影像组学在卵巢癌免疫治疗预后中的研究进展
Research Progress of Imaging Histology in the Prognosis of Ovarian Cancer Immunotherapy
DOI: 10.12677/acm.2024.1451488, PDF,   
作者: 郑子金, 吕晋谊, 朱前勇*:河南大学人民医院妇产科,河南 郑州;刘 宁:郑州大学人民医院妇产科,河南 郑州;李柯静:新乡医学院人民医院妇产科,河南 郑州
关键词: 影像组学卵巢癌免疫治疗预后Imaging Histology Ovarian Cancer Immunotherapy Prognosis
摘要: 卵巢癌的免疫治疗为提高患者生存率开辟了新途径,尤其是免疫检查点抑制剂在卵巢癌治疗中的应用。然而,免疫治疗效果受多因素综合影响,及早预测免疫治疗疗效指标对于指导治疗更为重要。影像组学技术通过整合CT、MRI、PET-CT等多种影像学技术建立预测模型,深度挖掘图像信息,以无创方式全面评估肿瘤整体情况。本文综述了目前影像组学在卵巢癌免疫治疗预后中的研究进展,强调其在提高治疗效果预测准确性、个体化治疗方案制定方面的潜力。
Abstract: Immunotherapy for ovarian cancer has opened a new way to improve the survival rate of patients, especially the application of immune checkpoint inhibitors in the treatment of ovarian cancer. However, the effect of immunotherapy is affected by a combination of factors, and early prediction of immunotherapy efficacy indicators is more important to guide treatment. Imaging histology technology integrates CT, MRI, PET-CT and other imaging technologies to establish a prediction model, deeply exploits image information, and comprehensively evaluates the overall condition of the tumor in a non-invasive manner. This article reviews the current research progress of imaging histology in the prognosis of ovarian cancer immunotherapy, emphasizing its potential in improving the accuracy of treatment effect prediction and individualized treatment plan development.
文章引用:郑子金, 刘宁, 李柯静, 吕晋谊, 朱前勇. 影像组学在卵巢癌免疫治疗预后中的研究进展[J]. 临床医学进展, 2024, 14(5): 759-766. https://doi.org/10.12677/acm.2024.1451488

参考文献

[1] Sung, H., Ferlay, J., Siegel, R.L., et al. (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CaA Cancer Journal for Clinicians, 71, 209-249. [Google Scholar] [CrossRef] [PubMed]
[2] Schuurman, M.S., Kruitwagen, R., Portielje, J.E.A., Roes, E.M., Lemmens, V. and Van Deraa, M.A. (2018) Treatment and Outcome of Elderly Patients with Advanced Stage Ovarian Cancer: A Nationwide Analysis. Gynecologic Oncology, 149, 270-274. [Google Scholar] [CrossRef] [PubMed]
[3] Kaiser, J. and Couzin-Frankel, J. (2018) Cancer Immunotherapy Sweeps Nobel for Medicine. Science (New York, NY), 362, 13. [Google Scholar] [CrossRef] [PubMed]
[4] 薛春燕, 谢荃沁, 徐云, 等. PD-L1和PD-L2在卵巢癌组织中的表达及对患者预后判断的价值[J]. 现代免疫学, 2020, 40(1): 1-8.
[5] Akkin, S., Varan, G. and Bilensoy, E. (2021) A Review on Cancer Immunotherapy and Applications of Nanotechnology to Chemoimmunotherapy of Different Cancers. Molecules (Basel, Switzerland), 26, Article No. 3382. [Google Scholar] [CrossRef] [PubMed]
[6] Gillies, R.J., Anderson, A.R., Gatenby, R.A., et al. (2010) The Biology Underlying Molecular Imaging in Oncology: From Genome to Anatome and Back Again. Clinical Radiology, 65, 517-521. [Google Scholar] [CrossRef] [PubMed]
[7] Khorrami, M., Prasanna, P., Gupta, A., et al. (2020) Changes in CT Radiomic Features Associated with Lymphocyte Distribution Predict Overall Survival and Response to Immunotherapy in Non-Small Cell Lung Cancer. Cancer Immunology Research, 8, 108-119. [Google Scholar] [CrossRef
[8] Wu, J., Mayer, A.T. and Li, R. (2022) Integrated Imaging and Molecular Analysis to Decipher Tumor Microenvironment in the Era of Immunotherapy. Seminars in Cancer Biology, 84, 310-328. [Google Scholar] [CrossRef] [PubMed]
[9] Xiong, S. and Tang, K. (2022) A Diagnostic Dilemma of a Pulmonary Nodule of a Patient Who Suffered Advanced Ovarian Cancer: A Case Report and a Hypothesis. International Journal of Surgery Case Reports, 94, Article ID: 107111. [Google Scholar] [CrossRef] [PubMed]
[10] 孟靖涵, 何秀丽. IOTA SR与CA125、HE4、ROMA、RMI1、GI-RADS对卵巢良恶性肿瘤的诊断价值比较[J]. 中国实用妇科与产科杂志, 2021, 37(1): 100-104.
[11] Ferreira-Junior, J.R., Koenigkam-Santos, M., Magalhães Tenório, A.P., et al. (2020) CT-Based Radiomics for Prediction of Histologic Subtype and Metastatic Disease in Primary Malignant Lung Neoplasms. International Journal of Computer Assisted Radiology and Surgery, 15, 163-172. [Google Scholar] [CrossRef] [PubMed]
[12] Li, S., Liu, J., Xiong, Y., et al. (2022) Application Values of 2d and 3d Radiomics Models Based on Ct Plain Scan in Differentiating Benign from Malignant Ovarian Tumors. BioMed Research International, 2022, Article ID: 5952296. [Google Scholar] [CrossRef] [PubMed]
[13] Yao, F., Ding, J., Lin, F., et al. (2022) Nomogram Based on Ultrasound Radiomics Score and Clinical Variables for Predicting Histologic Subtypes of Epithelial Ovarian Cancer. The British Journal of Radiology, 95, Article ID: 20211332. [Google Scholar] [CrossRef] [PubMed]
[14] Sato, S. and Itamochi, H. (2014) Neoadjuvant Chemotherapy in Advanced Ovarian Cancer: Latest Results and Place in Therapy. Therapeutic Advances in Medical Oncology, 6, 293-304. [Google Scholar] [CrossRef] [PubMed]
[15] Sehouli, J. and Grabowski, J.P. (2017) Surgery for Recurrent Ovarian Cancer: Options and Limits. Best Practice & Research Clinical Obstetrics & Gynaecology, 41, 88-95. [Google Scholar] [CrossRef] [PubMed]
[16] Nougaret, S., Sadowski, E., Lakhman, Y., et al. (2022) The BUMPy Road of Peritoneal Metastases in Ovarian Cancer. Diagnostic and Interventional Imaging, 103, 448-459. [Google Scholar] [CrossRef] [PubMed]
[17] Gerestein, C.G., Eijkemans, M.J., Bakker, J., et al. (2011) Nomogram for Suboptimal Cytoreduction at Primary Surgery for Advanced Stage Ovarian Cancer. Anticancer Research, 31, 4043-4049.
[18] Lorusso, D., Sarno, I., Di Donato, V., et al. (2014) Is Postoperative Computed Tomography Evaluation a Prognostic Indicator in Patients with Optimally Debulked Advanced Ovarian Cancer? Oncology, 87, 293-299. [Google Scholar] [CrossRef] [PubMed]
[19] Weinberger, V., Fischerova, D., Semeradova, I., et al. (2016) Prospective Evaluation of Ultrasound Accuracy in the Detection of Pelvic Carcinomatosis in Patients with Ovarian Cancer. Ultrasound in Medicine & Biology, 42, 2196-2202. [Google Scholar] [CrossRef] [PubMed]
[20] Zikan, M., Fischerova, D., Semeradova, I., et al. (2017) Accuracy of Ultrasound in Prediction of Rectosigmoid Infiltration in Epithelial Ovarian Cancer. Ultrasound in Obstetrics & Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology, 50, 533-538. [Google Scholar] [CrossRef] [PubMed]
[21] Gupta, A., Jha, P., Baran, T.M., et al. (2022) Ovarian Cancer Detection in Average-Risk Women: Classic-versus Nonclassic-Appearing Adnexal Lesions at Us. Radiology, 303, 603-610. [Google Scholar] [CrossRef] [PubMed]
[22] Jha, P., Gupta, A., Baran, T.M., et al. (2022) Diagnostic Performance of the Ovarian-Adnexal Reporting and Data System (O-Rads) Ultrasound Risk Score in Women in the United States. JAMA Network Open, 5, E2216370. [Google Scholar] [CrossRef] [PubMed]
[23] Antil, N., Wang, H., Kaffas, A.E., et al. (2023) In Vivo Ultrasound Molecular Imaging in the Evaluation of Complex Ovarian Masses: A Practical Guide to Correlation with ex Vivo Immunohistochemistry. Advanced Biology, 7, E2300091. [Google Scholar] [CrossRef] [PubMed]
[24] Willmann, J.K., Bonomo, L., Testa, A.C., et al. (2017) Ultrasound Molecular Imaging with Br55 in Patients with Breast and Ovarian Lesions: First-in-Human Results. Journal of Clinical Oncology: Official Journal of the American Society of Clinical Oncology, 35, 2133-2140. [Google Scholar] [CrossRef
[25] Fang, J., Zhao, W., Li, Q., et al. (2020) Correlation Analysis of Conventional Ultrasound Characteristics and Strain Elastography with Ki-67 Status in Breast Cancer. Ultrasound in Medicine & Biology, 46, 2972-2978. [Google Scholar] [CrossRef] [PubMed]
[26] Jin, X., Ai, Y., Zhang, J., et al. (2020) Noninvasive Prediction of Lymph Node Status for Patients with Early-Stage Cervical Cancer Based on Radiomics Features from Ultrasound Images. European Radiology, 30, 4117-4124. [Google Scholar] [CrossRef] [PubMed]
[27] Nero, C., Ciccarone, F., Boldrini, L., et al. (2020) Germline BRCA 1-2 Status Prediction through Ovarian Ultrasound Images Radiogenomics: A Hypothesis Generating Study (PROBE Study). Scientific Reports, 10, Article No. 16511. [Google Scholar] [CrossRef] [PubMed]
[28] Moro, F., Giudice, M.T., Bolomini, G., et al. (2023) Imaging in Gynecological Disease: Clinical and Ultrasound Characteristics of Recurrent Ovarian Stromal Cell Tumors. Ultrasound in Obstetrics & Gynecology: The Official Journal of the International Society of Ultrasound in Obstetrics and Gynecology.
[29] Yao, F., Ding, J., Hu, Z., et al. (2021) Ultrasound-Based Radiomics Score: A Potential Biomarker for the Prediction of Progression-Free Survival in Ovarian Epithelial Cancer. Abdominal Radiology (New York), 46, 4936-4945. [Google Scholar] [CrossRef] [PubMed]
[30] Rundo, F., Banna, G.L., Prezzavento, L., et al. (2020) 3D Non-Local Neural Network: A Non-Invasive Biomarker for Immunotherapy Treatment Outcome Prediction. Case-Study: Metastatic Urothelial Carcinoma. Journal of Imaging, 6, Article No. 133. [Google Scholar] [CrossRef] [PubMed]
[31] Yin, X., Liao, H., Yun, H., et al. (2022) Artificial Intelligence-Based Prediction of Clinical Outcome in Immunotherapy and Targeted Therapy of Lung Cancer. Seminars in Cancer Biology, 86, 146-159. [Google Scholar] [CrossRef] [PubMed]
[32] 高境泽, 吴霞. 卵巢癌组织中CXCR3表达与免疫细胞浸润的相关性研究[J]. 现代免疫学, 2021, 41(3): 177-183.
[33] Yang, M., Lu, J., Zhang, G., et al. (2021) CXCL13 Shapes Immunoactive Tumor Microenvironment and Enhances the Efficacy of PD-1 Checkpoint Blockade in High-Grade Serous Ovarian Cancer. Journal for Immunotherapy of Cancer, 9, e001136. [Google Scholar] [CrossRef] [PubMed]
[34] Brenna, E. and Pedroza-Pacheco, I. (2022) Harnessing CXCL13 in Ovarian Cancer. Nature Reviews Immunology, 22, Article No. 145. [Google Scholar] [CrossRef] [PubMed]
[35] Xu, W., Zhu, C., Ji, D., et al. (2023) CT-Based Radiomics Prediction of CXCL13 Expression in Ovarian Cancer. Medical Physics, 50, 6801-6814. [Google Scholar] [CrossRef] [PubMed]
[36] Wan, S., Zhou, T., Che, R., et al. (2023) CT-Based Machine Learning Radiomics Predicts CCR5 Expression Level and Survival in Ovarian Cancer. Journal of Ovarian Research, 16, Article No. 1. [Google Scholar] [CrossRef] [PubMed]
[37] 姚晋, 闵鹏秋, 黄娟. 腹膜转移瘤的CT征象[J]. 中国普外基础与临床杂志, 2005, 12(6): 620-623.
[38] Ahrens, E.T. and Bulte, J.W. (2013) Tracking Immune Cells in Vivo Using Magnetic Resonance Imaging. Nature Reviews Immunology, 13, 755-763. [Google Scholar] [CrossRef] [PubMed]
[39] Taylor, E.N., Wilson, C.M., Franco, S., et al. (2022) Monitoring Therapeutic Responses to Silicified Cancer Cell Immunotherapy Using Pet/Mri in a Mouse Model of Disseminated Ovarian Cancer. International Journal of Molecular Sciences, 23, Article ID: 10525. [Google Scholar] [CrossRef] [PubMed]
[40] Bouchlaka, M.N., Ludwig, K.D., Gordon, J.W., et al. (2016) (19)F-MRI for Monitoring Human NK Cells in Vivo. Oncoimmunology, 5, E1143996. [Google Scholar] [CrossRef
[41] Zhang, H., Mao, Y., Chen, X., et al. (2019) Magnetic Resonance Imaging Radiomics in Categorizing Ovarian Masses and Predicting Clinical Outcome: A Preliminary Study. European Radiology, 29, 3358-3371. [Google Scholar] [CrossRef] [PubMed]
[42] Cadour, F., Cautela, J., Rapacchi, S., 等. 免疫检查点抑制剂性心肌炎的心脏MRI表现及预后价值[J]. 国际医学放射学杂志, 2022, 45(4): 481.
[43] Aide, N., Hicks, R.J., Le Tourneau, C., et al. (2019) FDG PET/CT for Assessing Tumour Response to Immunotherapy: Report on the EANM Symposium on Immune Modulation and Recent Review of the Literature. European Journal of Nuclear Medicine and Molecular Imaging, 46, 238-250. [Google Scholar] [CrossRef] [PubMed]
[44] (2020) Expert Consensus on Assessing Tumor Response to Immune Checkpoint Inhibitors by PET/CT (2020 Edition). Chinese Journal of Oncology, 42, 697-705.
[45] Kaira, K., Higuchi, T., Naruse, I., et al. (2018) Metabolic Activity by (18)F-FDG-PET/CT Is Predictive of Early Response after Nivolumab in Previously Treated NSCLC. European Journal of Nuclear Medicine and Molecular Imaging, 45, 56-66. [Google Scholar] [CrossRef] [PubMed]
[46] 李敏, 李绪清, 颜士杰, 等. 18F-脱氧葡萄糖PET/CT联合CA125在诊断卵巢癌复发转移中的应用价值[J]. 重庆医科大学学报, 2017, 42(12): 1635-1638.
[47] Wang, X., Xu, C., Grzegorzek, M., et al. (2022) Habitat Radiomics Analysis of Pet/Ct Imaging in High-Grade Serous Ovarian Cancer: Application to Ki-67 Status and Progression-Free Survival. Frontiers in Physiology, 13, Article ID: 948767. [Google Scholar] [CrossRef] [PubMed]
[48] Mu, W., Jiang, L., Shi, Y., et al. (2021) Non-Invasive Measurement of PD-L1 Status and Prediction of Immunotherapy Response Using Deep Learning of PET/CT Images. Journal for Immunotherapy of Cancer, 9, e002118. [Google Scholar] [CrossRef] [PubMed]
[49] Mapelli, P., Incerti, E., Fallanca, F., et al. (2016) Imaging Biomarkers in Ovarian Cancer: The Role of 18F-FDG PET/CT. The Quarterly Journal of Nuclear Medicine and Molecular Imaging, 60, 93-102.
[50] Peng, H., Dong, D., Fang, M.J., et al. (2019) Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma. Clinical Cancer Research: An Official Journal of the American Association for Cancer Research, 25, 4271-4279. [Google Scholar] [CrossRef