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Impact of Computer-aided Optical Diagnosis (CAD) in Predicting Histology of Diminutive Rectosigmoid Polyps: a Multicenter Prospective Trial (ABC Study).

Impact of Computer-aided Optical Diagnosis (CAD) in Predicting Histology of Diminutive Rectosigmoid Polyps: a Multicenter Prospective Trial (Artificial Intelligence BLI Characterization - ABC Study).

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT04607083
Acronym
ABC
Enrollment
1134
Registered
2020-10-28
Start date
2020-10-22
Completion date
2021-03-30
Last updated
2021-06-10

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Colonic Adenomatous Polyp

Brief summary

Recently, a CNN-based artificial intelligence (AI) system for polyp characterization has been developed by Fujifilm Co., Tokyo, Japan. It works in conjunction with BLI system. In the present study we prospectively evaluate whether the evaluation of the endoscopist combined with the CAD system output achieve \> 90% accuracy in characterization (i.e. as adenomas or non-adenomas) of diminutive rectosigmoid polyps having histopathology as reference standard. Consecutive adult outpatients undergoing elective colonoscopy, in which at least one diminutive (\<5 mm) rectosigmoid polyp is detected are included. During endoscopic procedures all polyps identified by the endoscopist are documented for size, location and morphology. All diminutive polyps are characterized by a three sequential steps process: I) endoscopist prediction: the endoscopist evaluates the polyp by using BLI through the BASIC classification; the confidence level (high vs. low) in histology prediction is recorded; II) AI prediction: the AI system is switched on and the output of the automatic evaluation is recorded; this outcome is rated as stable or unstable, depending of the consistency over time of the outcome; III) combined prediction: a final classification is provided by endoscopist in light of the results of the first and of the second step; the confidence level is recorded. All polyps are resected and retrieved in separate jars and sent for pathology assessment. Only polyps characterized with high confidence will be included in the per-polyp analysis; the high-confidence characterization rate will be also calculated; the rate of polyps characterized with a CAD stable outcome will be calculated. Operative characteristics (sensitivity, specificity, positive and negative predictive value and accuracy) in distinguishing adenomatous from non-adenomatous polyps, evaluated with high confidence, will be calculated for each diminutive polyp and for each diminutive rectosigmoid polyp, having histopathology report as reference standard. The post-polypectomy surveillance intervals will be calculated on the basis of polyp histology (reference standard) in all patients according to both USMSTF and ESGE guidelines.

Interventions

DIAGNOSTIC_TESTPolyp carachterization by combing endoscopist evaluation and Ai output

A polyp characterization (adenoma vs. non adenoma) is provided by endoscopist in light of the results of this own evaluation and of the Ai system output. The confidence level (high vs. low) in polyp characterization is recorded. The combined evaluation is compared with histopathology results.

Sponsors

Valduce Hospital
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to 85 Years

Inclusion criteria

* Consecutive adult outpatients undergoing elective colonoscopy, in which at least one diminutive (\<5 mm) rectosigmoid polyp is detected.

Exclusion criteria

* patients with CRC history or hereditary polyposis syndromes or hereditary non-polyposis colorectal cancer * patients with inadequate bowel preparation * patients scheduled for partial examinations * polyps could not be resected due to ongoing anticoagulation preventing resection and pathologic assessment * patients undergoing urgent colonoscopy

Design outcomes

Primary

MeasureTime frameDescription
Agreement of combined prediction with PIVI I statement6 monthsTo prospectively evaluate whether the evaluation of the endoscopist combined with the CAD system output achieve \> 90% accuracy in characterization (i.e. as adenomas or non-adenomas) of diminutive rectosigmoid polyps (i.e. PIVI I threshold) having histopathology as reference standard.

Secondary

MeasureTime frameDescription
Endoscopist prediction6 monthsto calculate the performance measures (sensitivity, specificity, positive and negative predictive value) of the endoscopist alone in characterizing diminutive rectosigmoid polyps
Ai prediction6 months\- to calculate the performance measures (sensitivity, specificity, positive and negative predictive value) of the AI system alone in characterizing diminutive rectosigmoid polyps
Agreement of combined prediction with PIVI II statement6 months\- to evaluate if the evaluation of the endoscopist combined with the CAD system output achieve \> 90% accuracy in the assignment of post-polypectomy surveillance intervals, according to US and EU guidelines, when combined with the histopathology assessment of polyps \>5 mm in size

Countries

Italy

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026