In situ generated 2,5-pyrazinedicarboxylate and oxalate ligands leading to a Eu-MOF for selective capture of C2H2 from C2H2/CO2.

The separation of C2H2/CO2 mixtures is a very important but highly challenging task due to their comparable physical natures and relative sizes. Herein, we report a europium-based 3D microporous MOF with a 4-connected two-nodal net with {4·53·62}2{42·62·82} topology, {[Eu2(pzdc)(ox)2(H2O)4]·5H2O}n (1) (H2pzdc = 2,5-pyrazinedicarboxylic acid, H2ox = oxalic acid), prepared by a hydrothermal method involving in situ generation of 2,5-pyrazinedicarboxylate and oxalate ligands. Two different temperatures were utilized to create two porous materials (1a and 1b) with channels of 4.8 × 5.4 Å and 4.1 × 6.3 Å, and 4.8 × 5.4 and 4.6 × 8.7 Å2, respectively. 1b shows a superior ability to selectively capture C2H2 from C2H2/CO2 as compared with 1a. At 1 bar and 298 K, 1b takes up 4.10 mmol g-1 C2H2 and 1.84 mmol g-1 CO2, respectively. In addition, at 298 K and 1 bar, 1b has a high selectivity for C2H2 over CO2, with an IAST selectivity of 12.7 while the value for 1a is 3.2. The separation of C2H2/CO2 with 1b also exhibits good reusability.

Immune checkpoint inhibitors and anti-vascular endothelial growth factor antibody/tyrosine kinase inhibitors with or without transarterial chemoembolization as first-line treatment for advanced hepatocellular carcinoma (CHANCE2201): a target trial emulation study.

Background: The role of transarterial chemoembolization (TACE) in the treatment of advanced hepatocellular carcinoma (HCC) is unconfirmed. This study aimed to assess the efficacy and safety of immune checkpoint inhibitors (ICIs) plus anti-vascular endothelial growth factor (anti-VEGF) antibody/tyrosine kinase inhibitors (TKIs) with or without TACE as first-line treatment for advanced HCC. Methods: This nationwide, multicenter, retrospective cohort study included advanced HCC patients receiving either TACE with ICIs plus anti-VEGF antibody/TKIs (TACE-ICI-VEGF) or only ICIs plus anti-VEGF antibody/TKIs (ICI-VEGF) from January 2018 to December 2022. The study design followed the target trial emulation framework with stabilized inverse probability of treatment weighting (sIPTW) to minimize biases. The primary outcome was overall survival (OS). Secondary outcomes included progression-free survival (PFS), objective response rate (ORR), and safety. The study is registered with ClinicalTrials.gov, NCT05332821. Findings: Among 1244 patients included in the analysis, 802 (64.5%) patients received TACE-ICI-VEGF treatment, and 442 (35.5%) patients received ICI-VEGF treatment. The median follow-up time was 21.1 months and 20.6 months, respectively. Post-application of sIPTW, baseline characteristics were well-balanced between the two groups. TACE-ICI-VEGF group exhibited a significantly improved median OS (22.6 months [95% CI: 21.2-23.9] vs 15.9 months [14.9-17.8]; P < 0.0001; adjusted hazard ratio [aHR] 0.63 [95% CI: 0.53-0.75]). Median PFS was also longer in TACE-ICI-VEGF group (9.9 months [9.1-10.6] vs 7.4 months [6.7-8.5]; P < 0.0001; aHR 0.74 [0.65-0.85]) per Response Evaluation Criteria in Solid Tumours (RECIST) version 1.1. A higher ORR was observed in TACE-ICI-VEGF group, by either RECIST v1.1 or modified RECIST (41.2% vs 22.9%, P < 0.0001; 47.3% vs 29.7%, P < 0.0001). Grade ≥3 adverse events occurred in 178 patients (22.2%) in TACE-ICI-VEGF group and 80 patients (18.1%) in ICI-VEGF group. Interpretation: This multicenter study supports the use of TACE combined with ICIs and anti-VEGF antibody/TKIs as first-line treatment for advanced HCC, demonstrating an acceptable safety profile. Funding: National Natural Science Foundation of China, National Key Research and Development Program of China, Jiangsu Provincial Medical Innovation Center, Collaborative Innovation Center of Radiation Medicine of Jiangsu Higher Education Institutions, and Nanjing Life Health Science and Technology Project.

Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study.

RATIONALE AND OBJECTIVES: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cancer (BC) patients who underwent total mastectomy (TM) and had 1-2 positive sentinel lymph nodes (SLNs). MATERIALS AND METHODS: In total, 494 patients were retrospectively enrolled from two hospitals, and were divided into the training (n = 286), internal validation (n = 122), and external validation (n = 86) cohorts. Features were extracted from DCE-MRI images for each patient and screened. Six ML classifies were trained and the best classifier was evaluated to calculate radiomics (Rad)-scores. A combined model was developed based on Rad-scores and clinical risk factors, then the calibration, discrimination, reclassification, and clinical usefulness were evaluated. RESULTS: 14 radiomics features were ultimately selected. The random forest (RF) classifier showed the best performance, with the highest average area under the curve (AUC) of 0.833 in the validation cohorts. The combined model incorporating RF-based Rad-scores, tumor size, lymphovascular invasion, and proportion of positive SLNs resulted in the best discrimination ability, with AUCs of 0.903, 0.890, and 0.836 in the training, internal validation, and external validation cohorts, respectively. Furthermore, the combined model significantly improved the classification accuracy and clinical benefit for NSLN metastasis prediction. CONCLUSION: A RF-based combined model using DCE-MRI images exhibited a promising performance for predicting NSLN metastasis in Chinese BC patients who underwent TM and had 1-2 positive SLNs, thereby aiding in individualized clinical treatment decisions.

Multitask deep learning for prediction of microvascular invasion and recurrence-free survival in hepatocellular carcinoma based on MRI images.

BACKGROUND AND AIMS: Accurate preoperative prediction of microvascular invasion (MVI) and recurrence-free survival (RFS) is vital for personalised hepatocellular carcinoma (HCC) management. We developed a multitask deep learning model to predict MVI and RFS using preoperative MRI scans. METHODS: Utilising a retrospective dataset of 725 HCC patients from seven institutions, we developed and validated a multitask deep learning model focused on predicting MVI and RFS. The model employs a transformer architecture to extract critical features from preoperative MRI scans. It was trained on a set of 234 patients and internally validated on a set of 58 patients. External validation was performed using three independent sets (n = 212, 111, 110). RESULTS: The multitask deep learning model yielded high MVI prediction accuracy, with AUC values of 0.918 for the training set and 0.800 for the internal test set. In external test sets, AUC values were 0.837, 0.815 and 0.800. Radiologists' sensitivity and inter-rater agreement for MVI prediction improved significantly when integrated with the model. For RFS, the model achieved C-index values of 0.763 in the training set and ranged between 0.628 and 0.728 in external test sets. Notably, PA-TACE improved RFS only in patients predicted to have high MVI risk and low survival scores (p < .001). CONCLUSIONS: Our deep learning model allows accurate MVI and survival prediction in HCC patients. Prospective studies are warranted to assess the clinical utility of this model in guiding personalised treatment in conjunction with clinical criteria.

Evaluation of exogenous therapeutic protein activity under confinement and crowding effects.

Dysfunction of intracellular proteins is frequently associated with various diseases, such as cancer. The exogenous proteins in cells are usually assembled with specific configurations due to physiological confinement/crowding to exhibit novel features in the protein structure, folding or conformational stability, distinguished with their behaviors in buffer solutions. Here, we synthesized exogenous proteins under confined/crowded conditions, to explore protein activity within cells. The findings suggested that the confinement and crowding effects on protein activity are heterogeneous; they showed an inhibitory effect on HRP by decreasing Km from ∼9.5- and ∼21.7-fold and Vmax from ∼6.8- and ∼20.2-fold lower than that of dilute solutions. Interestingly, the effects on Cyt C seem to be more complicated, and crowding exerts a positive effect by increasing Km ∼ 3.6-fold and Vmax ∼ 1.5-fold higher than that of dilute solutions; however, confinement exhibits a negative effect by decreasing Km ∼2.0 and Vmax ∼8.3 times. Additionally, in contrast to traditional nanoparticle-based confinement models, we synthesized a biodegradable nanoparticle to mimic the confined space, and the biggest advantage of this novel model is that the particles can be degraded and thus it can provide more intuitive observations of the properties of the target proteins under confinement and after release. Furthermore, we also evaluated protein activity in different cellular environments, indicating that the exogenous protein activity was closely related to the crowdedness of cellular environments, and the inhibition of protein activity in MDA-MB-231 cancer cells was more obvious than in HEK293 normal cells. Finally, SAXS analysis revealed the correlation between the protein conformation and the different environments. Our work will provide a unique method for precisely assessing whether the target cellular environments are native matrix in which specific exogenous protein drugs are delivered to function or whether they display a therapeutic role, which is of great significance for screening and development of new drugs.

Three-Dimensional Voxel-Wise Quantitative Assessment of Imaging Features in Hepatocellular Carcinoma.

Voxel-wise quantitative assessment of typical characteristics in three-dimensional (3D) multiphase computed tomography (CT) imaging, especially arterial phase hyperenhancement (APHE) and subsequent washout (WO), is crucial for the diagnosis and therapy of hepatocellular carcinoma (HCC). However, this process is still missing in practice. Radiologists often visually estimate these features, which limit the diagnostic accuracy due to subjective interpretation and qualitative assessment. Quantitative assessment is one of the solutions to this problem. However, performing voxel-wise assessment in 3D is difficult due to the misalignments between images caused by respiratory and other physiological motions. In this paper, based on the Liver Imaging Reporting and Data System (v2018), we propose a registration-based quantitative model for the 3D voxel-wise assessment of image characteristics through multiple CT imaging phases. Specifically, we selected three phases from sequential CT imaging phases, i.e., pre-contrast phase (Pre), arterial phase (AP), delayed phase (DP), and then registered Pre and DP images to the AP image to extract and assess the major imaging characteristics. An iterative reweighted local cross-correlation was applied in the proposed registration model to construct the fidelity term for comparison of intensity features across different imaging phases, which is challenging due to their distinct intensity appearance. Experiments on clinical dataset showed that the means of dice similarity coefficient of liver were 98.6% and 98.1%, those of surface distance were 0.38 and 0.54 mm, and those of Hausdorff distance were 4.34 and 6.16 mm, indicating that quantitative estimation can be accomplished with high accuracy. For the classification of APHE, the result obtained by our method was consistent with those acquired by experts. For the WO, the effectiveness of the model was verified in terms of WO volume ratio.

Automatic volumetric diagnosis of hepatocellular carcinoma based on four-phase CT scans with minimum extra information.

Summary: We built a deep-learning based model for diagnosis of HCC with typical images from four-phase CT and MEI, demonstrating high performance and excellent efficiency. Objectives: The aim of this study was to develop a deep-learning-based model for the diagnosis of hepatocellular carcinoma. Materials and methods: This clinical retrospective study uses CT scans of liver tumors over four phases (non-enhanced phase, arterial phase, portal venous phase, and delayed phase). Tumors were diagnosed as hepatocellular carcinoma (HCC) and non-hepatocellular carcinoma (non-HCC) including cyst, hemangioma (HA), and intrahepatic cholangiocarcinoma (ICC). A total of 601 liver lesions from 479 patients (56 years ± 11 [standard deviation]; 350 men) are evaluated between 2014 and 2017 for a total of 315 HCCs and 286 non-HCCs including 64 cysts, 178 HAs, and 44 ICCs. A total of 481 liver lesions were randomly assigned to the training set, and the remaining 120 liver lesions constituted the validation set. A deep learning model using 3D convolutional neural network (CNN) and multilayer perceptron is trained based on CT scans and minimum extra information (MEI) including text input of patient age and gender as well as automatically extracted lesion location and size from image data. Fivefold cross-validations were performed using randomly split datasets. Diagnosis accuracy and efficiency of the trained model were compared with that of the radiologists using a validation set on which the model showed matched performance to the fivefold average. Student's t-test (T-test) of accuracy between the model and the two radiologists was performed. Results: The accuracy for diagnosing HCCs of the proposed model was 94.17% (113 of 120), significantly higher than those of the radiologists, being 90.83% (109 of 120, p-value = 0.018) and 83.33% (100 of 120, p-value = 0.002). The average time analyzing each lesion by our proposed model on one Graphics Processing Unit was 0.13 s, which was about 250 times faster than that of the two radiologists who needed, on average, 30 s and 37.5 s instead. Conclusion: The proposed model trained on a few hundred samples with MEI demonstrates a diagnostic accuracy significantly higher than the two radiologists with a classification runtime about 250 times faster than that of the two radiologists and therefore could be easily incorporated into the clinical workflow to dramatically reduce the workload of radiologists.

DeepRecS: From RECIST Diameters to Precise Liver Tumor Segmentation.

Liver tumor segmentation (LiTS) is of primary importance in diagnosis and treatment of hepatocellular carcinoma. Known automated LiTS methods could not yield satisfactory results for clinical use since they were hard to model flexible tumor shapes and locations. In clinical practice, radiologists usually estimate tumor shape and size by a Response Evaluation Criteria in Solid Tumor (RECIST) mark. Inspired by this, in this paper, we explore a deep learning (DL) based interactive LiTS method, which incorporates guidance from user-provided RECIST marks. Our method takes a three-step framework to predict liver tumor boundaries. Under this architecture, we develop a RECIST mark propagation network (RMP-Net) to estimate RECIST-like marks in off-RECIST slices. We also devise a context-guided boundary-sensitive network (CGBS-Net) to distill tumors' contextual and boundary information from corresponding RECIST(-like) marks, and then predict tumor maps. To further refine the segmentation results, we process the tumor maps using a 3D conditional random field (CRF) algorithm and a morphology hole-filling operation. Verified on two clinical contrast-enhanced abdomen computed tomography (CT) image datasets, our proposed approach can produce promising segmentation results, and outperforms the state-of-the-art interactive segmentation methods.

PA-ResSeg: A phase attention residual network for liver tumor segmentation from multiphase CT images.

PURPOSE: Liver tumor segmentation is a crucial prerequisite for computer-aided diagnosis of liver tumors. In the clinical diagnosis of liver tumors, radiologists usually examine multiphase CT images as these images provide abundant and complementary information of tumors. However, most known automatic segmentation methods extract tumor features from CT images merely of a single phase, in which valuable multiphase information is ignored. Therefore, it is highly demanded to develop a method effectively incorporating multiphase information for automatic and accurate liver tumor segmentation. METHODS: In this paper, we propose a phase attention residual network (PA-ResSeg) to model multiphase features for accurate liver tumor segmentation. A phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase. The PA block consists of an intraphase attention (intra-PA) module and an interphase attention (inter-PA) module to capture channel-wise self-dependencies and cross-phase interdependencies, respectively. Thus, it enables the network to learn more representative multiphase features by refining the PV features according to the channel dependencies and recalibrating the ART features based on the learned interdependencies between phases. We propose a PA-based multiscale fusion (MSF) architecture to embed the PA blocks in the network at multiple levels along the encoding path to fuse multiscale features from multiphase images. Moreover, a 3D boundary-enhanced loss (BE-loss) is proposed for training to make the network more sensitive to boundaries. RESULTS: To evaluate the performance of our proposed PA-ResSeg, we conducted experiments on a multiphase CT dataset of focal liver lesions (MPCT-FLLs). Experimental results show the effectiveness of the proposed method by achieving a dice per case (DPC) of 0.7787, a dice global (DG) of 0.8682, a volumetric overlap error (VOE) of 0.3328, and a relative volume difference (RVD) of 0.0443 on the MPCT-FLLs. Furthermore, to validate the effectiveness and robustness of PA-ResSeg, we conducted extra experiments on another multiphase liver tumor dataset and obtained a DPC of 0.8290, a DG of 0.9132, a VOE of 0.2637, and a RVD of 0.0163. The proposed method shows its robustness and generalization capability in different datasets and different backbones. CONCLUSIONS: The study demonstrates that our method can effectively model information from multiphase CT images to segment liver tumors and outperforms other state-of-the-art methods. The PA-based MSF method can learn more representative multiphase features at multiple scales and thereby improve the segmentation performance. Besides, the proposed 3D BE-loss is conducive to tumor boundary segmentation by enforcing the network focus on boundary regions and marginal slices. Experimental results evaluated by quantitative metrics demonstrate the superiority of our PA-ResSeg over the best-known methods.

Drug-eluting beads transarterial chemoembolization by CalliSpheres is effective and well tolerated in treating intrahepatic cholangiocarcinoma patients: A preliminary result from CTILC study.

This study aimed to investigate the efficacy and safety of drug-eluting beads (DEB) transarterial chemoembolization (TACE) treatment in Chinese intrahepatic cholangiocarcinoma (ICC) patients.37 ICC patients underwent DEB-TACE treatment in CTILC study (registered on clinicaltrials.gov with registry No. NCT03317483) were included in this present study. Treatment response was assessed according to modified Response Evaluation Criteria in Solid Tumors (mRECIST). Overall survival (OS) was calculated from the time of DEB-TACE operation until the date of death from any causes. Liver function change and adverse events (AEs) were recorded during and after DEB-TACE operation.3 (8.1%) patients achieved complete response (CR) and 22 (59.5%) patients achieved partial response (PR), with objective response rate (ORR) of 67.6%. After DEB-TACE treatment, mean OS was 376 days (95%CI: 341-412 days). Multivariate logistic regression analysis revealed that Bilobar disease (P = .040, OR: 0.105, 95% CI: 0.012-0.898) and portal vein invasion (P = .038, OR: 0.104, 95% CI: 0.012-0.881) could independently predict less possibility of ORR. Patients with ALB abnormal, TP abnormal, ALT abnormal and AST abnormal were increased at 1-week post DEB-TACE treatment (P = .034, P = .001, P < .001, P = .006, respectively), while returned to the levels at baseline after 1 to 3 months (all P > .050). Besides, most of the AEs were mild including pain, fever, vomiting, and nausea in this study.DEB-TACE was effective and well tolerated in treating ICC patients, and bilobar disease as well as portal vein invasion were independently correlated with less probability of ORR achievement.

Efficient synthesis of ß-lactam antibiotics with in situ product removal by a newly isolated penicillin G acylase.

A penicillin G acylase (PGA) from Achromobacter xylosoxidans PX02 was newly isolated, and site-directed mutagenesis at three important positions αR141, αF142, ßF24 was carried out for improving the enzymatic synthesis of ß-lactam antibiotics. The efficient mutant ßF24A was selected, and the (Ps/Ph)ini (ratio between the initial rate of synthesis and hydrolysis of the activated acyl donor) dramatically increased from 1.42-1.50 to 23.8-24.1 by means of the optimization of reaction conditions. Interestingly, the efficient enzymatic synthesis of ampicillin (99.1% conversion) and amoxicillin (98.7% conversion) from a high concentration (600 mM) of substrate 6-APA in the low acyl donor/nucleus ratio (1.1:1) resulted in a large amount of products precipitation from aqueous reaction solution. Meanwhile, the by-product D-phenylglycine was hardly precipitated, and 93.5% yield of precipitated ampicillin (561 mM) and 94.6% yield of precipitated amoxicillin (568 mM) were achieved with high purity (99%), which significantly simplified the downstream purification. This was the first study to achieve efficient ß-lactam antibiotics synthesis process with in situ product removal, with barely any by-product formation. The effect enzymatic synthesis of antibiotics in aqueous reaction solution with in situ product removal provides a promising model for the industrial semi-synthesis of ß-lactam antibiotics.

The Comprehensive Analysis of Efficacy and Safety of CalliSpheres® Drug-Eluting Beads Transarterial Chemoembolization in 367 Liver Cancer Patients: A Multiple-Center, Cohort Study.

This study aimed to investigate the efficacy, safety, and prognostic factors of drug-eluting beads transarterial chemoembolization (DEB-TACE) in treating Chinese patients with liver cancer. A total of 367 liver cancer patients from 24 medical centers were consecutively enrolled in this multiple-center, prospective cohort study, including 275 hepatocellular carcinoma (HCC) cases, 37 intrahepatic cholangiocarcinoma (ICC) cases, and 55 secondary liver cancer cases. All the patients received CalliSpheres® DEB-TACE treatment. Treatment response, overall survival (OS), change of liver function, and adverse events (AEs) were assessed. DEB-TACE treatment achieved 19.9% complete response (CR) and 79.6% objective response rate (ORR), with mean OS of 384 days [95% confidence interval (CI): 375-393 days]. CR and ORR were both higher in HCC patients compared with primary ICC patients and secondary liver cancer patients, while no difference was discovered in OS. Portal vein invasion was an independent risk factor for CR, while portal vein invasion, previous conventional TACE (cTACE) treatment, and abnormal blood creatinine (BCr) were independent risk factors for ORR. In addition, largest nodule size ≥5.0 cm, abnormal albumin (ALB), and abnormal total bilirubin (TBIL) independently correlated with unfavorable OS. Most liver function indexes were recovered to baseline levels at 1-3 months after DEB-TACE. Common AEs were pain, fever, vomiting, and nausea; most of them were at mild grade. CalliSpheres® DEB-TACE is efficient and well tolerated in Chinese liver cancer patients. Portal vein invasion, previous cTACE treatment, largest nodule size, abnormal BCr, ALB, and TBIL correlate with worse prognosis independently.

Efficacy and Safety of Drug-Eluting Beads Transarterial Chemoembolization by CalliSpheres® in 275 Hepatocellular Carcinoma Patients: Results From the Chinese CalliSpheres® Transarterial Chemoembolization in Liver Cancer (CTILC) Study.

The purpose of this study was to investigate the efficacy and safety of drug-eluting beads transarterial chemoembolization (DEB-TACE) treatment in Chinese hepatocellular carcinoma (HCC) patients and the prognostic factors for treatment response as well as survival. A total of 275 HCC patients were included in this prospective study. Treatment response was assessed by modified Response Evaluation Criteria in Solid Tumors (mRECIST), and progression-free survival (PFS) as well as overall survival (OS) were determined. Liver function and adverse events (AEs) were assessed before and after DEB-TACE operation. Complete response (CR), partial response (PR), and objective response rate (ORR) were 22.9%, 60.7%, and 83.6%, respectively. The mean PFS was 362 (95% CI: 34.9-375) days, the 6-month PFS rate was 89.4 ± 2.1%, while the mean OS was 380 (95% CI: 370-389) days, and the 6-month OS rate was 94.4 ± 1.7%. Multivariate logistic regression revealed that portal vein invasion (p = 0.011) was an independent predictor of worse clinical response. Portal vein invasion (p = 0.040), previous cTACE treatment (p = 0.030), as well as abnormal serum creatinine level (BCr) (p = 0.017) were independent factors that predicted worse ORR. In terms of survival, higher Barcelona Clinic Liver Cancer (BCLC) stage (p = 0.029) predicted for worse PFS, and abnormal albumin (ALB) (p = 0.011) and total serum bilirubin (TBIL) (p = 0.009) predicted for worse OS. The number of patients with abnormal albumin, total protein (TP), TBIL, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) were augmented at 1 week posttreatment and were similar at 1-3 months compared with baseline. The most common AEs were pain, fever, nausea, and vomiting, and no severe AEs were observed in this study. DEB-TACE was effective and tolerable in treating Chinese HCC patients, and portal vein invasion, previous cTACE treatment, abnormal BCr, ALB, and TBIL appear to be important factors that predict worse clinical outcome.

A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma.

Purpose: Accurate lymph node (LN) status evaluation for intrahepatic cholangiocarcinoma (ICC) patients is essential for surgical planning. This study aimed to develop and validate a prediction model for preoperative LN status evaluation in ICC patients. Methods and Materials: A group of 106 ICC patients, who were diagnosed between April 2011 and February 2016, was used for prediction model training. Image features were extracted from T1-weighted contrast-enhanced MR images. A support vector machine (SVM) model was built by using the most LN status-related features, which were selected using the maximum relevance minimum redundancy (mRMR) algorithm. The mRMR method ranked each feature according to its relevance to the LN status and redundancy with other features. An SVM score was calculated for each patient to reflect the LN metastasis (LNM) probability from the SVM model. Finally, a combination nomogram was constructed by incorporating the SVM score and clinical features. An independent group of 42 patients who were diagnosed from March 2016 to November 2017 was used to validate the prediction models. The model performances were evaluated on discrimination, calibration, and clinical utility. Results: The SVM model was constructed based on five selected image features. Significant differences were found between patients with LNM and non-LNM in SVM scores in both groups (the training group: 0.5466 (interquartile range (IQR), 0.4059-0.6985) vs. 0.3226 (IQR, 0.0527-0.4659), P<0.0001; the validation group: 0.5831 (IQR, 0.3641-0.8162) vs. 0.3101 (IQR, 0.1029-0.4661), P=0.0015). The combination nomogram based on the SVM score, the CA 19-9 level, and the MR-reported LNM factor showed better discrimination in separating patients with LNM and non-LNM, comparing to the SVM model alone (AUC: the training group: 0.842 vs. 0.788; the validation group: 0.870 vs. 0.787). Favorable clinical utility was observed using the decision curve analysis for the nomogram. Conclusion: The nomogram, incorporating the SVM score, CA 19-9 level and the MR-reported LNM factor, provided an individualized LN status evaluation and helped clinicians guide the surgical decisions.

An investigation of efficacy, safety, and prognostic factors of drug-eluting beads-transarterial chemoembolization operation with CalliSpheres® Microspheres in treating Chinese hepatocellular carcinoma patients.

BACKGROUND: We aimed to investigate treatment response, survival profiles, safety profiles, and predictive factors of drug-eluting beads-transarterial chemoembolization (DEB-TACE) with CalliSpheres® Microspheres (CSM) in treating Chinese hepatocellular carcinoma (HCC) patients. METHODS: A total of 66 HCC patients about to receive DEB-TACE with CSM therapy were consecutively enrolled in this prospective cohort study. Treatment response was recorded. Besides, progression-free survival (PFS) and overall survival (OS) were also recorded. All adverse events including pain, nausea, vomiting, fever, and liver function damage were recorded during hospitalization. RESULTS: 37.9% of patients achieved complete response (CR) and 81.8% of patients achieved an objective response rate (ORR). For survival, mean PFS and OS were 13.7 (11.7-15.8) months and 18.8 (95% CI: 16.3-21.2) months, respectively. Multivariate logistic regression analysis revealed that a number of nodules ≥2 was an independent factor for worse CR; moreover, multivariate Cox's regression analysis disclosed that largest sample size ≥5 cm was an independent factor for shorter PFS, and Child-Pugh B and BCLC stage B/C were independent predictive factors for unfavorable OS. As to AEs, numbers of patients suffered liver function damage, pain, nausea, vomiting, and fever were 29 (43.9%), 27 (40.9%), 22 (33.3%), 13 (19.7%), and 37 (56.1%), respectively. CONCLUSION: Drug-eluting beads-transarterial chemoembolization with CSM is an effective and tolerated treatment for Chinese HCC patients, and number of nodules ≥2, largest nodule size ≥5 cm, Child-Pugh stage B, and BCLC stage B/C correlates with unfavorable prognosis.

Efficacy and safety of CalliSpheres® drug-eluting beads transarterial chemoembolization in patients with secondary liver cancer: a preliminary result from CTILC study.

BACKGROUND: This study aimed to assess the treatment response, short-term overall survival (OS) and safety profiles of drug-eluting beads transarterial chemoembolization (DEB-TACE) in patients with secondary liver cancer. METHODS: Fifty-five patients with secondary liver cancer underwent DEB-TACE were enrolled in this prospective cohort study. Treatment response was assessed by modified Response Evaluation Criteria in Solid Tumors (mRECIST). OS was calculated from the time of DEB-TACE operation until the date of death. RESULTS: The complete response (CR) and objective response rate (ORR) at 1-3 months post DEB-TACE were 12.7% and 67.3%. Mean OS was 383 d (95% CI: 360-406), and 6-month OS rate was 93.4%±3.7%. Subgroup analysis revealed previous conventional TACE (cTACE) treatment was correlated with worse ORR (P=0.028), and it was a risk factor for ORR achievement (P=0.021). As for liver function, the percentages of abnormal TP (P=0.031), TBIL (P=0.022), ALT (P=0.002) and AST (P=0.035) were increased at 1 week post DEB-TACE compared to baseline, while these four indexes returned to baseline (all P>0.05) at 1-3 months post DEB-TACE. As to safety profiles, 41 (66.1%), 28 (45.2%), 17 (27.4%), 8 (12.9%) and 6 (9.7%) cases had pain, vomiting, fever, nausea and other adverse events (AEs) respectively during DEB-TACE operation, while 26 (41.9%), 9 (14.5%), 8 (12.9%), 4 (6.5%), 1 (1.6%) and 2 (3.2%) cases had pain, fever, vomiting, nausea, bone marrow toxicity and other AEs respectively at 1 month after DEB-TACE operation. CONCLUSIONS: DEB-TACE was efficient and well tolerated in treating patients with secondary liver cancer.

Positron emission tomography/computed tomography manifestations of primary hepatic myxoid liposarcoma: A case report.

RATIONALE: Computed tomography (CT) and magnetic resonance imaging (MRI) are conventional used to evaluate liver tumors. Detection of fat component is considered an important clue to the imaging diagnosis of hepatic myxoid liposarcoma. However, the positron emission tomography (PET)/CT report of hepatic myxoid liposarcoma is scarce, and the metabolic characteristics of primary hepatic myxoid liposarcoma are still unknown. PATIENT CONCERNS: In this report, we report the PET/CT manifestations of a rare case of primary hepatic myxoid liposarcoma that was confirmed by pathologic examination. A 29-year-old male patient presented with a nodule adjacent to the umbilicus that had been present for 2 weeks. PET/CT showed a hypoattenuating mass with moderate uptake (maximum standardized uptake value [SUVmax] 1.9), except for a slightly hyperattenuating focal area with intense uptake (SUVmax 3.1). DIAGNOSES: The final pathologic diagnosis was confirmed, by means of laparotomy, to be a primary hepatic myxoid liposarcoma with multiple extrahepatic metastases. INTERVENTIONS: The lesions in the left liver and periumbilical abdominal wall were punctured. During the laparotomy, hepatic malignant tumors were confirmed and metastasized widely in the abdominal cavity. OUTCOMES: An abdominal CT performed 4 months after initial presentation showed extensive metastasis. LESSONS: Primary hepatic myxoid liposarcoma may manifest as moderate metabolism with less fat on PET/CT. PET/CT is not only valuable in reflecting the round cell component of hepatic myxoid liposarcoma, but also in estimating its origin.

Diagnostic value of computed tomography (CT) histogram analysis in thyroid benign solitary coarse calcification nodules.

This study was to investigate the diagnostic value of the computed tomography (CT) histogram in thyroid benign solitary coarse calcification nodules (BSCNs). A total of 89 thyroid solitary coarse calcification nodules (coarse calcification ≥5 mm, no definite soft tissue around calcification) confirmed either by surgery or histopathological examination in 86 cases enrolled from January 2009 to December 2015 were evaluated. These included 56 BSCNs and 33 malignant solitary coarse calcification nodules (MSCNs). Overall, 27 cut-off values were calculated by N (4≤N≤30) times of 50 Hounsfield units (HU) in the range of 200 to 1500 HU, and each cut-off value and the differences in the corresponding area percentages in the CT histogram were recorded for BSCN and MSCN. The optimal cut-off value and the corresponding area percentage were established by receiver operating characteristic (ROC) curve analysis. In the 19 groups with an ROC area under curve (AUC) of more than 0.7, at a cut-off value of 800 HU and at an area percentage of no more than 93.8%, the ROC AUC reached the maximum of 0.79, and the accuracy, sensitivity, and specificity were 75.3%, 80.4%, and 66.7%, respectively. At a cut-off value of 1050 HU and at an area percentage of no more than 93.6%, the accuracy, sensitivity, and specificity were 71.9%, 60.7%, and 90.9%, respectively. At a cut-off of 1150 HU and area of no more than 98.4%, the accuracy, sensitivity, and specificity were 70.8%, 57.1%, and 93.9%, respectively. At a cut-off of 600 HU and area of no more than 12.1%, the accuracy, sensitivity, and specificity were 61.8%, 39.3%, and 100.0%, respectively. Compared with the cut-off value of 800 HU and an area percentage of no more than 93.8%, the sensitivity of cut-off values and minimum areas of 1050 HU and 93.6%, of 1150 HU and 98.4%, and of 600 HU and 12.1%, was gradually decreasing; however, their specificity was gradually increasing. This can provide an important basis for reducing the misdiagnosis and unnecessary surgical trauma.

In vivo evaluation of bronchial injury of irreversible electroporation in a porcine lung ablation model by using laboratory, pathological, and CT findings.

Irreversible electroporation (IRE) creates permanent pores in the cell membrane, leading to irreversible cell death. In this study, the impact of IRE on bronchial injury was comprehensively examined in a timed series study. Altogether, 8 Bama miniature pigs were included in this study and were randomly assigned to experimental and control groups. The experimental group underwent IRE that was guided and monitored by spiral computed tomography (CT). The monopole probe of the IRE was positioned at the right pulmonary hilum. Specimens were collected at 0 h, 2 h, 2 d, 7 d, and 14 d after the IRE procedure for a pathological examination. A small amount of needle-tract bleeding occurred in two animals, and mild pneumothorax occurred in another. IRE can elicit acute bronchial inflammation, bleeding, and mucosal injury, but severe complications were not found. Pathological examinations and transmission electron microscopy (TEM) showed dead vascular epithelium cells in the region of the ablation, while the bronchioli and the vascular extracellular matrix were preserved. At 2 hours post-IRE, there were marked increases in bronchoalveolar macrophages (P<0.001), but the inflammation could recover after 14 days and showed no statistical significance when compared with the control group at the same time. In conclusion, CT-guided IRE ablation can elicit acute but recoverable bronchial inflammation, bleeding, and mucosal injury in porcine lung tissues. However, longer follow-up is still required to establish an evaluation of the long-term safety.