A novel nomogram for predicting endoscopic remission in refractory Crohn's disease with ustekinumab administration.

Ustekinumab (UST) is a human IgG1 monoclonal antibody that targets to the share p40 subunit of interleukin-12(IL-12) and IL-23. Evidence has shown that UST therapy is well tolerated and effective in inducing clinical response in refractory CD(Crohn's disease) and dose escalation is effective in recapturing response in over half of the patients. However, no predictive factor has been reported to be helpful for UST treatment in clinical practice. Additionally, there were few reports about therapeutic drug monitoring (TDM) of UST administration in China due to its late launch time in Chinese market and lack of experience in clinical use. Herein, we establish and validate the first UST-trough concentrations (TCs) -related nomogram in China for predicting endoscopic remission in refractory CD to facilitate clinical decision making.

Noninvasive evaluation of neutrophil extracellular traps signature predicts clinical outcomes and immunotherapy response in hepatocellular carcinoma.

Background: Neutrophil extracellular traps (NETs) have been shown to play a pivotal role in promoting metastasis and immune escape in hepatocellular carcinoma (HCC). Therefore, noninvasive tests to detect the formation of NETs in tumors can have significant implications for the treatment and prognoses of patients. Here, we sought to develop and validate a computed tomography (CT)-based radiomics model to predict the gene expression profiles that regulate the formation of NETs in HCC. Methods: This study included 1133 HCC patients from five retrospective cohorts. Based on the mRNA expression levels of 69 biomarkers correlated with NET formation, a 6-gene score (NETs score, NETS) was constructed in cohort 1 from TCIA database (n=52) and validated in cohort 2 (n=232) from ICGC database and cohort 3 (n=365) from TCGA database. And then based on the radiomics features of CT images, a radiomics signature (RNETS) was developed in cohort 1 to predict NETS status (high- or low-NETS). We further employed two cohorts from Nanfang Hospital (Guangzhou, China) to evaluate the predictive power of RNETS in predicting prognosis in cohort 4 (n=347) and the responses to PD-1 inhibitor of HCC patients in cohort 5 (n=137). Results: For NETS, in cohort 1, the area under the curve (AUC) values predicting 1, 2, and 3-year overall survival (OS) were 0.836, 0.879, and 0.902, respectively. The low-NETS was associated with better survival and higher levels of immune cell infiltration. The RNETS yielded an AUC value of 0.853 in distinguishing between high-NETS or low-NETS and patients with low-RNETS were associated with significantly longer survival time in cohort 1 (P<0.001). Notably, the RNETS was competent in predicting disease-free survival (DFS) and OS in cohort 4 (P<0.001). In cohort 5, the RNETS was found to be an independent risk factor for progression-free survival (PFS) (P<0.001). In addition, the objective response rate of HCC patients treated with PD-1 inhibitor was significantly higher in the low-RNETS group (27.8%) than in the high-RNETS group (10.8%). Conclusions: This study revealed that RNETS as a radiomics biomarker could effectively predict prognosis and immunotherapy response in HCC patients.

Artificial intelligence CT radiomics to predict early recurrence of intrahepatic cholangiocarcinoma: a multicenter study.

OBJECTIVES: In this multicenter study, we sought to develop and validate a preoperative model for predicting early recurrence (ER) risk after curative resection of intrahepatic cholangiocarcinoma (ICC) through artificial intelligence (AI)-based CT radiomics approach. MATERIALS AND METHODS: A total of 311 patients (Derivation: 160; Internal and two external validations: 36, 74 and 61) from 8 medical centers who underwent curative resection were collected retrospectively. In derivation cohort, radiomics and clinical-radiomics models for ER prediction were constructed by LightGBM (a machine learning algorithm). A clinical model was also developed for comparison. Model performance was validated in internal and two external cohorts by ROC. In addition, we investigated the interpretability of the LightGBM model. RESULTS: The combined clinical-radiomics model that included 15 radiomic features and 3 clinical features (CA19-9 > 1000 U/ml, vascular invasion and tumor margin), resulting in the area under the curves (AUCs) of 0.974 (95% CI 0.946-1.000) in the derivation cohort, and 0.871-0.882 (95% CI 0.672-0.962) in the internal and external validation cohorts, respectively, which are higher than the AJCC 8th TNM staging system (AUCs: 0.686-0.717, p all < 0.05). Especially, the sensitivity of this machine learning model could reach 94.6% on average for all the cohorts. CONCLUSIONS: This AI-driven combined radiomics model may provide as a useful tool to preoperatively predict ER and improve therapeutic management of ICC patients.

Hepatic Tumor Stiffness Measured by Shear Wave Elastography Is Prognostic for HCC Progression Following Treatment With Anti-PD-1 Antibodies Plus Lenvatinib: A Retrospective Analysis of Two Independent Cohorts.

Background: The clinical significance of liver stiffness (LS) measured by shear wave elastography (SWE) in programmed cell death protein-1 (PD-1) inhibitors treated advanced hepatocellular carcinoma (HCC) patients remains unknown. This study aimed to explore the prognostic value of baseline LS by SWE prior to PD-1 inhibitor treatment in combination with lenvatinib. Methods: We retrospectively evaluated patients (n=133) with HCC who received anti-PD-1 antibodies plus lenvatinib at two high-volume medical centres, between January 2020 and June 2021. Univariate and multivariate logistic regression analysis were used to develop a novel nomogram. RNA sequencing and immunohistochemical staining were used to assess the heterogeneity of biological and immune characteristics associated with tumor stiffness. Results: The objective response rate (ORR) and disease control rate (DCR) of the whole population were 23.4% and 72.2%, respectively. A LS value of the baseline tumorous foci of 19.53 kPa had the maximum sum of sensitivity and specificity, making it the optimal cut-off value for predicting PD-1 inhibitor efficacy. The nomogram comprised baseline tumor LS and albumin-bilirubin grade (ALBI), which provided favorable calibration and discrimination in the training dataset with an AUC of 0.840 (95%CI: 0.750-0.931) and a C-index of 0.828. Further, it showed acceptable discrimination in the validation cohort, with an AUC of 0.827 (95%CI: 0.673-0.980) and C-index of 0.803. The differentially expressed genes enriched in high stiffness tumors were predominantly associated with metabolic pathways, while those enriched in low stiffness tumors were related to DNA damage repair. Furthermore, patients with high stiffness tumors had a relatively lower infiltration of immune cells and histone deacetylase pathway inhibitors were identified as candidate drugs to promote the efficacy of immunotherapy. Conclusions: Baseline LS value of tumorous foci by SWE-that is, before administration of a PD-1 inhibitor in combination with lenvatinib-is a convenient predictor of PD-1 inhibitor efficacy in patients with advanced HCC, which has potential to be used for pretreatment stratification to optimize treatment of advanced HCC.

A Novel Post-Operative ALRI Model Accurately Predicts Clinical Outcomes of Resected Hepatocellular Carcinoma Patients.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the leading malignant tumors worldwide. Prognosis and long-term survival of HCC remain unsatisfactory, even after radical resection, and many non-invasive predictors have been explored for post-operative patients. Most prognostic prediction models were based on preoperative clinical characteristics and pathological findings. This study aimed to investigate the prognostic value of a newly constructed nomogram, which incorporated post-operative aspartate aminotransferase to lymphocyte ratio index (ALRI). METHODS: A total of 771 HCC patients underwent radical resection from three medical centers were enrolled and grouped into the training cohort (n = 416) and validation cohort (n = 355). Prognostic prediction potential of ALRI was assessed by receiver operating curve (ROC) analysis. The Cox regression model was used to identify independent prognostic factors. Nomograms for overall survival (OS) and disease-free survival (DFS) were constructed and further validated externally. RESULTS: The ROC analysis ranked ALRI as the most effective prediction marker for resected HCC patients, with the cut-off value determined at 22.6. Higher ALRI level positively correlated with larger tumor size, higher tumor node metastasis (TNM) stage, and inversely with lower albumin level and shorter OS and DFS. Nomograms for OS and DFS were capable of discriminating HCC patients into different risk-groups. CONCLUSIONS: Post-operative ALRI was of prediction value for HCC prognosis. This novel nomogram may categorize HCC patients into different risk groups, and offer individualized surveillance reference for post-operative patients.

Preoperative Gadoxetic Acid-Enhanced MRI Based Nomogram Improves Prediction of Early HCC Recurrence After Ablation Therapy.

PURPOSE: This study aimed to identify preoperative gadoxetic acid-enhanced MRI features and establish a nomogram for predicting early recurrence (≤ 2 years) of hepatocellular carcinoma (HCC) after ablation therapy. METHODS: A total of 160 patients who underwent gadoxetic acid-enhanced MRI and ablation HCC therapy from January 2015 to June 2018, were included retrospectively and divided into a training cohort (n = 112) and a validation cohort (n = 48). Independent clinical risk factors and gadoxetic acid-enhanced MRI features associated with early recurrence were identified by univariate and multivariate logistic regression analysis and used for construction of a nomogram. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility. RESULTS: Alpha-fetoprotein (AFP) level, tumor number, arterial peritumoral enhancement, satellite nodule and peritumoral hypointensity at hepatobiliary phases in the training cohort were identified as independent risk factors for early recurrence after ablation. A new nomogram that was constructed with these five features showed an area under the curve (AUC) of 0.843 (95%CI 0.771-0.916) and 0.835 (95%CI 0.713-0.956) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) suggested that the nomogram had good consistency and clinical utility. CONCLUSIONS: A new nomogram that was constructed using four preoperative gadoxetic acid-enhanced MRI features and serum AFP level can predict the risk of early HCC recurrence after ablation therapy with AUC up to 0.843. The strong performance of this nomogram may help hepatologists to categorize patients' recurrent risk to guide selecting treatment options and improve postoperative management.

Effect of Pegylated Interferon Plus Tenofovir Combination on Higher Hepatitis B Surface Antigen Loss in Treatment-naive Patients With Hepatitis B e Antigen -positive Chronic Hepatitis B: A Real-world Experience.

PURPOSE: The loss of serum hepatitis B surface antigen (HBsAg) in patients with chronic hepatitis B (CHB) is considered an ideal clinical outcome but rarely achieved with current standard of care. We evaluated the effectiveness in inducing HBsAg seroclearance in a real-world clinical cohort of Chinese patients with CHB treated with a combination of pegylated interferon (Peg-IFN) with tenofovir disoproxil fumarate (TDF) or monotherapy with each agent. METHODS: A total of 330 patients with CHB were assigned to receive Peg-IFN plus TDF for 48 weeks (Peg-IFN plus TDF group), Peg-IFN alone for 48 weeks (Peg-IFN group), or TDF alone for 144 weeks (TDF group). The primary end point was the percentages of patients who achieved HBsAg seroclearance at week 72. Differences from the baseline characteristics and treatment data were compared using the χ2 test for categorical variables or 1-way ANOVA for continuous variables. A Kaplan-Meier test was performed to compare the HBsAg loss among the 3 groups. Discrimination of responders versus nonresponders was quantified using AUC curves. Optimal cut-offs were selected based on Youden's J statistic defined as J = sensitivity + specificity-1. FINDINGS: At week 72, the Kaplan-Meier cumulative HBsAg loss was 11.5% in the Peg-IFN plus TDF group, 5.7% in the Peg-IFN group, and 0% in the TDF group. The percentage of patients with HBsAg loss was comparable in the Peg-IFN plus TDF and Peg-IFN groups (P = 0.143), but both were significantly higher than that in the TDF group (P = 0.000 and P = 0.010). In addition, a significantly higher percentage of patients in the combination group and Peg-IFN group had serum HBsAg of <100 IU/mL compared with the TDF group (32.7% vs 23.6% vs 9.2%; P < 0.001) but no significant differences in the percentages of patients with HBsAg <1000 IU/mL, the undetectable serum HBV DNA and hepatitis B e antigen seroconversion. Our model predicted serum HBsAg loss at week 72 (AUC = 0.846) if the HBsAg level was reduced by > 1.5 log10 IU/mL from baseline at treatment week 24, an optimal timepoint for prediction of HBsAg loss in this cohort. IMPLICATIONS: A 48-week course of Peg-IFN and TDF combination therapy led to profound reduction in serum HBsAg level, resulting in a significantly higher rate of HBsAg loss compared with TDF monotherapy. Patients with steep HBsAg decline >1.5 log10 IU/mL at week 24 well signaled a higher probability of achieving HBsAg loss at week 72.

Development and Validation of a Contrast-Enhanced CT-Based Radiomics Nomogram for Prediction of Therapeutic Efficacy of Anti-PD-1 Antibodies in Advanced HCC Patients.

Background: There is no study accessible now assessing the prognostic aspect of radiomics for anti-PD-1 therapy for patients with HCC. Aim: The aim of this study was to develop and validate a radiomics nomogram by incorporating the pretreatment contrast-enhanced Computed tomography (CT) images and clinical risk factors to estimate the anti-PD-1 treatment efficacy in Hepatocellular Carcinoma (HCC) patients. Methods: A total of 58 patients with advanced HCC who were refractory to the standard first-line of therapy, and received PD-1 inhibitor treatment with Toripalimab, Camrelizumab, or Sintilimab from 1st January 2019 to 31 July 2020 were enrolled and divided into two sets randomly: training set (n = 40) and validation set (n = 18). Radiomics features were extracted from non-enhanced and contrast-enhanced CT scans and selected by using the least absolute shrinkage and selection operator (LASSO) method. Finally, a radiomics nomogram was developed based on by univariate and multivariate logistic regression analysis. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility. Results: Eight radiomics features from the whole tumor and peritumoral regions were selected and comprised of the Fusion Radiomics score. Together with two clinical factors (tumor embolus and ALBI grade), a radiomics nomogram was developed with an area under the curve (AUC) of 0.894 (95% CI, 0.797-0.991) and 0.883 (95% CI, 0.716-0.998) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) confirmed that nomogram had good consistency and clinical usefulness. Conclusions: This study has developed and validated a radiomics nomogram by incorporating the pretreatment CECT images and clinical factors to predict the anti-PD-1 treatment efficacy in patients with advanced HCC.

CCR5 expression in inflammatory bowel disease and its correlation with inflammatory cells and ß-arrestin2 expression.

OBJECTIVE: To elucidate the correlation of expression of CC chemokine receptor 5 (CCR5) with degrees of inflammatory cells infiltration and expression of ß-arrestin2 in biopsic intestinal mucosa of the patients with inflammatory bowel disease (IBD). METHODS: Paraffin sections were derived from 53 patients with active IBD, 26 patients with remissive IBD and 30 healthy people. Immunohistochemical envision two-step method was used to test the expression of CCR5 and ß-arrestin2 in biopsic intestinal mucosa. HE and toluidine blue staining were used to detect the pathological cytological analysis and classification in lamina propria of colonic mucosa. RESULTS: The positive rate, strong positive rate and immunohistochemical score of CCR5 expression in active IBD were significantly higher than that in normal controls and remissive IBD (p < .05). CCR5 expression had no obvious correlation with clinical severity, lesion distribution and endoscopic classification of active IBD. Neutrophils, eosinophils and lymphocytes in active IBD were significantly higher than that in normal controls and remissive IBD (p < .05), while the lymphocyte grade had a positive correlation with CCR5 expression (p = .042, r = .286). Mastocytes in active IBD, remissive IBD and normal controls had no obvious difference (p > .05). ß-arrestin2 expression was significantly lower in active IBD than that in remissive IBD and normal controls, and it had a negative correlation with CCR5 expression (p = .01, r = -.247). CONCLUSIONS: CCR5 is highly expressed in active IBD, and it has positive correlation with lymphocyte grade and negative correlation with expression of ß-arrestin2.