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

• URL: http://arxiv.org/abs/2501.08097v1
• Date: Tue, 14 Jan 2025 13:10:29 GMT
• Title: Guiding the classification of hepatocellular carcinoma on 3D CT-scans using deep and handcrafted radiological features
• Authors: E. Sarfati, A. Bône, M-M. Rohé, C. Aubé, M. Ronot, P. Gori, I. Bloch,
• Abstract summary: 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.
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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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