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

arXiv: https://arxiv.org/abs/2501.08097 · HTML: https://mlsanity.com/paper/2501.08097/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 27 references that are in the map.
- First posted on arXiv 14 Jan 2025.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [Med3D: Transfer Learning for 3D Medical Image Analysis](https://mlsanity.com/paper/1904.00625/) (2019)
- [Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies](https://mlsanity.com/paper/2302.08427/) (2023)
- [Decoupled conditional contrastive learning with variable metadata for prostate lesion detection](https://mlsanity.com/paper/2308.09542/) (2023)
- [Weakly-supervised positional contrastive learning: application to cirrhosis classification](https://mlsanity.com/paper/2307.04617/) (2023)
- [Non-Redundant Combination of Hand-Crafted and Deep Learning Radiomics: Application to the Early Detection of Pancreatic Cancer](https://mlsanity.com/paper/2308.11389/) (2023)

## Abstract (arXiv)

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 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.