NCT05493930

Brief Summary

In this study, we aim to develop and validate an easy-to-use machine learning prediction model to preoperatively identify the lymph node metastasis status for rectal cancer patients by using these clinical data from three hospitals.

Trial Health

100
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
6,578

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jan 2010

Longer than P75 for all trials

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

January 1, 2010

Completed
6 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

December 31, 2015

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

December 31, 2015

Completed
6.6 years until next milestone

First Submitted

Initial submission to the registry

August 5, 2022

Completed
4 days until next milestone

First Posted

Study publicly available on registry

August 9, 2022

Completed
Last Updated

August 9, 2022

Status Verified

August 1, 2022

Enrollment Period

6 years

First QC Date

August 5, 2022

Last Update Submit

August 6, 2022

Conditions

Outcome Measures

Primary Outcomes (1)

  • diagnosis of lymph node metastasis

    The lymph node metastasis (LNM) status was determined based on the pathological diagnosis of the surgical specimens.

    through study completion, an average of 1 month

Study Arms (3)

development set;

RC patients from the Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College

Other: The hospital where the treatment is performed

external validation sets 1

RC patients from Changhai Hospital, Naval Medical University

Other: The hospital where the treatment is performed

external validation sets 2

RC patients from the Second Affiliated Hospital of Harbin Medical University

Other: The hospital where the treatment is performed

Interventions

development set;external validation sets 1external validation sets 2

Eligibility Criteria

Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

rectal cancer patients with/without lymph node metastasis

You may qualify if:

  • American Joint Committee on Cancer (AJCC) stages I -III rectal cancer
  • underwent radical surgery

You may not qualify if:

  • other malignancies
  • received treatment with endoscopic submucosal dissection (ESD)
  • metastatic lesions
  • did not undergo lymph node dissection
  • had unavailable assessed lymph node status
  • received neoadjuvant therapy

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Related Publications (1)

  • Guan X, Yu G, Zhang W, Wen R, Wei R, Jiao S, Zhao Q, Lou Z, Hao L, Liu E, Gao X, Wang G, Zhang W, Wang X. An easy-to-use artificial intelligence preoperative lymph node metastasis predictor (LN-MASTER) in rectal cancer based on a privacy-preserving computing platform: multicenter retrospective cohort study. Int J Surg. 2023 Mar 1;109(3):255-265. doi: 10.1097/JS9.0000000000000067.

MeSH Terms

Conditions

Rectal Neoplasms

Condition Hierarchy (Ancestors)

Colorectal NeoplasmsIntestinal NeoplasmsGastrointestinal NeoplasmsDigestive System NeoplasmsNeoplasms by SiteNeoplasmsDigestive System DiseasesGastrointestinal DiseasesIntestinal DiseasesRectal Diseases

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
RETROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Chief of Colorectal Cancer Surgery

Study Record Dates

First Submitted

August 5, 2022

First Posted

August 9, 2022

Study Start

January 1, 2010

Primary Completion

December 31, 2015

Study Completion

December 31, 2015

Last Updated

August 9, 2022

Record last verified: 2022-08