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Childhood Brain Cancer Progression and Outcome Data
Patient Health Records & Digital Health
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About
Clinical parameters and tumour size measurements for paediatric patients diagnosed with high-grade glioma offer a vital resource for investigating childhood brain cancer. By examining fifty-seven individual cases, researchers can delve into the nuances of disease progression and identify factors influencing mortality through binary outcome variables. This collection facilitates a deeper understanding of the pathological landscape of high-grade gliomas in children, aiding in the identification of critical symptoms and specific tumour characteristics that impact survival and long-term health.
Columns
- Age: The numerical age of the child at the time of diagnosis, which spans from 2 to 17 years within this group.
- Gender: The biological sex of the paediatric patient recorded in the study.
- Headache: A boolean indicator reflecting the status of headaches as a reported symptom, found in 70% of the cases.
- Epilepsy: A boolean field indicating the presence or absence of epileptic episodes.
- Hemparesis: A description or indicator relating to the specific site of the tumour within the brain.
- increaseICT: A boolean status indicating whether the patient experienced associated increased intracranial tension.
- Pathology: The specific classification of the disease, with Glioblastoma Multiforme (GBM) being the most common at 68%.
- Pathology_Grade: The numerical grade assigned to the tumour pathology, typically ranging between 3 and 4.
- Thalamic_extension: A record of whether the disease has extended into the thalamus.
- Bil_extension: An indicator of disease infiltration into the contralateral hemisphere of the brain.
Distribution
The data is provided in a CSV format titled
HGG dataset.csv with a file size of 9.53 kB. It consists of 57 valid records across 22 columns. The dataset demonstrates high integrity, with 100% validity for the primary clinical fields such as age, gender, and symptom status, and no recorded missing or mismatched values.Usage
This resource is ideally suited for training and validating machine learning models to predict paediatric oncology outcomes. It serves as a practical foundation for survival analysis and for studying the correlations between clinical symptoms, such as intracranial tension or epilepsy, and tumour pathology. Additionally, it is a valuable tool for academic research into the progression of high-grade gliomas and for teaching biostatistics in a medical context.
Coverage
The scope is focused exclusively on a paediatric population diagnosed with high-grade glioma. The age range of the subjects is 2 to 17 years. While the data represents 57 unique patients, providing a detailed view of clinical and pathological parameters, no specific geographic or temporal bounds are explicitly stated beyond the clinical nature of the study.
License
CC0: Public Domain
Who Can Use It
Oncologists and paediatric medical researchers can leverage these records to identify markers of disease progression and mortality. Data scientists can utilise the binary variables and clinical counts to develop predictive diagnostic tools. Furthermore, medical students and health analysts can use the high-validity samples to explore the statistical prevalence of symptoms in rare brain tumours.
Dataset Name Suggestions
- Paediatric High-Grade Glioma Clinical and Pathological Records
- Childhood Brain Cancer Progression and Outcome Data
- Paediatric HGG Symptom and Tumour Infiltration Archive
- High-Grade Glioma Paediatric Patient Clinical Registry
- Paediatric Oncology: HGG Clinical Parameters and Pathology Grade
Attributes
Original Data Source: Childhood Brain Cancer Progression and Outcome Data
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