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Completed

NCT Number: NCT03234725

Analysis of New Endoscopic Features and Variable Stiffness in Colonoscopy: Prospective Randomised Trial

The aim of the present study is to develop and evaluate a computer-based methods for automated and improved detection and classification of different colorectal lesions, especially polyps. For this purpose first, pit pattern and vascularization features of up to 1000 polyps with a size of 10 mm or smaller will be detected and stored in our web based picture database made by a zoom BLI colonoscopy. These polyps are going to be imaged and subsequently removed for histological analysis. The polyp images are analyzed by a newly developed deep learning computer algorithm. The results of the deep learning automatic classification (sensitivity, specificity, negative predictive value, positive predictive value and accuracy) are compared to those of human observers, who were blinded to the histological gold standard.

In a second approach we are planning to use LCI of the colon, rather than the usual white light. Here, we will determine, whether this technique could improve the detection of flat neoplastic lesions, laterally spreading tumors, small pedunculated adenomas and serrated polyps. The polyps are called serrated because of their appearance under the microscope after they have been removed. They tend to be located up high in the colon, far away from the rectum. They have been definitely shown to be a type of precancerous polyp and it is possible that using LCI will make it easier to see them, as they can be quite difficult to see with standard white light.

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Key information

Age range

18 year–99 year

Sex eligibility

All sexes

Study type

Observational

Primary location

Bács Kiskun County and Teaching Hospital

Kecskemét, Nyiri Street 38, 6000, Hungary

About this study

Computer-based Classification and Differentiation of Colorectal Polyps Using Blue Light Imaging (BLI)

Purpose

Recent studies have shown that optical chromoendoscopy with narrow-band imaging (NBI) of Fuji Intelligent Color Enhancement (FICE) is a powerful diagnostic tool for the differentiation between neoplastic and non-neoplastic colorectal polyps. Linked color imaging (LCI) and blue laser imaging (BLI) are two new imaging systems used in endoscopy which are recently developed. BLI was developed to compensate for the limitations of NBI. BLI shows a bright image of the digestive mucosa, enabling the detailed visualization of both the microstructure and microvasculature. The ELUXEO™ endoscopic system powered by Fujifilm's unique 4-LED (light-emitting diode) Multi Light™ technology sets a new standard in light intensity and electronic chromoendoscopy imaging. By combining four different wavelengths and the specific application of intensified from light spectra created by the integrated light source, this technology allows to easily switch between the three imaging modes White Light, Blue Light Imaging (BLI) and Linked Colour Imaging (LCI). Blue light imaging (BLI) is a new system for image-enhanced electronic chromoendoscopy, since the 410 nm LED visualizes vascular microarchitecture, similar to narrow band imaging, and a 450 nm provides white light by excitation. According to three recently published reports, the diagnostic ability of polyp characterization using blue light imaging compares favorably with narrow band imaging. No published data are available to date regarding computer assisted polyp characterization with blue light imaging.

The aim of the present study is to develop and evaluate a computer-based method for automated classification of small colorectal polyps on the basis of pit pattern and vascularization features. In this prospective study up to 1000 polyps with a size of 10 mm or smaller should be detected and stored in our web based picture database made by a zoom BLI colonoscopy. These polyps were imaged and subsequently removed for histological analysis. The polyp images were analyzed by a newly developed deep learning computer algorithm. The proposed computer-based method consists of several steps: picture annotation, preprocessing, vessel segmentation, feature extraction and classification, parameterization, and finally train and test of the multiple neural layer algorithms. The results of the deep learning automatic classification (sensitivity, specificity, negative predictive value, positive predictive value and accuracy) were compared to those of human observers, who were blinded to the histological gold standard.

Condition Colorectal Polyps with a size less then 10 mm

Study Type:

Observational

Study Design:

Observational Model: Cohort Time Perspective: Prospective

Official Title:

Computer-based Classification and Differentiation of Colorectal Polyps Using Fujifilm Blue Light Imaging (BLI)

Linked color imaging (LCI) and magnifying blue laser imaging (BLI) are two new imaging systems used in endoscopy which are recently developed. The newly developed LCI system (FUJIFILM Co.) creates clear and bright endoscopic images by using short-wavelength narrow-band laser light combined with white laser light on the basis of BLI technology. LCI makes red areas appear redder and white areas appear whiter. Thus, it is easier to recognize a slight difference in color of the mucosa. The aim the present study to determine if using LCI of the colon, rather than the usual white light on the colon, will improve the detection of flat neoplastic lesions, laterally spreading tumors, small pedunculated adenomas and serrated polyps. The polyps are called serrated because of their appearance under the microscope after they have been removed. They tend to be located up high in the colon, far away from the rectum. They have been definitely shown to be a type of precancerous polyp and it is possible that using LCI will make it easier to see them, as they can be quite difficult to see with standard white light.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • The patient must sign, understand and provide written consent for the procedure.
  • Undergoing colonoscopy at our endoscopy unit for any indication in Propofol deep sedation
  • Intact colon and rectum
  • ASA (American Society of Anesthesiology) risk class 1, 2 or 3

Exclusion criteria

  • Patients with inflammatory bowel disease;
  • Patients with poor bowel preparation; (Boston score <4)
  • Female patients with pregnancy;
  • Patients with mechanical bowel obstruction;
  • Patients with diverticulitis or toxic megacolon;
  • Patients with a history of radiation therapy to abdomen or pelvis;
  • Patients with a history of severe cardiovascular, pulmonary, liver or renal disease and high ASA (>3) risk of propofol sedation;
  • Personal history of coagulation disorders or use of anticoagulants;
  • Patients who are currently enrolled in another clinical investigation in which the intervention might compromise the safety of the patient's participation in this study.

Treatment and study plan

Primary outcomes

  1. diagnostic value of the computer algorithm

    Time frame: 2 years

    diagnostic value of the computer algorithm (sensitivity, specificity, negative predictive value, positive predictive value, accuracy) [ Time Frame: 10 months ] [ Designated as safety issue: No ]

  2. Number of detected serrated polyps

    Time frame: 2 years

    Number of Detected Proximal Serrated lesions, flat polyps and colorectal adenomas in proximal colon

  3. Number of detected polyps

    Time frame: 2 years

    Quantity of total number of colorectal adenomas found in the colon during colonoscopy was recorded and compared.

  4. the accuracy of the NICE (NBI International Colorectal Endoscopic) criteria using FICE versus BLI Eluxeo technology

    Time frame: 2 years

    the accuracy of the NICE criteria using FICE versus BLI Eluxeo technology without optical zoom for differentiating between the non-neoplastic and neoplastic histotypes in diagnoses with high-confidence on a video-library of 120 polyps reviewed by 5 experts. 5 experts will review pictures from a web-library of subcentimetric polyps removed and histologically verified and will assess each of the three NICE criteria (colour/vascularization/surface), and classify the lesion as neoplastic or non-neoplastic with low or high confidence.

  5. Inter-observer agreement among the 5 experts

    Time frame: 2 years

    Inter-observer agreement among the 5 experts [ Time Frame: up to 6 months ] [ Designated as safety issue: No ] The inter-observer agreement, among the 5 experts, on the final diagnosis (neoplastic or non-neoplastic) and on each individual NICE criterion for each polyp will be determined by using K statistics.

  6. Cecal intubation rate

    Time frame: 2 years

    The proportion of colonoscopy procedures resulting in successful intubation of the cecum.

  7. Propofol need for deep sedation

    Time frame: 2 years

    The main efficacy parameter is the amount of Propofol used for deep sedation during colonoscopy, expressed as the mean for each group.

Secondary outcomes

  1. diagnostic interobserver variability based on the computer algorithm

    Time frame: 2 years

    diagnostic interobserver variability based on the computer algorithm

  2. the accuracy of the NICE criteria using FICE versus BLI Eluxeo technology with 50x optical zoom for differentiating between the non-neoplastic and neoplastic histotypes

    Time frame: 2 years

    the accuracy of the NICE criteria using FICE versus BLI Eluxeo technology with 50x optical zoom for differentiating between the non-neoplastic and neoplastic histotypes in diagnoses with high-confidence on a video-library of 120 polyps reviewed by 5 experts (ML, SZM, OL, SZA, DZS)5 experts will review pictures from a web-library of subcentimetric polyps removed and histologically verified and will assess each of the three NICE criteria (colour/vascularization/surface), and classify the lesion as neoplastic or non-neoplastic with low or high confidence.

  3. Comparison of accuracy of BLI and LCI pictures

    Time frame: 2 years

    Comparison of accuracy of BLI and LCI pictures with and without zoom on the final diagnosis (neoplastic or non-neoplastic polyp) as compared to histology

  4. Improvement of adenoma detection rate by using LCI imaging comparing with that under white endoscopy

    Time frame: 2 years

    Improvement of adenoma detection rate by using LCI imaging comparing with that under white endoscopy

  5. Time-to-cecum

    Time frame: 2 years

    Time from initial insertion of colonoscope until successful intubation of the cecum (min)

  6. Ancillary maneuvers to facilitate procedure

    Time frame: 2 years

    A number of added maneuvers, including abdominal pressure, repositioning of the patient, endoscope loop reduction techniques, used to facilitate advancement of the endoscope during the procedure.

Sponsors and collaborators

Lead sponsor

Bács-Kiskun County Teaching Hospital

Other

Collaborators

  • Endo-Kapszula Privat Medical Center

Registry information

Official study title

Prospective Randomised Trial to Analyse the Advantages of the New Virtual Chromoendoscopy Features and the Variable Stiffness in Connection With Our Colonoscopic Examinations

Acronym: ELUFIBLI

Important dates

Study start
2016
Primary completion
2018
Study completion
2018
First posted
Jul 31, 2017
Registry last updated
Jul 10, 2019

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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