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arXiv · 2501.08097

Guiding the classification of hepatocellular carcinoma on 3D CT-scans using deep and handcrafted radiological features

E. Sarfati·A. Bône·M-M. Rohé·C. Aubé·M. Ronot·P. Gori·I. Bloch
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Abstract

Hepatocellular carcinoma is the most spread primary liver cancer across the world ($\sim$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 histology-proven HCC from CT images in order to reduce radiologists' inter-variability. 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.

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E. Sarfati, A. Bône, M-M. Rohé, C. Aubé, M. Ronot, P. Gori, I. Bloch. 2025-01-14. Guiding the classification of hepatocellular carcinoma on 3D CT-scans using deep and handcrafted radiological features. https://arxiv.org/abs/2501.08097

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