Guiding the classification of hepatocellular carcinoma on 3D CT-scans using deep and handcrafted radiological features
This addresses the need for more consistent HCC diagnosis in clinical radiology, though it is incremental as it builds on existing LI-RADS protocols.
The paper tackled the problem of automatically predicting histology-proven hepatocellular carcinoma (HCC) from 3D CT-scans to reduce radiologists' inter-variability, achieving improvements of 6 to 18 AUC points over deep learning baselines and performing on par with expert radiologists.
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.