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Completed

NCT Number: NCT06256185

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

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.

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Key information

Conditions

Lymph Node Metastasis Lymphatic Metastasis Neoplasm Metastasis Neoplasms Neoplastic Processes Pathologic Processes Pathological Conditions, Signs and Symptoms

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

Zhongshan Hospital Affiliated to Fudan University

Shanghai, Shanghai Municipality, 200032, China

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Esophagectomy

Procedure

Resection of esophageal tumor and lymph node dissection

Primary outcomes

  1. 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

  2. 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

  3. 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

Sponsors and collaborators

Lead sponsor

Shanghai Zhongshan Hospital

Other

Registry information

Official study title

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

Important dates

Study start
2010
Primary completion
2019
Study completion
2023
First posted
Feb 13, 2024
Registry last updated
Feb 13, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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