An observational study in Colorectal Polyps, sponsored by Peking Union Medical College Hospital. Recruiting at 1 site in China. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2025-01-14.
Sponsored by Peking Union Medical College Hospital · Observational
Background: Colonoscopy with optical diagnosis based on the appearance of polyps can guide the selection of endoscopic treatment methods, reduce unnecessary polypectomy procedures and the need for tissue pathological diagnosis, and formulate follow-up strategies in a timely manner [1]. This approach significantly alleviates the economic burden on patients and the healthcare system and can effectively ease the tension on clinical resources [2]. Various endoscopic polyp classification methods, including Pit Pattern [3], NICE [4], WASP [5], and MS [6], are used to determine pathological types. However, mastering these classification methods requires endoscopists to undergo extensive training, and due to the inherent flaws in each method, no single endoscopic classification method can accurately diagnose all types of polyps to meet the requirements of optical diagnosis. This limitation has hindered the widespread application of optical diagnosis in clinical practice [7]. The application of artificial intelligence technology in this field, known as computer-aided diagnosis (CADx), has seen rapid development in recent years. Numerous large-scale, prospective studies have demonstrated that the accuracy of CADx technology for optical diagnosis of minute lesions (\<5mm) has essentially met the threshold set by European and American endoscopy societies for optical diagnosis [8,9]. However, the diagnostic efficacy of CADx for polyps ≥5mm remains unclear. Moreover, current research is mostly limited to distinguishing between common adenomas and hyperplastic polyps, with little attention given to serrated lesions, which are also precancerous lesions and progress even more rapidly, and are more challenging for endoscopists to assess. These reasons prevent CADx from being widely applied in clinical practice for real-time accurate judgment of polyp pathological types.
182 studies on the registry are indexed under Adenoma; 28 are open to participants now.
This study's planned enrollment of 400 is below the median of 600 across 51 observational studies indexed under Adenoma.
Browse Adenoma studies →Peking Union Medical College Hospital is the lead sponsor of 1,155 studies on the registry; 475 are open to participants now.
Of its 5 completed or terminated interventional studies of FDA-regulated products, 0 (0%) have results posted.
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Patients aged 18 years or older undergoing routine colonoscopy screening
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Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Creatinine clearance (CrCl)
How quickly the kidneys clear creatinine, a waste product, from the blood. A study may estimate it from a blood test with age, weight and sex (often by the Cockcroft–Gault formula), or measure it using blood and urine collected over a set time. Given in mL/min; higher is better.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
NYHA class (heart failure)
The New York Heart Association’s four classes of how much heart failure limits physical activity.
Studies set their own limit — many outside heart failure leave out class III–IV, while heart failure studies may ask for a particular class. Check the class this study names.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Diagnostic Test: Real-time Artificial Intelligence Model for Diagnosing Colorectal Polyp Pathology and Endoscopic Classification
During the AI model development phase, the aim is to include as many samples as possible. Given the focus on the diagnostic accuracy of serrated lesions, we retrospectively collected approximately 400 cases serrated lesions with pathological diagnosis by the department of pathology at Peking Union Medical College Hospital to date. Additionally, we matched with 400 cases each of hyperplastic polyps, conventional adenomas, and early-stage colorectal cancer, totaling approximately 1600 cases. The model employs mainstream AI classification algorithms to construct the model and compare the predictive performance of different models. Utilizing the dataset established in the first phase, which contains static images of polyp lesions along with their corresponding pathological diagnosis and endoscopic classifications, we developed and optimized the AI model. Then the model will be be compared with endoscopists in a prospective cohort to investigate the efficacy.
Accuracy of Optical Diagnosis for Colorectal Polyps
The accuracy of the AI model's optical diagnosis is compared with that of endoscopists, with pathological diagnosis serving as the gold standard.
Time frame: 2 years
Other Assessment Parameters of Optical Diagnosis
Including sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of Optical Diagnosis
Time frame: 2 years
Accuracy in Determining Endoscopic Classification of Colorectal Polyps
Using the endoscopic classification judgment of experienced endoscopists as the gold standard, the study investigates the accuracy of the AI model in determining the endoscopic classification of lesions. The endoscopic classifications include Pit Pattern, CP, NICE, JNET, WASP, and MS.
Time frame: 2 years
Other Assessment Parameters in Determining Endoscopic Classification
The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the AI Model in determining endoscopic classification of colorectal polyps
Time frame: 2 years
Plan to share: Yes — All IPD collected throughout the trial
Time frame: Beginning 3 months after publication with no end date
Access criteria: Any investigators who wish to utilize the data for pertinent research with an appropriate request
Start is estimated for Jan 2025; primary completion for Dec 2026, 23 months later.
Of 22 observational studies under Adenoma, half reached primary completion within 45 months of starting (the median), counting those still running; a quarter within 14 and three quarters within 103. This study's planned 23 months is below that.
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