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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
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06621147
Acronym
MLA-MNSCLCNET
Enrollment
1776
Registered
2024-10-01
Start date
2000-01-01
Completion date
2024-05-31
Last updated
2024-10-01

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Lung Cancer - Non Small Cell

Keywords

lung cancer, machine learning, non-small cell neuroendocrine tumor

Brief 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.

Interventions

PROCEDURESurgery

Surgery

Sponsors

Second Affiliated Hospital of Nanchang University
CollaboratorOTHER
Hongquan Xing
Lead SponsorOTHER

Study design

Observational model
CASE_CONTROL
Time perspective
RETROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

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

Exclusion criteria

* Small cell lung cancer * Age less than 18 years

Design outcomes

Primary

MeasureTime frameDescription
Overall survivalThe 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).

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026