Borrowing strength from adults: Transferability of AI algorithms for paediatric brain and tumour segmentation.
Purpose: AI brain tumour segmentation and brain extraction algorithms promise better diagnostic and follow-up of brain tumours in adults. The development of such tools for paediatric populations is restricted by limited training data but careful adaption of adult algorithms to paediatric population...
| Publicado en: | European Journal of Radiology Vol. 151 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Jun2022
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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=156765075&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156765075 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0720048X 3S7 jtl: European Journal of Radiology issn: 0720048X maglogo: N pubinfo: dt: Jun2022 vid: 151 pid: 1004 pub: Elsevier B.V. artinfo: ui: 156765075 156765075 NLM35405580 156765075 10.1016/j.ejrad.2022.110291 NLM35405580 156765075 ppct: 1 formats: tig: atl: Borrowing strength from adults: Transferability of AI algorithms for paediatric brain and tumour segmentation. aug: au: Drai, Maxime Testud, Benoit Brun, Gilles Hak, Jean-François Scavarda, Didier Girard, Nadine Stellmann, Jan-Patrick affil: APHM La Timone, Department of Neuroradiology, Marseille, France sug: subj: Brain Neoplasms Pathology Glioma Pathology Brain Neoplasms Glioma Child Algorithms Retrospective Design Brain Pathology Male Brain Artificial Intelligence Adult Magnetic Resonance Imaging Methods Image Processing, Computer Assisted Methods Child: 6-12 years Adult: 19-44 years Male ab: Purpose: AI brain tumour segmentation and brain extraction algorithms promise better diagnostic and follow-up of brain tumours in adults. The development of such tools for paediatric populations is restricted by limited training data but careful adaption of adult algorithms to paediatric population might be a solution. Here, we aim exploring the transferability of algorithms for brain (HD-BET) and tumour segmentation (HD-GLIOMA) in adults to paediatric imaging studies.Method: In a retrospective cohort, we compared automated segmentation with expert masks. We used the dice coefficient for evaluating the similarity and multivariate regressions for the influence of covariates. We explored the feasibility of automatic tumor classification based on diffusion data.Results: In 42 patients (mean age 7 years, 9 below 2 years, 26 males), segmentation was excellent for brain extraction (mean dice 0.99, range 0.85-1), moderate for segmentation of contrast-enhancing tumours (mean dice 0.67, range 0-1), and weak for non-enhancing T2-signal abnormalities (mean dice 0.41). Precision was better for enhancing tumour parts (p < 0.001) and for malignant histology (p = 0.006 and p = 0.012) but independent from myelinisation as indicated by the age (p = 0.472). Automated tumour grading based on mean diffusivity (MD) values from automated masks was good (AUC = 0.86) but tended to be less accurate than MD values from expert masks (AUC = 1, p = 0.208).Conclusion: HD-BET provides a reliable extraction of the paediatric brain. HD-GLIOMA works moderately for contrast-enhancing tumours parts. Without optimization, brain tumor AI algorithms trained on adults and used on paediatric patients may yield acceptable results depending on the clinical scenario. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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