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

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.

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Key information

Conditions

Lung Cancer - Non Small Cell Bronchial Neoplasms Carcinoma, Bronchogenic Carcinoma, Non-Small-Cell Lung Lung Diseases Lung Neoplasms Neoplasms Neoplasms by Site Respiratory Tract Diseases Respiratory Tract Neoplasms Thoracic Neoplasms

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

About this study

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.

Who can participate

Healthy volunteers accepted: No

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

Inclusion criteria

Pathologic Diagnosis of Non-Small Cell Neuroendocrine Carcinoma
Known Surgical Information

Exclusion criteria

Small cell lung cancer
Age less than 18 years

Treatment and study plan

Surgery

Procedure

Surgery

Primary outcomes

Overall survival

Time frame: The estimated time period for assessing events was January 2000 through December 2021. It is the time from random assignment to death from any cause (last follow-up for lost patients; end of follow-up for patients still alive at the end of the study).

Overall survival refers to the duration of time from the start of diagnosis until death from any cause. It is a common endpoint used in clinical trials and medical research, particularly in oncology, to measure the effectiveness of a treatment or intervention. Overall survival provides a comprehensive picture of how well patients are doing and is often expressed as a percentage or a median time (e.g., the median overall survival time).

Sponsors and collaborators

Lead sponsor

Hongquan Xing

Other

Collaborators

Second Affiliated Hospital of Nanchang University

Registry information

Official study title

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

Acronym: MLA-MNSCLCNET

Important dates

Study start: 2000
Primary completion: 2021
Study completion: 2024
First posted: Oct 1, 2024
Registry last updated: Oct 1, 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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