Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy

Sponsor

Shandong University (Other)

Overall Status

Recruiting

CT.gov ID

NCT05576506

Collaborator

(none)

Enrollment

Location

14.8

Anticipated Duration (Months)

3.4

Patients Per Site Per Month

Study Details

Study Description

Brief Summary

The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of colorectal cancer other colorectal disease by marking and analyzing the characteristics of hyperspectral images based on the pathological results of colonoscopic biopsy, so as to improve the objectiveness and intelligence of early colorectal cancer diagnosis.

Condition or Disease	Intervention/Treatment	Phase
Colorectal Neoplasms Colorectal Polyp Colorectal Adenoma Colorectal SSA

Detailed Description

Prospectively collect the hyperspectral image information of ordinary colonoscopic biopsy tissue. The colonoscopic biopsy tissue is from the Endoscopy Center of Qilu Hospital of Shandong University. The hyperspectral images are marked based on the biopsy pathological results, and the deep convolutional neural network (DCNN) model is used. With training and verification, develop the Hyperspectral Imaging Artificial Intelligence Diagnostic System (HSIAIDS) .A portion of colonoscopic biopsy tissue will be collected as a prospective test set to prospectively test the diagnostic performance of the HSIAIDS algorithm.

Study Design

Study Type:

Observational

Anticipated Enrollment :

50 participants

Observational Model:

Cohort

Time Perspective:

Prospective

Official Title:

Application of Hyperspectral Imaging Analysis Technology in the Diagnosis of Colorectal Cancer Based on Colonoscopic Biopsy

Actual Study Start Date :

Oct 8, 2022

Anticipated Primary Completion Date :

Dec 31, 2023

Anticipated Study Completion Date :

Dec 31, 2023

Arms and Interventions

Arm	Intervention/Treatment
Deep learning algorithm group After the patient has passed the screening, a routine colonoscopy will be performed, and the target tissue with suspected inflammation or neoplasia will be biopsied. The clinical investigators use the hyperspectral microscope to collect image information of the biopsy tissue in the endoscopy room. After collecting information, biopsy specimens will be routinely processed and sent for pathological diagnosis.

Arm

Intervention/Treatment

Deep learning algorithm group

After the patient has passed the screening, a routine colonoscopy will be performed, and the target tissue with suspected inflammation or neoplasia will be biopsied. The clinical investigators use the hyperspectral microscope to collect image information of the biopsy tissue in the endoscopy room. After collecting information, biopsy specimens will be routinely processed and sent for pathological diagnosis.

Outcome Measures

Primary Outcome Measures

Accuracy of HSI artificial intelligence model to identify colorectal adenoma and cancer [1 year]
Accuracy of hyperspectral imaging (HSI) artificial intelligence model to identify colorectal hyperplastic polyp, adenoma, SSL and colorectal cancer. Accuracy of artificial intelligence models Accuracy = (true positives + true negatives) / total number of subjects * 100%
Sensitivity [1 year]
Sensitivity of HSI artificial intelligence model Sensitivity = number of true positives / (number of true positives + number of false negatives) * 100%.
Specificity [1 year]
Specificity of HSI Artificial Intelligence Model Specificity = number of true negatives / (number of true negatives + number of false positives))*100%
Negative predictive values(NPV) [1 year]
Negative predictive values for HSI artificial intelligence model = number of true negatives / (number of true negatives + number of false negatives)*100%
AUC (95% CI) [1 year]
area under the receiver operating characteristic curve (AUC)

Secondary Outcome Measures

To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition [1 year]
To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition

Eligibility Criteria

Criteria

Ages Eligible for Study:

18 Years to 75 Years

Sexes Eligible for Study:

All

Accepts Healthy Volunteers:

Inclusion Criteria:

patients aged 18-75 years who undergo the colonoscopy examination and biopsy

Exclusion Criteria:

patients with severe cardiac, cerebral, pulmonary or renal dysfunction or psychiatric disorders who cannot participate in colonoscopy
patients with previous surgical procedures on the gastrointestinal tract.
patients with contraindications to biopsy
patients who refuse to sign the informed consent form

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Qilu hosipital	Jinan	Shandong	China	250012

Sponsors and Collaborators

Shandong University

Investigators

Study Chair: Xiuli Zuo, MD,PhD, Study Principal investigator

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

Responsible Party:

Xiuli Zuo, Director of Qilu Hospital gastroenterology department, Shandong University

ClinicalTrials.gov Identifier:

NCT05576506

Other Study ID Numbers:

2022-SDU-QILU-G003

First Posted:

Oct 12, 2022

Last Update Posted:

Dec 21, 2022

Last Verified:

Oct 1, 2022

Studies a U.S. FDA-regulated Drug Product:

Studies a U.S. FDA-regulated Device Product:

Keywords provided by Xiuli Zuo, Director of Qilu Hospital gastroenterology department, Shandong University

Hyperspectral imaging

Artifitial intelligence

colorectal cancer

Additional relevant MeSH terms:

Colorectal Neoplasms

Adenoma

Study Results

No Results Posted as of Dec 21, 2022