NCT06621147

Application of Machine Learning Algorithms to Identify Optimal Candidates for Primary Tumor Resection in Patients with Metastatic Non-small Cell Neuroendocrine Tumors

Application of Machine Learning Algorithms to Identify Optimal Candidates for Primary Tumor Resection in Patients with Metastatic Non-small Cell Neuroendocrine Tumors: Propensity Score Matching

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 1,776 (actual)
• Sponsor: Hongquan Xing · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

This study was based on public use data from the SEER database. The study did not require informed consent from the SEER registered cases, and the authors obtained Limited-Use Data Agreements from SEER.

Detailed description

This study utilized publicly available data from the SEER (Surveillance, Epidemiology, and End Results) database, which is a comprehensive source of information on cancer statistics in the United States. The authors did not need to obtain informed consent from individuals whose cases are registered in the SEER database because the data is anonymized and is meant for public use. Instead, the authors acquired Limited-Use Data Agreements with SEER, which are legal contracts that allow researchers to access and use specific datasets under certain conditions while ensuring that the privacy of the individuals in the database is maintained. This agreement outlines the terms of data use, ensuring that the researchers adhere to guidelines for the ethical handling of data while still enabling them to conduct their research.

Conditions

• Lung Cancer - Non Small Cell

Interventions

Type	Name	Description
PROCEDURE	Surgery	Surgery

Timeline

• Start date: 2000-01-01
• Primary completion: 2021-12-31
• Completion: 2024-05-31
• First posted: 2024-10-01
• Last updated: 2024-10-01