Author/Authors :
Faranoush، Mohammad نويسنده MAHAK Pediatric Cancer Treatment and Research Center (MPCTRC), Tehran, Iran , , TORABI-NAMI، Mohammad نويسنده PhD Candidate, Department of Neuroscience, Institute for Cognitive Science Studies (ICSS), SBMU, Tehran, Iran. , , Mehrvar، Azim نويسنده MAHAK Pediatric Cancer Treatment and Research Center (MPCTRC), Tehran, Iran , , HedayatiAsl، Amir Abbas نويسنده MAHAK Pediatric Cancer Treatment and Research Center (MPCTRC), Tehran, Iran , , Tashvighi، Maryam نويسنده MAHAK Pediatric Cancer Treatment and Research Center (MPCTRC), Tehran, Iran , , Ravan Parsa، Reza نويسنده Behphar Scientific Committee, Behphar Group, Tehran, Iran , , Fazeli، Mohammad Ali نويسنده MAHAK Pediatric Cancer Treatment and Research Center (MPCTRC), Tehran, Iran , , Sobuti، Behdad نويسنده MAHAK Pediatric Cancer Treatment and Research Center (MPCTRC), Tehran, Iran , , Mehrvar، Narjes نويسنده MAHAK Pediatric Cancer Treatment and Research Center (MPCTRC), Tehran, Iran , , Jafarpour Boroujeni، Ali Akbar نويسنده , , Zangooei، Rokhsareh نويسنده MAHAK Pediatric Cancer Treatment and Research Center (MPCTRC), Tehran, Iran , , Alebouyeh، Mardawij نويسنده MAHAK Pediatric Cancer Treatment and Research Center (MPCTRC), Tehran, Iran , , Abolghasemi، Mohammadreza نويسنده School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran , , Vahabie، Abdol-Hossein نويسنده School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran , , Vossough، Parvaneh نويسنده MAHAK Pediatric Cancer Treatment and Research Center (MPCTRC), Tehran, Iran ,
Abstract :
Background: Labeling, gathering mutual information, clustering and classification
of central nervous system tumors may assist in predicting not only distinct diagnoses
based on tumor-specific features but also prognosis. This study evaluates the epidemi-
ological features of central nervous system tumors in children who referred to Mahak’s
Pediatric Cancer Treatment and Research Center in Tehran, Iran.
Methods: This cohort (convenience sample) study comprised 198 children (?15
years old) with central nervous system tumors who referred to Mahakʹs Pediatric
Cancer Treatment and Research Center from 2007 to 2010. In addition to the descriptive
analyses on epidemiological features and mutual information, we used the Least
Squares Support Vector Machines method in MATLAB software to propose a
preliminary predictive model of pediatric central nervous system tumor feature-label
analysis.
Results:Of patients, there were 63.1% males and 36.9% females. Patientsʹ mean±SD
age was 6.11±3.65 years. Tumor location was as follows: supra-tentorial (30.3%), infra-
tentorial (67.7%) and 2% (spinal). The most frequent tumors registered were: high-grade
glioma (supra-tentorial) in 36 (59.99%) patients and medulloblastoma (infra-tentorial)
in 65 (48.51%) patients. The most prevalent clinical findings included vomiting,
headache and impaired vision. Gender, age, ethnicity, tumor stage and the presence of
metastasis were the features predictive of supra-tentorial tumor histology.
Conclusion: Our data agreed with previous reports on the epidemiology of central
nervous system tumors. Our feature-label analysis has shown how presenting features may
partially predict diagnosis. Timely diagnosis and management of central nervous system
tumors can lead to decreased disease burden and improved survival. This may be further
facilitated through development of partitioning, risk prediction and prognostic models.
