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

NCT06256185 · Status: COMPLETED · Phase: NA · Type: INTERVENTIONAL · Enrollment: 1267

Last updated 2024-02-13

No results posted yet for this study

Summary

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.

Conditions

  • Lymph Node Metastasis

Interventions

PROCEDURE

esophagectomy

Resection of esophageal tumor and lymph node dissection

Sponsors & Collaborators

  • Shanghai Zhongshan Hospital

    lead OTHER

Study Design

Allocation
NA
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2010-01-15
Primary Completion
2019-12-15
Completion
2023-07-15

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06256185 on ClinicalTrials.gov