Machine Learning to Predict Lymph Node Metastasis in T1 Esophageal Squamous Cell Carcinoma

February 9, 2024 updated by: Shanghai Zhongshan Hospital

Machine Learning to Predict Lymph Node Metastasis in T1 Esophageal Squamous Cell Carcinoma: A Multicenter Study

Existing models do poorly when it comes to quantifying the risk of Lymph node metastases (LNM). This study generated elastic net regression (ELR), random forest (RF), extreme gradient boosting (XGB), and a combined (ensemble) model of these for LNM in patients with T1 esophageal squamous cell carcinoma.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

Lymph node metastases (LNM) is a relatively uncommon but possible complication of T1 esophageal squamous cell carcinoma (ESCC). Existing models do poorly when it comes to quantifying this risk. This study aimed to develop a machine learning model for LNM in patients with T1 esophageal squamous cell carcinoma.

Patients with T1 squamous cell carcinoma treated with surgery between January 2010 and September 2021 from 3 institutions were included in this study. Machine-learning models were developed using data on patients' age and sex, depth of tumor invasion, tumor size, tumor location, macroscopic tumor type, lymphatic and vascular invasion, and histologic grade. Elastic net regression (ELR), random forest (RF), extreme gradient boosting (XGB), and a combined (ensemble) model of these was generated. Use Area Under Curve (AUC) to evaluate the predictive ability of the model. The contribution to the model of each factor was calculated. In order to better meet clinical needs, the investigators have designed the model as a user-friendly website.

Study Type

Interventional

Enrollment (Actual)

1267

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • China
    • Shanghai
      • Shanghai, Shanghai, China, 200032
        • Zhongshan Hospital Affiliated to Fudan University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • (I) thoracic ESCC
  • (II) no history of concomitant or prior malignancy
  • (III) tumor with pT1 staging
  • (IV) 15 or more lymph nodes examined

Exclusion Criteria:

  • underwent neoadjuvant treatment or endoscopic submucosal dissection before surgery

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: N/A
  • Interventional Model: Single Group Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: Arm used for predicting lymph node metastasis
Procedure: esophagectomy
Resection of esophageal tumor and lymph node dissection

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Model performance: discrimination
Time Frame: 8 weeks
Draw the ROC curve of the model and obtain their AUC values, and select the best prediction model based on the results of the validation set
8 weeks
Variable importance
Time Frame: 6 weeks
Calculate the importance level of variables used in the model and sort them, and analyze the reasons for the most important variables
6 weeks
Sub-analysis (ML Model vs. Logistic Model vs. NCCN Guideline)
Time Frame: 8 weeks
Apply NCCN guidelines and logistic models for prediction, and compare their performance with the model obtained in this study to determine the actual application benefits of the model
8 weeks

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Shanghai Zhongshan Hospital

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 15, 2010

Primary Completion (Actual)

December 15, 2019

Study Completion (Actual)

July 15, 2023

Study Registration Dates

First Submitted

January 23, 2024

First Submitted That Met QC Criteria

February 9, 2024

First Posted (Actual)

February 13, 2024

Study Record Updates

Last Update Posted (Actual)

February 13, 2024

Last Update Submitted That Met QC Criteria

February 9, 2024

Last Verified

February 1, 2024

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 81902396

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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