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

Not Applicable

Completed

• Conditions: Lymph Node Metastasis
• Interventions: Procedure: esophagectomy

• Registration Number: NCT06256185
• Lead Sponsor: Shanghai Zhongshan Hospital

Brief 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.

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 Timeline

• Study Start: 2010-01-15
• Primary Completion: 2019-12-15
• Study Completion: 2023-07-15
• Study First Submitted: 2024-01-23
• Status Verified: 2024-02
• Study First Submitted QC: 2024-02-09
• Last Update Submitted: 2024-02-09
• Study First Posted: 2024-02-13
• Last Update Posted: 2024-02-13

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 1267

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 & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Arm && Interventions

Group	Intervention	Description
Arm used for predicting lymph node metastasis	esophagectomy	-

Primary Outcome Measures

Name	Time	Method
Sub-analysis (ML Model vs. Logistic Model vs. NCCN Guideline)	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
Model performance: discrimination	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
Variable importance	6 weeks	Calculate the importance level of variables used in the model and sort them, and analyze the reasons for the most important variables

Trial Locations

Locations (1)

Zhongshan Hospital Affiliated to Fudan University; 🇨🇳 Shanghai, Shanghai, China