Ignite Creation Date:
2024-05-06 @ 8:07 PM
Last Modification Date:
2024-10-26 @ 3:20 PM
Study NCT ID:
NCT06256185
Status:
COMPLETED
Last Update Posted:
2024-02-13
First Post:
2024-01-23
Brief Title:
Machine Learning to Predict Lymph Node Metastasis in T1 Esophageal Squamous Cell Carcinoma
Sponsor:
Shanghai Zhongshan Hospital
Organization:
Shanghai Zhongshan Hospital
Study Overview
Official Title:
Machine Learning to Predict Lymph Node Metastasis in T1 Esophageal Squamous Cell Carcinoma A Multicenter Study
Status:
COMPLETED
Status Verified Date:
2024-02
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
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
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 Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None