Development and Validation of a Deep Learning-Based Survival Prediction Model for Pediatric Glioma Patients: A Retrospective Study Using the SEER Database and Chinese Data

NCT06199388 | Completed

• 1 other identifier: TDLL-202312-05
• Study Type: observational
• Target: 9,532
• Locations: 1 country
• Sites: 1

Brief Summary

Accurately predicting the survival of pediatric glioma patients is crucial for informed clinical decision-making and selecting appropriate treatment strategies. However, there is a lack of prognostic models specifically tailored for pediatric glioma patients. This study aimed to address this gap by developing a time-dependent deep learning model to aid physicians in making more accurate prognostic assessments and treatment decisions.

Conditions

Glioma

Outcome Measures

Primary Outcomes (2)

• overall survival

  The primary outcome was overall survival (OS), which was defined as the time interval from the pediatric glioma diagnosis until death or the end of follow-up in SEER registry

  2000.01-2018.12

• overall survival

  The primary outcome was overall survival (OS), which was defined as the time interval from the pediatric glioma diagnosis until death or the end of follow-up in Chinese registry

  2010.01-2018.12

Study Arms (2)

SEER database

The model was trained using the Surveillance, Epidemiology, and End Results (SEER) Registry database. To identify specific tumor types, the International Classification of Diseases for Oncology, 3rd Edition codes (ICD-O-3) were used, including codes 9450, 9394, 9421, 9384, 9383, 9424, 9400, 9420, 9410, 9411, 9380, 9382, 9391, 9393, 9390, 9401, 9381, 9451, 9440, 9441, 9442, 9430, and 9380, covering astrocytic tumors, oligodendroglia tumors, oligoastrocytic tumors, ependymal tumors, and other gliomas. Inclusion criteria comprised all primary brain tumors (C71.0-C71.9, C72.3, C72.8, C75.3) diagnosed between 2000 and 2018, among patients under 21 years old, and meeting the third edition of the ICD-O-3 classification. Only patients with available survival time were included, and those with unknown or missing clinical features were excluded.

Other: Survival state

Chinese cohort

To assess the generalizability of the final model, an external validation cohort from China was used. This cohort consisted of 258 pediatric glioma patients diagnosed at Tangdu Hospital in Xi\'an, China, between January 2010 and December 2018. These patients had complete clinical data and comprehensive follow-up records.

Other: Survival state

Interventions

Survival state OTHER

We recorded clinically relevant information and survival status of pediatric glioma patients

Chinese cohort; SEER database

Eligibility Criteria

• Age: Up to 21 Years
• Sex: all
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Probability Sample

Study Population

the US Surveillance, Epidemiology, and End Results (SEER) between January 2000 and December 2018 and a Chinese registry (The Tangdu Hospital of the Fourth Military Medical Universitye) between January 2010 and December 2018

You may not qualify if:

• Only patients with available survival time were included, and those with unknown or missing clinical features were excluded.

Sponsors & Collaborators

• Tang-Du Hospital: lead

Study Sites (1)

Tangdu Hospital

Xi'an, Shannxi, 710000, China

Location

Related Publications (2)

• Thomas L, Li F, Pencina M. Using Propensity Score Methods to Create Target Populations in Observational Clinical Research. JAMA. 2020 Feb 4;323(5):466-467. doi: 10.1001/jama.2019.21558. No abstract available.

  PMID: 31922529 RESULT

• Doll KM, Rademaker A, Sosa JA. Practical Guide to Surgical Data Sets: Surveillance, Epidemiology, and End Results (SEER) Database. JAMA Surg. 2018 Jun 1;153(6):588-589. doi: 10.1001/jamasurg.2018.0501. No abstract available.

  PMID: 29617544 RESULT

MeSH Terms

Conditions

Glioma

Condition Hierarchy (Ancestors)

Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Glandular and Epithelial; Neoplasms, Nerve Tissue

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 27, 2023
• First Posted: January 10, 2024
• Study Start: September 20, 2022
• Primary Completion: August 16, 2023
• Study Completion: December 20, 2023
• Last Updated: January 10, 2024

Record last verified: 2023-12

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)