Analysis of New Endoscopic Features and Variable Stiffness in Colonoscopy: Prospective Randomised Trial
Condition: Colorectal Adenoma; Colorectal Adenomatous Polyp
MeSH terms: Adenoma; Adenomatous Polyps
Study type: Observational

Summary: 
      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.
    

Detailed description: 
      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.
    

Observational model: Cohort
Time perspective: Prospective
Sampling method: Probability Sample
Study population: 
        All adult patients who did not meet the exclusion criteria were included in the study
      
Keywords: Blue light imaging; Linked color imaging; Optical histology; Adenoma detection rate; Variable stiffness; Deep learning