Colorectal Adenoma Clinical Trial
Official title:
Prospective Randomised Trial to Analyse the Advantages of the New Virtual Chromoendoscopy Features and the Variable Stiffness in Connection With Our Colonoscopic Examinations
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.
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.
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