e-journal

A Statistical Modeling Approach for Tumor-Type Identification in Surgical Neuropathology Using Tissue Mass Spectrometry Imaging

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Abstract—Current clinical practice involves classification of biopsiedorresectedtumortissuebasedonahistopathologicalevaluation by a neuropathologist. In this paper, we propose a method for computer-aided histopathological evaluation using mass spectrometry imaging. Specifically, mass spectrometry imaging can be used to acquire the chemical composition of a tissue section and, hence, provides a framework to study the molecular composition of the sample while preserving the morphological features in the tissue.Theproposedclassificationframeworkusesstatisticalmodeling to identify the tumor type associated with a given sample. In addition,ifthetumortypeforagiventissuesampleisunknownor there is a great degree of uncertainty associated with assigning the tumor type to one of the known tumor models, then the algorithm rejects the given sample without classification. Due to the modular nature of the proposed framework, new tumor models can be added without the need to retrain the algorithm on all existing tumor models.

Index Terms—Classification, mass spectrometry (MS), neuropathology, statistical model.

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Ketersediaan

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Informasi Detail

Judul Seri

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No. Panggil

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Penerbit

: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS., 2013

Deskripsi Fisik

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, VOL. 17, NO. 3, MAY 2013 p. 734-744

Bahasa

English

ISBN/ISSN

2168-2194

Klasifikasi

NONE

Tipe Isi

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Tipe Media

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Tipe Pembawa

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Edisi

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Subjek

TEKNOLOGI KEDOKTERAN

Info Detail Spesifik

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Pernyataan Tanggungjawab

Behnood Gholami ... [et al.]

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