Real-time Artificial Intelligence-based Endocytoscopic Diagnosis of Colorectal Neoplasms

Real-time Artificial Intelligence-based Endocytoscopic Diagnosis of Colorectal Neoplasms: a Single Center, Prospective Clinical Study

Colorectal cancer (CRC) is the third most common malignancy and the second leading cause of cancer-related death worldwide. Colonoscopy is considered the preferred method of screening for colorectal cancer, and resection of colorectal lesions can significantly reduce the incidence and mortality of colorectal cancer. In order to improve the qualitative and quantitative diagnosis of colorectal lesions, many endoscopic techniques, such as image-enhanced endoscopy (IEE), including narrowband imaging (NBI), magnifying endoscopy, pigment endoscopy, confocal laser endoscopy, and endocytoscopy (EC) are applied clinically. However, with the increasing number of endoscopic resection, the costs associated with the pathological diagnosis of endoscopic resection and resection specimens increase year by year. In clinical practice, some non-neoplastic colorectal lesions may not require resection, so it is important to distinguish neoplastic from non-neoplastic during colonoscopy. The application of EC is intended to achieve the purpose of real-time histopathological endoscopic diagnosis without biopsy. Several studies have shown that EC is effective in identifying the nature of colorectal lesions and judging the depth of invasion in CRC. Based on the endoscopic diagnosis, the endoscopist can determine the treatment plan for the colorectal lesions. The latest EC is an integrated endoscope with a contact light microscopy system with a maximum magnification of 520 x. EC can demonstrate the atypical of gland structure and cells after staining and display the super-amplified surface microvessels of the lesion under the EC-NBI mode. However, the judgment of endocytoscopic images needs a lot of experience to improve the diagnostic accuracy. Moreover, endoscopists have certain subjective judgments and errors in endocytoscopic diagnosis. There is an artificial intelligence system which has been developed to identify colorectal neoplasms. However, there is still a lack of prospective clinical verification based on Chinese population. In the study, the investigators performed a prospective clinical study to determine the diagnostic accuracy of artificial intelligence system.

Study Overview

• Status: Recruiting
• Conditions: Colorectal Neoplasms
• Intervention / Treatment: Diagnostic test: artificial intelligence system

• Study Type: Observational
• Enrollment (Estimated): 350

Contacts and Locations

• Study Contact: Name: Mingqing Liu, Doctor; Phone Number: +8613204300453; Email: liumq23@mails.jlu.edu.cn

Study Locations

• China
  • Jilin
    • Changchun, Jilin, China, 130021
      • Recruiting
      • First Hospital of Jilin University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

The investigators analyzed only endoscopically or surgically resected colorectal lesions that had been observed with EC-NBI and EC-stained by endoscopists and artificial intelligence system before treatment that were ultimately performed histopathologic examination.

Description

Inclusion Criteria:

• colorectal lesions

Exclusion Criteria:

• non-epithelial tumors
• a history of inflammatory bowel disease
• chemotherapy or radiation therapy for colorectal cancer
• lesions without clear EC images
• specific pathological types
• familial adenomatous polyposis

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

colorectal lesion

Diagnostic test: artificial intelligence system; The colorectal lesions had been observed with EC-NBI and EC-stained by endoscopists before treatment that were ultimately performed histopathologic examination. The endocytoscopies (CF-H290ECI, Olympus, Tokyo, Japan) have a maximum magnification of ×520, focusing depth, 35 μm; field of view, 570 × 500μm. During EC-NBI , the endoscopist pushed the button of the endoscope to switch from white-light imaging to NBI and observed the lesion with full magnification. After endocytoscopic observation, the artificial intelligence system will be open and display the predictive result. Finally, the endoscopist performed EC-stained mode diagnosis after staining the lesion surface with 1.0% methylene blue. After endocytoscopic observation, the artificial intelligence system will be open again and display the predictive result.; Other Names: endocytoscopy

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Time Frame
sensitivity	December 2024
specificity	December 2024
accuracy	December 2024
positive predictive value	December 2024
negative predictive value	December 2024
high confidence diagnosis rate	December 2024

Collaborators and Investigators

• Sponsor: The First Hospital of Jilin University

Study record dates

Study Major Dates

• Study Start (Actual): April 1, 2024
• Primary Completion (Estimated): December 31, 2024
• Study Completion (Estimated): December 31, 2024

Study Registration Dates

• First Submitted: March 22, 2024
• First Submitted That Met QC Criteria: March 22, 2024
• First Posted (Actual): March 28, 2024

Study Record Updates

• Last Update Posted (Actual): April 18, 2024
• Last Update Submitted That Met QC Criteria: April 16, 2024
• Last Verified: April 1, 2024

More Information

Terms related to this study

• Keywords: artificial intelligence; endocytoscopy
• Additional Relevant MeSH Terms: Digestive System Diseases; Neoplasms by Site; Gastrointestinal Neoplasms; Digestive System Neoplasms; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases; Intestinal Neoplasms; Rectal Diseases; Neoplasms; Colorectal Neoplasms
• Other Study ID Numbers: 24K056-001

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No