Efficacy of nab­paclitaxel vs. Gemcitabine in combination with S­1 for advanced pancreatic cancer: A multicenter phase II randomized trial.

Patients with advanced pancreatic cancer (PC) need a cost-effective treatment regimen. The present study was designed to compare the efficacy and safety of nab-paclitaxel plus S-1 (AS) and gemcitabine plus S-1 (GS) regimens in patients with chemotherapy-naïve advanced PC. In this open-label, multicenter, randomized study named AvGmPC, eligible patients with chemotherapy-naïve advanced PC were randomly assigned (1:1) to receive AS (125 mg/m2 nab-paclitaxel, days 1 and 8; 80-120 mg S-1, days 1-14) or GS (1,000 mg/m2 gemcitabine, days 1 and 8; 80-120 mg S-1, days 1-14). The treatment was administered every 3 weeks until intolerable toxicity or disease progression occurred. The primary endpoint was progression-free survival (PFS). Between December 2018 and March 2022, 101 of 106 randomized patients were treated and evaluated for analysis (AS, n=49; GS, n=52). As of the data cutoff, the median follow-up time was 11.37 months [95% confidence interval (CI), 9.31-13.24]. The median PFS was 7.16 months (95% CI, 5.19-12.32) for patients treated with AS and 6.41 months (95% CI, 3.72-8.84) for patients treated with GS (HR=0.78; 95% CI, 0.51-1.21; P=0.264). The AS regimen showed a slightly improved overall survival (OS; 13.27 vs. 10.64 months) and a significantly improved ORR (44.90 vs. 15.38%; P=0.001) compared with the GS regimen. In the subgroup analyses, PFS and OS benefits were observed in patients treated with the AS regimen who had KRAS gene mutations and high C-reactive protein (CRP) levels (≥5 mg/l). The most common grade ≥3 adverse events were neutropenia, anemia and alopecia in the two groups. Thrombocytopenia occurred more frequently in the GS group than in the AS group. While the study did not meet the primary endpoint, the response benefit observed for AS may be suggestive of meaningful clinical activity in this population. In particular, promising survival benefits were observed in the subsets of patients with KRAS gene mutations and high CRP levels, which is encouraging and warrants further investigation. This trial was retrospectively registered as ChiCTR1900024588 on July 18, 2019.

Insulin Glargine is More Suitable Than Exenatide in Preventing Muscle Loss in Non-Obese Type 2 Diabetic Patients with NAFLD.

AIM: This study investigated the effects of insulin glargine and exenatide on the muscle mass of patients with newly diagnosed type 2 diabetes (T2DM) and nonalcoholic fatty liver disease (NAFLD). METHODS: We performed a post-hoc analysis of our previously study, a 24-week randomized controlled multicenter clinical trial (ClinicalTrials.gov, NCT02303730). Seventy-six patients were randomly assigned 1:1 to receive insulin glargine or exenatide treatment. The changes in psoas muscle area (PMA) (mm2) were obtained with the cross-sectional Dixonfat magnetic resonance images at the fourth lumber vertebra. RESULTS: There were no significant differences in age, BMI, gender, and PMA in insulin glargine and exenatide groups at baseline. After treatment, PMA tended to increase by 13.13 (-215.52, 280.80) mm2 in the insulin glargine group and decrease by 149.09 (322.90-56.39) mm2 in the exenatide group (both p>0.05). Subgroup analysis showed a 560.64 (77.88, 1043.40) (mm2) increase of PMA in the insulin group relative to the Exenatide group in patients with BMI<28 kg/m2 (p0.031) after adjusting for gender, age, and research center. Interaction analysis showed an interaction between BMI and treatment (p0.009). However, no interaction was observed among subgroups with a BMI≥28 kg/m2 or with different genders and ages. CONCLUSION: Compared to exenatide, insulin glargine can relativity increase PMA in patients with T2DM having BMI<28 kg/m2 and NAFLD.

Magnetic resonance elastography-derived stiffness: potential imaging biomarker for differentiation of benign and malignant pancreatic masses.

OBJECTIVE: This study sought to determine the diagnostic performance of magnetic resonance elastography (MRE) for pancreatic solid masses, compared with diffusion-weighted imaging (DWI) and serum CA19-9, to establish a threshold for differentiating between pancreatic ductal adenocarcinoma (PDAC) and benign tumors in pancreas. MATERIALS AND METHODS: Between July 2021 to January 2023, 75 adult patients confirmed with pancreatic solid tumors were enrolled in this prospective and consecutive study. All patients underwent MRE and DWI examinations that were both performed with a spin echo-EPI sequence. Stiffness maps and apparent diffusion coefficient (ADC) maps were generated, with MRE-derived mass stiffness and stiffness ratio (computing as the ratio of mass stiffness to the parenchyma stiffness) and DWI-derived ADC values obtained by placing regions of interest over the focal tumors on stiffness and ADC maps. Further analysis of comparing diagnostic performances was assessed by calculating the area under ROC curves. RESULTS: PDAC had significantly higher tumor stiffness [3.795 (2.879-4.438) kPa vs. 2.359 (2.01-3.507) kPa, P = 0.0003], stiffness ratio [1.939 (1.562-2.511) vs. 1.187 (1.031-1.453), P < 0.0001] and serum CA19-9 level [276 (31.73-1055) vs. 10.45 (7.825-14.15), P < 0.0001] than other pancreatic masses. Mass stiffness, stiffness ratio and serum CA19-9 showed good diagnostic performance for differentiation with AUC of 0.7895, 0.8392 and 0.9136 respectively. The sensitivity/specificity/positive predictive value/negative predictive value for differentiating malignant from benign pancreatic tumors with mass stiffness (cutoff, > 2.8211 kPa) and stiffness ratio (cutoff, > 1.5117) were 78.4/66.7/82.9/60% and 77.8/83.3/90.3/65.2% respectively. The combined performance of Mass stiffness, stiffness ratio and serum CA19-9 got an AUC of 0.9758. CONCLUSION: MRE holds excellent clinical potential in discriminating pancreatic ductal adenocarcinoma from other pancreatic solid masses according to their mechanical properties.

Preoperative identification of cytokeratin 19 status of hepatocellular carcinoma based on diffusion kurtosis imaging.

PURPOSE: To explore the potential value of diffusion kurtosis imaging (DKI) for identification of cytokeratin 19 (CK19) status of HCCs. METHODS: This study was approved by the local institute review board and written informed consent was obtained. 73 patients with pathologically confirmed HCCs were included in this prospective study. All the diffusion-weighted (DW) images were acquired using a 3.0-T MR scanner with 4 b-values (0, 800, 1500 and 2000 s/mm2). The mean diffusion value (MD) and mean kurtosis coefficient (MK) from DKI, apparent diffusion coefficient (ADC) from DW imaging (b = 0, 500 s/mm2), and tumor-to-liver signal intensity ratios on ADC map (SIRADC) and DW images with b-value of 500 s/mm2 (SIRb500) were calculated and compared between CK19-positive (n = 23) and CK19-negative (n = 50) HCC groups. Univariate and multivariate logistic regression analyses were used to identify risk factors for the positive expression of CK19. RESULTS: Increased a-fetoprotein level (p = 0.021) and SIRb500 (p = 0.006) and decreased ADC (p = 0.021) and MD (p < 0.001) were significantly correlated with CK19-positive HCCs at univariate analysis. Decreased MD value (odds ratio: 0.042, p = 0.002) and a-fetoprotein level (odds ratio: 5.139, p = 0.015) were the independent risk factors for CK19-positive HCCs at multivariate analysis. The area under the curve of MD value by receiver operating characteristic analysis was 0.823 with a sensitivity of 86.96% and a specificity of 76% for the prediction of CK19-positive HCCs. CONCLUSION: The decreased MD value derived from DKI is potential quantitative biomarker for predicting CK19-positive HCCs.

Preoperative assessment of microvascular invasion of hepatocellular carcinoma using non-Gaussian diffusion-weighted imaging with a fractional order calculus model: A pilot study.

PURPOSE: To assess the clinical potential of a set of new diffusion parameters (D, ß, and µ) derived from fractional order calculus (FROC) diffusion model in predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC). MATERIALS AND METHODS: Between January 2019 to November 2020, a total of 63 patients with HCC were enrolled in this study. Diffusion-weighted images were acquired by using ten b-values (0-2000 s/mm2). The FROC model parameters including diffusion coefficient (D), fractional order parameter (ß), a microstructural quantity (µ) together with a conventional apparent diffusion coefficient (ADC) were calculated. Intraclass coefficients were calculated for assessing the agreement of parameters quantified by two radiologists. The differences of these values between the MVI-positive and MVI-negative HCC groups were compared by using independent sample t-test or the Mann-Whitney U test. Then the parameters showing significant differences between subgroups, including the ß and D, were integrated to develop a comprehensive predictive model via binary logistic regression. The diagnostic performance was evaluated by receiver operating characteristic (ROC) analysis. RESULTS: Among all the studied diffusion parameters, significant differences were found in D, ß, and ADC between the MVI-positive and MVI-negative groups. MVI-positive HCCs showed significantly higher ß values (0.65 ± 0.17 vs. 0.51 ± 0.13, P = 0.001), along with lower D values (0.84 ± 0.11 µm2/ms vs. 1.03 ± 0.13 µm2/ms, P < 0.001) and lower ADC values (1.38 ± 0.46 µm2/ms vs. 2.09 ± 0.70 µm2/ms, P < 0.001) than those of MVI-negative HCCs. According to the ROC analysis, the combination of D and ß demonstrated the largest area under the ROC curve (0.920) compared with individual parameters (D: 0.912; ß: 0.733; and ADC: 0.831) for differentiating MVI-positive from MVI-negative HCCs. CONCLUSIONS: The FROC parameters can be used as noninvasive quantitative imaging markers for preoperatively predicting the MVI status of HCCs.

A novel model for detecting advanced fibrosis in patients with nonalcoholic fatty liver disease.

AIMS: The study aimed to develop a novel noninvasive model to detect advanced fibrosis based on routinely available clinical and laboratory tests. MATERIALS AND METHODS: A total of 309 patients who underwent liver biopsy were randomly divided into the estimation group (n = 201) and validation group (n = 108). The model was developed using multiple regression analysis in the estimation group and further verified in the validation group. Diagnostic accuracy was evaluated using the receiver operating characteristic (ROC) curve. RESULTS: The model was named NAFLD Fibrosis Index (NFI): -10.844 + 0.046 × age - 0.01 × platelet count + 0.19 × 2h postprandial plasma glucose (PG) + 0.294 × conjugated bilirubin - 0.015 × ALT + 0.039 × AST + 0.109 × total iron binding capacity -0.033 × parathyroid hormone (PTH). The area under the ROC curve (AUC) of NFI was 0.86 (95% CI: 0.79-0.93, p < 0.001) in the estimation group and 0.80 (95% CI: 0.69-0.91, p < 0.001) in the validation group, higher than NFS, FIB4, APRI, and BARD, and similar to FibroScan (NFI AUC = 0.77, 95% CI: 0.66-0.89, p = 0.001 vs. FibroScan AUC = 0.76, 95% CI: 0.62-0.90, p = 0.002). By applying the low cut-off value (-2.756), advanced fibrosis could be excluded among 49.3% and 48% of patients in the estimation group (sensitivity: 93.1%, NPV: 97.9%, specificity: 55.2%, and PPV: 26.0%) and validation group (sensitivity: 81.3%, NPV: 94.2%, specificity: 53.3%, and PPV: 23.2%), respectively, allowing them to avoid liver biopsy. CONCLUSIONS: The study has established a novel model for advanced fibrosis, the diagnostic accuracy of which is superior to the current clinical scoring systems and is similar to FibroScan.

Differentiating Cytokeratin 19 expression of hepatocellular carcinoma by using multi-b-value diffusion-weighted MR imaging with mono-exponential, stretched exponential, intravoxel incoherent motion, diffusion kurtosis imaging and fractional order calculus models.

PURPOSE: To evaluate Cytokeratin 19 expression in hepatocellular carcinoma (HCC) with diffusion parameters derived from mono-exponential model (MEM), stretched exponential model (SEM), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM) imaging, and fractional order calculus (FROC) model and compare their predictive performance. METHOD: 61 patients with pathologically confirmed primary HCC were included in this prospective study. All the DWIs were acquired using a 3.0 T MR scanner with 10b-values (0-2000 s/mm2). The apparent diffusion coefficient (ADC), distributed diffusion coefficient (DDC), heterogeneity index (α), apparent kurtosis coefficient (AK), apparent diffusion coefficient (AD), pseudo-diffusion coefficient (Dp), true diffusion coefficient (Dt), perfusion fraction (f), diffusion coefficient (D), fractional order parameter (ß), and a microstructural quantity (µ) were calculated. The diagnostic efficacy of various diffusion parameters for predicting CK19 expression of HCC was compared. RESULTS: ADC, DDC, Dt, Dp, AD, and D were significantly lower in CK19-positive HCCs than in CK19-negative HCCs (P ≤ 0.05). ß was significantly higher in CK19-positive group (P = 0.001). AD (AUC = 0.845) had the greatest AUC values in differentiating CK19-positive and CK19-negative HCC with individual parameters. The combination of ß, AD, and Dp generated the highest area under the ROC curve (AUC = 0.881) compared with individual parameters. CONCLUSIONS: ADC, DDC, Dt, Dp, D, and ß may serve as noninvasive and quantitative imaging markers and holds great potential in accurately accessing CK19 status of HCC. More importantly, the combination of different diffusion parameters yielded better diagnostic performance.

Progression-Free Survival Prediction in Small Cell Lung Cancer Based on Radiomics Analysis of Contrast-Enhanced CT.

Purposes and Objectives: The aim of this study was to predict the progression-free survival (PFS) in patients with small cell lung cancer (SCLC) by radiomic signature from the contrast-enhanced computed tomography (CT). Methods: A total of 186 cases with pathological confirmed small cell lung cancer were retrospectively assembled. First, 1,218 radiomic features were automatically extracted from tumor region of interests (ROIs) on the lung window and mediastinal window, respectively. Then, the prognostic and robust features were selected by machine learning methods, such as (1) univariate analysis based on a Cox proportional hazard (CPH) model, (2) redundancy removing using the variance inflation factor (VIF), and (3) multivariate importance analysis based on random survival forests (RSF). Finally, PFS predictive models were established based on RSF, and their performances were evaluated using the concordance index (C-index) and the cumulative/dynamic area under the curve (C/D AUC). Results: In total, 11 radiomic features (6 for mediastinal window and 5 for lung window) were finally selected, and the predictive model constructed from them achieved a C-index of 0.7531 and a mean C/D AUC of 0.8487 on the independent test set, better than the predictions by single clinical features (C-index = 0.6026, mean C/D AUC = 0.6312), and single radiomic features computed in lung window (C-index = 0.6951, mean C/D AUC = 0.7836) or mediastinal window (C-index = 0.7192, mean C/D AUC = 0.7964). Conclusion: The radiomic features computed from tumor ROIs on both lung window and mediastinal window can predict the PFS for patients with SCLC by a high accuracy, which could be used as a useful tool to support the personalized clinical decision for the diagnosis and patient management of patients with SCLC.

PKCα/ZFP64/CSF1 axis resets the tumor microenvironment and fuels anti-PD1 resistance in hepatocellular carcinoma.

BACKGROUND & AIMS: Despite remarkable advances in treatment, most patients with hepatocellular carcinoma (HCC) respond poorly to anti-programmed cell death 1 (anti-PD1) therapy. A deeper insight into the tolerance mechanism of HCC against this therapy is urgently needed. METHODS: We performed next-generation sequencing, multiplex immunofluorescence, and dual-color immunohistochemistry and constructed an orthotopic HCC xenograft tumor model to identify the key gene associated with anti-PD1 tolerance. A spontaneously tumorigenic transgenic mouse model, an in vitro coculture system, mass cytometry, and multiplex immunofluorescence were used to explore the biological function of zinc finger protein 64 (ZFP64) on tumor progression and immune escape. Molecular and biochemical strategies like RNA-sequencing, chromatin immunoprecipitation-sequencing and mass spectrometry were used to gain insight into the underlying mechanisms of ZFP64. RESULTS: We showed that ZFP64 is frequently upregulated in tumor tissues from patients with anti-PD1-resistant HCC. Elevated ZFP64 drives anti-PD1 resistance by shifting macrophage polarization toward an alternative activation phenotype (M2) and fostering an inhibitory tumor microenvironment. Mechanistically, we primarily demonstrated that protein kinase C alpha (PKCα) directly phosphorylates ZFP64 at S226, leading to its nuclear translocation and the transcriptional activation of macrophage colony-stimulating factor (CSF1). HCC-derived CSF1 transforms macrophages to the M2 phenotype to drive immune escape and anti-PD1 tolerance. Notably, Gö6976, a protein kinase inhibitor, and lenvatinib, a multi-kinase inhibitor, reset the tumor microenvironment and restore sensitivity to anti-PD1 by blocking the PKCα/ZFP64/CSF1 axis. CONCLUSIONS: We propose that the PKCα/ZFP64/CSF1 axis is critical for triggering immune evasion and anti-PD1 tolerance. Inhibiting this axis with Gö6976 or lenvatinib overcomes anti-PD1 resistance in HCC. LAY SUMMARY: Despite remarkable treatment progress, most patients with hepatocellular carcinoma respond poorly to anti-PD1 therapy (a type of immunotherapy). A deeper insight into the tolerance mechanisms to this therapy is urgently needed. Herein, we unravel a previously unexplored mechanism linking tumor progression, macrophage polarization, and anti-PD1 resistance, and offer an attractive novel target for anti-PD1 combination therapy, which may benefit patients with hepatocellular carcinoma.

Staging Chronic Hepatitis B Related Liver Fibrosis with a Fractional Order Calculus Diffusion Model.

RATIONALE AND OBJECTIVES: Accurately staging liver fibrosis is of great clinical significance. We aimed to evaluate the clinical potential of the non-Gaussian fractional order calculus (FROC) diffusion model in staging liver fibrosis. MATERIALS AND METHODS: A total of 82 patients with chronic hepatitis B (CHB) were included in this prospective study. Diffusion weighted imaging (DWI)-derived parameters including the diffusion coefficient (D), fractional order parameter (ß) and microstructural quantity (µ) sourced from FROC-DWI, and apparent diffusion coefficient (ADC) derived from mono-exponential DWI, as well as the aspartate aminotransferase-to-platelet ratio index (APRI) and fibrosis-4 (FIB-4) were calculated. Their correlations with fibrosis stages and the diagnostic efficacy in predicting liver fibrosis were assessed and compared. RESULTS: D (r = -0.667), ß (r = -0.671), µ (r = -0.481), and ADC (r = -0.665) displayed significant correlations with fibrosis stages (p < 0.001). D, ß and ADC (p < 0.01) were independently associated with fibrosis; and compared to inflammatory activity, fibrosis was the independent factor significantly correlated with D, ß and ADC (p < 0.001). There were no significant differences between the area under curves of D, ß, µ or their combinations and ADC for predicting different fibrosis stages (p > 0.05). The diagnostic performance of the combined index with four diffusion metrics was better than D, ß, µ or ADC used alone (p < 0.05) as well as APRI or FIB-4 (p < 0.01) in fibrosis staging. CONCLUSION: FROC-DWI was valuable in staging liver fibrosis in patients with CHB, but there were no significant differences between the FROC-DWI parameters and the classical ADC. However, the combined DWI-derived index including D, ß, µ and ADC offered the best diagnostic efficacy and may serve as a reliable tool for fibrosis evaluation, superior to APRI and FIB-4.

Performance of liver stiffness measurements obtained with FibroScan is affected by glucose metabolism in patients with nonalcoholic fatty liver disease.

BACKGROUND: The performance of liver stiffness measurements (LSMs) obtained using FibroScan can be affected by several factors, and cut-off values are different for fibrosis caused by various aetiologies. The study aims to evaluate the diagnostic accuracy of LSM in nonalcoholic fatty liver disease (NAFLD) patients with abnormal glucose metabolism and investigate whether the LSM value would be affected by metabolic indicators. METHODS: The study involved 91 NAFLD patients with abnormal glucose metabolism who underwent liver biopsy. The diagnostic accuracy of LSM value was evaluated by the receiver operator characteristic (ROC) curves, with the biopsy results taken as the gold standard. Multivariate linear regression and subgroup analysis were performed to determine the correlated indicators. RESULTS: The areas under the ROC curves (AUROCs) of LSM values for detecting fibrosis stage ≥1, 2, 3 and 4 were 0.793 (95% confidence interval [CI]: 0.695-0.871), 0.764 (95% CI: 0.663-0.846), 0.837 (95% CI: 0.744-0.906) and 0.902 (95% CI: 0.822-0.955), with cut-off values of 6.3, 7.6, 8.3 and 13.8 kPa, respectively. Multivariate linear regression demonstrated that haemoglobin A1c (HbA1c, ß = 0.205, P = 0.026) and alanine aminotransferase (ALT, ß = 0.192, P = 0.047) were independently associated with the LSM value after adjustment for fibrosis stage, ballooning and inflammation grade from liver biopsy. Subgroup analysis demonstrated that LSM values were slightly higher in patients with HbA1c ≥7% than in those with HbA1c < 7% and in patients with body mass index (BMI) ≥30 kg/m2 than in those with BMI < 30 kg/m2. CONCLUSIONS: FibroScan was valuable for the evaluation of liver fibrosis in NAFLD patients with abnormal glucose metabolism. FibroScan is recommended to evaluate severe fibrosis, especially to exclude advanced fibrosis. Glucose metabolism state may affect LSM values.

Comparative study of evaluating the microcirculatory function status of primary small HCC between the CE (DCE-MRI) and Non-CE (IVIM-DWI) MR Perfusion Imaging.

PURPOSE: To compare the difference of evaluating the microcirculatory function status of primary small HCC between DCE-MRI with two-compartmental pharmacokinetic model and IVIM-DWI. METHODS: 27 patients (22 men, 5 women; mean age, 49 years; range 36-65 years) with primary single sHCC who underwent IVIM-DWI and DCE-MRI before the operation were included in this retrospective study. The MR perfusion parameters are Ktrans, Ve, Kep, D, D* and f. Pathological results include pathological grade (low grade ≤ II, high grade > II) and MVD. The perfusion parameters and pathological results of sHCC were analyzed and compared in their relevance, sensitivity and specificity. Statistical methods included Spearman and ROC curve analysis. RESULTS: The perfusion parameters (Ktrans, Kep, D*, f) were significantly positive correlated (r = 0.892, 0.808, 0.589 and 0.543, P = 0.000, 0.000, 0.001 and 0.003 with MVD of sHCC. The parameter Ve and D values were negatively correlated (r = - 0.454 and - 0.399, P = 0.017 and 0.039, respectively) with the pathological grade. Regarding the evaluation MVD of sHCC, the evaluation of the sensitivity and specificity performance was present in descending order: Ktrans > Kep > PF > D*. In the evaluation pathological grade of sHCC, the sensitivity and specificity were better by parameters D than Ve. CONCLUSION: DCE-MRI is better than IVIM-DWI for evaluation microcirculation functional status of sHCC. But for evaluating the pathological grade, IVIM-DWI is better than DCE-MRI. Combination of the two imaging techniques may provide more comprehensive evaluation in microcirculation functional status of the sHCC.

Histological Subtypes Classification of Lung Cancers on CT Images Using 3D Deep Learning and Radiomics.

RATIONALE AND OBJECTIVES: Histological subtypes of lung cancers are critical for clinical treatment decision. In this study, we attempt to use 3D deep learning and radiomics methods to automatically distinguish lung adenocarcinomas (ADC), squamous cell carcinomas (SCC), and small cell lung cancers (SCLC) respectively on Computed Tomography images, and then compare their performance. MATERIALS AND METHODS: 920 patients (mean age 61.2, range, 17-87; 340 Female and 580 Male) with lung cancer, including 554 patients with ADC, 175 patients with lung SCC and 191 patients with SCLC, were included in this retrospective study from January 2013 to August 2018. Histopathologic analysis was available for every patient. The classification models based on 3D deep learning (named the ProNet) and radiomics (named com_radNet) were designed to classify lung cancers into the three types mentioned above according to histopathologic results. The training, validation and testing cohorts counted 0.70, 0.15, and 0.15 of the whole datasets respectively. RESULTS: The ProNet model used to classify the three types of lung cancers achieved the F1-scores of 90.0%, 72.4%, 83.7% in ADC, SCC, and SCLC respectively, and the weighted average F1-score of 73.2%. For com_radNet, the F1-scores achieved 83.1%, 75.4%, 85.1% in ADC, SCC, and SCLC, and the weighted average F1-score was 72.2%. The area under the receiver operating characteristic curve of the ProNet model and com_radNet were 0.840 and 0.789, and the accuracy were 71.6% and 74.7% respectively. CONCLUSION: The ProNet and com_radNet models we developed can achieve high performance in distinguishing ADC, SCC, and SCLC and may be promising approaches for non-invasive predicting histological subtypes of lung cancers.

Solubilization of Hexyl Aminolevulinate by Surfactants for Tumor Fluorescence Detection.

Hexyl aminolevulinate (HAL) is a lipophilic derivative of 5-aminolevulinic acid (5-ALA) and can induce more protoporphyrin IX (PpIX) formation and stronger fluorescence intensity (FI) than 5-ALA, which will greatly facilitate photodynamic diagnosis and therapy. The main drawback of HAL is its low solubility in neutral aqueous media. In this study, surfactants were used to increase HAL solubility in the cell culture medium and serum, followed by in vitro fluorescence formation measurement in human pancreatic cancer cells (SW1990) and in vivo fluorescence detection in tumor-bearing mice. The results showed that Tween 80 (TW80) and Kolliphor® HS 15 (HS15) increased the solubility of HAL in the selected media. Although TW80 and HS15 exhibited in vitro cytotoxicity at high concentrations (5 mg mL-1 ), they facilitated fluorescent signal formation at the early stage of cell incubation. When surfactants were used, the FI should be determined without the routine washing process because surfactant-containing culture medium caused the loss of synthesized PpIX during the washing process. When HAL dissolved in TW80 solution was injected intraperitoneally into pancreatic cancer-bearing mice at a dose of 50 mg kg-1 , the tumors exhibited red fluorescence, which indicated that systemic administration of surfactant-solubilized HAL might be applicable for tumor fluorescence detection in vivo.

Efficacy of exenatide and insulin glargine on nonalcoholic fatty liver disease in patients with type 2 diabetes.

BACKGROUND: The aim of this study was to investigate the efficacy of exenatide and insulin glargine in patients with newly diagnosed type 2 diabetes mellitus (T2DM) and nonalcoholic fatty liver disease (NAFLD). METHODS: We performed a 24-week randomized controlled multicentre clinical trial. Seventy-six patients were randomly assigned 1:1 to receive exenatide or insulin glargine treatment. The endpoints included changes in liver fat content (LFC), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) measured by magnetic resonance spectroscopy, blood glucose, liver enzymes, lipid profile, body weight, and Fibrosis-4 index (FIB-4). RESULTS: LFC, VAT, SAT, and FIB-4 were significantly reduced after exenatide treatment (ΔLFC, -17.55 ± 12.93%; ΔVAT, -43.57 ± 68.20 cm2 ; ΔSAT, -28.44 ± 51.48 cm2 ; ΔFIB-4, -0.10 ± 0.26; all P < .05). In comparison, only LFC (ΔLFC, -10.49 ± 11.38%; P < .05), and not VAT, SAT, or FIB-4 index (all P > .05), was reduced after insulin glargine treatment. Moreover, exenatide treatment resulted in greater reductions in alanine transaminase (ALT), aspartate transaminase (AST), and gamma glutamyl transpeptidase (GGT) than insulin glargine (P < 0.05). The body weight, waist circumference, postprandial plasma glucose, and low-density lipoprotein cholesterol (LDL-C) in the exenatide group also presented greater reductions than the insulin glargine group (P < .05). The proportion of adverse events were comparable between the two groups. CONCLUSION: Both exenatide and insulin glargine reduced LFC in patients with drug-naive T2DM and NAFLD; however, exenatide showed greater reductions in body weight, visceral fat area, liver enzymes, FIB-4, postprandial plasma glucose, and LDL-C.

Dextran-based Nanomicelle System with Directly Ester-bound Gadolinium Chelates as a Magnetic Resonance Imaging Contrast Agent for Tumor Detection.

There is a strong need to develop MRI contrast agents (CAs) with lower in-vivo retention, stronger signal enhancement, and more specific imaging. Here, we report a novel dextran (DEX)-based nanomicelle system as an MRI CA with superior tumor imaging and relatively short intravascular persistence. Gadolinium (Gd)-chelate (DTPA-Gd) was conjugated directly to DEX hydroxyl via a degradable ester bond. DEX-DTPA-Gd was then modified with dodecylsuccinic anhydride to obtain the amphiphilic derivative, 2-dodecylsuccinic acid (DSA)-grafted DEX-DTPA-Gd. Nanomicelles were prepared by dissolving DSA-DEX-DTPA-Gd in water using ultrasonication. The physicochemical properties, cytotoxicity, and MRI efficiency of the synthesized CA were evaluated. The synthesized DSA-DEX-DTPA-Gd self-assembled into nanomicelles with an average diameter of 67.80 ± 5.21 nm. Within the given Gd concentration range, DSA-DEX-DTPA-Gd and Magnevist® exhibited similar cytotoxicity. DEX-based CAs resulted in a greater contrast enhancement of T1-weighted signal intensity in the tumor region than Magnevist®, and the tumors were clearly defined for at least 3 h. Simultaneously, the ester bond in DSA-DEX-DTPA-Gd facilitated the elimination of Gd chelates, compared with the relatively more stable amide linker. The DEX-based nanomicelle system with directly ester-bound DTPA-Gd may serve as an MRI CA with superior tumor imaging and relatively rapid elimination.

Assessing PD-L1 Expression Level by Radiomic Features From PET/CT in Nonsmall Cell Lung Cancer Patients: An Initial Result.

RATIONALE AND OBJECTIVES: To explore the potential value of radiomic features-derived approach in assessing PD-L1 expression status in nonsmall cell lung cancer (NSCLC) patients. MATERIALS AND METHODS: A cohort of 399 stage I-IV NSCLC patients were enrolled. Tumor segmentation was performed to select essential primary lesions of NSCLC cases after PET/CT images acquisition. Features were extracted, then filtered with automatic relevance determination and minimized with LASSO model based on its relevance of PD-L1 expression status. Finally, we built predictive models with features from the CT, the PET, and the PET/CT images, respectively, for differentiating different status of specific PD-L1 types. Five-fold cross validation was practiced to evaluate the signatures' accuracy, and the receiver operating characteristic as well as the corresponding area under the curve (AUC) was reckoned for each model. RESULTS: With the total of 24 selected features which were significantly associated with PD-L1 expression levels, models based on CT-, PET-, PET/CT-derived features were built and compared. For PD-L1 (SP142) expression level over 1% prediction, models that comprised radiomic features from the CT, the PET, and the PET/CT images resulted in an AUC of 0.97, 0.61, and 0.97, respectively; models for over 50% prediction resulted with AUC of 0.80, 0.65, and 0.77. For PD-L1 (28-8) expression level prediction, predictive models of over 1% expression scored at 0.86, 0.62, and 0.85; and signatures of over 50% expression reached the score of AUCs at 0.91, 0.75, and 0.88, respectively. CONCLUSION: The radiomic-based predictive approach, especially CT-derived predictive model, may anticipate PD-L1 expression status in NSCLC patients relatively accurate. It may be helpful in guiding immunotherapy in clinical practice and deserves further analysis.

Assessing EGFR gene mutation status in non-small cell lung cancer with imaging features from PET/CT.

OBJECTIVE: The aim of this study was to investigate whether quantitative and qualitative features extracted from PET/computed tomography (CT) can be used as imaging biomarkers for evaluating epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer patients. METHODS: Eighty patients with stage II and III non-small cell lung cancer from January 2017 to December 2017 were included in this study. All patients underwent PET/CT examination before operation. Patients with 30 EGFR positive and 50 EGFR negative were confirmed by pathological verification and gene detection. Least absolute shrinkage and selection operator was used for analysis and selection of imaging features. Support vector machine was used to classify EGFR positive/negative using the selected features. Ten-fold cross validation was used to estimate the accuracy. RESULTS: A total of 512 quantitative features (radiomic features) were extracted from PET/CT (256 for PET and 256 for CT), and 12 qualitative features (semantic features) were extracted from CT. A total of 35 features were finally retained after least absolute shrinkage and selection operator (31 quantitative features and 4 qualitative features). The 35 selected features were significantly associated with EGFR mutation status. A predictive model was built using PET/CT data. Its performance was revealed as 0.953 using the area under the receiver operating characteristic curve. CONCLUSION: A predictive model using PET/CT images might be used to detect EGFR mutation status in non-small cell lung cancer patients.

Stearic Acid-Grafted Chitooligosaccharide Nanomicelle System with Biocleavable Gadolinium Chelates as a Multifunctional Agent for Tumor Imaging and Drug Delivery.

PURPOSE: Theranostic nanoplatforms are promising approaches for diagnosis and treatment. Here, we report a drug-loaded nanomicelle system with biocleavable gadolinium (Gd) chelates as a multifunctional biodegradable agent for simultaneous magnetic resonance imaging (MRI) and drug delivery. METHODS: Self-assembled nanomicelles based on stearic acid-grafted chitooligosaccharide were utilized as vehicles. Gd chelates, DTPA-Gds, were linked to the nanomicelles via redox-responsive disulfide bonds, and hydrophobic drugs were encapsulated in the micelle cores. MRI and cargo delivery were investigated in orthotopic pancreatic tumor-bearing mice. RESULTS: In vivo MRI demonstrated that the biodegradable agent was cleaved by endogenous thiols after intravenous injection, and the released DTPA-Gds were eliminated rapidly. At the same time, the agent resulted in a greater contrast enhancement of T1-weighted MR signal intensity at the tumor region than Magnevist®, and the tumor boundaries were clearly defined for at least 2 h. In addition, the agent possessed high drug-loading and tumor-targeting capacities. Loading content and encapsulation efficiency of docetaxel were 3.2% and 99.4%, respectively. Compared with Taxotere®, the commercially available docetaxel injection, the docetaxel-loaded agent significantly increased the drug concentration in tumor tissue in vivo. CONCLUSION: The fabricated multifunctional agent may serve as a biodegradable nanoscale MRI contrast agent and as a drug delivery system for tumor diagnosis and treatment.