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

NCT06335654 | Completed

• 1 other identifier: 24K056-001
• Study Type: observational
• Target: 680
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Colorectal Neoplasms

Keywords

endocytoscopy; artificial intelligence

Outcome Measures

Primary Outcomes (1)

• To evaluate the diagnostic performance and high confidence diagnosis rate of EndoBRAIN in diagnosing neoplastic lesions in a clinical setting.EndoBRAIN in diagnosing neoplastic lesions in a clinical setting.

  The sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and high confidence diagnosis rate will be calculated for comparison with final histology as the gold standard for diagnosis

  8 months

Secondary Outcomes (4)

• To evaluate the performance and high-confidence diagnostic rate of EndoBRAIN in diagnosing adenomas of rectosigmoid colon ≤5 mm;

  8 months

• To evaluate the performance and high-confidence diagnostic rate of Endobrain under EC-stained mode in the diagnosis of invasive cancer;

  8 months

• To evaluate the Influencing factors on the diagnosis of colorectal lesions by EndoBRAIN.

  8 months

• To compare the diagnostic performance of diagnosing the histology of colorectal lesions by EndoBRAIN, by endoscopists, and by endoscopists combined with EndoBRAIN;

  8 months

Study Arms (1)

Patients with one or more colorectal lesions detected

During endocytoscopy, the Clinician inspect for the presence of colorectal lesions as per routine clinical practice with the EndoBRAIN turned off. When a colorectal lesion is encountered, the Clinician will make a prediction on the histology based on routine clinical practice. Following this, the EndoBRAIN function will be switched on and the Clinician will take note of the EndoBRAIN prediction for the every image of colorectal lesion. In addition, other colorectal lesion features such as the size, location and shape will be recorded, which is similar to what is performed in routine clinical practice. The colorectal lesion will be resected and sent for pathological examination, which will form the "gold standard" for the diagnosis of colorectal lesion histology.

Diagnostic Test: artificial intelligence system

Interventions

artificial intelligence system DIAGNOSTIC_TEST

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.

Also known as: endocytoscopy

Patients with one or more colorectal lesions detected

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-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.

You may qualify if:

• Those patients who, during the endoscopic examination, discovered at least one colorectal lesion and received treatment and obtained a pathological diagnosis
• consent obtained for the study

You may not qualify if:

• 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

Sponsors & Collaborators

• The First Hospital of Jilin University: lead

Study Sites (1)

The First hospital of Jilin University

Changchun, Jilin, 130021, China

Location

Biospecimen

Retention: SAMPLES WITH DNA

Post-operative colorectal lesion specimen

MeSH Terms

Conditions

Colorectal Neoplasms

Condition Hierarchy (Ancestors)

Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases; Rectal Diseases

Study Officials

• Hong Xu, PHD

  The First Hospital of Jilin University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Director, Head of Gastroenterology and Endoscopy Center, Principal Investigator, Clinical Professor

Study Record Dates

• First Submitted: March 22, 2024
• First Posted: March 28, 2024
• Study Start: April 1, 2024
• Primary Completion: December 19, 2024
• Study Completion: December 19, 2024
• Last Updated: December 9, 2025

Record last verified: 2025-12

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)