Clinical trial NCT06199388: 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
Official title: 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
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Glioma
Lead sponsor: Tang-Du Hospital
Sponsor class: OTHER
First posted: 2024-01-10
Enrollment: 9532
Sex: ALL
Maximum age (years): 21
Standard ages: CHILD
ADULT
Observational model: COHORT
Time perspective: RETROSPECTIVE
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

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\&#39;an, China, between January 2010 and December 2018. These patients had complete clinical data and comprehensive follow-up records.

Eligibility criteria: 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.

Primary outcomes:
measure: overall survival
timeFrame: 2000.01-2018.12
measure: overall survival
timeFrame: 2010.01-2018.12
Secondary outcomes:
Other outcomes:
Locations:
city: Xi'an
country: China
facility: Tangdu Hospital
state: Shannxi
Study officials:
References:
citation: 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.
citation: 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.