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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1493735
This article is part of the Research Topic Myeloid Cell Immunity and Tumor Immunotherapy View all articles

Myeloid response evaluated by noninvasive CT imaging predicts post-surgical survival and immune checkpoint therapy benefits in patients with hepatocellular carcinoma

Provisionally accepted
Kangqiang Peng Kangqiang Peng 1*Xiao Zhang Xiao Zhang 2Zhongliang Li Zhongliang Li 2Yongchun Wang Yongchun Wang 3Hong-Wei Sun Hong-Wei Sun 2Wei Zhao Wei Zhao 2*Jielin Pan Jielin Pan 2*Xiao-Yang Zhang Xiao-Yang Zhang 4*Xiaoling Wu Xiaoling Wu 5*Xiangrong Yu Xiangrong Yu 2Chong Wu Chong Wu 6Yulan Weng Yulan Weng 6*Xiaowen Lin Xiaowen Lin 2*DingJie Liu DingJie Liu 2Meixiao Zhan Meixiao Zhan 2Jing Xu Jing Xu 1Limin Zheng Limin Zheng 6*Yaojun Zhang Yaojun Zhang 1*Ligong Lu Ligong Lu 2*
  • 1 Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China
  • 2 Zhuhai People's Hospital, Zhuhai, Guangdong Province, China
  • 3 Department of Pathology, Xiangya Hospital, Central South University, Changsha, Hunan Province, China
  • 4 School of Medicine and Bioinformatics Engineering, Northeastern University, Shenyang, Liaoning Province, China
  • 5 First Affiliated Hospital of Jinan University, Guangzhou, Guangdong Province, China
  • 6 Sun Yat-sen University, Guangzhou, Guangdong Province, China

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

    The potential of preoperative CT in the assessment of myeloid immune response and its application in predicting prognosis and immune-checkpoint therapy outcomes in hepatocellular carcinoma (HCC) has not been explored.Methods: 165 patients with pathological slides and multi-phase CT images were included to develop a radiomics signature for predicting imaging-based myeloid response score (iMRS).Overall survival (OS) and recurrence-free survival (RFS) were assessed according to the iMRS risk group and validated in a surgical resection cohort (n = 98).The complementary advantage of iMRS incorporating significant clinicopathologic factors was investigated by the Cox proportional hazards analysis. Additionally, the iMRS in interring the benefits of immune checkpoint therapy was explored in an immunotherapy cohort (n = 36). We showed that AUCs of the optimal radiomics signature for iMRS were 0.941 (95% confidence interval [CI], 0.909-0.973) and 0.833 (0.798-0.868) in the training and test cohorts, respectively. High iMRS was associated with poor RFS and OS. The prognostic performance of the Clinical-iMRS nomogram was better than that of a single parameter (P < 0.05), with 1-, 3-, and 5-year C-index for RFS of 0.729, 0.709, and 0.713 in the training, test, and surgical resection cohorts, respectively. A high iMRS score predicted a higher proportion of objective response (vs progressive disease or stable disease; odds ratio, 2.311; 95% CI, 1.144-4.672; P = 0.020; AUC, 0.718) in patients treated with anti-PD-1 and PD-L1.Conclusions: iMRS may provide a promising method for predicting local myeloid immune responses in HCC patients, inferring postsurgical prognosis, and evaluating benefits of immune checkpoint therapy.

    Keywords: Hepatocellular Carcinoma, Radiomics, Myeloid Cells, prognosis, Immunotherapy

    Received: 09 Sep 2024; Accepted: 12 Nov 2024.

    Copyright: © 2024 Peng, Zhang, Li, Wang, Sun, Zhao, Pan, Zhang, Wu, Yu, Wu, Weng, Lin, Liu, Zhan, Xu, Zheng, Zhang and Lu. 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:
    Kangqiang Peng, Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China
    Wei Zhao, Zhuhai People's Hospital, Zhuhai, Guangdong Province, China
    Jielin Pan, Zhuhai People's Hospital, Zhuhai, Guangdong Province, China
    Xiao-Yang Zhang, School of Medicine and Bioinformatics Engineering, Northeastern University, Shenyang, 110167, Liaoning Province, China
    Xiaoling Wu, First Affiliated Hospital of Jinan University, Guangzhou, 510630, Guangdong Province, China
    Yulan Weng, Sun Yat-sen University, Guangzhou, 510275, Guangdong Province, China
    Xiaowen Lin, Zhuhai People's Hospital, Zhuhai, Guangdong Province, China
    Limin Zheng, Sun Yat-sen University, Guangzhou, 510275, Guangdong Province, China
    Yaojun Zhang, Sun Yat-sen University Cancer Center (SYSUCC), Guangzhou, China
    Ligong Lu, Zhuhai People's Hospital, Zhuhai, Guangdong Province, China

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