非小细胞肺癌免疫微环境的CT与PET/CT组学研究进展
Research Progress in CT and PET/CT Radiomics of the Tumor Immune Microenvironment in Non-Small Cell Lung Cancer
DOI: 10.12677/acm.2025.1582319, PDF,    科研立项经费支持
作者: 黄晓舟, 钱丹飞：绍兴文理学院医学院，浙江 绍兴；杨建峰*：绍兴市人民医院(绍兴文理学院附属第一医院)放射科，浙江 绍兴
关键词: 非小细胞肺癌；免疫微环境；影像组学；肿瘤浸润淋巴细胞；Non-Small Cell Lung Cancer； Tumor Immune Microenvironment； Radiomics； Tumor-Infiltrating Lymphocytes
摘要: 免疫疗法已在非小细胞肺癌(NSCLC)治疗中发挥重要作用。肿瘤免疫微环境特征强烈影响NSCLC免疫治疗的潜在反应，准确评估NSCLC免疫微环境状态对制定免疫治疗策略和评估患者预后具有重要意义。CT影像组学利用定量分析纹理特征反映肿瘤异质性，已被应用于评估免疫微环境的研究。本文就影像组学在NSCLC免疫微环境研究中的应用现状进行综述，并对其应用前景进行展望。
Abstract: Immunotherapy has played a pivotal role in the treatment of non-small cell lung cancer (NSCLC). The characteristics of the tumor immune microenvironment (TIME) significantly influence potential responses to immunotherapy in NSCLC, making accurate assessment of TIME status crucial for developing treatment strategies and evaluating patient prognosis. CT radiomics, through quantitative analysis of texture features that reflect tumor heterogeneity, has been increasingly applied in TIME evaluation research. This review summarizes current applications of radiomics in NSCLC TIME studies and discusses future perspectives.
文章引用：黄晓舟, 钱丹飞, 杨建峰. 非小细胞肺癌免疫微环境的CT与PET/CT组学研究进展[J]. 临床医学进展, 2025, 15(8): 942-949. https://doi.org/10.12677/acm.2025.1582319

参考文献

[1] Goldstraw, P., Ball, D., Jett, J.R., Le Chevalier, T., Lim, E., Nicholson, A.G., et al. (2011) Non-Small-Cell Lung Cancer. The Lancet, 378, 1727-1740. [Google Scholar] [CrossRef] [PubMed]
[2] Shintani, Y., Kimura, T., Funaki, S., Ose, N., Kanou, T. and Fukui, E. (2023) Therapeutic Targeting of Cancer-Associated Fibroblasts in the Non-Small Cell Lung Cancer Tumor Microenvironment. Cancers, 15, Article 335. [Google Scholar] [CrossRef] [PubMed]
[3] Pellat, A. and Barat, M. (2023) Tumor Microenvironment: A New Application for Radiomics. Diagnostic and Interventional Imaging, 104, 93-94. [Google Scholar] [CrossRef] [PubMed]
[4] Jin, M. and Jin, W. (2020) The Updated Landscape of Tumor Microenvironment and Drug Repurposing. Signal Transduction and Targeted Therapy, 5, Article No. 166. [Google Scholar] [CrossRef] [PubMed]
[5] Binnewies, M., Roberts, E.W., Kersten, K., Chan, V., Fearon, D.F., Merad, M., et al. (2018) Understanding the Tumor Immune Microenvironment (TIME) for Effective Therapy. Nature Medicine, 24, 541-550. [Google Scholar] [CrossRef] [PubMed]
[6] Chen, D.S. and Mellman, I. (2017) Elements of Cancer Immunity and the Cancer-Immune Set Point. Nature, 541, 321-330. [Google Scholar] [CrossRef] [PubMed]
[7] Gajewski, T.F., Corrales, L., Williams, J., Horton, B., Sivan, A. and Spranger, S. (2017) Cancer Immunotherapy Targets Based on Understanding the T Cell-Inflamed versus Non-T Cell-Inflamed Tumor Microenvironment. In: Kalinski, P., Ed., Tumor Immune Microenvironment in Cancer Progression and Cancer Therapy, Springer, 19-31. [Google Scholar] [CrossRef] [PubMed]
[8] Galon, J. and Bruni, D. (2019) Approaches to Treat Immune Hot, Altered and Cold Tumours with Combination Immunotherapies. Nature Reviews Drug Discovery, 18, 197-218. [Google Scholar] [CrossRef] [PubMed]
[9] Ugel, S., Canè, S., De Sanctis, F. and Bronte, V. (2021) Monocytes in the Tumor Microenvironment. Annual Review of Pathology: Mechanisms of Disease, 16, 93-122. [Google Scholar] [CrossRef] [PubMed]
[10] McGranahan, N. and Swanton, C. (2017) Clonal Heterogeneity and Tumor Evolution: Past, Present, and the Future. Cell, 168, 613-628. [Google Scholar] [CrossRef] [PubMed]
[11] McLaughlin, J., Han, G., Schalper, K.A., Carvajal-Hausdorf, D., Pelekanou, V., Rehman, J., et al. (2016) Quantitative Assessment of the Heterogeneity of PD-L1 Expression in Non-Small-Cell Lung Cancer. JAMA Oncology, 2, 46-54. [Google Scholar] [CrossRef] [PubMed]
[12] Jiménez-Sánchez, A., Memon, D., Pourpe, S., Veeraraghavan, H., Li, Y., Vargas, H.A., et al. (2017) Heterogeneous Tumor-Immune Microenvironments among Differentially Growing Metastases in an Ovarian Cancer Patient. Cell, 170, 927-938.e20. [Google Scholar] [CrossRef] [PubMed]
[13] Mansfield, A.S., Aubry, M.C., Moser, J.C., Harrington, S.M., Dronca, R.S., Park, S.S., et al. (2016) Temporal and Spatial Discordance of Programmed Cell Death-Ligand 1 Expression and Lymphocyte Tumor Infiltration between Paired Primary Lesions and Brain Metastases in Lung Cancer. Annals of Oncology, 27, 1953-1958. [Google Scholar] [CrossRef] [PubMed]
[14] Santos, R., Ursu, O., Gaulton, A., Bento, A.P., Donadi, R.S., Bologa, C.G., et al. (2016) A Comprehensive Map of Molecular Drug Targets. Nature Reviews Drug Discovery, 16, 19-34. [Google Scholar] [CrossRef] [PubMed]
[15] Chen, P., Liu, Y., Wen, Y. and Zhou, C. (2022) Non‐Small Cell Lung Cancer in China. Cancer Communications, 42, 937-970. [Google Scholar] [CrossRef] [PubMed]
[16] Incorvaia, L., Fanale, D., Badalamenti, G., Barraco, N., Bono, M., Corsini, L.R., et al. (2019) Programmed Death Ligand 1 (PD-L1) as a Predictive Biomarker for Pembrolizumab Therapy in Patients with Advanced Non-Small-Cell Lung Cancer (NSCLC). Advances in Therapy, 36, 2600-2617. [Google Scholar] [CrossRef] [PubMed]
[17] Chen, L. and Han, X. (2015) Anti-PD-1/PD-l1 Therapy of Human Cancer: Past, Present, and Future. Journal of Clinical Investigation, 125, 3384-3391. [Google Scholar] [CrossRef] [PubMed]
[18] Riley, J.L. (2009) PD‐1 Signaling in Primary T Cells. Immunological Reviews, 229, 114-125. [Google Scholar] [CrossRef] [PubMed]
[19] Lages, C.S., Lewkowich, I., Sproles, A., Wills‐Karp, M. and Chougnet, C. (2010) Partial Restoration of T‐Cell Function in Aged Mice by in Vitro Blockade of the PD‐1/PD‐L1 Pathway. Aging Cell, 9, 785-798. [Google Scholar] [CrossRef] [PubMed]
[20] Taube, J.M., Klein, A., Brahmer, J.R., Xu, H., Pan, X., Kim, J.H., et al. (2014) Association of PD-1, PD-1 Ligands, and Other Features of the Tumor Immune Microenvironment with Response to Anti-PD-1 Therapy. Clinical Cancer Research, 20, 5064-5074. [Google Scholar] [CrossRef] [PubMed]
[21] 朱闻捷, 朱豪华, 刘雨桃, 等. 程序性死亡蛋白1/程序性死亡蛋白配体1抑制剂治疗晚期非小细胞肺癌的疗效及疗效和预后预测标志物的真实世界研究[J]. 中华肿瘤杂志, 2022, 44(5): 416-424.
[22] Hanna, N., Johnson, D., Temin, S., Baker, S., Brahmer, J., Ellis, P.M., et al. (2017) Systemic Therapy for Stage IV Non-Small-Cell Lung Cancer: American Society of Clinical Oncology Clinical Practice Guideline Update. Journal of Clinical Oncology, 35, 3484-3515. [Google Scholar] [CrossRef] [PubMed]
[23] Rocco, D., Malapelle, U., Del Re, M., Della Gravara, L., Pepe, F., Danesi, R., et al. (2020) Pharmacodynamics of Current and Emerging PD-1 and PD-L1 Inhibitors for the Treatment of Non-Small Cell Lung Cancer. Expert Opinion on Drug Metabolism & Toxicology, 16, 87-96. [Google Scholar] [CrossRef] [PubMed]
[24] Doroshow, D.B., Bhalla, S., Beasley, M.B., Sholl, L.M., Kerr, K.M., Gnjatic, S., et al. (2021) PD-L1 as a Biomarker of Response to Immune-Checkpoint Inhibitors. Nature Reviews Clinical Oncology, 18, 345-362. [Google Scholar] [CrossRef] [PubMed]
[25] Mok, T.S.K., Wu, Y., Kudaba, I., Kowalski, D.M., Cho, B.C., Turna, H.Z., et al. (2019) Pembrolizumab versus Chemotherapy for Previously Untreated, Pd-L1-Expressing, Locally Advanced or Metastatic Non-Small-Cell Lung Cancer (KEYNOTE-042): A Randomised, Open-Label, Controlled, Phase 3 Trial. The Lancet, 393, 1819-1830. [Google Scholar] [CrossRef] [PubMed]
[26] Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R.G.P.M., Granton, P., et al. (2012) Radiomics: Extracting More Information from Medical Images Using Advanced Feature Analysis. European Journal of Cancer, 48, 441-446. [Google Scholar] [CrossRef] [PubMed]
[27] Aerts, H.J.W.L., Velazquez, E.R., Leijenaar, R.T.H., Parmar, C., Grossmann, P., Carvalho, S., et al. (2014) Decoding Tumour Phenotype by Noninvasive Imaging Using a Quantitative Radiomics Approach. Nature Communications, 5, Article No. 4006. [Google Scholar] [CrossRef] [PubMed]
[28] Liu, Z., Wang, S., Dong, D., Wei, J., Fang, C., Zhou, X., et al. (2019) The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics, 9, 1303-1322. [Google Scholar] [CrossRef] [PubMed]
[29] Wen, Q., Yang, Z., Dai, H., Feng, A. and Li, Q. (2021) 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. Frontiers in Oncology, 11, Article 620246. [Google Scholar] [CrossRef] [PubMed]
[30] Mu, W., Katsoulakis, E., Whelan, C.J., Gage, K.L., Schabath, M.B. and Gillies, R.J. (2021) Radiomics Predicts Risk of Cachexia in Advanced NSCLC Patients Treated with Immune Checkpoint Inhibitors. British Journal of Cancer, 125, 229-239. [Google Scholar] [CrossRef] [PubMed]
[31] Li, B., Su, J., Liu, K. and Hu, C. (2024) Deep Learning Radiomics Model Based on PET/CT Predicts PD-L1 Expression in Non-Small Cell Lung Cancer. European Journal of Radiology Open, 12, Article ID: 100549. [Google Scholar] [CrossRef] [PubMed]
[32] Jiang, M., Sun, D., Guo, Y., Guo, Y., Xiao, J., Wang, L., et al. (2020) Assessing PD-L1 Expression Level by Radiomic Features from PET/CT in Nonsmall Cell Lung Cancer Patients: An Initial Result. Academic Radiology, 27, 171-179. [Google Scholar] [CrossRef] [PubMed]
[33] Vaidya, P., Bera, K., Patil, P.D., Gupta, A., Jain, P., Alilou, M., et al. (2020) Novel, Non-Invasive Imaging Approach to Identify Patients with Advanced Non-Small Cell Lung Cancer at Risk of Hyperprogressive Disease with Immune Checkpoint Blockade. Journal for ImmunoTherapy of Cancer, 8, e001343. [Google Scholar] [CrossRef] [PubMed]
[34] Liu, Y., Wu, M., Zhang, Y., Luo, Y., He, S., Wang, Y., et al. (2021) Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer. Frontiers in Oncology, 11, Article 657615. [Google Scholar] [CrossRef] [PubMed]
[35] Bracci, S., Dolciami, M., Trobiani, C., Izzo, A., Pernazza, A., D’Amati, G., et al. (2021) Quantitative CT Texture Analysis in Predicting PD-L1 Expression in Locally Advanced or Metastatic NSCLC Patients. La radiologia medica, 126, 1425-1433. [Google Scholar] [CrossRef] [PubMed]
[36] Ren, Q., Xiong, F., Zhu, P., Chang, X., Wang, G., He, N., et al. (2022) Assessing the Robustness of Radiomics/Deep Learning Approach in the Identification of Efficacy of Anti-PD-1 Treatment in Advanced or Metastatic Non-Small Cell Lung Carcinoma Patients. Frontiers in Oncology, 12, Article 952749. [Google Scholar] [CrossRef] [PubMed]
[37] Wang, C., Ma, J., Shao, J., Zhang, S., Liu, Z., Yu, Y., et al. (2022) Predicting EGFR and PD-L1 Status in NSCLC Patients Using Multitask AI System Based on CT Images. Frontiers in Immunology, 13, Article 813072. [Google Scholar] [CrossRef] [PubMed]
[38] Zhou, J., Zou, S., Kuang, D., Yan, J., Zhao, J. and Zhu, X. (2021) A Novel Approach Using FDG-PET/CT-Based Radiomics to Assess Tumor Immune Phenotypes in Patients with Non-Small Cell Lung Cancer. Frontiers in Oncology, 11, Article 769272. [Google Scholar] [CrossRef] [PubMed]
[39] Altorki, N.K., Markowitz, G.J., Gao, D., Port, J.L., Saxena, A., Stiles, B., et al. (2018) The Lung Microenvironment: An Important Regulator of Tumour Growth and Metastasis. Nature Reviews Cancer, 19, 9-31. [Google Scholar] [CrossRef] [PubMed]
[40] Tumeh, P.C., Harview, C.L., Yearley, J.H., Shintaku, I.P., Taylor, E.J.M., Robert, L., et al. (2014) PD-1 Blockade Induces Responses by Inhibiting Adaptive Immune Resistance. Nature, 515, 568-571. [Google Scholar] [CrossRef] [PubMed]
[41] Tozaki, M., Sakamoto, M., Oyama, Y., Maruyama, K. and Fukuma, E. (2010) Predicting Pathological Response to Neoadjuvant Chemotherapy in Breast Cancer with Quantitative 1H MR Spectroscopy Using the External Standard Method. Journal of Magnetic Resonance Imaging, 31, 895-902. [Google Scholar] [CrossRef] [PubMed]
[42] Xie, W., Jiang, S., Xin, F., Jiang, Z., Pan, W., Zhou, X., et al. (2024) Prediction of CD8+T Lymphocyte Infiltration Levels in Gastric Cancer from Contrast‐Enhanced CT and Clinical Factors Using Machine Learning. Medical Physics, 51, 7108-7118. [Google Scholar] [CrossRef] [PubMed]
[43] Liao, H., Zhang, Z., Chen, J., Liao, M., Xu, L., Wu, Z., et al. (2019) Preoperative Radiomic Approach to Evaluate Tumor-Infiltrating CD8+ T Cells in Hepatocellular Carcinoma Patients Using Contrast-Enhanced Computed Tomography. Annals of Surgical Oncology, 26, 4537-4547. [Google Scholar] [CrossRef] [PubMed]
[44] Tong, H., Sun, J., Fang, J., Zhang, M., Liu, H., Xia, R., et al. (2022) A Machine Learning Model Based on PET/CT Radiomics and Clinical Characteristics Predicts Tumor Immune Profiles in Non-Small Cell Lung Cancer: A Retrospective Multicohort Study. Frontiers in Immunology, 13, Article 859323. [Google Scholar] [CrossRef] [PubMed]
[45] Chen, Y., Xu, T., Jiang, C., You, S., Cheng, Z. and Gong, J. (2022) CT-Based Radiomics Signature to Predict CD8+ Tumor Infiltrating Lymphocytes in Non-Small-Cell Lung Cancer. Acta Radiologica, 64, 1390-1399. [Google Scholar] [CrossRef] [PubMed]
[46] Mazzaschi, G., Milanese, G., Pagano, P., Madeddu, D., Gnetti, L., Trentini, F., et al. (2020) Integrated CT Imaging and Tissue Immune Features Disclose a Radio-Immune Signature with High Prognostic Impact on Surgically Resected NSCLC. Lung Cancer, 144, 30-39. [Google Scholar] [CrossRef] [PubMed]
[47] Trentini, F., Mazzaschi, G., Milanese, G., Pavone, C., Madeddu, D., Gnetti, L., et al. (2021) Validation of a Radiomic Approach to Decipher NSCLC Immune Microenvironment in Surgically Resected Patients. Tumori Journal, 108, 86-92. [Google Scholar] [CrossRef] [PubMed]
[48] Yoon, H.J., Kang, J., Park, H., Sohn, I., Lee, S. and Lee, H.Y. (2020) Deciphering the Tumor Microenvironment through Radiomics in Non-Small Cell Lung Cancer: Correlation with Immune Profiles. PLOS ONE, 15, e0231227. [Google Scholar] [CrossRef] [PubMed]
[49] Kim, H., Kwon, H.J., Han, Y.B., Park, S.Y., Kim, E.S., Kim, S.H., et al. (2019) Increased CD3+ T Cells with a Low FOXP3+/CD8+ T Cell Ratio Can Predict Anti-PD-1 Therapeutic Response in Non-Small Cell Lung Cancer Patients. Modern Pathology, 32, 367-375. [Google Scholar] [CrossRef] [PubMed]
[50] Li, H., Yu, H., Lan, S., Zhao, D., Liu, Y. and Cheng, Y. (2021) Aberrant Alteration of Circulating Lymphocyte Subsets in Small Cell Lung Cancer Patients Treated with Radiotherapy. Technology in Cancer Research & Treatment, 20, 1-10. [Google Scholar] [CrossRef] [PubMed]
[51] Geng, Y., Shao, Y., He, W., Hu, W., Xu, Y., Chen, J., et al. (2015) Prognostic Role of Tumor-Infiltrating Lymphocytes in Lung Cancer: A Meta-analysis. Cellular Physiology and Biochemistry, 37, 1560-1571. [Google Scholar] [CrossRef] [PubMed]
[52] Hendry, S., Salgado, R., Gevaert, T., Russell, P.A., John, T., Thapa, B., et al. (2017) Assessing Tumor-Infiltrating Lymphocytes in Solid Tumors: A Practical Review for Pathologists and Proposal for a Standardized Method from the International Immuno-Oncology Biomarkers Working Group: Part 2: Tils in Melanoma, Gastrointestinal Tract Carcinomas, Non-Small Cell Lung Carcinoma and Mesothelioma, Endometrial and Ovarian Carcinomas, Squamous Cell Carcinoma of the Head and Neck, Genitourinary Carcinomas, and Primary Brain Tumors. Advances in Anatomic Pathology, 24, 311-335. [Google Scholar] [CrossRef] [PubMed]
[53] Zeng, D., Yu, Y., Ou, Q., Li, X., Zhong, R., Xie, C., et al. (2016) Prognostic and Predictive Value of Tumor-Infiltrating Lymphocytes for Clinical Therapeutic Research in Patients with Non-Small Cell Lung Cancer. Oncotarget, 7, 13765-13781. [Google Scholar] [CrossRef] [PubMed]
[54] Chen, L., Chen, L., Ni, H., Shen, L., Wei, J., Xia, Y., et al. (2023) Prediction of CD3 T Cells and CD8 T Cells Expression Levels in Non-Small Cell Lung Cancer Based on Radiomic Features of CT Images. Frontiers in Oncology, 13, Article 1104316. [Google Scholar] [CrossRef] [PubMed]
[55] Patel, S.A., Nilsson, M.B., Yang, Y., Le, X., Tran, H.T., Elamin, Y.Y., et al. (2023) IL6 Mediates Suppression of T-and NK-Cell Function in EMT-Associated TKI-Resistant EGFR-Mutant NSCLC. Clinical Cancer Research, 29, 1292-1304. [Google Scholar] [CrossRef] [PubMed]
[56] Chuang, T., Lai, W., Gabre, J.L., Lind, D.E., Umapathy, G., Bokhari, A.A., et al. (2023) ALK Fusion NSCLC Oncogenes Promote Survival and Inhibit NK Cell Responses via SERPINB4 Expression. Proceedings of the National Academy of Sciences of the United States of America, 120, e2216479120. [Google Scholar] [CrossRef] [PubMed]
[57] Meng, X., Xu, H., Liang, Y., Liang, M., Song, W., Zhou, B., et al. (2024) Enhanced CT-Based Radiomics Model to Predict Natural Killer Cell Infiltration and Clinical Prognosis in Non-Small Cell Lung Cancer. Frontiers in Immunology, 14, Article 1334886. [Google Scholar] [CrossRef] [PubMed]
[58] Hou, R., Xia, W., Zhang, C., Shao, Y., Zhu, X., Feng, W., et al. (2023) Dosiomics and Radiomics Improve the Prediction of Post‐Radiotherapy Neutrophil‐lymphocyte Ratio in Locally Advanced Non‐Small Cell Lung Cancer. Medical Physics, 51, 650-661. [Google Scholar] [CrossRef] [PubMed]
[59] Weisberg, E.M., Chu, L.C., Park, S., Yuille, A.L., Kinzler, K.W., Vogelstein, B., et al. (2020) Deep Lessons Learned: Radiology, Oncology, Pathology, and Computer Science Experts Unite around Artificial Intelligence to Strive for Earlier Pancreatic Cancer Diagnosis. Diagnostic and Interventional Imaging, 101, 111-115. [Google Scholar] [CrossRef] [PubMed]
[60] Kang, J., Rancati, T., Lee, S., Oh, J.H., Kerns, S.L., Scott, J.G., et al. (2018) Machine Learning and Radiogenomics: Lessons Learned and Future Directions. Frontiers in Oncology, 8, Article 228. [Google Scholar] [CrossRef] [PubMed]
[61] Harding‐Theobald, E., Louissaint, J., Maraj, B., Cuaresma, E., Townsend, W., Mendiratta‐Lala, M., et al. (2021) Systematic Review: Radiomics for the Diagnosis and Prognosis of Hepatocellular Carcinoma. Alimentary Pharmacology & Therapeutics, 54, 890-901. [Google Scholar] [CrossRef] [PubMed]
[62] Duron, L., Savatovsky, J., Fournier, L. and Lecler, A. (2021) Can We Use Radiomics in Ultrasound Imaging? Impact of Preprocessing on Feature Repeatability. Diagnostic and Interventional Imaging, 102, 659-667. [Google Scholar] [CrossRef] [PubMed]
[63] Qi, Y., Zhao, T. and Han, M. (2022) The Application of Radiomics in Predicting Gene Mutations in Cancer. European Radiology, 32, 4014-4024. [Google Scholar] [CrossRef] [PubMed]

为你推荐