Clinical trial NCT04840056: Prediction of Gastric Cancer in Intestinal Metaplasia and Atrophic Gastritis
Official title: Prediction of Gastric Cancer in Intestinal Metaplasia and Atrophic Gastritis - Application of Artificial Intelligence in Histology and Clinical Data
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Gastric Cancer
Intestinal Metaplasia
Atrophic Gastritis
Keywords: artificial intelligence
Lead sponsor: Chinese University of Hong Kong
Sponsor class: OTHER
First posted: 2021-04-09
Enrollment: 1300
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Clinical data will be collected retrospectively using the Clinical Data Analysis and Reporting System (CDARS) and Clinical management System (CMS). A cluster-wide cohort (New Territories East Cluster, NTEC) consisting of patients with history of histologically-proven gastric IM and AG will be identified and included for subsequent analysis. The data collection period for the retrospective data will be 2000-2020.

Histology slides will be retrieved retrospectively when available (within NTEC). Whole slide imaging technique will be utilized for the development of training and validation cohorts with machine learning algorithms in the pathology part.

Brief summary: The primary objectives of this study are:

* To identify clinical or histological factors associated with gastric cancer development in patients with IM and AG
* To establish a machine learning algorithm for prediction of future gastric cancer risks and individual risk stratification in patient with IM and AG

Detailed description: This is a two-part retrospective study including a clinical data part and a pathology part. A training cohort will be developed from approximately 70% of included cases. It will be followed by a validation cohort with the remaining cases.

Clinical data will be collected retrospectively using the Clinical Data Analysis and Reporting System (CDARS) and Clinical management System (CMS). A cluster-wide cohort (New Territories East Cluster, NTEC) consisting of patients with history of histologically-proven gastric IM and AG will be identified and included for subsequent analysis. The data collection period for the retrospective data will be 2000-2020.

Histology slides will be retrieved retrospectively when available (within NTEC). Whole slide imaging technique will be utilized for the development of training and validation cohorts with machine learning algorithms in the pathology part.

Eligibility criteria: Inclusion Criteria:

* Adults \>= 18 years of age
* Histologically proven atrophic gastritis or intestinal metaplasia (at antrum and/or body and/or angular of stomach)

Exclusion Criteria:

- none

Primary outcomes:
measure: Gastric cancer and gastric dysplasia
timeFrame: 20 years
Secondary outcomes:
measure: Overall accuracy of machine learning model
timeFrame: 20 years
measure: Sensitivity of machine learning model
timeFrame: 20 years
measure: Specificity of machine learning model
timeFrame: 20 years
measure: Positive predictive value of machine learning model
timeFrame: 20 years
measure: Negative predictive value of machine learning model
timeFrame: 20 years
measure: Area under the receiver operating characteristic curve of machine learning model
timeFrame: 20 years
Other outcomes:
Locations:
city: Shatin
country: Hong Kong
facility: Prince of Wales Hospital
state: New Territories
Study officials:
References: