Multi-step validation of a Post-Endoscopic Retrograde Cholangiopancreatography Pancreatitis prediction system integrating multi-modal data: A multi-center study.

BACKGROUND AND STUDY AIMS: The impact of various categories of information on the prediction of Post Endoscopic Retrograde Cholangiopancreatography Pancreatitis (PEP) remains uncertain. We aimed to comprehensively investigate the risk factors associated with PEP by constructing and validating a model incorporating multi-modal data through multiple steps. PATIENTS AND METHODS: A total of 1,916 cases underwent ERCP were retrospectively collected from multiple centers for model construction. Through literature research, 49 electronic health record (EHR) features and one image feature related to PEP were identified. The EHR features were categorized into baseline, diagnosis, technique, and prevent strategies, covering pre-ERCP, intra-ERCP, and peri-ERCP phases. We first incrementally constructed models 1-4 incorporating these four feature categories, then added the image feature into models 1-4 and developed models 5-8. All models underwent testing and comparison using both internal and external test sets. Once the optimal model was selected, we conducted comparison among multiple machine learning algorithms. RESULTS: Compared with model 2 incorporating baseline and diagnosis features, adding technique and prevent strategies (model 4) greatly improved the sensitivity (63.89% vs 83.33%, p<0.05) and specificity (75.00% vs 85.92%, p<0.001). Similar tendency was observed in internal and external tests. In model 4, the top three features ranked by weight were previous pancreatitis, NSAIDS, and difficult cannulation. The image-based feature has the highest weight in model 5-8. Lastly, model 8 employed Random Forest algorithm showed the best performance. CONCLUSIONS: We firstly developed a multi-modal prediction model for identifying PEP with clinical-acceptable performance. The image and technique features are crucial for PEP prediction.

The effect of incorporating domain knowledge with deep learning in identifying benign and malignant gastric whitish lesions: A retrospective study.

BACKGROUND AND AIM: Early whitish gastric neoplasms can be easily misdiagnosed; differential diagnosis of gastric whitish lesions remains a challenge. We aim to build a deep learning (DL) model to diagnose whitish gastric neoplasms and explore the effect of adding domain knowledge in model construction. METHODS: We collected 4558 images from two institutions to train and test models. We first developed two sole DL models (1 and 2) using supervised and semi-supervised algorithms. Then we selected diagnosis-related features through literature research and developed feature-extraction models to determine features including boundary, surface, roundness, depression, and location. Then predictions of the five feature-extraction models and sole DL model were combined and inputted into seven machine-learning (ML) based fitting-diagnosis models. The optimal model was selected as ENDOANGEL-WD (whitish-diagnosis) and compared with endoscopists. RESULTS: Sole DL 2 had higher sensitivity (83.12% vs 68.67%, Bonferroni adjusted P = 0.024) than sole DL 1. Adding domain knowledge, the decision tree performed best among the seven ML models, achieving higher specificity than DL 1 (84.38% vs 72.27%, Bonferroni adjusted P < 0.05) and higher accuracy than DL 2 (80.47%, Bonferroni adjusted P < 0.001) and was selected as ENDOANGEL-WD. ENDOANGEL-WD showed better accuracy compared with 10 endoscopists (75.70%, P < 0.001). CONCLUSIONS: We developed a novel system ENDOANGEL-WD combining domain knowledge and traditional DL to detect gastric whitish neoplasms. Adding domain knowledge improved the performance of traditional DL, which provided a novel solution for establishing diagnostic models for other rare diseases potentially.

An artificial intelligence system for chronic atrophic gastritis diagnosis and risk stratification under white light endoscopy.

BACKGROUND AND AIMS: The diagnosis and stratification of gastric atrophy (GA) predict patients' gastric cancer progression risk and determine endoscopy surveillance interval. We aimed to construct an artificial intelligence (AI) system for GA endoscopic identification and risk stratification based on the Kimura-Takemoto classification. METHODS: We constructed the system using two trained models and verified its performance. First, we retrospectively collected 869 images and 119 videos to compare its performance with that of endoscopists in identifying GA. Then, we included original image cases of 102 patients to validate the system for stratifying GA and comparing it with endoscopists with different experiences. RESULTS: The sensitivity of model 1 was higher than that of endoscopists (92.72% vs. 76.85 %) at image level and also higher than that of experts (94.87% vs. 85.90 %) at video level. The system outperformed experts in stratifying GA (overall accuracy: 81.37 %, 73.04 %, p = 0.045). The accuracy of this system in classifying non-GA, mild GA, moderate GA, and severe GA was 80.00 %, 77.42 %, 83.33 %, and 85.71 %, comparable to that of experts and better than that of seniors and novices. CONCLUSIONS: We established an expert-level system for GA endoscopic identification and risk stratification. It has great potential for endoscopic assessment and surveillance determinations.

Initial experience of the treatment of large glioma with microwave ablation-assisted surgical resection.

Aim: This study aimed to investigate the preliminary clinical outcomes of microwave ablation (MWA)-assisted surgical treatment for large glioma. Materials and Methods: In total, six cases of large glioma (diameter >4 cm) were described. All cases were treated with MWA-assisted surgical resection, which was performed using ultrasound to guide the accurate placement of the antenna in the central region of the tumor. The MWA power was 40-45 W, and 6 min was applied. Changes in the ablation area were observed using intraoperative Doppler ultrasound and contrast-enhanced ultrasound (CEUS). Ten patients treated with surgical resection alone were included in the control group. Data on surgical times (i.e., the time from the incision of the dura to the removal of the tumor), intraoperative blood loss, and complications were recorded. Results: The median patient age was 45 years (range: 36.5-60.3 years). The median lesion diameter was 4.9 cm (range: 4.3-5.8). The microwave power was 40-45 W, and the median ablation time was 240 s (range: 208-297 s). The intra-tumoral vascular flow was significantly reduced after MWA. The median surgical time was shorter (38.5 min [range: 34.3-42.8 min]) and the median intraoperative blood loss was less (400 mL, [range: 400-450 mL]) in the combination treatment group than in the surgery-alone group. During the ablation process, no obvious additional neurological deficits were detected; however, a tube-shaped carbonide was found after the operation. Conclusion: MWA may be a useful complement to conventional techniques for the surgical resection of large glioma.

Development and Evaluation of a Surveillance System for Follow-Up After Colorectal Polypectomy.

Importance: The adherence of physicians and patients to published colorectal postpolypectomy surveillance guidelines varies greatly, and patient follow-up is critical but time consuming. Objectives: To evaluate the accuracy of an automatic surveillance (AS) system in identifying patients after polypectomy, assigning surveillance intervals for different risks of patients, and proactively following up with patients on time. Design, Setting, and Participants: In this diagnostic/prognostic study, endoscopic and pathological reports of 47 544 patients undergoing colonoscopy at 3 hospitals between January 1, 2017, and June 30, 2022, were collected to develop an AS system based on natural language processing. The performance of the AS system was fully evaluated in internal and external tests according to 5 guidelines worldwide and compared with that of physicians. A multireader, multicase (MRMC) trial was conducted to evaluate use of the AS system and physician guideline adherence, and prospective data were collected to evaluate the success rate in contacting patients and the association with reduced human workload. Data analysis was conducted from July to September 2022. Exposures: Assistance of the AS system. Main Outcomes and Measures: The accuracy of the system in identifying patients after polypectomy, stratifying patient risk levels, and assigning surveillance intervals in internal (Renmin Hospital of Wuhan University), external 1 (Wenzhou Central Hospital), and external 2 (The First People's Hospital of Yichang) test sets; the accuracy of physicians and their time burden with and without system assistance; and the rate of successfully informed patients of the system were evaluated. Results: Test sets for 16 106 patients undergoing colonoscopy (mean [SD] age, 51.90 [13.40] years; 7690 females [47.75%]) were evaluated. In internal, external 1, and external 2 test sets, the system had an overall accuracy of 99.91% (95% CI, 99.83%-99.95%), 99.54% (95% CI, 99.30%-99.70%), and 99.77% (95% CI, 99.41%-99.91%), respectively, for identifying types of patients and achieved an overall accuracy of at least 99.30% (95% CI, 98.67%-99.63%) in the internal test set, 98.89% (95% CI, 98.33%-99.27%) in external test set 1, and 98.56% (95% CI, 95.86%-99.51%) in external test set 2 for stratifying patient risk levels and assigning surveillance intervals according to 5 guidelines. The system was associated with increased mean (SD) accuracy among physicians vs no AS system in 105 patients (98.67% [1.28%] vs 78.10% [18.01%]; P = .04) in the MRMC trial. In a prospective trial, the AS system successfully informed 82 of 88 patients (93.18%) and was associated with reduced burden of follow-up time vs no AS system (0 vs 2.86 h). Conclusions and Relevance: This study found that an AS system was associated with improved adherence to guidelines among physicians and reduced workload among physicians and nurses.

Exploring the challenge of early gastric cancer diagnostic AI system face in multiple centers and its potential solutions.

BACKGROUND: Artificial intelligence (AI) performed variously among test sets with different diversity due to sample selection bias, which can be stumbling block for AI applications. We previously tested AI named ENDOANGEL, diagnosing early gastric cancer (EGC) on single-center videos in man-machine competition. We aimed to re-test ENDOANGEL on multi-center videos to explore challenges applying AI in multiple centers, then upgrade ENDOANGEL and explore solutions to the challenge. METHODS: ENDOANGEL was re-tested on multi-center videos retrospectively collected from 12 institutions and compared with performance in previously reported single-center videos. We then upgraded ENDOANGEL to ENDOANGEL-2022 with more training samples and novel algorithms and conducted competition between ENDOANGEL-2022 and endoscopists. ENDOANGEL-2022 was then tested on single-center videos and compared with performance in multi-center videos; the two AI systems were also compared with each other and endoscopists. RESULTS: Forty-six EGCs and 54 non-cancers were included in multi-center video cohort. On diagnosing EGCs, compared with single-center videos, ENDOANGEL showed stable sensitivity (97.83% vs. 100.00%) while sharply decreased specificity (61.11% vs. 82.54%); ENDOANGEL-2022 showed similar tendency while achieving significantly higher specificity (79.63%, p < 0.01) making fewer mistakes on typical lesions than ENDOANGEL. On detecting gastric neoplasms, both AI showed stable sensitivity while sharply decreased specificity. Nevertheless, both AI outperformed endoscopists in the two competitions. CONCLUSIONS: Great increase of false positives is a prominent challenge for applying EGC diagnostic AI in multiple centers due to high heterogeneity of negative cases. Optimizing AI by adding samples and using novel algorithms is promising to overcome this challenge.

The effectiveness of a computer-aided system in improving the detection rate of gastric neoplasm and early gastric cancer: study protocol for a multi-centre, randomized controlled trial.

BACKGROUND: This protocol is for a multi-centre randomised controlled trial to determine whether the computer-aided system ENDOANGEL-GC improves the detection rates of gastric neoplasms and early gastric cancer (EGC) in routine oesophagogastroduodenoscopy (EGD). METHODS: Study design: Prospective, single-blind, parallel-group, multi-centre randomised controlled trial. SETTINGS: The computer-aided system ENDOANGEL-GC was used to monitor blind spots, detect gastric abnormalities, and identify gastric neoplasms during EGD. PARTICIPANTS: Adults who underwent screening, diagnosis, or surveillance EGD. Randomisation groups: 1. Experiment group, EGD examinations with the assistance of the ENDOANGEL-GC; 2. Control group, EGD examinations without the assistance of the ENDOANGEL-GC. RANDOMISATION: Block randomisation, stratified by centre. PRIMARY OUTCOMES: Detection rates of gastric neoplasms and EGC. SECONDARY OUTCOMES: Detection rate of premalignant gastric lesions, biopsy rate, observation time, and number of blind spots on EGD. BLINDING: Outcomes are undertaken by blinded assessors. SAMPLE SIZE: Based on the previously published findings and our pilot study, the detection rate of gastric neoplasms in the control group is estimated to be 2.5%, and that of the experimental group is expected to be 4.0%. With a two-sided α level of 0.05 and power of 80%, allowing for a 10% drop-out rate, the sample size is calculated as 4858. The detection rate of EGC in the control group is estimated to be 20%, and that of the experiment group is expected to be 35%. With a two-sided α level of 0.05 and power of 80%, a total of 270 cases of gastric cancer are needed. Assuming the proportion of gastric cancer to be 1% in patients undergoing EGD and allowing for a 10% dropout rate, the sample size is calculated as 30,000. Considering the larger sample size calculated from the two primary endpoints, the required sample size is determined to be 30,000. DISCUSSION: The results of this trial will help determine the effectiveness of the ENDOANGEL-GC in clinical settings. TRIAL REGISTRATION: ChiCTR (Chinese Clinical Trial Registry), ChiCTR2100054449, registered 17 December 2021.

Explainable artificial intelligence incorporated with domain knowledge diagnosing early gastric neoplasms under white light endoscopy.

White light endoscopy is the most pivotal tool for detecting early gastric neoplasms. Previous artificial intelligence (AI) systems were primarily unexplainable, affecting their clinical credibility and acceptability. We aimed to develop an explainable AI named ENDOANGEL-ED (explainable diagnosis) to solve this problem. A total of 4482 images and 296 videos with focal lesions from 3279 patients from eight hospitals were used for training, validating, and testing ENDOANGEL-ED. A traditional sole deep learning (DL) model was trained using the same dataset. The performance of ENDOANGEL-ED and sole DL was evaluated on six levels: internal and external images, internal and external videos, consecutive videos, and man-machine comparison with 77 endoscopists in videos. Furthermore, a multi-reader, multi-case study was conducted to evaluate the ENDOANGEL-ED's effectiveness. A scale was used to compare the overall acceptance of endoscopists to traditional and explainable AI systems. The ENDOANGEL-ED showed high performance in the image and video tests. In man-machine comparison, the accuracy of ENDOANGEL-ED was significantly higher than that of all endoscopists in internal (81.10% vs. 70.61%, p < 0.001) and external videos (88.24% vs. 78.49%, p < 0.001). With ENDOANGEL-ED's assistance, the accuracy of endoscopists significantly improved (70.61% vs. 79.63%, p < 0.001). Compared with the traditional AI, the explainable AI increased the endoscopists' trust and acceptance (4.42 vs. 3.74, p < 0.001; 4.52 vs. 4.00, p < 0.001). In conclusion, we developed a real-time explainable AI that showed high performance, higher clinical credibility, and acceptance than traditional DL models and greatly improved the diagnostic ability of endoscopists.

D-dimer for assessment of treatment response, and survival to drug-eluting beads transarterial chemoembolization in hepatocellular carcinoma.

BACKGROUND: D-dimer exhibits a certain prognostic value in hepatocellular carcinoma (HCC) patients who underwent hepatectomy and microwave ablation, while its value in estimating the clinical benefit of drug-eluting beads transarterial chemoembolization (DEB-TACE) remains unclear. Hence, this study aimed to investigate the correlation of D-dimer with tumor features, response and survival to DEB-TACE in HCC patients. METHODS: Fifty-one HCC patients treated with DEB-TACE were recruited. Their serum samples at baseline and after DEB-TACE were collected and proposed for D-dimer detection by the immunoturbidimetry method. RESULTS: Elevated D-dimer levels were related to a higher Child‒Pugh stage (P = 0.013), tumor nodule number (P = 0.031), largest tumor size (P = 0.004), and portal vein invasion (P = 0.050) in HCC patients. Then, patients were classified by the median value of D-dimer, and it was observed that patients with D-dimer >0.7 mg/L achieved a lower complete response rate (12.0% vs. 46.2%, P = 0.007) but a similar objective response rate (84.0% vs. 84.6%, P = 1.000) compared to those with D-dimer ≤0.7 mg/L. The Kaplan‒Meier curve showed that D-dimer >0.7 mg/L (vs. ≤0.7 mg/L) was related to shorter overall survival (OS) (P = 0.013). Further univariate Cox regression analyses showed that D-dimer >0.7 mg/L (vs. ≤0.7 mg/L) was related to unfavorable OS [hazard ratio (HR): 5.524, 95% confidence interval (CI): 1.209-25.229, P = 0.027], but it failed to independently estimate OS (HR: 10.303, 95%CI: 0.640-165.831, P = 0.100) in multivariate Cox regression analyses. Moreover, D-dimer was elevated during DEB-TACE therapy (P<0.001). CONCLUSION: D-dimer may be helpful for monitoring prognosis to DEB-TACE therapy in HCC, while further large-scale-study validation is warranted.

A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video).

BACKGROUND: White light (WL) and weak-magnifying (WM) endoscopy are both important methods for diagnosing gastric neoplasms. This study constructed a deep-learning system named ENDOANGEL-MM (multi-modal) aimed at real-time diagnosing gastric neoplasms using WL and WM data. METHODS: WL and WM images of a same lesion were combined into image-pairs. A total of 4201 images, 7436 image-pairs, and 162 videos were used for model construction and validation. Models 1-5 including two single-modal models (WL, WM) and three multi-modal models (data fusion on task-level, feature-level, and input-level) were constructed. The models were tested on three levels including images, videos, and prospective patients. The best model was selected for constructing ENDOANGEL-MM. We compared the performance between the models and endoscopists and conducted a diagnostic study to explore the ENDOANGEL-MM's assistance ability. RESULTS: Model 4 (ENDOANGEL-MM) showed the best performance among five models. Model 2 performed better in single-modal models. The accuracy of ENDOANGEL-MM was higher than that of Model 2 in still images, real-time videos, and prospective patients. (86.54 vs 78.85%, P = 0.134; 90.00 vs 85.00%, P = 0.179; 93.55 vs 70.97%, P < 0.001). Model 2 and ENDOANGEL-MM outperformed endoscopists on WM data (85.00 vs 71.67%, P = 0.002) and multi-modal data (90.00 vs 76.17%, P = 0.002), significantly. With the assistance of ENDOANGEL-MM, the accuracy of non-experts improved significantly (85.75 vs 70.75%, P = 0.020), and performed no significant difference from experts (85.75 vs 89.00%, P = 0.159). CONCLUSIONS: The multi-modal model constructed by feature-level fusion showed the best performance. ENDOANGEL-MM identified gastric neoplasms with good accuracy and has a potential role in real-clinic.

Safety and efficacy of percutaneous microwave ablation using combined computed tomography and ultrasound-guided imaging in patients with hepatocellular carcinoma: A retrospective study.

Background: We retrospectively evaluated the safety and efficacy of percutaneous microwave ablation (MWA) using combined computed tomography (CT) and ultrasound (US)-guided imaging in patients with Barcelona Clinic Liver Cancer (BCLC)-A1-3 hepatocellular carcinoma (HCC). Methods: We included 88 consecutive patients with single HCC who were treated with transcatheter arterial chemoembolization (TACE) using our database. The patients were divided into three groups. The combination group received MWA under the guidance of nonenhanced CT and US, CT group received MWA under the guidance of nonenhanced CT alone and US group received MWA under the guidance of US alone. The study endpoints included the treatment time, number of puncture, local recurrence rate, and adverse events. Results: The median treatment time and mean puncture number were 38.6 (30-45) min, 1.2 (1-2) times (combination group); 45.8 (35-56) min, 4.2 (3-7) times (CT group); and 36.7 (30-47) min, 1.1 (1-2) times (US group), respectively. The median puncture number was significantly less than in the CT group. The local recurrence rate in the combination group was significantly inferior to that in the US group. There was a statistically significant difference between the combination group and CT group in Grade C complication rate. Conclusions: Combining CT-and US-guide MWA in patients with BCLC-A1-3 HCC appeared to be much better than the use of guidance of CT or US alone.

Development and validation of a feature extraction-based logical anthropomorphic diagnostic system for early gastric cancer: A case-control study.

Background: Prompt diagnosis of early gastric cancer (EGC) is crucial for improving patient survival. However, most previous computer-aided-diagnosis (CAD) systems did not concretize or explain diagnostic theories. We aimed to develop a logical anthropomorphic artificial intelligence (AI) diagnostic system named ENDOANGEL-LA (logical anthropomorphic) for EGCs under magnifying image enhanced endoscopy (M-IEE). Methods: We retrospectively collected data for 692 patients and 1897 images from Renmin Hospital of Wuhan University, Wuhan, China between Nov 15, 2016 and May 7, 2019. The images were randomly assigned to the training set and test set by patient with a ratio of about 4:1. ENDOANGEL-LA was developed based on feature extraction combining quantitative analysis, deep learning (DL), and machine learning (ML). 11 diagnostic feature indexes were integrated into seven ML models, and an optimal model was selected. The performance of ENDOANGEL-LA was evaluated and compared with endoscopists and sole DL models. The satisfaction of endoscopists on ENDOANGEL-LA and sole DL model was also compared. Findings: Random forest showed the best performance, and demarcation line and microstructures density were the most important feature indexes. The accuracy of ENDOANGEL-LA in images (88.76%) was significantly higher than that of sole DL model (82.77%, p = 0.034) and the novices (71.63%, p<0.001), and comparable to that of the experts (88.95%). The accuracy of ENDOANGEL-LA in videos (87.00%) was significantly higher than that of the sole DL model (68.00%, p<0.001), and comparable to that of the endoscopists (89.00%). The accuracy (87.45%, p<0.001) of novices with the assistance of ENDOANGEL-LA was significantly improved. The satisfaction of endoscopists on ENDOANGEL-LA was significantly higher than that of sole DL model. Interpretation: We established a logical anthropomorphic system (ENDOANGEL-LA) that can diagnose EGC under M-IEE with diagnostic theory concretization, high accuracy, and good explainability. It has the potential to increase interactivity between endoscopists and CADs, and improve trust and acceptability of endoscopists for CADs. Funding: This work was partly supported by a grant from the Hubei Province Major Science and Technology Innovation Project (2018-916-000-008) and the Fundamental Research Funds for the Central Universities (2042021kf0084).

A deep learning-based system for real-time image reporting during esophagogastroduodenoscopy: a multicenter study.

BACKGROUND AND STUDY AIMS: Endoscopic reports are essential for the diagnosis and follow-up of gastrointestinal diseases. This study aimed to construct an intelligent system for automatic photo documentation during esophagogastroduodenoscopy (EGD) and test its utility in clinical practice. PATIENTS AND METHODS: Seven convolutional neural networks trained and tested using 210,198 images were integrated to construct the endoscopic automatic image reporting system (EAIRS). We tested its performance through man-machine comparison at three levels: internal, external, and prospective test. Between May 2021 and June 2021, patients undergoing EGD at Renmin Hospital of Wuhan University were recruited. The primary outcomes were accuracy for capturing anatomical landmarks, completeness for capturing anatomical landmarks, and detected lesions. RESULTS: The EAIRS outperformed endoscopists in retrospective internal and external test. A total of 161 consecutive patients were enrolled in the prospective test. The EAIRS achieved an accuracy of 95.2% in capturing anatomical landmarks in the prospective test. It also achieved higher completeness on capturing anatomical landmarks compared with endoscopists: (93.1% vs. 88.8%), and was comparable to endoscopists on capturing detected lesions: (99.0% vs. 98.0%). CONCLUSIONS: The EAIRS can generate qualified image reports and could be a powerful tool for generating endoscopic reports in clinical practice.

Treatment efficacy and safety of drug-eluting beads transarterial chemoembolization versus conventional transarterial chemoembolization in hepatocellular carcinoma patients with arterioportal fistula.

This study aimed to compare the treatment efficacy and tolerance between drug-eluting beads transarterial chemoembolization (DEB-TACE) and conventional transarterial chemoembolization (cTACE) in hepatocellular carcinoma (HCC) patients with arterioportal fistula (APF). A total of 44 HCC patients with APF scheduled for DEB-TACE (N = 24, as DEB-TACE group) or cTACE (N = 20, as cTACE group) were recruited. Treatment response, hepatic function, and adverse events were assessed or recorded. Besides, progression-free survival (PFS) and overall survival (OS) were calculated. Total treatment response was better in the DEB-TACE group compared with the cTACE group (P = .012). Meanwhile, the objective response rate (87.5% versus 60.0%) was higher (P = .013), while the disease control rate (95.8% versus 85.0%) was similar in the DEB-TACE group compared to the cTACE group (P = .213). Besides, PFS (mean value: 12.2 (95%CI: 9.9-14.6) months versus 7.8 (95%CI: 5.6-10.0) months) (P = .037), but not OS (mean value: 20.0 (95%CI: 18.1-21.9) months versus. 18.6 (95%CI: 15.4-21.8) months) (P = .341) was prolonged in DEB-TACE group compared with cTACE group. Regarding the safety, Child-Pugh stage, albumin level, and bilirubin level after treatment were all similar between the DEB-TACE group and cTACE group (all P > .05); moreover, no difference was found in the occurrence of adverse events during or after treatment between the two groups (all P > .05). Moreover, subsequent analyses found that embolic materials for APF (microspheres) in the DEB-TACE group did not affect the treatment efficacy (all P > .05). DEB-TACE promotes treatment response and PFS compared with cTACE and shows good safety in HCC patients with APF.

FAM83H-AS1/miR-485-5p/MEF2D axis facilitates proliferation, migration and invasion of hepatocellular carcinoma cells.

BACKGROUND: Abundant evidence has manifested that long noncoding RNAs (lncRNAs) are closely implicated in human cancers, including hepatocellular carcinoma (HCC). Remarkably, lncRNA FAM83H antisense RNA 1 (FAM83H-AS1) has been reported to be a tumor-propeller in multiple cancers. However, its effect on HCC progression remains unknown. METHODS: FAM83H-AS1 expression was analyzed by RT-qPCR. Colony formation, EdU, and flow cytometry as well as transwell assays were implemented to analyze the biological functions of FAM83H-AS1 on HCC progression. Luciferase reporter, RIP and RNA pull-down assays were implemented to detect the interaction among FAM83H-AS1, microRNA-485-5p (miR-485-5p), and myocyte enhancer factor 2D (MEF2D) in HCC cells. RESULTS: FAM83H-AS1 expression in HCC cells was markedly elevated. FAM83H-AS1 accelerated cell proliferation, migration and invasion whereas inhibiting cell apoptosis in HCC. Besides, we confirmed that FAM83H-AS1 acts as a miR-485-5p sponge in HCC cells. Additionally, MEF2D was verified to be a direct target of miR-485-5p. FAM83H-AS1 could upregulate MEF2D expression via sponging miR-485-5p. Further, rescue experiments testified that MEF2D upregulation or miR-485-5p downregulation offset the repressive effect of FAM83H-AS1 depletion on HCC cell progression. CONCLUSIONS: FAM83H-AS1 facilitates HCC malignant progression via targeting miR-485-5p/MEF2D axis, suggesting that FAM83H-AS1 may be a promising biomarker for HCC treatment in the future.

Effect of microwave ablation on platelet and coagulation function in patients with BCLC-A hepatocellular carcinoma.

AIM: To preliminarily evaluate the effect of microwave ablation (MWA) alone on platelet (PLT) and coagulation function in patients with BCLC-A hepatocellular carcinoma (B-A-HCC) using a retrospective method. MATERIALS AND METHODS: A total of 36 patients with 48 B-A-HCCs were radically treated with MWA alone under the guidance of ultrasound between April and October 2018. PLT coagulation indexes were measured before and after MWA at 1 day, 3 days, 1 week, and 2 weeks, and blood samples (after morning fasting) were collected from cubital veins. Coagulation indexes included prothrombin time (PT), prothrombin activity (PTA), thrombin time (TT), Activated Partial Thromboplastin Time (APTT), international standardized ratio (INR), plasma fibrinogen (FIB), plasma antithrombin III (AT-III), and D dimer (DD). Overall survival (OS), recurrence-free survival (RFS), local tumor progression (LTP), and adverse reactions were also recorded. RESULTS: All patients were radically treated with MWA alone. The median size of the lesion was 2.6 (1.5-7.0) cm3. On the first day after MWA, the level of PLT decreased significantly compared with the values before MWA and gradually returned to preoperative levels one week after MWA. One day after MWA, the levels of PT, INR, and AT-III increased markedly and the level of PTA decreased significantly, all of them gradually returned to baseline after 3 days to a week of time. 1, 3, and 7 days after MWA, the levels of FIB, and DD increased significantly, and the level of TT decreased significantly; all of them gradually returned to baseline at 2 weeks. At 6 months posttreatment, the OS and RFS rates were 100% and 91.7%, the LTP rates was 5.6%, no significant adverse reactions. CONCLUSION: PLT and coagulation indexes were abnormal in patients with B-A-HCC who were radically treated with MWA alone after treatment; without specific treatment, they all gradually returned to baseline within a week or two.

Multi-step validation of a deep learning-based system for the quantification of bowel preparation: a prospective, observational study.

BACKGROUND: Inadequate bowel preparation is associated with a decrease in adenoma detection rate (ADR). A deep learning-based bowel preparation assessment system based on the Boston bowel preparation scale (BBPS) has been previously established to calculate the automatic BBPS (e-BBPS) score (ranging 0-20). The aims of this study were to investigate whether there was a statistically inverse relationship between the e-BBPS score and the ADR, and to determine the threshold of e-BBPS score for adequate bowel preparation in colonoscopy screening. METHODS: In this prospective, observational study, we trained and internally validated the e-BBPS system using retrospective colonoscopy images and videos from the Endoscopy Center of Wuhan University, annotated by endoscopists. We externally validated the system using colonoscopy images and videos from the First People's Hospital of Yichang and the Third Hospital of Wuhan. To prospectively validate the system, we recruited consecutive patients at Renmin Hospital of Wuhan University aged between 18 and 75 years undergoing colonoscopy. The exclusion criteria included: contraindication to colonoscopy, family polyposis syndrome, inflammatory bowel disease, history of surgery for colorectal or colorectal cancer, known or suspected bowel obstruction or perforation, patients who were pregnant or lactating, inability to receive caecal intubation, and lumen obstruction. We did colonoscopy procedures and collected withdrawal videos, which were reviewed and the e-BBPS system was applied to all colon segments. The primary outcome of this study was ADR, defined as the proportion of patients with one or more conventional adenomas detected during colonoscopy. We calculated the ADR of each e-BBPS score and did a correlation analysis using Spearman analysis. FINDINGS: From May 11 to Aug 10, 2020, 616 patients underwent screening colonoscopies, which evaluated. There was a significant inverse correlation between the e-BBPS score and ADR (Spearman's rank -0·976, p<0·010). The ADR for the e-BBPS scores 1-8 was 28·57%, 28·68%, 26·79%, 19·19%, 17·57%, 17·07%, 14·81%, and 0%, respectively. According to the 25% ADR standard for screening colonoscopy, an e-BBPS score of 3 was set as a threshold to guarantee an ADR of more than 25%, and so high-quality endoscopy. Patients with scores of more than 3 had a significantly lower ADR than those with a score of 3 or less (ADR 15·93% vs 28·03%, p<0·001, 95% CI 0·28-0·66, odds ratio 0·43). INTERPRETATION: The e-BBPS system has potential to provide a more objective and refined threshold for the quantification of adequate bowel preparation. FUNDING: Project of Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision and Hubei Province Major Science and Technology Innovation Project.

Drug-eluting beads-transarterial chemoembolization plus microwave ablation is an effective and safe treatment strategy in treating hepatocellular carcinoma adjacent to gallbladder.

The present study aimed to compare the efficacy and safety of drug-eluting beads-transarterial chemoembolization (DEB-TACE) plus microwave ablation (MWA) versus (vs.) surgery in treating patients with hepatocellular carcinoma (HCC) adjacent to gallbladder. Totally 54 patients with HCC adjacent to gallbladder were included and divided into two groups: DEB-TACE plus MWA group (n = 24) and surgery group (n = 30). Treatment response, relapse-free survival (RFS), progression-free survival (PFS), overall survival (OS) and adverse events were assessed and documented. For DEB-TACE plus MWA group, complete response rate, objective response rate and disease control rate were 79.2%, 95.8% and 100.0% after one-month post treatment, respectively. In terms of survival profiles, DEB-TACE plus MWA group presented similar RFS (28.2 (95% CI: 12.5-43.9) months vs. 26.6 (95% CI: 19.2-34.1) months) (P = 0.930), PFS (21.2 (95% CI: 1.6-40.8) months vs. 26.6 (95% CI: 19.2-34.1) months) (P = 0.541), and OS (41.4 (95% CI: 35.0-47.9) months vs. 59.7 (95% CI: 51.7-67.7) months) (P = 0.138) compared with surgery group, and further multivariate Cox's regression analysis validated that, after adjustment of confounding factors, DEB-TACE plus MWA group exhibited no difference of RPS, PFS or OS compared with surgery group. Regarding safety, the intraoperative adverse event incidence was higher in DEB-TACE plus MWA group compared with surgery group (P = 0.008), while two groups exhibited no difference of postoperative adverse event incidence (P = 0.618). In conclusion, DEB-TACE plus MWA presents to be an optional treatment strategy in patients with HCC adjacent to gallbladder.