Transanal endoscopic microsurgery versus endoscopic mucosal resection for large rectal adenomas (TREND-study)

Background: Recent non-randomized studies suggest that extended endoscopic mucosal resection (EMR) is equally effective in removing large rectal adenomas as transanal endoscopic microsurgery (TEM). If equally effective, EMR might be a more cost-effective approach as this strategy does not require expensive equipment, general anesthesia and hospital admission. Furthermore, EMR appears to be associated with fewer complications. The aim of this study is to compare the cost-effectiveness and cost-utility of TEM and EMR for the resection of large rectal adenomas. Methods/design. Multicenter randomized trial among 15 hospitals in the Netherlands. Patients with a rectal adenoma 3 cm, located between 115 cm ab ano, will be randomized to a TEM- or EMR-treatment strategy. For TEM, patients will be treated under general anesthesia, adenomas will be dissected en-bloc by a full-thickness excision, and patients will be admitted to the hospital. For EMR, no or conscious sedation is used, lesions will be resected through the submucosal plane i

Competence Measurement During Colonoscopy Training: The Use of Self-Assessment of Performance Measures

OBJECTIVES: We evaluated a new assessment technique for colonoscopy training. METHODS: We prospectively evaluated colonoscopy skills during training using the Rotterdam Assessment Form for colonoscopy. The questionnaire covers cecal intubation, procedural time, and subjective grading of performance. Individual learning curves are compared with a group reference. RESULTS: Nineteen trainees self-assessed 2,887 colonoscopies. The cecal intubation rate improved from 65% at baseline to 78% and 85% after 100 and 200 colonoscopies, respectively. In our training program the 90% threshold was reached after 280 colonoscopies on average. Cecal intubation time improved from 13:10 minutes at baseline to 9:30 and 8:30 after 100 and 200 colonoscopies, respectively. CONCLUSIONS: This novel self-assessment form allows individual learning curves to be compared with a group reference, provides data on the development of dexterity skills and individual training targets, and stimulates trainees to identify steps for self-improvement

A second-generation virtual reality simulator for colonoscopy: validation and initial experience

Background and study aims: Simulators are increasingly used in skills training for physicians; however data on systematic evaluation of the performance of these simulators are scarce compared with those used in aviation. The objectives of this study were to determine the expert validity, the construct validity, and the training value of the novel Olympus simulator as judged by experts. Patients and methods: Participants were novices and experts. Novices had no prior experience in flexible endoscopy; experts had all performed more than 1000 colonoscopies. Participants filled out a questionnaire on their impression of the realism of the colonoscopy exercises performed. These included a dexterity exercise and a virtual colonoscopy. Test parameters used were points acquired in a game, time to reach the cecum, maximum insertion force, and "patient pain." Results: Novices (n = 26) scored a median of 973 points (range -118-1393), experts (n = 23) scored 1212 points (range 89- 1375). This difference did not reach significance (P = 0.073). Experts performed virtual colonoscopy significantly faster than novices (220 vs. 780 s, P < 0.001) but used more insertion force (11.8 vs. 11.6 N: P = 0.147). Maximum pain score was higher in the expert group: 86% vs. 73%. (P = 0.018). The realism was graded 6.5 on a 10-point scale. Experts considered the Olympus simulator beneficial for the training of novice endoscopists. Conclusions: The novel Olympus simulator discriminates excellently between the measured levels of expertise. The prototype offers a good realistic representation of colonoscopy according to experts. Although the software development is continuing, the device can already be implemented in the training program of novice endoscopists

Simulated colonoscopy training leads to improved performance during patient-based assessment

Background: Virtual reality (VR) endoscopy simulators are increasingly being used in the training of novice endoscopists. There are, however, insufficient data regarding the effect of simulator training on the early learning curve of novice endoscopists. Objective: The aim of this study was to assess the clinical performance of novice endoscopists during colonoscopy after intensive and prolonged training on a VR endoscopy simulator. Design: Prospective study. Setting: Single university medical center. Patients: Patient-based assessment (PBA) of performance was carried out on patients routinely scheduled for colonoscopy. Interventions: Eighteen trainees without any endoscopic experience were included in the study. They were divided into 2 groups. The simulator-training program consisted of either 50 (group I) or 100 (group II) VR colonoscopies. After 10, 30, and 50 (group I) and after 20, 60, and 100 (group II) VR colonoscopies, trainees underwent both simulator-based assessment and PBA. Main Outcome Measurements: Cecal intubation time, colonic insertion depth, and cecal intubation rate. Results: Eighteen novices participated in the study. All completed VR training and assessments. The mean cecal intubation time on the SBA decreased from a baseline of 9.50 minutes to 2.20 minutes at completion of the training (P = .002). Colonic insertion depth during PBA improved from 29.4 cm to 63.7 cm (P < .001). The learning effect of simulator training ceased after 60 colonoscopies. Limitations: Single-center study, no formal sample size calculation. Conclusions: VR training by using a colonoscopy simulator leads to a significant improvement in performance with the simulator itself and, more importantly, to significantly improved performances during patient-based colonoscopy. This study demonstrates the rationale for intensive simulator training in the early learning curve of novices performing colonoscopy

A learning system for cancer detection

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Evaluation and comparison of textural feature representation for the detection of early stage cancer in endoscopy

Esophageal cancer is the fastest rising type of cancer in the Western world. The novel technology of High Definition (HD) endoscopy enables physicians to find texture patterns related to early cancer. It encourages the development of a Computer-Aided Decision (CAD) system in order to help physicians with faster identification of early cancer and decrease the miss rate. However, an appropriate texture feature extraction, which is needed for classification, has not been studied yet. In this paper, we compare several techniques for texture feature extraction, including co-occurrence matrix features, LBP and Gabor features and evaluate their performance in detecting early stage cancer in HD endoscopic images. In order to exploit more image characteristics, we introduce an efficient combination of the texture and color features. Furthermore, we add a specific preprocessing step designed for endoscopy images, which improves the classification accuracy. After reducing the feature dimensionality using Principal Component Analysis (PCA), we classify selected features with a Support Vector Machine (SVM). The experimental results validated by an expert gastroenterologist show that the proposed feature extraction is promising and reaches a classification accuracy up to 96.48%

Multi-modal classification of polyp malignancy using CNN features with balanced class augmentation

Colorectal polyps are an indicator of colorectal cancer (CRC). Classification of polyps during colonoscopy is still a challenge for which many medical experts have come up with visual models albeit with limited success. In this paper, a classification approach is proposed to differentiate between polyp malignancy, using features extracted from the Global Average Pooling (GAP) layer of a Convolutional Neural Network (CNNs). Two recent endoscopic modalities are used to improve the algorithm prediction: Blue Laser Imaging (BLI) and Linked Color Imaging (LCI). Furthermore, a new strategy of per-class data augmentation is adopted to tackle an unbalanced class distribution and to improve the decision of the classifiers. As a result, we increase the performance compared to state-of-the-art methods (0.97 vs 0.90 AUC). Our method for automatic polyp malignancy classification facilitates future advances towards patient safety and may avoid time-consuming and costly histopathological assessment