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

Colorectal Neoplasms Clinical Trial

Official title:

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

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT05576506
• Other study ID #: 2022-SDU-QILU-G003
• Status: Recruiting
• Start date: October 8, 2022
• Est. completion date: December 31, 2023

Study information

• Verified date: October 2022
• Source: Shandong University
• Contact: Xiuli Zuo, MD,PhD
• Phone: 15588818685
• Email: zuoxiuli[@]sina.com
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Recruiting
• Enrollment: 50
• Est. completion date: December 31, 2023
• Est. primary completion date: December 31, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 75 Years

Eligibility

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

Related Conditions & MeSH terms

• Adenoma
• Colorectal Adenoma
• Colorectal Neoplasms
• Colorectal Polyp
• Colorectal SSA

Locations

Country	Name	City	State
China	Qilu hosipital	Jinan	Shandong

Sponsors (1)

Lead Sponsor	Collaborator
Shandong University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Accuracy of HSI artificial intelligence model to identify colorectal adenoma and cancer	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%	1 year
Primary	Sensitivity	Sensitivity of HSI artificial intelligence model Sensitivity = number of true positives / (number of true positives + number of false negatives) * 100%.	1 year
Primary	Specificity	Specificity of HSI Artificial Intelligence Model Specificity = number of true negatives / (number of true negatives + number of false positives))*100%	1 year
Primary	Negative predictive values(NPV)	Negative predictive values for HSI artificial intelligence model = number of true negatives / (number of true negatives + number of false negatives)*100%	1 year
Primary	AUC (95% CI)	area under the receiver operating characteristic curve (AUC)	1 year
Secondary	To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition	To record and evaluate any unknown risks and adverse events of hyperspectral imaging in specimen image acquisition	1 year