Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 71, 209–249 (2021).
Allaire, M. et al. What to do about hepatocellular carcinoma: Recommendations for health authorities from the International Liver Cancer Association. JHEP Rep. 4, 100578 (2022).
Park, J. W. et al. Global patterns of hepatocellular carcinoma management from diagnosis to death: The BRIDGE Study. Liver Int. 35, 2155–2166 (2015).
Allemani, C. et al. Global surveillance of trends in cancer survival 2000–14 (CONCORD-3): Analysis of individual records for 37 513 025 patients diagnosed with one of 18 cancers from 322 population-based registries in 71 countries. Lancet 391, 1023–1075 (2018).
Cammarota, A. et al. Immunotherapy in hepatocellular carcinoma: How will it reshape treatment sequencing?. Ther. Adv. Med. Oncol. 15, 17588359221148028 (2023).
Zou, H. et al. Clinical outcomes associated with monotherapy and combination therapy of immune checkpoint inhibitors as first-line treatment for advanced hepatocellular carcinoma in real-world practice: A systematic literature review and meta-analysis. Cancers 30(15), 260 (2022).
Zongyi, Y. & Xiaowu, L. Immunotherapy for hepatocellular carcinoma. Cancer Lett. 470, 8–17 (2020).
Zhong, C. et al. Immunotherapy for hepatocellular carcinoma: Current limits and prospects. Front. Oncol. 11, 589680 (2021).
Prieto, J., Melero, I. & Sangro, B. Immunological landscape and immunotherapy of hepatocellular carcinoma. Nat. Rev. Gastroenterol. Hepatol. 12, 681–700 (2015).
Salmaninejad, A. et al. PD-1 and cancer: Molecular mechanisms and polymorphisms. Immunogenetics 70, 73–86 (2018).
Chen, J., Jiang, C. C., Jin, L. & Zhang, X. D. Regulation of PD-L1: A novel role of pro-survival signalling in cancer. Ann. Oncol. 27, 409–416 (2016).
Gong, J., Chehrazi-Raffle, A., Reddi, S. & Salgia, R. Development of PD-1 and PD-L1 inhibitors as a form of cancer immunotherapy: A comprehensive review of registration trials and future considerations. J. Immunother. Cancer 6, 8 (2018).
Herbst, R. S. et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 515, 563–567 (2014).
Kim, H. D. et al. Association between expression level of PD1 by tumor-infiltrating CD8(+) T cells and features of hepatocellular carcinoma. Gastroenterology 155, 1936–1950 (2018).
Bertucci, F. et al. PDL1 expression is an independent prognostic factor in localized GIST. Oncoimmunology. 4, e1002729 (2015).
Sanmamed, M. F. & Chen, L. A paradigm shift in cancer immunotherapy: From enhancement to normalization. Cell 175, 313–326 (2018).
Jung, H. I. et al. Overexpression of PD-L1 and PD-L2 Is associated with poor prognosis in patients with hepatocellular carcinoma. Cancer Res Treat. 49, 246–254 (2017).
Umemoto, Y. et al. Prognostic impact of programmed cell death 1 ligand 1 expression in human leukocyte antigen class I-positive hepatocellular carcinoma after curative hepatectomy. J. Gastroenterol. 50, 65–75 (2015).
Xiang, X. et al. Prognostic value of PD-L1 expression in patients with primary solid tumors. Oncotarget 9, 5058–5072 (2018).
Dai, X. et al. Association of PD-L1 and HIF-1α coexpression with poor prognosis in hepatocellular carcinoma. Transl. Oncol. 11, 559–566 (2018).
Li, Z. et al. Expression and clinical significance of PD-1 in hepatocellular carcinoma tissues detected by a novel mouse anti-human PD-1 monoclonal antibody. Int. J. Oncol. 52, 2079–2092 (2018).
Takada, K. et al. Clinical significance of PD-L1 protein expression in surgically resected primary lung adenocarcinoma. J. Thorac. Oncol. 11, 1879–1890 (2016).
Brahmer, J. et al. Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N. Engl. J. Med. 373, 123–135 (2015).
Lambin, P. et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur. J. Cancer. 48, 441–446 (2012).
Aerts, H. J. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014).
Gong, X. Q. et al. Progress of MRI radiomics in hepatocellular carcinoma. Front. Oncol. 11, 698373 (2021).
Mao, Q. et al. Role of radiomics in the diagnosis and treatment of gastrointestinal cancer. World J. Gastroenterol. 28, 6002–6016 (2022).
Iseda, N. et al. ARID1A deficiency is associated with high programmed death ligand 1 expression in hepatocellular carcinoma. Hepatol. Commun. 5, 675–688 (2021).
Ihling, C. et al. Observational study of PD-L1, TGF-β, and immune cell infiltrates in hepatocellular carcinoma. Front. Med. 6, 15 (2019).
Bracci, S. et al. Quantitative CT texture analysis in predicting PD-L1 expression in locally advanced or metastatic NSCLC patients. Radiol. Med. 126, 1425–1433 (2021).
Greiner, M., Pfeiffer, D. & Smith, R. D. Principles and practical application of the receiver-operating characteristic analysis for diagnostic tests. Prev. Vet. Med. 45, 23–41 (2000).
Wang, W. et al. A radiomics-based biomarker for cytokeratin 19 status of hepatocellular carcinoma with gadoxetic acid-enhanced MRI. Eur. Radiol. 30, 3004–3014 (2020).
Kudo, M. Immune checkpoint inhibition in hepatocellular carcinoma: Basics and ongoing clinical trials. Oncology 92, 50–62 (2017).
Ma, J. et al. PD1(Hi) CD8(+) T cells correlate with exhausted signature and poor clinical outcome in hepatocellular carcinoma. J. Immunother. Cancer. 7, 331 (2019).
Semaan, A. et al. CXCL12 expression and PD-L1 expression serve as prognostic biomarkers in HCC and are induced by hypoxia. Virchows Arch. 470, 185–196 (2017).
Chen, C. L. et al. PD-L1 expression as a predictive biomarker for cytokine-induced killer cell immunotherapy in patients with hepatocellular carcinoma. Oncoimmunology. 5, e1176653 (2016).
Li, X. S., Li, J. W., Li, H. & Jiang, T. Prognostic value of programmed cell death ligand 1 (PD-L1) for hepatocellular carcinoma: A meta-analysis. Biosci. Rep. 40, BSR20200459 (2020).
Li, H. et al. Programmed cell death-1 (PD-1) checkpoint blockade in combination with a mammalian target of rapamycin inhibitor restrains hepatocellular carcinoma growth induced by hepatoma cell-intrinsic PD-1. Hepatology 66, 1920–1933 (2017).
Gu, X. et al. Increased programmed death ligand-1 expression predicts poor prognosis in hepatocellular carcinoma patients. Onco Targets Ther. 9, 4805–4813 (2016).
Yasuoka, H. et al. Increased both PD-L1 and PD-L2 expressions on monocytes of patients with hepatocellular carcinoma was associated with a poor prognosis. Sci. Rep. 10, 10377 (2020).
Sangro, B. et al. Diagnosis and management of toxicities of immune checkpoint inhibitors in hepatocellular carcinoma. J. Hepatol. 72, 320–341 (2020).
Sangro, B. et al. Association of inflammatory biomarkers with clinical outcomes in nivolumab-treated patients with advanced hepatocellular carcinoma. J. Hepatol. 73, 1460–1469 (2020).
Moon, S. H. et al. Correlations between metabolic texture features, genetic heterogeneity, and mutation burden in patients with lung cancer. Eur. J. Nucl. Med. Mol. Imaging. 46, 446–454 (2019).
Yoon, J. et al. Utility of CT radiomics for prediction of PD-L1 expression in advanced lung adenocarcinomas. Thorac. Cancer. 11, 993–1004 (2020).
Zheng, Y. M. et al. A CT-based radiomics signature for preoperative discrimination between high and low expression of programmed death ligand 1 in head and neck squamous cell carcinoma. Eur. J. Radiol. 146, 110093 (2022).
Mu, W. et al. Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images. J. Immunother. Cancer. 9, e002118 (2021).
Wen, Q., Yang, Z., Dai, H., Feng, A. & Li, Q. Radiomics study for predicting the expression of PD-L1 and tumor mutation burden in non-small cell lung cancer based on CT images and clinicopathological features. Front. Oncol. 11, 620246 (2021).
Zhou, J. et al. A novel approach using FDG-PET/CT-Based radiomics to assess tumor immune phenotypes in patients with non-small cell lung cancer. Front. Oncol. 11, 769272 (2021).
Jiang, M. et al. Assessing PD-L1 expression level by radiomic features from PET/CT in nonsmall cell lung cancer patients: An initial result. Acad. Radiol. 27, 171–179 (2020).
Lo Gullo, R. et al. Assessing PD-L1 expression status using radiomic features from contrast-enhanced breast MRI in breast cancer patients: Initial results. Cancers 13, 6273 (2021).
Yao, Z. et al. Preoperative diagnosis and prediction of hepatocellular carcinoma: Radiomics analysis based on multi-modal ultrasound images. BMC Cancer 18, 1089 (2018).
Tian, Y. et al. Assessing PD-L1 expression level via preoperative MRI in HCC based on integrating deep learning and radiomics features. Diagnostics 11, 1875 (2021).
Hectors, S. J. et al. MRI radiomic eatures predict immuno-oncological characteristics of hepatocellular carcinoma. Eur. Radiol. 30, 3759–3769 (2020).
Tao, Y. Y. et al. Radiomic analysis based on magnetic resonance imaging for predicting PD-L2 expression in hepatocellular carcinoma. Cancers 15, 365 (2023).
Hui, T. C. H., Chuah, T. K., Low, H. M. & Tan, C. H. Predicting early recurrence of hepatocellular carcinoma with texture analysis of preoperative MRI: A radiomics study. Clin. Radiol. 73, 1056 (2018).
Li, Y. et al. Texture analysis of multi-phase MRI images to detect expression of Ki67 in hepatocellular carcinoma. Clin. Radiol. 74, 813 (2019).
Zhang, R. et al. A nomogram based on bi-regional radiomic eatures from multimodal magnetic resonance imaging for preoperative prediction of microvascular invasion in hepatocellular carcinoma. Quant. Imaging Med. Surg. 9, 1503–1515 (2019).
Davnall, F. et al. Assessment of tumor heterogeneity: An emerging imaging tool for clinical practice?. Insights Imaging. 3, 573–589 (2012).
Ng, F., Ganeshan, B., Kozarski, R., Miles, K. A. & Goh, V. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: Contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology 266, 177–184 (2013).
Zhang, J. et al. Machine learning: An approach to preoperatively predict PD-1/PD-L1 expression and outcome in intrahepatic cholangiocarcinoma using MRI biomarkers. ESMO Open. 5, e000910 (2020).
Liao, H. et al. Preoperative radiomic approach to evaluate tumor-infiltrating CD8(+) T cells in hepatocellular carcinoma patients using contrast-enhanced computed tomography. Ann. Surg. Oncol. 26, 4537–4547 (2019).
Just, N. Improving tumour heterogeneity MRI assessment with histograms. Br. J. Cancer. 111, 2205–2213 (2014).
Shi, G. et al. Evaluation of multiple prognostic factors of hepatocellular carcinoma with intra-voxel incoherent motions imaging by extracting the histogram metrics. Cancer Manag. Res. 12, 6019–6031 (2020).
Sukowati, C., El-Khobar, K. E. & Tiribelli, C. Immunotherapy against programmed death-1/programmed death ligand 1 in hepatocellular carcinoma: Importance of molecular variations, cellular heterogeneity, and cancer stem cells. World J. Stem Cells. 13, 795–824 (2021).
Hu, K. et al. CLEC1B expression and PD-L1 expression predict clinical outcome in hepatocellular carcinoma with tumor hemorrhage. Transl. Oncol. 11, 552–558 (2018).
Calderaro, J. et al. Programmed death ligand 1 expression in hepatocellular carcinoma: Relationship with clinical and pathological features. Hepatology 64, 2038–2046 (2016).
Zhang, Q., Zhou, K., Liang, W. & Xiong, W. Prognostic and clinicopathological significance of PD-1 expression in hepatocellular carcinoma: A meta-analysis. J. Int. Med. Res. 48, 300060520962675 (2020).