A Transformer‐Based microvascular invasion classifier enhances prognostic stratification in HCC following radiofrequency ablation

作者：Wentao Wang, Yueyue Wang, Danjun Song, Yingting Zhou, Rongkui Luo, Si-Qi Ying, Li Yang, Wei Sun, Jiabin Cai, Xi Wang, Zhen Bao, Jiaping Zheng, Mengsu Zeng, Qiang Gao, Xiaoying Wang, Jian Zhou, Manning Wang, Guoliang Shao, Shengxiang Rao, Kai Zhu · 发表于：Liver International · 年份：2024 · DOI：10.1111/liv.15846 · 被引用次数：18 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、Radiomics and Machine Learning in Medical Imaging、MRI in cancer diagnosis

BACKGROUND & AIMS: We aimed to develop a Transformer-based deep learning (DL) network for prognostic stratification in hepatocellular carcinoma (HCC) patients undergoing RFA. METHODS: A Swin Transformer DL network was trained to establish associations between magnetic resonance imaging (MRI) datasets and the ground truth of microvascular invasion (MVI) based on 696 surgical resection (SR) patients with solitary HCC ≤3 cm, and was validated in an external cohort (n = 180). The multiphase MRI-based DL risk outputs using an optimal threshold of .5 was employed as a MVI classifier for prognosis stratification in the RFA cohort (n = 180). RESULTS: Over 90% of all enrolled patients exhibited hepatitis B virus infection. Liver cirrhosis was significantly more prevalent in the RFA cohort compared to the SR cohort (72.2% vs. 44.1%, p < .001). The MVI risk outputs exhibited good performance (area under the curve values = .938 and .883) for predicting MVI in the training and validation cohort, respectively. The RFA patients at high risk of MVI classified by the MVI classifier demonstrated significantly lower recurrence-free survival (RFS) and overall survival rates at 1, 3 and 5 years compared to those classified as low risk (p < .001). Multivariate cox regression modelling of a-fetoprotein > 20 ng/mL [hazard ratio (HR) = 1.53; 95% confidence interval (95% CI): 1.02-2.33, p = .047], high risk of MVI (HR = 3.76; 95% CI: 2.40-5.88, p < .001) and unfavourable tumour location (HR = 2.15; 95...