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

NCT06621147 | Completed

• 1 other identifier: NanchangU
• Study Type: observational
• Target: 1,776
• Locations: 0 countries
• Sites: N/A

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.

Conditions

Lung Cancer - Non Small Cell

Keywords

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

Outcome Measures

Primary Outcomes (1)

• Overall survival

  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).

  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).

Interventions

Surgery PROCEDURE

Surgery

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

patients with metastatic non-small cell neuroendocrine tumors

You may qualify if:

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

You may not qualify if:

• Small cell lung cancer
• Age less than 18 years

Sponsors & Collaborators

• Hongquan Xing: lead
• Second Affiliated Hospital of Nanchang University: collaborator

Biospecimen

Retention: SAMPLES WITHOUT DNA

Demographic data

MeSH Terms

Conditions

Carcinoma, Non-Small-Cell Lung; Lung Neoplasms

Interventions

Surgical Procedures, Operative

Condition Hierarchy (Ancestors)

Carcinoma, Bronchogenic; Bronchial Neoplasms; Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Design

• Study Type: observational
• Observational Model: CASE CONTROL
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Doctor

Study Record Dates

• First Submitted: September 27, 2024
• First Posted: October 1, 2024
• Study Start: January 1, 2000
• Primary Completion: December 31, 2021
• Study Completion: May 31, 2024
• Last Updated: October 1, 2024

Record last verified: 2024-09