Clinical trial NCT04607083: Impact of Computer-aided Optical Diagnosis (CAD) in Predicting Histology of Diminutive Rectosigmoid Polyps: a Multicenter Prospective Trial (ABC Study).
Official title: Impact of Computer-aided Optical Diagnosis (CAD) in Predicting Histology of Diminutive Rectosigmoid Polyps: a Multicenter Prospective Trial (Artificial Intelligence BLI Characterization - ABC Study).
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Colonic Adenomatous Polyp
Lead sponsor: Valduce Hospital
Sponsor class: OTHER
First posted: 2020-10-28
Enrollment: 1134
Sex: ALL
Minimum age (years): 18
Maximum age (years): 85
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Consecutive adult outpatients undergoing elective colonoscopy, in which at least one diminutive (\<5 mm) rectosigmoid polyp is detected

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.


Eligibility criteria: 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

Primary outcomes:
measure: Agreement of combined prediction with PIVI I statement
timeFrame: 6 months
Secondary outcomes:
measure: Endoscopist prediction
timeFrame: 6 months
measure: Ai prediction
timeFrame: 6 months
measure: Agreement of combined prediction with PIVI II statement
timeFrame: 6 months
Other outcomes:
Locations:
city: Como
country: Italy
facility: Gastroenterology Unit, Valduce Hospital
Study officials:
References:
citation: Rondonotti E, Hassan C, Tamanini G, Antonelli G, Andrisani G, Leonetti G, Paggi S, Amato A, Scardino G, Di Paolo D, Mandelli G, Lenoci N, Terreni N, Andrealli A, Maselli R, Spadaccini M, Galtieri PA, Correale L, Repici A, Di Matteo FM, Ambrosiani L, Filippi E, Sharma P, Radaelli F. Artificial intelligence-assisted optical diagnosis for the resect-and-discard strategy in clinical practice: the Artificial intelligence BLI Characterization (ABC) study. Endoscopy. 2023 Jan;55(1):14-22. doi: 10.1055/a-1852-0330. Epub 2022 May 13.