Validation of artificial intelligence-based bowel preparation assessment in screening colonoscopy (with video).

BACKGROUND AND AIMS: Accurate bowel preparation assessment is essential for determining colonoscopy screening intervals. Patients with suboptimal bowel preparation are at a high risk of missing >5 mm adenomas and should undergo an early repeat colonoscopy. In this study, we used artificial intelligence (AI) to evaluate bowel preparation and validated the ability of the system to accurately identify patients who are at high risk of having >5 mm adenomas missed due to inadequate bowel preparation. METHODS: This prospective, single-center, observational study was conducted at the Eighth Affiliated Hospital, Sun Yat-sen University, from October 8, 2021, to November 9, 2022. Eligible patients who underwent screening colonoscopy were consecutively enrolled. The AI assessed bowel preparation using the e-Boston Bowel Preparation Scale (e-BBPS) while endoscopists made evaluations using BBPS. If both BBPS and e-BBPS deemed preparation adequate, the patient immediately underwent a second colonoscopy; otherwise, the patient underwent bowel re-cleansing before the second colonoscopy. RESULTS: Among the 393 patients, 72 adenomas >5 mm in size were detected; 27 adenomas >5 mm in size were missed. In unqualified-AI patients, the >5 mm adenoma miss rate (AMR) was significantly higher than in qualified-AI patients (35.71% vs 13.19% [P = .0056]; odds ratio [OR], .2734 [95% CI, .1139-.6565]), as were the AMR (50.89% vs 20.79% [P < .001]; OR, .2532 [95% CI, .1583-.4052]) and >5 mm polyp miss rate (35.82% vs 19.48% [P = .0152]; OR, .4335 [95% CI, .2288-.8213]). CONCLUSIONS: This study confirmed that patients classified as inadequate by AI exhibited an unacceptable >5 mm AMR, providing key evidence for implementing AI in guiding bowel re-cleansing and potentially standardizing future colonoscopy screening. (Clinical trial registration number: NCT05145712.).

Current status of quality control in screening esophagogastroduodenoscopy and the emerging role of artificial intelligence.

Esophagogastroduodenoscopy (EGD) screening is being implemented in countries with a high incidence of upper gastrointestinal (UGI) cancer. High-quality EGD screening ensures the yield of early diagnosis and prevents suffering from advanced UGI cancer and minimal operational-related discomfort. However, performance varied dramatically among endoscopists, and quality control for EGD screening remains suboptimal. Guidelines have recommended potential measures for endoscopy quality improvement and research has been conducted for evidence. Moreover, artificial intelligence offers a promising solution for computer-aided diagnosis and quality control during EGD examinations. In this review, we summarized the key points for quality assurance in EGD screening based on current guidelines and evidence. We also outline the latest evidence, limitations, and future prospects of the emerging role of artificial intelligence in EGD quality control, aiming to provide a foundation for improving the quality of EGD screening.

Effect of artificial intelligence on novice-performed colonoscopy: a multicenter randomized controlled tandem study.

BACKGROUND AND AIMS: The efficacy and safety of colonoscopy performed by artificial intelligence (AI)-assisted novices remain unknown. The aim of this study was to compare the lesion detection capability of novices, AI-assisted novices, and experts. METHODS: This multicenter, randomized, noninferiority tandem study was conducted across 3 hospitals in China from May 1, 2022, to November 11, 2022. Eligible patients were randomized into 1 of 3 groups: the CN group (control novice group, withdrawal performed by a novice independently), the AN group (AI-assisted novice group, withdrawal performed by a novice with AI assistance), or the CE group (control expert group, withdrawal performed by an expert independently). Participants underwent a repeat colonoscopy conducted by an AI-assisted expert to evaluate the lesion miss rate and ensure lesion detection. The primary outcome was the adenoma miss rate (AMR). RESULTS: A total of 685 eligible patients were analyzed: 229 in the CN group, 227 in the AN group, and 229 in the CE group. Both AMR and polyp miss rate were lower in the AN group than in the CN group (18.82% vs 43.69% [P < .001] and 21.23% vs 35.38% [P < .001], respectively). The noninferiority margin was met between the AN and CE groups of both AMR and polyp miss rate (18.82% vs 26.97% [P = .202] and 21.23% vs 24.10% [P < .249]). CONCLUSIONS: AI-assisted colonoscopy lowered the AMR of novices, making them noninferior to experts. The withdrawal technique of new endoscopists can be enhanced by AI-assisted colonoscopy. (Clinical trial registration number: NCT05323279.).

Human-Like Artificial Intelligent System for Predicting Invasion Depth of Esophageal Squamous Cell Carcinoma Using Magnifying Narrow-Band Imaging Endoscopy: A Retrospective Multicenter Study.

INTRODUCTION: Endoscopic evaluation is crucial for predicting the invasion depth of esophagus squamous cell carcinoma (ESCC) and selecting appropriate treatment strategies. Our study aimed to develop and validate an interpretable artificial intelligence-based invasion depth prediction system (AI-IDPS) for ESCC. METHODS: We reviewed the PubMed for eligible studies and collected potential visual feature indices associated with invasion depth. Multicenter data comprising 5,119 narrow-band imaging magnifying endoscopy images from 581 patients with ESCC were collected from 4 hospitals between April 2016 and November 2021. Thirteen models for feature extraction and 1 model for feature fitting were developed for AI-IDPS. The efficiency of AI-IDPS was evaluated on 196 images and 33 consecutively collected videos and compared with a pure deep learning model and performance of endoscopists. A crossover study and a questionnaire survey were conducted to investigate the system's impact on endoscopists' understanding of the AI predictions. RESULTS: AI-IDPS demonstrated the sensitivity, specificity, and accuracy of 85.7%, 86.3%, and 86.2% in image validation and 87.5%, 84%, and 84.9% in consecutively collected videos, respectively, for differentiating SM2-3 lesions. The pure deep learning model showed significantly lower sensitivity, specificity, and accuracy (83.7%, 52.1% and 60.0%, respectively). The endoscopists had significantly improved accuracy (from 79.7% to 84.9% on average, P = 0.03) and comparable sensitivity (from 37.5% to 55.4% on average, P = 0.27) and specificity (from 93.1% to 94.3% on average, P = 0.75) after AI-IDPS assistance. DISCUSSION: Based on domain knowledge, we developed an interpretable system for predicting ESCC invasion depth. The anthropopathic approach demonstrates the potential to outperform deep learning architecture in practice.

Development and Validation of a Deep Learning-Based Histologic Diagnosis System for Diagnosing Colorectal Sessile Serrated Lesions.

OBJECTIVES: The histopathologic diagnosis of colorectal sessile serrated lesions (SSLs) and hyperplastic polyps (HPs) is of low consistency among pathologists. This study aimed to develop and validate a deep learning (DL)-based logical anthropomorphic pathology diagnostic system (LA-SSLD) for the differential diagnosis of colorectal SSL and HP. METHODS: The diagnosis framework of the LA-SSLD system was constructed according to the current guidelines and consisted of 4 DL models. Deep convolutional neural network (DCNN) 1 was the mucosal layer segmentation model, DCNN 2 was the muscularis mucosa segmentation model, DCNN 3 was the glandular lumen segmentation model, and DCNN 4 was the glandular lumen classification (aberrant or regular) model. A total of 175 HP and 127 SSL sections were collected from Renmin Hospital of Wuhan University during November 2016 to November 2022. The performance of the LA-SSLD system was compared to 11 pathologists with different qualifications through the human-machine contest. RESULTS: The Dice scores of DCNNs 1, 2, and 3 were 93.66%, 58.38%, and 74.04%, respectively. The accuracy of DCNN 4 was 92.72%. In the human-machine contest, the accuracy, sensitivity, and specificity of the LA-SSLD system were 85.71%, 86.36%, and 85.00%, respectively. In comparison with experts (pathologist D: accuracy 83.33%, sensitivity 90.91%, specificity 75.00%; pathologist E: accuracy 85.71%, sensitivity 90.91%, specificity 80.00%), LA-SSLD achieved expert-level accuracy and outperformed all the senior and junior pathologists. CONCLUSIONS: This study proposed a logical anthropomorphic diagnostic system for the differential diagnosis of colorectal SSL and HP. The diagnostic performance of the system is comparable to that of experts and has the potential to become a powerful diagnostic tool for SSL in the future. It is worth mentioning that a logical anthropomorphic system can achieve expert-level accuracy with fewer samples, providing potential ideas for the development of other artificial intelligence models.

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.

Effect of a deep learning-based automatic upper GI endoscopic reporting system: a randomized crossover study (with video).

BACKGROUND AND AIMS: EGD is essential for GI disorders, and reports are pivotal to facilitating postprocedure diagnosis and treatment. Manual report generation lacks sufficient quality and is labor intensive. We reported and validated an artificial intelligence-based endoscopy automatic reporting system (AI-EARS). METHODS: The AI-EARS was designed for automatic report generation, including real-time image capturing, diagnosis, and textual description. It was developed using multicenter datasets from 8 hospitals in China, including 252,111 images for training, 62,706 images, and 950 videos for testing. Twelve endoscopists and 44 endoscopy procedures were consecutively enrolled to evaluate the effect of the AI-EARS in a multireader, multicase, crossover study. The precision and completeness of the reports were compared between endoscopists using the AI-EARS and conventional reporting systems. RESULTS: In video validation, the AI-EARS achieved completeness of 98.59% and 99.69% for esophageal and gastric abnormality records, respectively, accuracies of 87.99% and 88.85% for esophageal and gastric lesion location records, and 73.14% and 85.24% for diagnosis. Compared with the conventional reporting systems, the AI-EARS achieved greater completeness (79.03% vs 51.86%, P < .001) and accuracy (64.47% vs 42.81%, P < .001) of the textual description and completeness of the photo-documents of landmarks (92.23% vs 73.69%, P < .001). The mean reporting time for an individual lesion was significantly reduced (80.13 ± 16.12 seconds vs 46.47 ± 11.68 seconds, P < .001) after the AI-EARS assistance. CONCLUSIONS: The AI-EARS showed its efficacy in improving the accuracy and completeness of EGD reports. It might facilitate the generation of complete endoscopy reports and postendoscopy patient management. (Clinical trial registration number: NCT05479253.).

Complementary Effect of the Proportion of Overspeed Frames of Withdrawal and Withdrawal Time on Reflecting Colonoscopy Quality: A Retrospective, Observational Study.

INTRODUCTION: Constructing quality indicators that reflect the defect of colonoscopy operation for quality audit and feedback is very important. Previously, we have established a real-time withdrawal speed monitoring system to control withdrawal speed below the safe speed. We aimed to explore the relationship between the proportion of overspeed frames (POF) of withdrawal and the adenoma detection rate (ADR) and to conjointly analyze the influence of POF and withdrawal time on ADR to evaluate the feasibility of POF combined with withdrawal time as a quality control indicator. METHODS: The POF was defined as the proportion of frames with instantaneous speed ≥44 in the whole colonoscopy video. First, we developed a system for the POF of withdrawal based on a perceptual hashing algorithm. Next, we retrospectively collected 1,804 colonoscopy videos to explore the relationship between POF and ADR. According to withdrawal time and POF cutoff, we conducted a complementary analysis on the effects of POF and withdrawal time on ADR. RESULTS: There was an inverse correlation between the POF and ADR (Pearson correlation coefficient -0.836). When withdrawal time was >6 minutes, the ADR of the POF ≤10% was significantly higher than that of POF >10% (25.30% vs 16.50%; odds ratio 0.463, 95% confidence interval 0.296-0.724, P < 0.01). When the POF was ≤10%, the ADR of withdrawal time >6 minutes was higher than that of withdrawal time ≤6 minutes (25.30% vs 21.14%; odds ratio 0.877, 95% confidence interval 0.667-1.153, P = 0.35). DISCUSSION: The POF was strongly correlated with ADR. The combined assessment of the POF and withdrawal time has profound significance for colonoscopy quality control.

Assessment of the Role of Artificial Intelligence in the Association Between Time of Day and Colonoscopy Quality.

Importance: Time of day was associated with a decline in adenoma detection during colonoscopy. Artificial intelligence (AI) systems are effective in improving the adenoma detection rate (ADR), but the performance of AI during different times of the day remains unknown. Objective: To validate whether the assistance of an AI system could overcome the time-related decline in ADR during colonoscopy. Design, Setting, and Participants: This cohort study is a secondary analysis of 2 prospective randomized controlled trials (RCT) from Renmin Hospital of Wuhan University. Consecutive patients undergoing colonoscopy were randomly assigned to either the AI-assisted group or unassisted group from June 18, 2019, to September 6, 2019, and July 1, 2020, to October 15, 2020. The ADR of early and late colonoscopy sessions per half day were compared before and after the intervention of the AI system. Data were analyzed from March to June 2022. Exposure: Conventional colonoscopy or AI-assisted colonoscopy. Main Outcomes and Measures: Adenoma detection rate. Results: A total of 1780 patients (mean [SD] age, 48.61 [13.35] years, 837 [47.02%] women) were enrolled. A total of 1041 procedures (58.48%) were performed in early sessions, with 357 randomized into the unassisted group (34.29%) and 684 into the AI group (65.71%). A total of 739 procedures (41.52%) were performed in late sessions, with 263 randomized into the unassisted group (35.59%) and 476 into the AI group (64.41%). In the unassisted group, the ADR in early sessions was significantly higher compared with that of late sessions (13.73% vs 5.70%; P = .005; OR, 2.42; 95% CI, 1.31-4.47). After the intervention of the AI system, as expected, no statistically significant difference was found (22.95% vs 22.06%, P = .78; OR, 0.96; 95% CI; 0.71-1.29). Furthermore, the AI systems showed better assistance ability on ADR in late sessions compared with early sessions (odds ratio, 3.81; 95% CI, 2.10-6.91 vs 1.60; 95% CI, 1.10-2.34). Conclusions and Relevance: In this cohort study, AI systems showed higher assistance ability in late sessions per half day, which suggests the potential to maintain high quality and homogeneity of colonoscopies and further improve endoscopist performance in large screening programs and centers with high workloads.

Age-Related Decline in Gangliosides GM1 and GD1a in Non-CNS Tissues of Normal Mice: Implications for Peripheral Symptoms of Parkinson's Disease.

The purpose of this study was to determine whether the age-related decline in a-series gangliosides (especially GM1), shown to be a factor in the brain-related etiology of Parkinson's disease (PD), also pertains to the peripheral nervous system (PNS) and aspects of PD unrelated to the central nervous system (CNS). Following Svennerholm's demonstration of the age-dependent decline in a-series gangliosides (both GM1 and GD1a) in the human brain, we previously demonstrated a similar decline in the normal mouse brain. The present study seeks to determine whether a similar a-series decline occurs in the periphery of normal mice as a possible prelude to the non-CNS symptoms of PD. We used mice of increasing age to measure a-series gangliosides in three peripheral tissues closely associated with PD pathology. Employing high-performance thin-layer chromatography (HPTLC), we found a substantial decrease in both GM1 and GD1a in all three tissues from 191 days of age. Motor and cognitive dysfunction were also shown to worsen, as expected, in synchrony with the decrease in GM1. Based on the previously demonstrated parallel between mice and humans concerning age-related a-series ganglioside decline in the brain, we propose the present findings to suggest a similar a-series decline in human peripheral tissues as the primary contributor to non-CNS pathologies of PD. An onset of sporadic PD would thus be seen as occurring simultaneously throughout the brain and body, albeit at varying rates, in association with the decline in a-series gangliosides. This would obviate the need to postulate the transfer of aggregated α-synuclein between brain and body or to debate brain vs. body as the origin of PD.

Development and validation of an artificial intelligence-based system for predicting colorectal cancer invasion depth using multi-modal data.

OBJECTIVES: Accurate endoscopic optical prediction of the depth of cancer invasion is critical for guiding an optimal treatment approach of large sessile colorectal polyps but was hindered by insufficient endoscopists expertise and inter-observer variability. We aimed to construct a clinically applicable artificial intelligence (AI) system for the identification of presence of cancer invasion in large sessile colorectal polyps. METHODS: A deep learning-based colorectal cancer invasion calculation (CCIC) system was constructed. Multi-modal data including clinical information, white light (WL) and image-enhanced endoscopy (IEE) were included for training. The system was trained using 339 lesions and tested on 198 lesions across three hospitals. Man-machine contest, reader study and video validation were further conducted to evaluate the performance of CCIC. RESULTS: The overall accuracy of CCIC system using image and video validation was 90.4% and 89.7%, respectively. In comparison with 14 endoscopists, the accuracy of CCIC was comparable with expert endoscopists but superior to all the participating senior and junior endoscopists in both image and video validation set. With CCIC augmentation, the average accuracy of junior endoscopists improved significantly from 75.4% to 85.3% (P = 0.002). CONCLUSIONS: This deep learning-based CCIC system may play an important role in predicting the depth of cancer invasion in colorectal polyps, thus determining treatment strategies for these large sessile colorectal polyps.

Systemic deficiency of GM1 ganglioside in Parkinson's disease tissues and its relation to the disease etiology.

Following our initial reports on subnormal levels of GM1 in the substantia nigra and occipital cortex of Parkinson's disease (PD) patients, we have examined additional tissues from such patients and found these are also deficient in the ganglioside. These include innervated tissues intimately involved in PD pathology such as colon, heart and others, somewhat less intimately involved, such as skin and fibroblasts. Finally, we have analyzed GM1 in peripheral blood mononuclear cells, a type of tissue apparently with no direct innervation, and found those too to be deficient in GM1. Those patients were all afflicted with the sporadic form of PD (sPD), and we therefore conclude that systemic deficiency of GM1 is a characteristic of this major type of PD. Age is one factor in GM1 decline but is not sufficient; additional GM1 suppressive factors are involved in producing sPD. We discuss these and why GM1 replacement offers promise as a disease-altering therapy.

Real-time automated diagnosis of colorectal cancer invasion depth using a deep learning model with multimodal data (with video).

BACKGROUND AND AIMS: The optical diagnosis of colorectal cancer (CRC) invasion depth with white light (WL) and image-enhanced endoscopy (IEE) remains challenging. We aimed to construct and validate a 2-modal deep learning-based system, incorporated with both WL and IEE images (named Endo-CRC) in estimating the invasion depth of CRC. METHODS: Samples were retrospectively obtained from 3 hospitals in China. We combined WL and IEE images into image pairs. Altogether, 337,278 image pairs from 268 noninvasive and superficial CRC and 181,934 image pairs from 82 deep CRC were used for training. A total of 296,644 and 4528 image pairs were used for internal and external tests and for comparison with endoscopists. Thirty-five videos were used for evaluating the real-time performance of the Endo-CRC system. Two deep learning models, solely using either WL (model W) or IEE images (model I), were constructed to compare with Endo-CRC. RESULTS: The accuracies of Endo-CRC in internal image tests with and without advanced CRC were 91.61% and 93.78%, respectively, and 88.65% in the external test, which did not include advanced CRC. In an endoscopist-machine competition, Endo-CRC achieved an expert comparable accuracy of 88.11% and the highest sensitivity compared with all endoscopists. In a video test, Endo-CRC achieved an accuracy of 100.00%. Compared with model W and model I, Endo-CRC had a higher accuracy (per image pair: 91.61% vs 88.27% compared with model I and 91.61% vs 81.32% compared with model W). CONCLUSIONS: The Endo-CRC system has great potential for assisting in CRC invasion depth diagnosis and may be well applied in clinical practice.

miR-497 defect contributes to gastric cancer tumorigenesis and progression via regulating CDC42/ITGB1/FAK/PXN/AKT signaling.

Gastric cancer (GC) is one of the leading causes of cancer-related death worldwide. MicroRNAs (miRNAs) are known to be important regulators of GC. This study aims to investigate the role of miRNA (miR)-497 in GC. We demonstrated that the expression of miR-497 was downregulated in human GC tissues. After N-methyl-N-nitrosourea treatment, the incidence of GC in miR-497 knockout mice was significantly higher than that in wild-type mice. miR-497 overexpression suppressed GC cell proliferation, cell-cycle progression, colony formation, anti-apoptosis ability, and cell migration and invasion capacity. Additionally, miR-497 overexpression decreased the expression levels of cell division cycle 42 (CDC42) and integrin ß1 (ITGB1) and inhibited the phosphorylation of focal adhesion kinase (FAK), paxillin (PXN), and serine-threonine protein kinase (AKT). Furthermore, overexpression of miR-497 inhibited the metastasis of GC cells in vivo, which could be counteracted by CDC42 restoration. Furthermore, the focal adhesion of GC cells was found to be regulated by miR-497/CDC42 axis via ITGB1/FAK/PXN/AKT signaling. Collectively, it is concluded that miR-497 plays an important role in the repression of GC tumorigenesis and progression, partly via the CDC42/ITGB1/FAK/PXN/AKT pathway.

A Gastrointestinal Endoscopy Quality Control System Incorporated With Deep Learning Improved Endoscopist Performance in a Pretest and Post-Test Trial.

INTRODUCTION: Gastrointestinal endoscopic quality is operator-dependent. To ensure the endoscopy quality, we constructed an endoscopic audit and feedback system named Endo.Adm and evaluated its effect in a form of pretest and posttest trial. METHODS: Endo.Adm system was developed using Python and Deep Convolutional Neural Ne2rk models. Sixteen endoscopists were recruited from Renmin Hospital of Wuhan University and were randomly assigned to undergo feedback of Endo.Adm or not (8 for the feedback group and 8 for the control group). The feedback group received weekly quality report cards which were automatically generated by Endo.Adm. We then compared the adenoma detection rate (ADR) and gastric precancerous conditions detection rate between baseline and postintervention phase for endoscopists in each group to evaluate the impact of Endo.Adm feedback. In total, 1,191 colonoscopies and 3,515 gastroscopies were included for analysis. RESULTS: ADR was increased after Endo.Adm feedback (10.8%-20.3%, P < 0.01,

A deep learning-based system for bile duct annotation and station recognition in linear endoscopic ultrasound.

BACKGROUND: Detailed evaluation of bile duct (BD) is main focus during endoscopic ultrasound (EUS). The aim of this study was to develop a system for EUS BD scanning augmentation. METHODS: The scanning was divided into 4 stations. We developed a station classification model and a BD segmentation model with 10681 images and 2529 images, respectively. 1704 images and 667 images were applied to classification and segmentation internal validation. For classification and segmentation video validation, 264 and 517 videos clips were used. For man-machine contest, an independent data set contained 120 images was applied. 799 images from other two hospitals were used for external validation. A crossover study was conducted to evaluate the system effect on reducing difficulty in ultrasound images interpretation. FINDINGS: For classification, the model achieved an accuracy of 93.3% in image set and 90.1% in video set. For segmentation, the model had a dice of 0.77 in image set, sensitivity of 89.48% and specificity of 82.3% in video set. For external validation, the model achieved 82.6% accuracy in classification. In man-machine contest, the models achieved 88.3% accuracy in classification and 0.72 dice in BD segmentation, which is comparable to that of expert. In the crossover study, trainees' accuracy improved from 60.8% to 76.3% (P < 0.01, 95% C.I. 20.9-27.2). INTERPRETATION: We developed a deep learning-based augmentation system for EUS BD scanning augmentation. FUNDING: Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision, Hubei Province Major Science and Technology Innovation Project, National Natural Science Foundation of China.

WNT4 secreted by tumor tissues promotes tumor progression in colorectal cancer by activation of the Wnt/ß-catenin signalling pathway.

BACKGROUND: Wingless and Int-related protein (Wnt) ligands are aberrantly expressed in patients with colorectal cancer (CRC). However, the aberrant level of Wnt ligands in serum have not been explored. Here, we aimed to identify the levels of WNT4 in serum and explored its oncogenic role in CRC. METHODS: The Oncomine database was used to analyze the relationship between WNT4 and the prognosis of CRC. ELISA was performed to measure WNT4 levels in serum and conditioned medium from fresh CRC tissues and adjacent normal tissues. Western blot and immunohistochemistry were carried out to measure the expression of WNT4 in human CRC tissues and adjacent normal tissues. The migration and invasion of CRC cells were determined by trans-well assay, and the effects of WNT4 on CRC invasion and metastasis in vivo were verified by tumor xenograft in nude mice. Cancer-associated fibroblasts (CAFs) and angiogenesis in subcutaneous nodules were detected by immunofluorescence (IF). In addition, the suspended spheres formation and tube formation assay were performed to explore the effects of WNT4 on CAFs and angiogenesis respectively. RESULTS: WNT4 was significantly upregulated in serum of CRC patients, and CRC tissues were identified as an important source of elevated WNT4 levels in CRC patients. Interestingly, elevated levels of WNT4 in serum were downregulated after tumor resection. Furthermore, we found that WNT4 contributed to epithelial-to-mesenchymal transition (EMT) and activated fibroblasts by activating the WNT4/ß-catenin pathway in vitro and in vivo. Moreover, angiogenesis was induced via the WNT4/ß-catenin/Ang2 pathway. Those effects could be reversed by ICG-001, a ß-catenin/TCF inhibitor. CONCLUSION: Our findings indicated that serum levels of WNT4 may be a potential biomarker for CRC. WNT4 secreted by colorectal cancer tissues promote the progression of CRC by inducing EMT, activate fibroblasts and promote angiogenesis through the canonical Wnt/ß-catenin signalling pathway.

Deep learning-based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video).

BACKGROUND AND AIMS: EUS is considered one of the most sensitive modalities for pancreatic cancer detection, but it is highly operator-dependent and the learning curve is steep. In this study, we constructed a system named BP MASTER (pancreaticobiliary master) for EUS training and quality control. METHODS: The standard procedure of pancreatic EUS was divided into 6 stations. We developed a station classification model and a pancreas/abdominal aorta/portal confluence segmentation model with 19,486 images and 2207 images, respectively. Then, we used 1920 images and 700 images for classification and segmentation internal validation, respectively. To test station recognition we used 396 videos clips. An independent data set containing 180 images was applied for comparing the performance between models and EUS experts. Seven hundred sixty-eight images from 2 other hospitals were used for external validation. A crossover study was conducted to test the system effect on reducing difficulty in ultrasonographics interpretation among trainees. RESULTS: The models achieved 94.2% accuracy in station classification and .836 dice in segmentation at internal validation. At external validation, the models achieved 82.4% accuracy in station classification and .715 dice in segmentation. For the video test, the station classification model achieved a per-frame accuracy of 86.2%. Compared with EUS experts, the models achieved 90.0% accuracy in classification and .77 and .813 dice in blood vessel and pancreas segmentation, which is comparable with that of experts. In the crossover study, trainee station recognition accuracy improved from 67.2% to 78.4% (95% confidence interval, .058-1.663; P < .01). CONCLUSIONS: The BP MASTER system has the potential to play an important role in shortening the pancreatic EUS learning curve and improving EUS quality control in the future.