Classifying Pediatric Central Nervous System Tumors through near Optimal Feature Selection and Mutual Information: A Single Center Cohort.
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 epidemiological features of central nervous system tum...
| Publicado en: | Middle East Journal of Cancer Vol. 4; no. 4; pp. 153 - 163 |
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| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Middle East Journal of Cancer
2013
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=100792598&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 100792598 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 20086709 B7LE jtl: Middle East Journal of Cancer issn: 20086709 maglogo: N pubinfo: dt: 2013 vid: 4 iid: 4 pid: 67247 pub: Middle East Journal of Cancer artinfo: ui: 100792598 100792598 103749062 100792598 ppf: 153 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Classifying Pediatric Central Nervous System Tumors through near Optimal Feature Selection and Mutual Information: A Single Center Cohort. aug: au: Faranoush, Mohammad Torabi-Nami, Mohammad Mehrvar, Azim HedayatiAsl, Amir Abbas Tashvighi, Maryam Parsa, Reza Ravan Fazeli, Mohammad Ali Sobuti, Behdad Mehrvar, Narjes Jafarpour, Ali Zangooei, Rokhsareh Alebouyeh, Mardawij Abolghasemi, Mohammadreza Vahabie, Abdol-Hossein Vossough, Parvaneh affil: MAHAK Pediatric Cancer Treatment and Research Center (MPCTRC), Tehran, Iran sug: subj: Central Nervous System Neoplasms Classification Serial Publications Human Pediatrics Child Central Nervous System Neoplasms Physiopathology Central Nervous System Neoplasms Diagnosis Access to Information Outcomes (Health Care) Prospective Studies Male Female Data Analysis Descriptive Statistics Brain Anatomy and Histology Central Nervous System Neoplasms Epidemiology Data Analysis Software Chi Square Test Spearman's Rank Correlation Coefficient Glioma Diagnosis Central Nervous System Neoplasms Surgery Surgery, Operative Child: 6-12 years Male Female ab: 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 epidemiological 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 Mahak's 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. Patients' mean±SD age was 6.11±3.65 years. Tumor location was as follows: supra-tentorial (30.3%), infratentorial (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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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