Machine learning-based prediction of glioma margin from 5-ALA induced PpIX fluorescence spectroscopy

被引:0
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作者
Pierre Leclerc
Cedric Ray
Laurent Mahieu-Williame
Laure Alston
Carole Frindel
Pierre-François Brevet
David Meyronet
Jacques Guyotat
Bruno Montcel
David Rousseau
机构
[1] Univ Lyon,
[2] Université Claude Bernard Lyon 1,undefined
[3] CNRS,undefined
[4] Institut Lumière Matière,undefined
[5] F-69622,undefined
[6] Villeurbanne,undefined
[7] France,undefined
[8] CREATIS,undefined
[9] Univ Lyon,undefined
[10] CNRS UMR5220,undefined
[11] INSERM U1044,undefined
[12] Université Claude Bernard Lyon1,undefined
[13] INSA Lyon,undefined
[14] Hospices Civils de Lyon,undefined
[15] Centre de Pathologie et de Neuropathologie Est,undefined
[16] Cancer Research Centre of Lyon,undefined
[17] Univ Lyon,undefined
[18] INSERM U1052,undefined
[19] CNRS UMR5286,undefined
[20] Lyon,undefined
[21] France,undefined
[22] Université Claude Bernard Lyon 1,undefined
[23] Laboratoire Angevin de Recherche en Ingénierie des Systèmes,undefined
[24] UMR INRA IRHS,undefined
[25] Université d’Angers,undefined
来源
Scientific Reports | / 10卷
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摘要
Gliomas are infiltrative brain tumors with a margin difficult to identify. 5-ALA induced PpIX fluorescence measurements are a clinical standard, but expert-based classification models still lack sensitivity and specificity. Here a fully automatic clustering method is proposed to discriminate glioma margin. This is obtained from spectroscopic fluorescent measurements acquired with a recently introduced intraoperative set up. We describe a data-driven selection of best spectral features and show how this improves results of margin prediction from healthy tissue by comparison with the standard biomarker-based prediction. This pilot study based on 10 patients and 50 samples shows promising results with a best performance of 77% of accuracy in healthy tissue prediction from margin tissue.
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