An Integral R-Banded Karyotype Analysis System of Bone Marrow Metaphases Based on Deep Learning.
Context.--: Conventional karyotype analysis, which provides comprehensive cytogenetic information, plays a significant role in the diagnosis and risk stratification of hematologic neoplasms. The main limitations of this approach include long turnaround time and laboriousness. Therefore, we developed...
| Publicado en: | Archives of Pathology & Laboratory Medicine Vol. 148; no. 8; pp. 905 - 914 |
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| Autores principales: | , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
College of American Pathologists
Aug2024
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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=178863288&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 178863288 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00039985 1FS jtl: Archives of Pathology & Laboratory Medicine issn: 00039985 maglogo: N pubinfo: dt: Aug2024 vid: 148 iid: 8 pid: 2550 pub: College of American Pathologists place: Northfield, Illinois artinfo: ui: 178863288 178863288 178863288 10.5858/arpa.2022-0533-OA 178863288 ppf: 905 ppct: 9 formats: fmt: @attributes: type: P tig: atl: An Integral R-Banded Karyotype Analysis System of Bone Marrow Metaphases Based on Deep Learning. aug: au: Jiyue Wang Chao Xia Yaling Fan Lu Jiang Guang Yang Zhijun Chen Jie Yang Bing Chen affil: Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China sug: subj: Bone Marrow Pathology Cell Division Karyotyping Methods Hematologic Neoplasms Diagnosis Hematologic Neoplasms Familial and Genetic Deep Learning Human Cell Physiology Cytogenetic Analysis Chromosomes Data Analysis Software Neural Networks (Computer) Models, Biological Image Processing, Computer Assisted ab: Context.--: Conventional karyotype analysis, which provides comprehensive cytogenetic information, plays a significant role in the diagnosis and risk stratification of hematologic neoplasms. The main limitations of this approach include long turnaround time and laboriousness. Therefore, we developed an integral R-banded karyotype analysis system for bone marrow metaphases, based on deep learning. Objective.--: To evaluate the performance of the internal models and the entire karyotype analysis system for R-banded bone marrow metaphase. Design.--: A total of 4442 sets of R-banded normal bone marrow metaphases and karyograms were collected. Accordingly, 4 deep learning-based models for different analytic stages of karyotyping, including denoising, segmentation, classification, and polarity recognition, were developed and integrated as an R-banded bone marrow karyotype analysis system. Five-fold cross validation was performed on each model. The whole system was implemented by 2 strategies of automatic and semiautomatic workflows. A test set of 885 metaphases was used to assess the entire system. Results.--: The denoising model achieved an intersection-over-union (IoU) of 99.20% and a Dice similarity coefficient (DSC) of 99.58% for metaphase acquisition. The segmentation model achieved an IoU of 91.95% and a DSC of 95.79% for chromosome segmentation. The accuracies of the segmentation, classification, and polarity recognition models were 96.77%, 98.77%, and 99.93%, respectively. The whole system achieved an accuracy of 93.33% with the automatic strategy and an accuracy of 99.06% with the semiautomatic strategy. Conclusions.--: The performance of both the internal models and the entire system is desirable. This deep learning-based karyotype analysis system has potential in a clinical application. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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