ISSN 1004-4140
CN 11-3017/P
| Citation: |
CHEN S Y, E L N, WANG R H. Advances in Multimodal Imaging Techniques for Immunotherapy of Non-small Cell Lung Cancer[J]. CT Theory and Applications, 2025, 34(2): 333-338. DOI: 10.15953/j.ctta.2024.209. (in Chinese).
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With the emergence of tumor immunotherapy, the immune status of non-small cell lung cancer (NSCLC) is closely linked to treatment selection. Therefore, it is crucial to identify potential beneficiaries using effective methods prior to treatment. Multimodal imaging techniques are advantageous as they are non-invasive, comprehensive, and repeatable, making them valuable for assessing immune status, evaluating early treatment, and predicting long-term outcomes in NSCLC patients. This article aims to review the progress in the application of CT, MRI and PET/CT in NSCLC immunotherapy.
| [1] |
RIBAS A, WOLCHOK J D. Cancer immunotherapy using checkpoint blockade[J]. Science, 2018, 359(6382): 1350-1355. DOI: 10.1126/science.aar4060.
|
| [2] |
ALTORKI N K, MARKOWITZ G J, GAO D, et al. The lung microenvironment: an important regulator of tumour growth and metastasis[J]. Nature Reviews Cancer, 2019, 19(1): 9-31. DOI: 10.1038/s41568-018-0081-9.
|
| [3] |
冯源, 兰晓莉. 影像组学介绍[J]. 中华核医学与分子影像杂志, 2023, 43(1): 55-60. DOI: 10.3760/cma.j.cn321828-20211130-00427.
FENG Y, LAN X L. Introduction to radiomics[J]. Chinese Journal of Nuclear Medicine and Molecular Imaging, 2023, 43(1): 55-60. DOI: 10.3760/cma.j.cn321828-20211130-00427. (in Chinese).
|
| [4] |
CHEN M, LU H, COPLEY S J, et al. A novel radiogenomics biomarker for predicting treatment response and pneumotoxicity from programmed cell death protein or ligand-1 inhibition immunotherapy in NSCLC[J]. Journal of Thoracic Oncology, 2023, 18(6): 718-730. DOI: 10.1016/j.jtho.2023.01.089.
|
| [5] |
田琪, 冯峰, 陈巧玲, 等. CT影像组学列线图评估非小细胞肺癌程序性死亡受体1表达[J]. 中国医学影像技术, 2023, 39(4): 543-548. DOI: 10.13929/j.issn.1003-3289.2023.04.013.
TIAN Q, FENG F, CHEN Q L, et al. CT radiomics nomogram for evaluating programmed death receptor 1 expression of non-small cell lung cancer[J]. Chinese Journal of Medical Imaging Technology, 2023, 39(4): 543-548. DOI: 10.13929/j.issn.1003-3289.2023.04.013. (in Chinese).
|
| [6] |
赵恒亮, 孟闫凯, 岳思宇, 等. 双能量CT定量参数与T1期非小细胞肺癌PD-L1表达的相关性研究[J]. 临床放射学杂志, 2023, 42(5): 754-759. DOI: 10.13437/j.cnki.jcr.2023.05.019.
ZHAO H L, MENG Y K, YUE S Y, et al. Correlation between DECT quantitative parameters and PD-L1 expression in T1 Stage non-small cell lung cancer[J]. Journal of Clinical Radiology, 2023, 42(5): 754-759. DOI: 10.13437/j.cnki.jcr.2023.05.019. (in Chinese).
|
| [7] |
TIAN P, HE B, MU W, et al. Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images[J]. Theranostics, 2021, 11(5): 2098-2107. DOI: 10.7150/thno.48027.
|
| [8] |
郑小霞, 马娅琼, 崔雅琼, 等. 基于双层探测器光谱CT参数图影像组学预测非小细胞肺癌PD-L1表达的研究[J]. 临床放射学杂志, 2023, 42(7): 1129-1138. DOI: 10.13437/j.cnki.jcr.2023.07.025.
ZHENG X X, MA Y Q, CUI Y Q, et al. Radiomics study for predicting the expression of PD-L1 in non-small cell lung cancer based on dual-layer detector CT parameter images[J]. Journal of Clinical Radiology, 2023, 42(7): 1129-1138. DOI: 10.13437/j.cnki.jcr.2023.07.025. (in Chinese).
|
| [9] |
KHORRAMI M, PRASANNA P, GUPTA A, et al. Changes in CT radiomic features associated with lymphocyte distribution predict overall survival and response to immunotherapy in non-small cell lung cancer[J]. Cancer Immunology Research, 2020, 8(1): 108-119. DOI: 10.1158/2326-6066.Cir-19-0476.
|
| [10] |
PARK C, JEONG D Y, CHOI Y, et al. Tumor-infiltrating lymphocyte enrichment predicted by CT radiomics analysis is associated with clinical outcomes of non-small cell lung cancer patients receiving immune checkpoint inhibitors[J]. Frontiers in Immunology, 2022, 13: 1038089. DOI: 10.3389/fimmu.2022.1038089.
|
| [11] |
HE L, LI Z H, YAN L X, et al. Development and validation of a computed tomography-based immune ecosystem diversity index as an imaging biomarker in non-small cell lung cancer[J]. European Radiology, 2022, 32(12): 8726-8736. DOI: 10.1007/s00330-022-08873-6.
|
| [12] |
林文俊, 李凯. CT影像组学对T1期非小细胞肺癌中CD8+T淋巴细胞浸润程度的分析[J]. 医学影像学杂志, 2023, 33(11): 1989-1993.
LIN W J, LI K. The study of CD8+T lymphocyte infiltration in T1 stage non-small cell lung cancer based on CT image radiomic[J]. Journal of Medical Imaging, 2023, 33(11): 1989-1993. (in Chinese).
|
| [13] |
BACKMAN M, STRELL C, LINDBERG A, et al. Spatial immunophenotyping of the tumour microenvironment in non-small cell lung cancer[J]. European Journal of Cancer, 2023, 185: 40-52. DOI: 10.1016/j.ejca.2023.02.012.
|
| [14] |
中国临床肿瘤学会血管靶向治疗专家委员会, 中国临床肿瘤学会非小细胞肺癌专家委员会. 肿瘤突变负荷应用于肺癌免疫治疗的专家共识[J]. 中国肺癌杂志, 2021, 24(11): 743-752. DOI: 10.3779/j.issn.1009-3419.2021.101.40.
Chinese Society of Clinical Oncology, Expert Committee on Tumor Vascular-Targeted Therapy, Chinese Society of Clinical Oncology, Expert Committee on Non-Small Cell Lung Cancer. Expert consensus on tumor mutational burden for immunotherapy in lung cancer[J]. Chinese Journal of Lung Cancer, 2021, 24(11): 743-752. DOI: 10.3779/j.issn.1009-3419.2021.101.40. (in Chinese).
|
| [15] |
HE B, DONG D, SHE Y, et al. Predicting response to immunotherapy in advanced non-small-cell lung cancer using tumor mutational burden radiomic biomarker[J]. Journal for Immuno Therapy of Cancer, 2020, 8(2). DOI: 10.1136/jitc-2020-000550.
|
| [16] |
YANG J, SHI W, YANG Z, et al. Establishing a predictive model for tumor mutation burden status based on CT radiomics and clinical features of non-small cell lung cancer patients[J]. Translational Lung Cancer Research, 2023, 12(4): 808-823. DOI: 10.21037/tlcr-23-171.
|
| [17] |
陈康, 孙步彤. PD-1/PD-L1抑制剂在晚期肿瘤患者中的相关肺炎发生率和发生风险: 一项荟萃分析[J]. 中国肺癌杂志, 2020, 23(11): 927-940. DOI: 10.3779/j.issn.1009-3419.2020.103.14.
CHEN K, SUN B T. Incidence and risk of PD-1/PD-L1 Inhibitor-associated pneumonia in advance cancer patients: A meta-analysis[J]. Chinese Journal of Lung Cancer, 2020, 23(11): 927-940. DOI: 10.3779/j.issn.1009-3419.2020.103.14. (in Chinese).
|
| [18] |
李瑞, 高鹏云, 杨晓玲, 等. 免疫检查点抑制剂相关肺炎的临床及影像学特点分析[J]. 临床放射学杂志, 2023, 42(1): 56-61. DOI: 10.13437/j.cnki.jcr.2023.01.030.
LI R, GAO P Y, YANG X L, et al. Analysis of clinical and imaging characteristics of immune checkpoint inhibitor-related pneumonitis[J]. Journal of Clinical Radiology, 2023, 42(1): 56-61. DOI: 10.13437/j.cnki.jcr.2023.01.030. (in Chinese).
|
| [19] |
SCHROEDER K E, ACHARYA L, MANI H, et al. Radiomic biomarkers from chest computed tomography are assistive in immunotherapy response prediction for non-small cell lung cancer[J]. Translational Lung Cancer Research, 2023, 12(5): 1023-1033. DOI: 10.21037/tlcr-22-763.
|
| [20] |
郑家雷, 莫缓缓, 赵艳, 等. 信迪利单抗联合化疗新辅助治疗Ⅲ期非小细胞肺癌的疗效[J]. 分子影像学杂志, 2023, 46(5): 811-816. DOI: 10.12122/j.issn.1674-4500.2023.05.06.
ZHENG J L, MO Y Y, ZHAO Y, et al. Efficacy of sintilimab combined with chemotherapy neoadjuvant in the treatment of stage Ⅲ non-small cell lung cancer[J]. Journal of Molecular Imaging, 2023, 46(5): 811-816. DOI: 10.12122/j.issn.1674-4500.2023.05.06. (in Chinese).
|
| [21] |
SHE Y, HE B, WANG F, et al. Deep learning for predicting major pathological response to neoadjuvant chemoimmunotherapy in non-small cell lung cancer: A multicentre study[J]. EBio Medicine, 2022, 86: 104364. DOI: 10.1016/j.ebiom.2022.104364.
|
| [22] |
LIN Q, WU H J, SONG Q S, et al. CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy[J]. Frontiers in Oncology, 2022, 12: 937277. DOI: 10.3389/fonc.2022.937277.
|
| [23] |
中华医学会肿瘤学分会, 中华医学会杂志社. 中华医学会肺癌临床诊疗指南(2023版)[J]. 中华肿瘤杂志, 2023, 45(7): 539-574. DOI: 10.3760/cma.j.cn112152-20230510-00200.
Oncology Society of Chinese Medical Association, Chinese Medical Association Publishing House. Chinese Medical Association guideline for clinical diagnosis and treatment of lung cancer (2023 edition)[J]. Chinese Journal of Oncology, 2023, 45(7): 539-574. DOI: 10.3760/cma.j.cn112152-20230510-00200. (in Chinese).
|
| [24] |
JAZIEH K, KHORRAMI M, SAAD A, et al. Novel imaging biomarkers predict outcomes in stage III unresectable non-small cell lung cancer treated with chemoradiation and durvalumab[J]. Journal for Immuno Therapy of Cancer, 2022, 10(3). DOI: 10.1136/jitc-2021-003778.
|
| [25] |
ZHU Z, CHEN M, HU G, et al. A pre-treatment CT-based weighted radiomic approach combined with clinical characteristics to predict durable clinical benefits of immunotherapy in advanced lung cancer[J]. European Radiology, 2023, 33(6): 3918-3930. DOI: 10.1007/s00330-022-09337-7.
|
| [26] |
SAAD M B, HONG L, AMINU M, et al. Predicting benefit from immune checkpoint inhibitors in patients with non-small-cell lung cancer by CT-based ensemble deep learning: A retrospective study[J]. The Lancet Digital Health, 2023, 5(7): e404-e420. DOI: 10.1016/s2589-7500(23)00082-1.
|
| [27] |
杜希剑, 章凯敏, 陈斌, 等. CT影像组学对非小细胞肺癌免疫治疗疗效的预测价值[J]. 实用放射学杂志, 2023, 39(4): 548-551, 599. DOI: 10.3969/j.issn.1002-1671.2023.04.008.
DU X J, ZHANG K M, CHEN B, et al. Predicting the efficacy of immunotherapy in non-small cell lung cancer based on CT radiomics[J]. Journal of Practical Radiology, 2023, 39(4): 548-551, 599. DOI: 10.3969/j.issn.1002-1671.2023.04.008. (in Chinese).
|
| [28] |
GONG J, BAO X, WANG T, et al. A short-term follow-up CT based radiomics approach to predict response to immunotherapy in advanced non-small-cell lung cancer[J]. Oncoimmunology, 2022, 11(1): 2028962. DOI: 10.1080/2162402x.2022.2028962.
|
| [29] |
PARK D, OH D, LEE M, et al. Importance of CT image normalization in radiomics analysis: Prediction of 3-year recurrence-free survival in non-small cell lung cancer[J]. European Radiology, 2022, 32(12): 8716-8725. DOI: 10.1007/s00330-022-08869-2.
|
| [30] |
BORTOLOTTO C, STELLA G M, MESSANA G, et al. Correlation between PD-L1 expression of non-small cell lung cancer and data from IVIM-DWI acquired during magnetic resonance of the thorax: Preliminary results[J]. Cancers, 2022, 14(22). DOI: 10.3390/cancers14225634.
|
| [31] |
BAO X, BIAN D, YANG X, et al. Multiparametric MRI for evaluation of pathological response to the neoadjuvant chemo-immunotherapy in resectable non-small-cell lung cancer[J]. European Radiology, 2023, 33(12): 9182-9193. DOI: 10.1007/s00330-023-09813-8.
|
| [32] |
KARAYAMA M, YOSHIZAWA N, SUGIYAMA M, et al. Intravoxel incoherent motion magnetic resonance imaging for predicting the long-term efficacy of immune checkpoint inhibitors in patients with non-small-cell lung cancer[J]. Lung Cancer, 2020, 143: 47-54. DOI: 10.1016/j.lungcan.2020.03.013.
|
| [33] |
XU X, LI J, YANG Y, et al. The correlation between PD-L1 expression and metabolic parameters of (18)FDG PET/CT and the prognostic value of PD-L1 in non-small cell lung cancer[J]. Clinical Imaging, 2022, 89: 120-127. DOI: 10.1016/j.clinimag.2022.06.016.
|
| [34] |
段梦月, 胡春峰, 鲍慧新. PET/CT相关参数与非小细胞肺癌PD-L1表达的相关性研究[J]. 中国CT和MRI杂志, 2023, 21(5): 28-30. DOI: 10.3969/j.issn.1672-5131.2023.05.010.
DUAN M Y, HU C F, BAO H X. Correlation between PET/CT related parameters and PD-L1 expression in non-small cell lung cancer[J]. Chinese Journal of CT and MRI, 2023, 21(5): 28-30. DOI: 10.3969/j.issn.1672-5131.2023.05.010. (in Chinese).
|
| [35] |
HU B, JIN H, LI X, et al. The predictive value of total-body PET/CT in non-small cell lung cancer for the PD-L1 high expression[J]. Frontiers in Oncology, 2022, 12: 943933. DOI: 10.3389/fonc.2022.943933.
|
| [36] |
WANG Y, ZHAO N, WU Z, et al. New insight on the correlation of metabolic status on (18)F-FDG PET/CT with immune marker expression in patients with non-small cell lung cancer[J]. European Journal of Nuclear Medicine and Molecular Imaging, 2020, 47(5): 1127-1136. DOI: 10.1007/s00259-019-04500-7.
|
| [37] |
CHENG Y, CHEN Z Y, HUANG J J, et al. Efficacy evaluation of neoadjuvant immunotherapy plus chemotherapy for non-small-cell lung cancer: Comparison of PET/CT with postoperative pathology[J]. European Radiology, 2023, 33(10): 6625-6635. DOI: 10.1007/s00330-023-09922-4.
|
| [38] |
ZHUANG F, HAORAN E, HUANG J, et al. Utility of (18)F-FDG PET/CT uptake values in predicting response to neoadjuvant chemoimmunotherapy in resec-table non-small cell lung cancer[J]. Lung Cancer, 2023, 178: 20-27. DOI: 10.1016/j.lungcan.2023.02.001.
|
| [39] |
SILVA S B, WANDERLEY C W S, GOMES MARIN J F, et al. Tumor glycolytic profiling through (18)F-FDG PET/CT predicts immune checkpoint inhibitor efficacy in advanced NSCLC[J]. Therapeutic Advances in Medical Oncology, 2022, 14: 17588359221138386. DOI: 10.1177/17588359221138386.
|
| [40] |
MU W, JIANG L, SHI Y, et al. Non-invasive measurement of PD-L1 status and prediction of immunotherapy response using deep learning of PET/CT images[J]. Journal for ImmunoTherapy of Cancer, 2021, 9(6). DOI: 10.1136/jitc-2020-002118.
|
| [41] |
TONG H, SUN J, FANG J, et al. 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[J]. Frontiers in Immunology, 2022, 13: 859323. DOI: 10.3389/fimmu.2022.859323.
|
| [42] |
MU W, TUNALI I, QI J, et al. Radiomics of (18)F fluorodeoxyglucose PET/CT images predicts severe immune-related adverse events in patients with NSCLC[J]. Radiology: Artificial Intelligence, 2020, 2(1): e190063. DOI: 10.1148/ryai.2019190063.
|
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