GUIDING THE CLASSIFICATION OF HEPATOCELLULAR CARCINOMA ON 3D CT-SCANS USING DEEP AND HANDCRAFTED RADIOLOGICAL FEATURES
Résumé
Hepatocellular carcinoma is the most spread primary liver cancer across the world (∼80% of the liver tumors). The gold standard for HCC diagnosis is liver biopsy. However, in the clinical routine, expert radiologists provide a visual diagnosis by interpreting hepatic CT-scans according to a standardized protocol, the LI-RADS, which uses five radiological criteria with an associated decision tree. In this paper, we propose an automatic approach to predict histologyproven HCC from CT images in order to reduce radiologists' intervariability. We first show that standard deep learning methods fail to accurately predict HCC from CT-scans on a challenging database, and propose a two-step approach inspired by the LI-RADS system to improve the performance. We achieve improvements from 6 to 18 points of AUC with respect to deep learning baselines trained with different architectures. We also provide clinical validation of our method, achieving results that outperform non-expert radiologists and are on par with expert ones.
Domaines
Imagerie médicaleOrigine | Fichiers produits par l'(les) auteur(s) |
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Licence |
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