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

Completed

• Conditions: Glioma
• Interventions: Other: Survival state

• Registration Number: NCT06199388
• Lead Sponsor: Tang-Du Hospital

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.

Detailed Description

This retrospective study focuses on survival prediction in pediatric glioma patients using a population-based approach. 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. 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.

Study Timeline

• Study Start: 2022-09-20
• Primary Completion: 2023-08-16
• Status Verified: 2023-12
• Study Completion: 2023-12-20
• Study First Submitted: 2023-12-27
• Study First Submitted QC: 2023-12-27
• Last Update Submitted: 2023-12-27
• Study First Posted: 2024-01-10
• Last Update Posted: 2024-01-10

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 9532

Inclusion Criteria

• 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, among patients under 21 years old, and meeting the third edition of the ICD-O-3 classification.

Exclusion Criteria

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

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Arm && Interventions

Group	Intervention	Description
SEER database	Survival state	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.
Chinese cohort	Survival state	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.

Primary Outcome Measures

Name	Time	Method
overall survival	2010.01-2018.12	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

Trial Locations

Locations (1)

Tangdu Hospital; 🇨🇳 Xi'an, Shannxi, China