculated the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). Two radiologists subjectively evaluated the image quality using a 5-point Likert scale. The differences between the groups of data were analyzed through a repeated measures ANOVA or the Friedman test. Last, our result show that the three reconstructions did not differ significantly in signal (p > 0.05) but had significant differences in noise, SNR, and CNR (p < 0.001). The subjective scores significantly differed among the three reconstruction modalities in soft tissue (p < 0.001) but not in lung tissue (p > 0.05). DLIR-H had the best noise reduction ability and improved SNR and CNR without distorting the image texture, followed by DLIR-M and ASIR-V 50%. In summary, DLIR can provide a higher image quality at the same dose, enhancing the physicians' diagnostic confidence and improving the diagnostic efficacy of LDCT for lung cancer screening.

Deep learning image reconstruction in pediatric abdominal and chest computed tomography: a comparison of image quality and radiation dose.

Background: Studies on the application of deep learning image reconstruction (DLIR) in pediatric computed tomography (CT) are limited and have so far been mostly based on phantom. The purpose of this study was to compare the image quality and radiation dose of DLIR with that of adaptive statistical iterative reconstruction-Veo (ASiR-V) during abdominal and chest CT for the pediatric population. Methods: A pediatric phantom was used for the pilot study, and 20 children were recruited for clinical verification. The preset scan parameter noise index (NI) was 5, 8, 11, 13, 15, and 18 for the phantom study, and 8 and 13 for the clinical pediatric study. We reconstructed CT images with ASiR-V 30%, ASiR-V 70%, DLIR-M (medium) and DLIR-H (high). The regions of interest (ROI) were marked on the organs of the abdomen (liver, kidney, and subcutaneous fat) and the chest (lung, mediastinum, and spine). The CT dose index volume (CTDIvol), CT value, image noise (N), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were measured and calculated. The subjective image quality was assessed by 3 radiologists blindly using a 5-point scale. The dose reduction efficiency of DLIR was estimated. Results: In the phantom study, the interobserver assessment of the data measurement demonstrated good agreement [intraclass correlation coefficient (ICC) =0.814 for abdomen, 0.801 for chest]. Within the same dose level, the N, SNR, and CNR were statistically different among reconstructions, while the CT value remained the same. The N increased and SNR decreased as the radiation dose decreased. The DLIR-H performed better than ASiR-V when the radiation dose was reduced, without sacrificing image quality. In the patient study, the interobserver assessment of the data measurement demonstrated good agreement (ICC =0.774 for abdomen, 0.751 for chest). DLIR-H had the highest subjective and objective scores in the abdomen. Conclusions: Application of DLIR could help to reduce radiation dose without sacrificing the image quality of pediatric CT scans. Further clinical validation is required.

Comparison of image quality and lesion diagnosis in abdominopelvic unenhanced CT between reduced-dose CT using deep learning post-processing and standard-dose CT using iterative reconstruction: A prospective study.

PURPOSE: To compare image quality and lesion diagnosis between reduced-dose abdominopelvic unenhanced computed tomography (CT) using deep learning (DL) post-processing and standard-dose CT using iterative reconstruction (IR). METHOD: Totally 251 patients underwent two consecutive abdominopelvic unenhanced CT scans of the same range, including standard and reduced doses, respectively. In group A, standard-dose data were reconstructed by (blend 30 %) IR. In group B, reduced-dose data were reconstructed by filtered back projection reconstruction to obtain group B1 images, and post-processed using the DL algorithm (NeuAI denosing, Neusoft medical, Shenyang, China) with 50 % and 100 % weights to obtain group B2 and B3 images, respectively. Then, CT values of the liver, the second lumbar vertebral centrum, the erector spinae and abdominal subcutaneous fat were measured. CT values, noise levels, signal-to-noise ratios (SNRs), contrast-to-noise ratios (CNRs), radiation doses and subjective scores of image quality were compared. Subjective evaluations of low-density liver lesions were compared by diagnostic results from enhanced CT or Magnetic Resonance Imaging. RESULTS: Groups B3 and B1 showed the lowest and highest noise levels, respectively (P < 0.001). The SNR and CNR in group B3 were highest (P < 0.001). The radiation dose in group B was reduced by 71.5 % on average compared to group A. Subjective scores in groups A and B2 were highest (P < 0.001). Diagnostic sensitivity and confidence for liver metastases in groups A and B2 were highest (P < 0.001). CONCLUSIONS: Reduced-dose abdominopelvic unenhanced CT combined with DL post-processing could ensure image quality and satisfy diagnostic needs.

Association of Circulating Cholesterol Level with Cognitive Function and Mild Cognitive Impairment in the Elderly: A Community-based Population Study.

BACKGROUND: The present study was designed to examine the association of circulating cholesterol with cognitive function in non-demented community aging adults. METHODS: This was a cross-sectional study including 1754 Chinese adults aged 55-80 years. The association between serum cholesterol levels and cognitive function was examined. Participants were categorized into four groups according to the quartile of circulating TC (total cholesterol), High Density Lipoprotein Cholesterol (HDL-c), Low Density Lipoprotein Cholesterol (LDL-c) levels and HDLc/ LDL-c ratio. The difference in cognitive performance among the groups was compared. Logistic regression model was used to determine the association of circulating cholesterol level with the risk of Mild Cognitive Impairment (MCI). RESULTS: Mild increase of serum LDL-c level correlated with better visual and executive, language, memory and delayed recall abilities. Higher circulating TC and HDL-c levels were found to be associated with poorer cognitive function, especially in aging female subjects. Higher circulating TC, HDL-c and HDL/LDL ratio indicated an increased risk of MCI, especially in female subjects. CONCLUSION: Slight increase in circulating LDL-c level might benefit cognitive function in aging adults. However, higher circulating TC and HDL-c levels might indicate a decline of cognitive function, especially in aging female subjects.

Impact of Deep Learning-based Optimization Algorithm on Image Quality of Low-dose Coronary CT Angiography with Noise Reduction: A Prospective Study.

RATIONALE AND OBJECTIVES: To evaluate deep learning (DL)-based optimization algorithm for low-dose coronary CT angiography (CCTA) image noise reduction and image quality (IQ) improvement. MATERIALS AND METHODS: A postprocessing platform for the CCTA image was built using a DL-based algorithm. Seventy subjects referred for CCTA were randomly divided into two groups (study group A with 80 kVp and control group B with 100 kVp). Group C was obtained by DL optimization of group A. Subjective IQ was blindly graded by two experienced radiologists on a four-point scale (4-excellent,1-poor). The image noise, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were calculated to evaluate IQ objectively. The difference between the time consumed of iterative reconstruction and DL algorithm was also recorded. RESULTS: The subjective IQ score of group C using the DL algorithm was significantly better than that of group A (p = 0.005). The noise of group C was significantly decreased, while SNR and CNR were significantly increased compared to group A (p < 0.001). The subjective IQ scores were lower in group A compared to group B (p = 0.037), whereas subjective IQ scores in group C were not significantly different (p = 0.874). For objective IQ, the noise of group A was significantly higher, while SNR and CNR were significantly lower than that of group B (p < 0.05). There was no significant difference in noise and SNR between group C and group B (p > 0.05), but CNR in group C was significantly higher than that in group B (p < 0.05). The DL algorithm processes the image twice as fast as the iterative reconstruction speed. CONCLUSION: The DL-based optimization algorithm could effectively improve the IQ of low-dose CCTA by noise reduction.