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ORIGINAL RESEARCH article

Front. Oncol.
Sec. Gastrointestinal Cancers: Hepato Pancreatic Biliary Cancers
Volume 14 - 2024 | doi: 10.3389/fonc.2024.1456748
This article is part of the Research Topic Hepatobiliary tumors: Molecular Targets and Therapeutics View all articles

Unveiling Tim-3 Immune Checkpoint Expression in Hepatocellular Carcinoma through Abdominal Contrast-Enhanced CT Habitat Radiomics

Provisionally accepted
Zhishen Tang Zhishen Tang 1Wei Wang Wei Wang 1Bo Gao Bo Gao 2Xuyang Liu Xuyang Liu 1Xiangyu Liu Xiangyu Liu 1Yingquan Zhuo Yingquan Zhuo 1Jun Du Jun Du 1Fujun Ai Fujun Ai 3Xianwu Yang Xianwu Yang 1Huajian Gu Huajian Gu 1*
  • 1 Department of Pediatric Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China
  • 2 Department of Radiology, Affiliated Hospital of Guizhou Medical University, Guiyang, Guizhou Province, China
  • 3 Department of Physiology and Pathology, School of Basic Medical Sciences, Guizhou Medical University, Guiyang, Guizhou Province, China

The final, formatted version of the article will be published soon.

    Immune checkpoint inhibitor (ICI) are critical adjuvant therapies for hepatocellular carcinoma (HCC), with T-cell Immunoglobulin and Mucin-domain containing-3 (Tim-3) being a significant target for ICI treatment. We collected data from 424 HCC patients from The Cancer Genome Atlas (TCGA) and 102 pathologically confirmed HCC patients from our center for prognostic analysis. Multivariate Cox analysis from both our center and the TCGA database indicated that high Tim-3 expression is an independent risk factor for poor prognosis. Higher Tim-3 expression levels in HCC patients were significantly associated with worse prognosis. We then used the Kmeans algorithm to cluster the regions of interest in the arterial phase enhancement and venous phase enhancement images from our center's patients. Using pyradiomics, we extracted radiomic features from three subregions as well as the entire tumor. Five machine learning methods were employed to construct Habitat models based on habitat features and Rad models based on traditional radiomics features. We compared the predictive performance of ten models using ROC, DCA curves, and calibration curves. Results showed that the Habitat model constructed using the LightGBM algorithm (training set vs. test set AUC 0.866 vs. 0.824) had the best performance in predicting Tim-3 expression status. This study confirmed the importance of Tim-3 as a prognostic marker and developed a habitat radiomics model to predict intratumoral Tim-3 infiltration, supplementing the evaluation of ICI therapy in HCC patients.

    Keywords: Hepatocellular Carcinoma, Tim-3 Expression, habitat radiomics, Immunotherapy, bioanalysis

    Received: 06 Jul 2024; Accepted: 11 Oct 2024.

    Copyright: © 2024 Tang, Wang, Gao, Liu, Liu, Zhuo, Du, Ai, Yang and Gu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Huajian Gu, Department of Pediatric Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China

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