Artificial Intelligence--Assisted Classification of Gliomas Using Whole Slide Images.
Context.--Glioma is the most common primary brain tumor in adults. The diagnosis and grading of different pathological subtypes of glioma is essential in treatment planning and prognosis. Objective.--To propose a deep learning--based approach for the automated classification of glioma histopathology...
| Published in: | Archives of Pathology & Laboratory Medicine Vol. 147; no. 8; pp. 916 - 925 |
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| Main Authors: | , , , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
College of American Pathologists
Aug2023
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=169944767&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 169944767 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Aug2023 vid: 147 iid: 8 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 169944767 169944767 169944767 10.5858/arpa.2021-0518-OA 169944767 ppf: 916 ppct: 9 formats: fmt: @attributes: type: P tig: atl: Artificial Intelligence--Assisted Classification of Gliomas Using Whole Slide Images. aug: au: Jose, Laya Sidong Liu Russo, Carlo Cong Cong Yang Song Rodriguez, Michael Di Ieva, Antonio affil: Computational NeuroSurgery Lab sug: subj: Glioma Classification Glioma Pathology Brain Neoplasms Classification Artificial Intelligence Utilization Deep Learning Methods World Health Organization Classification Human Neural Networks (Computer) Validation Studies Descriptive Statistics International Organization for Standardization ab: Context.--Glioma is the most common primary brain tumor in adults. The diagnosis and grading of different pathological subtypes of glioma is essential in treatment planning and prognosis. Objective.--To propose a deep learning--based approach for the automated classification of glioma histopathology images. Two classification methods, the ensemble method based on 2 binary classifiers and the multiclass method using a single multiclass classifier, were implemented to classify glioma images into astrocytoma, oligodendroglioma, and glioblastoma, according to the 5th edition of the World Health Organization classification of central nervous system tumors, published in 2021. Design.--We tested 2 different deep neural network architectures (VGG19 and ResNet50) and extensively validated the proposed approach based on The Cancer Genome Atlas data set (n ¼ 700). We also studied the effects of stain normalization and data augmentation on the glioma classification task. Results.--With the binary classifiers, our model could distinguish astrocytoma and oligodendroglioma (combined) from glioblastoma with an accuracy of 0.917 (area under the curve [AUC] = 0.976) and astrocytoma from oligodendroglioma (accuracy = 0.821, AUC = 0.865). The multiclass method (accuracy = 0.861, AUC = 0.961) outperformed the ensemble method (accuracy = 0.847, AUC = 0.933) with the best performance displayed by the ResNet50 architecture. Conclusions.--With the high performance of our model (.80%), the proposed method can assist pathologists and physicians to support examination and differential diagnosis of glioma histopathology images, with the aim to expedite personalized medical care. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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