Transfer learning radiomic model predicts intratumoral tertiary lymphoid structures in hepatocellular carcinoma: a multicenter study

作者：Shichao Long, Mengsi Li, Juan Chen, Linhui Zhong, Ganmian Dai, Deng Pan, Wenguang Liu, Yi Feng, Yue Ruan, Bocheng Zou, Xiong Chen, Kai Fu, Wenzheng Li · 发表于：Journal for ImmunoTherapy of Cancer · 年份：2025 · DOI：10.1136/jitc-2024-011126 · 被引用次数：25 · 研究领域：Hepatocellular Carcinoma Treatment and Prognosis、Radiomics and Machine Learning in Medical Imaging、Ferroptosis and cancer prognosis

BACKGROUND: Intratumoral tertiary lymphoid structures (iTLS) in hepatocellular carcinoma (HCC) are associated with improved survival and may influence treatment decisions. However, their non-invasive detection remains challenging in HCC. We aim to develop a non-invasive model using baseline contrast-enhanced MRI to predict the iTLS status. METHODS: A total of 660 patients with HCC who underwent surgery were retrospectively recruited from four centers between October 2015 and January 2023 and divided into training, internal test, and external validation sets. After features dimensionality and selection, corresponding features were used to construct transfer learning radiomic (TLR) models for diagnosing iTLS, and model interpretability was explored with pathway analysis in The Cancer Genome Atlas-Liver HCC. The performances of models were assessed using the area under the receiver operating characteristic curve (AUC). The log-rank test was used to evaluate the prognostic value of the TLR model. The combination therapy set of 101 patients with advanced HCC treated with first-line anti-programmed death 1 or ligand 1 plus antiangiogenic treatment between January 2021 and January 2024 was used to investigate the value of the TLR model for evaluating the treatment response. RESULTS: The presence of iTLS was identified in 46.0% (n=308) patients. The TLR model demonstrated excellent performance in predicting the presence of iTLS in training (AUC=0.91, 95% CI: 0.87, 0.94), internal tes...