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<dc:title xml:lang="fr">Histopathologie spectrale du cancer colique : développements d'outils chimiométriques pour le traitement automatisé des images tissulaires en spectroscopie moyen-infrarouge</dc:title>
<dcterms:alternative xml:lang="en">Spectral histopathology of colon cancer : developments of chemometric tools for automated processing of tissue images in mid-infrared spectroscopy</dcterms:alternative>
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<dcterms:abstract xml:lang="fr">Les cliniciens ont un besoin crucial de paramètres pour identifier en routine clinique, un patient dont la tumeur sera sensible ou non à un traitement. L’histopathologie spectrale par imagerie infrarouge mesure la composition biochimique des structures tissulaires sans coloration ni marquage. Bien que les études aient montré le potentiel diagnostique de cette technique, elle reste un outil de recherche sans être intégrée dans les services d’anatomopathologie. Ce travail de doctorat a porté sur des développements chimiométriques visant à rendre l’approche totalement automatisée pour un déploiement en routine clinique. Une méthode permettant d’éliminer les pixels parasites d’une image spectrale infrarouge a été élaborée ; ce développement est applicable aux tissus paraffinés et aux tissus congelés dans un gel biologique. Puis un algorithme de sélection non-supervisée de variables a été mis au point en vue d’optimiser l’exploitation du signal infrarouge. Ce travail trouve un intérêt dans l’identification de marqueurs spectroscopiques pour des applications médicales telle que la prédiction de la réponse au traitement des cancers du côlon métastasés.</dcterms:abstract>
<dcterms:abstract xml:lang="en">Clinicians have a crucial need for parameters to identify in clinical routine, a patient whose tumor will be sensitive or not to a treatment. Spectral histopathology by infrared imaging measures the biochemical composition of tissue structures without staining or labelling. Although studies have shown the diagnostic potential of this technique, it remains a research tool without being integrated into pathology departments. This doctoral work focused on chemometric developments aimed at making the approach fully automated for deployment in clinical routine. A method for removing outliers pixels from an infrared spectral image has been developed ; this development is applicable to paraffin-embedded tissues and to tissues frozen in a biological gel. Then, an algorithm of variable unsupervised selection was developed in order to optimize the exploitation of the infrared signal. This work finds an interest in the identification of spectroscopic markers for medical applications such as the prediction of the response to treatment of metastasized côlon cancers.</dcterms:abstract>
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