AI Prediction Model and Risk Stratification for Lung Meta... | NCT05816902

Studies
AI Prediction Model and Risk Stratification for Lung Metastasis in Colorectal Cancer

NCT05816902

Sponsor

Peking Union Medical College

Status

Completed

Last Update Posted

Apr 18, 2023Actual

Enrollment

2,779Actual

Phase

Not provided

Conditions

Colorectal Cancer
Lung Metastases

Interventions

The location of the patient's treatment

Countries

Not provided

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Apr 2023

Overall Recruitment Status or Expanded Access Status

Completed

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Jan 1, 2016Actual

Primary Completion Date

Dec 31, 2020Actual

Completion Date

Dec 31, 2020Actual

First Submitted Date

Apr 5, 2023

First Submission Date that Met QC Criteria

Apr 5, 2023

First Posted Date

Apr 18, 2023Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Apr 5, 2023

Last Update Posted Date

Apr 18, 2023Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

Xishan Wang, the Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College, Peking Union Medical CollegePrincipal Investigator

Lead Sponsor

Peking Union Medical CollegeOTHER

Collaborators

Name	Class
The Second Affiliated Hospital of Harbin Medical University	OTHER

Oversight Module

Has Data Monitoring Committee (DMC)

Not provided

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

Brief Summary

Background:

To assist clinicians with diagnosis and optimal treatment decision-making, we attempted to develop and validate an artificial intelligence prediction model for lung metastasis (LM) in colorectal cancer (CRC) patients.

Method:

The clinicopathological characteristics of 46037 CRC patients from the Surveillance, Epidemiology, and End Results (SEER) database and 2779 CRC patients from a multi-center external validation set were collected retrospectively. After feature selection by univariate and multivariate analyses, six machine learning (ML) models, including logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest, and balanced random forest (BRF), were developed and validated for the LM prediction. The optimization model with best performance was compared to the clinical predictor. In addition, stratified LM patients by risk score were utilized for survival analysis.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

2,779Actual

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
validation set 1		The validation set 1 was comprised of patients with CRC diagnosed and treated between January 1, 2016, and December 31, 2020, at the Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College.	Other: The location of the patient's treatment
validation set 2		The validation set 2 was comprised of patients with CRC diagnosed and treated between January 1, 2016, and December 31, 2020, at the Second Affiliated Hospital of Harbin Medical University.	Other: The location of the patient's treatment

Interventions

Name	Type	Description	Arm Group Labels	Other Names
The location of the patient's treatment	Other	The location of the patient's treatment	validation set 1 validation set 2

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
lung metastasis	diagnosed with lung metastasis	through study completion, an average of 3 month

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

patients with pathologic confirmation of a primary CRC diagnosis

Exclusion Criteria:

(1) patients with multiple primary cancers or other malignancies; (2) patients identified via autopsy or death certificate; and (3) patients with uncertain clinical data values

Accepts Healthy Volunteers

Not provided

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

Not provided

Maximum Age

Not provided

Standard Ages

ChildAdultOlder Adult

Study Population

patients with pathologic confirmation of a primary CRC diagnosis

Sampling Method

Non-Probability Sample

Condition Browse Module

MeSH Terms

ID	Term
D015179	Colorectal Neoplasms

Ancestor Terms

ID	Term
D007414	Intestinal Neoplasms
D005770	Gastrointestinal Neoplasms
D004067	Digestive System Neoplasms
D009371	Neoplasms by Site

Browse Leaves

Not provided

Browse Branches

Not provided