Is the Vascular Network Discriminant Enough to Classify Renal Cell Carcinoma?

Alexis Zubiolo 1, 2, 3 Eric Debreuve 1, 2, 4 Damien Ambrosetti 5, 3 Philippe Pognonec 6, 4 Xavier Descombes 1, 2
2 MORPHEME - Morphologie et Images
CRISAM - Inria Sophia Antipolis - Méditerranée , IBV - Institut de Biologie Valrose : U1091, Laboratoire I3S - SIS - Signal, Images et Systèmes
6 TIRO - UMR E4320 - Transporteurs en Imagerie et Radiothérapie en Oncologie
CEA - Commissariat à l'énergie atomique et aux énergies alternatives : DRF/BIAM, CNRS - Centre National de la Recherche Scientifique, TIRO-MATOs - UMR E4320 : UMR E4320
Abstract : The renal cell carcinoma (RCC) is the most frequent type of kidney cancer (between 90% and 95%). Twelve subtypes of RCC can be distinguished, among which the clear cell carcinoma (ccRCC) and the papillary carcinoma (pRCC) are the two most common ones (75% and 10% of the cases, respectively). After resection (i.e., surgical removal), the tumor is prepared for histological examination (fixation, slicing, staining, observation with a microscope). Along with protein expression and genetic tests, the histological study allows to classify the tumor and define its grade in order to make a prognosis and to take decisions for a potential additional chemotherapy treatment. Digital histology is a recent domain, since routinely, histological slices are studied directly under the microscope. The pioneer works deal with the automatic analysis of cells. However, a crucial factor for RCC classification is the tumoral architecture relying on the structure of the vascular network. For example, coarsely speaking, ccRCC is characterized by a ``fishnet'' structure while the pRCC has a tree-like structure. To our knowledge, no computerized analysis of the vascular network has been proposed yet. In this context, we developed a complete pipeline to extract the vascular network of a given histological slice and compute features of the underlying graph structure. Then, we studied the potential of such a feature-based approach in classifying a tumor into ccRCC or pRCC. Preliminary results on patient data are encouraging.
Type de document :
Communication dans un congrès
CBMI 2016 - International Workshop on Content-based Multimedia Indexing, Jun 2016, Bucarest, Romania. International Workshop on Content-based Multimedia Indexing, 〈10.1109/CBMI.2016.7500255〉
Liste complète des métadonnées

Littérature citée [20 références]  Voir  Masquer  Télécharger

https://hal.archives-ouvertes.fr/hal-01344247
Contributeur : Eric Debreuve <>
Soumis le : mardi 19 juillet 2016 - 17:09:30
Dernière modification le : lundi 5 novembre 2018 - 15:52:02

Fichier

cbmi-2016-hal.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

Collections

Citation

Alexis Zubiolo, Eric Debreuve, Damien Ambrosetti, Philippe Pognonec, Xavier Descombes. Is the Vascular Network Discriminant Enough to Classify Renal Cell Carcinoma?. CBMI 2016 - International Workshop on Content-based Multimedia Indexing, Jun 2016, Bucarest, Romania. International Workshop on Content-based Multimedia Indexing, 〈10.1109/CBMI.2016.7500255〉. 〈hal-01344247〉

Partager

Métriques

Consultations de la notice

549

Téléchargements de fichiers

114