To compare the diagnostic performance of two algorithms for HCC diagnosis: SonoVue-CEUS based on CEUS LI-RADS version 2017 and a modified algorithm incorporating Kupffer-phase findings for Sonazoid-CEUS.

This single center prospective study enrolled high-risk patients for HCC. Each participant underwent SonoVue-CEUS and Sonazoid-CEUS. Each liver observation was assigned two LI-RADS categories according to each algorithm: SonoVue-CEUS LI-RADS and modified Sonazoid-CEUS LI-RADS. For the latter method, observations at least 10 mm with non-rim arterial phase hyperenhancement were upgraded LR-4 to LR-5 if there was no washout with a Kupffer defect and were reassigned LR-M to LR-5 if there was early washout with mild Kupffer defect. The reference standard was pathologic confirmation.

Overall, 66 patients (mean age, 61.2 years ± 10.9; 54 male patients, 12 female patients) with 66 observations (mean size, 31 mm ± 16) were eventually enrolled. The results of Sonazoid-CEUS LI-RADS showed significant changes in sensitivity (82% vs. 65%, p < 0.001), accuracy (85% vs. 71%, p < 0.001) compared with the SonoVue-CEUS LI-RADS. There was no significant difference in specificity (93% vs. 87%, p = 0.26).

When incorporating Kupffer-phase findings, Sonazoid-CEUS LI-RADS had higher sensitivity without loss of specificity compared with SonoVue-CEUS LI-RADS.

Lung cancer remains a leading cause of cancer-related mortality worldwide. Our study investigates the involvement of the PD-L1/MALAT1/miR-200a-3p axis in lung tumor progression using a murine model of lung carcinogenesis. Lung tumors were induced in rats, which were divided into groups and sacrificed at different stages of tumor development. A histopathological examination was performed to assess tumor progression. Immunohistochemistry was applied to evaluate the expression of Ki-67 and programmed death-ligand 1 (PD-L1). The level of carcinoembryonic antigen (CEA) and expression analysis of metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), miR-200a-3p, and zinc finger E-box binding homeobox 1 (ZEB1) were evaluated for each stage of induction. Immunohistochemical analysis demonstrated a progressive upregulation of the proliferative marker Ki-67 and the immune checkpoint protein PD-L1 during the induction process, indicative of enhanced tumor proliferation and immune evasion. Additionally, CEA levels revealed a progressive increase across induction stages, with a significant increase in advanced tumor stages, highlighting its clinical relevance as a biomarker for lung cancer progression. Expression analysis revealed dynamic upregulation of MALAT1 and downregulation of miR-200a during lung tumor induction, which correlated with advanced tumor stages and elevated PD-L1 expression, suggesting that the negative correlation between MALAT1 and miR-200a is involved in the development of lung tumors. ZEB1 expression exhibited a notable increase in the advanced stages of induction, consistent with its association with aggressive lung cancer. Our findings underscore the interplay between molecular pathways involved in lung tumor development and the potential diagnostic and therapeutic implications of the PD-L1/MALAT1/miR-200a-3p axis.

Recent radiomics studies on predicting pathological outcomes of glioma have shown immense potential. However, the predictive ability remains suboptimal due to the tumor intrinsic heterogeneity. We aimed to achieve better pathological prediction outcomes by combining habitat analysis with deep learning.

387 cases of primary glioma from three hospitals were collected, along with their T1 contrast-enhanced and T2-weighted MR sequences, pathological reports and clinical histories. The training set consisted of 264 patients, 82 patients composed the test set, and 41 patients were used as the validation set for hyperparameter tuning and optimal model selection. All groups were sourced from different centers. Through radiomics, deep learning, habitat analysis and combined analysis, we extracted imaging features separately and jointly modeled them with clinical features. We identified the optimal models for predicting glioma grades, Ki67 expression levels, P53 mutation and IDH1 mutation.

Using a LightGBM model with DenseNet161 features based on habitat subregions, the best tumor grade prediction model was achieved. A LightGBM model with ResNet50 features based on habitat subregions yielded the best Ki67 expression level prediction model. An SVM model with Radiomics and Inception_v3 features provided the best prediction of P53 mutation. The best model for predicting IDH1 mutation was achieved by an MLP model with Radiomics features based on habitat subregions. Clinical features might be potentially helpful for the prediction with relatively weak evidence.

Habitat+Deep Learning feature extraction methods were optimal for predicting grades and Ki67 levels. Deep Learning is optimal for predicting P53 mutation, while the combination of Habitat+ Radiomics models yielded the best prediction for IDH1 mutation.

To predict the degree of pathological differentiation of hepatocellular carcinoma (HCC) by quantitative analysis the correlation between the perfusion parameters of contrast-enhanced ultrasound (CEUS) and the pathological grades of HCC using VueBox® software.

We enrolled 189 patients who underwent CEUS and liver biopsy at our hospital from July 2019 to September 2024 and were pathologically confirmed with primary HCC. The Edmondson-Steiner pathological classification system was used as the gold standard for dividing the patients into the low-grade and high-grade groups. The patients were randomly divided into training set and testing set in a ratio of 7:3, in which the parameters of the training set were analyzed by univariate analysis and then stepwise regression to construct the prediction model, and the diagnostic efficacy of the validation model was evaluated by discrimination, calibration, and clinical applicability.

A total of 189 patients with primary hepatocellular carcinoma were enrolled, including 118 patients in the low-grade group and 71 patients in the high-grade group; they were randomly divided into training set of 128 patients and testing set of 61 patients. The prediction model was constructed by logistic regression in the training set, and the final model included three variables: mTTI, FT, and maximum diameter of a single lesion, resulting in the equation was Y = - 2.360 + 1.674 X 1 + 1.019 X 2 + 0.753 X 3 ( 2 ) + 1.570 X 3 ( 3 ) .The area under the ROC curve (AUC) of the training set was 0.831, with a sensitivity of 82.0% and a specificity of 79.5%; the area under the ROC curve (AUC) of the testing set was 0.811, with a sensitivity of 81.0% and a specificity of 70.0%.

The regression model constructed by combining multiple parameters can effectively improve the diagnostic performance of CEUS in predicting the pathological differentiation grade of HCC, thus providing a clinical basis and empirical support for the use of CEUS as a diagnostic imaging method for this disease.

Sonazoid is a new ultrasound contrast agent with unique Kupffer phase imaging advantages and high mechanical index stability. This paper introduces the basic theories and advantages of Sonazoid ultrasound. Then, the application and latest advances of Sonazoid in the diagnosis and treatment of liver diseases are reviewed in detail. In addition, the advantages and disadvantages of Sonazoid ultrasound and its future directions are discussed. Sonazoid is expected to become an important tool for clinical ultrasound diagnosis and treatment.

To investigate the value of vascular morphology on high frame rate contrast-enhanced ultrasound (H-CEUS) and CEUS Li-RADS in predicting microvascular invasion (MVI), Ki-67 expression and recurrence of hepatocellular carcinoma (HCC).

This retrospective study enrolled 78 patients with single HCC diagnosed by postoperative pathology between January 1, 2021, and June 30, 2022. All patients underwent ultrasound and H-CEUS examination before operation. H-CEUS image features and CEUS Li-RADS were compared in different MVI status and Ki-67 level. Multiple logistic regression analysis was performed to select independent variables for MVI. Differences in recurrence among different H-CEUS image features, MVI status and Ki-67 level were further analyzed.

Tumor shape, vascular morphology, LR-M category, necrosis and AFP level were different between the MVI-positive group and MVI-negative group (p < 0.05). Vascular morphology and LR-M category were independent risk factors related to MVI (p < 0.05). Vascular morphology was also different between the high Ki-67 expression group and low Ki-67 expression group (p < 0.05). Vascular morphology, MVI status and Ki-67 expression were different between the recurrence group and no recurrence group (p < 0.05).

The vascular morphology of HCC on H-CEUS can indicate the risk of MVI status, Ki-67 expression and recurrence, which provides a feasible imaging technique for predicting the prognosis before operation.

H-CEUS shows the different vascular morphology of HCC in arterial phase and indicates the risk of MVI, Ki-67 expression and recurrence, which provides a feasible imaging technique for clinician to judge the risk of MVI pre-operation and adopt appropriate treatment.

H-CEUS can clearly show different vascular morphology of HCC in arterial phase. Vascular morphology on H-CEUS is associated with MVI status, Ki-67 expression and HCC recurrence. Preoperative MVI and Ki-67 expression prediction could help surgeons choose a more appropriate treatment plan.

The relationships between histopathology and imaging remain elusive, and investigating the underlying reasons for tumor microstructure leading to an imaging phenotype is of clinical importance. In the present study, a cross-sectional guided biopsy specimen was used to correlate prebioptic magnetic resonance imaging (MRI) with immunohistochemical staining of the histopathologic specimen using precise spatial biopsy localization.

Twenty-seven patients with mass-forming cholangiocarcinoma (CCA) were included in the present analysis. All patients were imaged with a 1.5 T clinical scanner at least 1 month prior to biopsy. The contrast-enhanced dynamic sequences were analyzed with quantified signal intensities. The bioptic specimens were obtained by cross-sectional guided biopsy and further analyzed for cell density, proliferation index (Ki67), tumor-infiltrating lymphocytes, tumor-stroma ratio (TSR), and collagen.

There were no statistically significant correlations between MRI signal intensities and cell count, TSR, Ki67 index, and CD45 count. Only a moderate correlation was observed between relative signal intensities of the venous phase and the collagen-stained area (r = 0.40, p = 0.04).

DCE-MRI is not associated with histopathological features in CCA. The complex interactions of tumor and tumor microenvironment are not reflected in the MRI phenotype.

To construct a gadoxetic acid-enhanced MRI (EOB-MRI) -based multivariable model to predict Ki-67 expression levels in hepatocellular carcinoma (HCC) using LI-RADS v2018 imaging features.

A total of 121 patients with HCC who underwent EOB-MRI were enrolled in this study. The patients were divided into three groups according to Ki-67 cut-offs: Ki-67 ≥ 20% (n = 86) vs. Ki-67 < 20% (n = 35); Ki-67 ≥ 30% (n = 73) vs. Ki-67 < 30% (n = 48); Ki-67 ≥ 50% (n = 45) vs. Ki-67 < 50% (n = 76). MRI features were analyzed to be associated with high Ki-67 expression using logistic regression to construct multivariable models. The performance characteristic of the models for the prediction of high Ki-67 expression was assessed using receiver operating characteristic curves.

The presence of mosaic architecture (p = 0.045), the presence of infiltrative appearance (p = 0.039), and the absence of targetoid hepatobiliary phase (HBP, p = 0.035) were independent differential factors for the prediction of high Ki-67 status (≥ 50% vs. < 50%) in HCC patients, while no features could predict high Ki-67 status with thresholds of 20% (≥ 20% vs. < 20%) and 30% (≥ 30% vs. < 30%) (p > 0.05). Four models were constructed including model A (mosaic architecture and infiltrated appearance), model B (mosaic architecture and targetoid HBP), model C (infiltrated appearance and targetoid HBP), and model D (mosaic architecture, infiltrated appearance and targetoid HBP). The model D yielded better diagnostic performance than the model C (0.776 vs. 0.669, p = 0.002), but a comparable AUC than model A (0.776 vs. 0.781, p = 0.855) and model B (0.776 vs. 0.746, p = 0.076).

Mosaic architecture, infiltrated appearance and targetoid HBP were sensitive imaging features for predicting Ki-67 index ≥ 50% and EOB-MRI model based on LI-RADS v2018 features may be an effective imaging approach for the risk stratification of patients with HCC before surgery.

Radiomics have been increasingly used in the clinical management of hepatocellular carcinoma (HCC), such as markers prediction. Ki-67 and cytokeratin 19 (CK-19) are important prognostic markers of HCC. Radiomics has been introduced by many researchers in the prediction of these markers expression, but its diagnostic value remains controversial. Therefore, this review aims to assess the diagnostic value of radiomics in predicting Ki-67 and CK-19 expression in HCC.

Original studies were systematically searched in PubMed, EMBASE, Cochrane Library, and Web of Science from inception to May 2023. All included studies were evaluated by the radiomics quality score. The C-index was used as the effect size of the performance of radiomics in predicting Ki-67and CK-19 expression, and the positive cutoff values of Ki-67 label index (LI) were determined by subgroup analysis and meta-regression.

We identified 34 eligible studies for Ki-67 (18 studies) and CK-19 (16 studies). The most common radiomics source was magnetic resonance imaging (MRI; 25/34). The pooled C-index of MRI-based models in predicting Ki-67 was 0.89 (95% CI:0.86-0.92) in the training set, and 0.87 (95% CI: 0.82-0.92) in the validation set. The pooled C-index of MRI-based models in predicting CK-19 was 0.86 (95% CI:0.81-0.90) in the training set, and 0.79 (95% CI: 0.73-0.84) in the validation set. Subgroup analysis suggested Ki-67 LI cutoff was a significant source of heterogeneity (I 2 = 0.0% P>0.05), and meta-regression showed that the C-index increased as Ki-67 LI increased.

Radiomics shows promising diagnostic value in predicting positive Ki-67 or CK-19 expression. But lacks standardized guidelines, which makes the model and variables selection dependent on researcher experience, leading to study heterogeneity. Therefore, standardized guidelines are warranted for future research.

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023427953.

The present study explored that the effects and its possible mechanisms of ring finger protein 20 (RNF20) in Postoperative survival rate of liver cancer in clinical. All the serum samples were collected from our hospital. Quantitative polymerase chain reaction (PCR) and microarray analysis, and RNA pull down assay were used in this study. We found that the serum RNF20 mRNA expression level in patients with liver cancer were down-regulated. Postoperative survival rate of RNF20 high expression was higher than that of RNF20 low expression. Then, over-expression of RNF20 diminished liver cancer cell proliferation and metastasis. RNF20 reduced Warburg effect of liver cancer. RNF20 expression regulated NOD-like receptor protein 3 (NLRP3) expression and increased NLRP3 Ubiquitination. NLRP3 participated in the effects of RNF20 on cell proliferation, and not affected on Warburg effect of liver cancer. Our study demonstrated that the serum RNF20 expression level was down-regulated in liver cancer, and promoted postoperative survival rate. RNF20 can reduce cancer progression of liver cancer by NLRP3 signal pathway, suggesting that it may prove to be a potential therapeutic target for postoperative survival rate of liver cancer.

Diabetes is not a single disease but rather, it is one aspect of metabolic syndrome. The pathologic aspects of diabetes involve cellular changes that need to be understood.

The main objective of this study was to explore the role of Ki67 in the liver of diabetic rats.

The study methodology involved the induction of diabetes in rats using Alloxan (120 mg/kg). A total of 20 albino rats were randomly assigned into two groups control group (N=10) and diabetes group (n=10). Diabetic group received the dose of alloxan, while the control group received similar dose of normal saline. Glucose level was monitored daily. After the end of the experiment (one -month period), all animals were terminated. Blood samples were taken to measure biochemical investigations including glucose, cholesterol, and triglycerides. Liver tissue was excised and washed with normal saline and fixed in buffered formalin (10%). Liver tissue was processed and stained by hematoxylin and eosin for routine histological examination and also stained by immunohistochemistry for Ki67 biomarker.

The results revealed the efficacy of the diabetic model. All biochemical investigations were significantly higher in the diabetic group compared with that of control group (p<0.001). Histological studies showed the existence of morphological alterations in cells and fatty changes in the diabetic group compared with the control group. The expression of Ki67 was significantly higher in the diabetic group compared with that in the control group (p=0.011).

Taken together, diabetes has adverse effects on the spleen from a histological point of view, and from the expression of Ki67.

Chronic hepatitis B-liver cirrhosis-hepatocellular carcinoma (CLH), commonly called the "liver cancer trilogy", is a crucial evolutionary phase in the emergence of hepatocellular carcinoma (HCC) in China. Previous studies on early diagnostic biomarkers of HCC were limited to the end-stage of HCC and did not focus on the evolutionary process of CLH.

11 monotonically changing differentially expressed genes (MCDEGs) highly correlated with CLH were screened through bioinformatic analysis and KPNA2 was identified for further research. The serum KPNA2 expression in different CLH states was detected by Enzyme linked immunosorbent assay (ELISA). A nomogram model was constructed using univariate and multivariate Cox regression methods.

The single-cell RNA-seq and bulk RNA-seq revealed that KPNA2 related to immune infiltration in HCC and may participate in cell cycle pathways in HCC. The serum KPNA2 expression was monotonically upregulated in CLH and was valuable for diagnosing different CLH states. Besides, chronic hepatitis B(CHB) patients, liver cirrhosis (LC) patients, and HCC patients were classified into subgroups with distinct serum KPNA2 expressions. Accordingly, patients with different serum KPNA2 expressions displayed various clinicopathological features. The AUC value of the nomogram model was 0.959 in predicting the likelihood of developing HCC in CHB patients or LC patients. Finally, we found that KPNA2 expression was negatively correlated with the IC50 of four chemotherapeutic drugs in HCC.

KPNA2 was a novel serum biomarker for diagnosing different CLH states, monitoring the dynamic evolution of CLH, and a new therapeutic target for intervening in the progression of CLH.

Hepatocellular cancer (HCC) is one of the most common tumors worldwide, and Ki-67 is highly important in the assessment of HCC. Our study aimed to evaluate the value of ultrasound radiomics based on intratumoral and peritumoral tissues in predicting Ki-67 expression levels in patients with HCC.

We conducted a retrospective analysis of ultrasonic and clinical data from 118 patients diagnosed with HCC through histopathological examination of surgical specimens in our hospital between September 2019 and January 2023. Radiomics features were extracted from ultrasound images of both intratumoral and peritumoral regions. To select the optimal features, we utilized the t-test and the least absolute shrinkage and selection operator (LASSO). We compared the area under the curve (AUC) values to determine the most effective modeling method. Subsequently, we developed four models: the intratumoral model, the peritumoral model, combined model #1, and combined model #2.

Of the 118 patients, 64 were confirmed to have high Ki-67 expression while 54 were confirmed to have low Ki-67 expression. The AUC of the intratumoral model was 0.796 (0.649-0.942), and the AUC of the peritumoral model was 0.772 (0.619-0.926). Furthermore, combined model#1 yielded an AUC of 0.870 (0.751-0.989), and the AUC of combined model#2 was 0.762 (0.605-0.918). Among these models, combined model#1 showed the best performance in terms of AUC, accuracy, F1-score, and decision curve analysis (DCA).

We presented an ultrasound radiomics model that utilizes both intratumoral and peritumoral tissue information to accurately predict Ki-67 expression in HCC patients. We believe that incorporating both regions in a proper manner can enhance the diagnostic performance of the prediction model. Nevertheless, it is not sufficient to include both regions in the region of interest (ROI) without careful consideration.