切换至 "中华医学电子期刊资源库"

中华消化病与影像杂志(电子版) ›› 2023, Vol. 13 ›› Issue (02) : 104 -110. doi: 10.3877/cma.j.issn.2095-2015.2023.02.009

综述

PET相关影像组学在肿瘤预后中的研究进展
黄文鹏1, 邱永康1, 杨琦1, 宋乐乐1, 陈钊1, 范岩1, 康磊1,()   
  1. 1. 100034 北京大学第一医院核医学科
  • 收稿日期:2022-11-11 出版日期:2023-04-01
  • 通信作者: 康磊

Research progress of PET related radiomics in tumor prognosis

Wenpeng Huang1, Yongkang Qiu1, Qi Yang1, Lele Song1, Zhao Chen1, Yan Fan1, Lei Kang1,()   

  1. 1. Department of Nuclear Medicine, Peking University First Hospital, Beijing 100034, China
  • Received:2022-11-11 Published:2023-04-01
  • Corresponding author: Lei Kang
  • Supported by:
    National Natural Science Foundation of China(82171970, 81871385); Beijing Science Foundation for Distinguished Young Scholars(JQ21025); Peking University Medicine Fund of Fostering Young Scholars′Scientific & Technological Innovation(BMU2022PY006); Clinical Medicine Plus X-Youth Scholars Project of Peking University(PKU2020LCXQ023)
引用本文:

黄文鹏, 邱永康, 杨琦, 宋乐乐, 陈钊, 范岩, 康磊. PET相关影像组学在肿瘤预后中的研究进展[J]. 中华消化病与影像杂志(电子版), 2023, 13(02): 104-110.

Wenpeng Huang, Yongkang Qiu, Qi Yang, Lele Song, Zhao Chen, Yan Fan, Lei Kang. Research progress of PET related radiomics in tumor prognosis[J]. Chinese Journal of Digestion and Medical Imageology(Electronic Edition), 2023, 13(02): 104-110.

影像组学作为致力于挖掘医学图像中高维可提取数据的定量分析方法,近年来在大数据分析时代和人工智能发展的推动下,在核医学肿瘤诊疗领域发展迅速,取得了一定的研究成果与进展,然而距离临床应用,仍面临诸多挑战。本文总结了影像组学在肿瘤正电子发射断层显像(PET)预后评估方面的研究进展,并对面临挑战和未来展望进行综述,以期为进一步的PET精准医学分析提供参考。

Radiomics provides a quantitative method to analyze the high-dimensional extractable data in medical images.Driven by the development of big data analysis and artificial intelligence, radiomics has developed rapidly and obtained remarkable progress in the field of tumor diagnosis and treatment in nuclear medicine recently.Radiomics still faces many challenges when it is considered for the clinical application.This review summarizes the research progress of radiomics in the prognosis evaluation of tumor using positron emission tomography(PET)imaging, as well as the challenges and future prospects, further it aims to provide some reference for the accurate medical analysis of PET imaging.

1
Hyun SHKim HSChoi SH,et al.Intratumoral heterogeneity of(18)F-FDG uptake predicts survival in patients with pancreatic ductal adenocarcinoma[J].Eur J Nucl Med Mol Imaging201643(8):1461-1468.
2
Sollini MAntunovic LChiti A,et al.Towards clinical application of image mining:a systematic review on artificial intelligence and radiomics[J].Eur J Nucl Med Mol Imaging201946(13):2656-2672.
3
Mayerhoefer MEMaterka ALangs G,et al.Introduction to Radiomics[J].J Nucl Med202061(4):488-495.
4
Lambin PRios-Velazquez ELeijenaar R,et al.Radiomics:extracting more information from medical images using advanced feature analysis[J].Eur J Cancer201248(4):441-446.
5
Li PWang XXu C,et al.18F-FDG PET/CT radiomic predictors of pathologic complete response(pCR)to neoadjuvant chemotherapy in breast cancer patients[J].Eur J Nucl Med Mol Imaging202047(5):1116-1126.
6
Jiang CZhao LXin B,et al.18F-FDG PET/CT radiomic analysis for classifying and predicting microvascular invasion in hepatocellular carcinoma and intrahepatic cholangiocarcinoma[J].Quantitative Imaging in Medicine and Surgery202212(8):4135-4150.
7
Zwanenburg AVallières MAbdalah M A,et al.The Image Biomarker Standardization Initiative:Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping[J].Radiology2020295(2):328-338.
8
Castiglioni IRundo LCodari M,et al.AI applications to medical images:From machine learning to deep learning[J].Phy Med202183:9-24.
9
Obermeyer ZEmanuel EJ.Predicting the Future-Big Data,Machine Learning,and Clinical Medicine[J].New Engl J Med2016375(13):1216-1219.
10
Cuaron JDunphy MRimner A.Role of FDG-PET scans in staging,response assessment,and follow-up care for non-small cell lung cancer[J].Front Oncol20122:208.
11
Li JLiu YDong W,et al.Identifying 18F-FDG PET-metabolic radiomic signature for lung adenocarcinoma prognosis via the leveraging of prognostic transcriptomic module[J].Quantitative Imaging in Medicine and Surgery202212(3):1893-1908.
12
Krarup MMKNygård LVogelius IR,et al.Heterogeneity in tumours:Validating the use of radiomic features on 18F-FDG PET/CT scans of lung cancer patients as a prognostic tool[J].Radiother Oncol2020144:72-78.
13
Kirienko MCozzi LAntunovic L,et al.Prediction of disease-free survival by the PET/CT radiomic signature in non-small cell lung cancer patients undergoing surgery[J].Eur J Nucl Med Mol Imaging201845(2):207-217.
14
Yang LXu PLi M,et al.PET/CT Radiomic Features:A Potential Biomarker for EGFR Mutation Status and Survival Outcome Prediction in NSCLC Patients Treated With TKIs[J].Front Oncol202212:894323.
15
Sepehri STankyevych OUpadhaya T,et al.Comparison and Fusion of Machine Learning Algorithms for Prospective Validation of PET/CT Radiomic Features Prognostic Value in Stage Ⅱ-Ⅲ Non-Small Cell Lung Cancer[J].Diagnostics(Basel,Switzerland)202111(4):675.
16
Chen YHWang TFChu SC,et al.Incorporating radiomic feature of pretreatment 18F-FDG PET improves survival stratification in patients with EGFR-mutated lung adenocarcinoma[J].PloS One202015(12):e0244502.
17
Yang BJi HSZhou CS,et al.18F-fluorodeoxyglucose positron emission tomography/computed tomography-based radiomic features for prediction of epidermal growth factor receptor mutation status and prognosis in patients with lung adenocarcinoma[J].Translational Lung Cancer Research20209(3):563-574.
18
Huang SYFranc BLHarnish RJ,et al.Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis[J].NPJ Breast Cancer20184:24.
19
Gómez OVHerraiz JLUdías JM,et al.Analysis of Cross-Combinations of Feature Selection and Machine-Learning Classification Methods Based on[18F]F-FDG PET/CT Radiomic Features for Metabolic Response Prediction of Metastatic Breast Cancer Lesions[J].Cancers202214(12):2922.
20
Roy SWhitehead TDLi S,et al.Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer[J].Eur J Nucl Med Mol Imaging202249(2):550-562.
21
Jiang CLi ATeng Y,et al.Optimal PET-based radiomic signature construction based on the cross-combination method for predicting the survival of patients with diffuse large B-cell lymphoma[J].Eur J Nucl Med Mol Imaging202249(8):2902-2916.
22
Aide NFruchart CNganoa C,et al.Baseline 18F-FDG PET radiomic features as predictors of 2-year event-free survival in diffuse large B cell lymphomas treated with immunochemotherapy[J].European Radiology202030(8):4623-4632.
23
Lue KHWu YFLin HH,et al.Prognostic Value of Baseline Radiomic Features of 18 F-FDG PET in Patients with Diffuse Large B-Cell Lymphoma[J].Diagnostics(Basel)202011(1):36.
24
Zhou YLi JZhang X,et al.Prognostic Value of Radiomic Features of 18F-FDG PET/CT in Patients With B-Cell Lymphoma Treated With CD19/CD22 Dual-Targeted Chimeric Antigen Receptor T Cells[J].Front Oncol202212:834288.
25
Lue KHWu YFLiu SH,et al.Prognostic Value of Pretreatment Radiomic Features of 18F-FDG PET in Patients With Hodgkin Lymphoma[J].Clinical Nuclear Medicine201944(10):e559-e565.
26
Zhou YZhu YChen Z,et al.Radiomic Features of 18F-FDG PET in Hodgkin Lymphoma Are Predictive of Outcomes[J].Contrast Media & Molecular Imaging20212021:6347404.
27
Wang HZhao SLi L,et al.Development and validation of an 18F-FDG PET radiomic model for prognosis prediction in patients with nasal-type extranodal natural killer/T cell lymphoma[J].European Radiology202030(10):5578-5587.
28
Zhou YMa XLPu LT,et al.Prediction of Overall Survival and Progression-Free Survival by the 18F-FDG PET/CT Radiomic Features in Patients with Primary Gastric Diffuse Large B-Cell Lymphoma[J].Contrast Media & Molecular Imaging20192019:5963607.
29
Gerstner ERZhang ZFink JR,et al.ACRIN 6684:Assessment of Tumor Hypoxia in Newly Diagnosed Glioblastoma Using 18F-FMISO PET and MRI[J].Clinical Cancer Research201622(20):5079-5086.
30
Muzi MWolsztynski EFink JR,et al.Assessment of the Prognostic Value of Radiomic Features in 18F-FMISO PET Imaging of Hypoxia in Postsurgery Brain Cancer Patients:Secondary Analysis of Imaging Data from a Single-Center Study and the Multicenter ACRIN 6684 Trial[J].Tomography20206(1):14-22.
31
Feliciani GFioroni FGrassi E,et al.Radiomic Profiling of Head and Neck Cancer:18F-FDG PET Texture Analysis as Predictor of Patient Survival[J].Contrast Media & Molecular Imaging20182018:3574310.
32
Dmytriw AAOrtega CAnconina R,et al.Nasopharyngeal Carcinoma Radiomic Evaluation with Serial PET/CT:Exploring Features Predictive of Survival in Patients with Long-Term Follow-Up[J].Cancers202214(13):3105.
33
Brodin N PVelten CLubin J,et al.A positron emission tomography radiomic signature for distant metastases risk in oropharyngeal cancer patients treated with definitive chemoradiotherapy[J].Physics and Imaging in Radiation Oncology202221:72-77.
34
Nakajo MKawaji KNagano H,et al.The Usefulness of Machine Learning-Based Evaluation of Clinical and Pretreatment[18F]-FDG-PET/CT Radiomic Features for Predicting Prognosis in Hypopharyngeal Cancer[J].Mol Imaging Biol2022.
35
Rishi AZhang GGYuan Z,et al.Pretreatment CT and 18 F-FDG PET-based radiomic model predicting pathological complete response and loco-regional control following neoadjuvant chemoradiation in oesophageal cancer[J].J Med Imaging Radiat Oncol202165(1):102-111.
36
Yh CKh LSc C,et al.Combining the radiomic features and traditional parameters of 18 F-FDG PET with clinical profiles to improve prognostic stratification in patients with esophageal squamous cell carcinoma treated with neoadjuvant chemoradiotherapy and surgery[J].Ann Nucl Med201933(9):657-670.
37
Jiang YYuan QLv W,et al.Radiomic signature of 18F fluorodeoxyglucose PET/CT for prediction of gastric cancer survival and chemotherapeutic benefits[J].Theranostics20188(21):5915-5928.
38
Xue XQYu WJShi X,et al.18F-FDG PET/CT-based radiomics nomogram for the preoperative prediction of lymph node metastasis in gastric cancer[J].Front Oncol202212:911168.
39
Blanc-Durand PVan Der Gucht AJreige M,et al.Signature of survival:a 18F-FDG PET based whole-liver radiomic analysis predicts survival after 90Y-TARE for hepatocellular carcinoma[J].Oncotarget20189(4):4549-4558.
40
Lee JWNa JOKang DY,et al.Prognostic Significance of FDG Uptake of Bone Marrow on PET/CT in Patients With Non-Small-Cell Lung Cancer After Curative Surgical Resection[J].Clinical Lung Cancer201718(2):198-206.
41
Lee JWBan MJPark JH,et al.Effect of F-18 Fluorodeoxyglucose Uptake by Bone Marrow on the Prognosis of Head and Neck Squamous Cell Carcinoma[J].J Clin Med20198(8):E1169.
42
Lee JWPark SHAhn H,et al.Predicting Survival in Patients with Pancreatic Cancer by Integrating Bone Marrow FDG Uptake and Radiomic Features of Primary Tumor in PET/CT[J].Cancers202113(14):3563.
43
Mori MPassoni PIncerti E,et al.Training and validation of a robust PET radiomic-based index to predict distant-relapse-free-survival after radio-chemotherapy for locally advanced pancreatic cancer[J].Radiother Oncol2020153:258-264.
44
Mapelli PBezzi CPalumbo D,et al.68Ga-DOTATOC PET/MR imaging and radiomic parameters in predicting histopathological prognostic factors in patients with pancreatic neuroendocrine well-differentiated tumours[J].Eur J Nucl Med Mol Imaging202249(7):2352-2363.
45
Lv LXin BHao Y,et al.Radiomic analysis for predicting prognosis of colorectal cancer from preoperative 18F-FDG PET/CT[J].J Transl Med202220(1):66.
46
Manfredi SLepage CHatem C,et al.Epidemiology and management of liver metastases from colorectal cancer[J].Ann Surg2006244(2):254-259.
47
Rahmim ABak-Fredslund KPAshrafinia S,et al.Prognostic modeling for patients with colorectal liver metastases incorporating FDG PET radiomic features[J].Eur J Radiol2019113:101-109.
48
Wilkinson JRMorris EJADowning A,et al.The rising incidence of anal cancer in England 1990-2010:a population-based study[J].Colorectal Dis201416(7):O234-239.
49
Brown P JZhong JFrood R,et al.Prediction of outcome in anal squamous cell carcinoma using radiomic feature analysis of pre-treatment FDG PET-CT[J].Eur J Nucl Med Mol Imaging201946(13):2790-2799.
50
Nakajo MJinguji MTani A,et al.Application of a Machine Learning Approach for the Analysis of Clinical and Radiomic Features of Pretreatment[18F]-FDG PET/CT to Predict Prognosis of Patients with Endometrial Cancer[J].Mol Imaging Biol202123(5):756-765.
51
Nakajo MJinguji MTani A,et al.Machine learning based evaluation of clinical and pretreatment 18F-FDG-PET/CT radiomic features to predict prognosis of cervical cancer patients[J].Abdominal Radiology(New York)202247(2):838-847.
52
Martone ME.FORCE11:Building the Future for Research Communications and e-Scholarship[J].BioScience201565(7):635.
53
Wilkinson MDDumontier MAalbersberg IJJ,et al.The FAIR Guiding Principles for scientific data management and stewardship[J].Scientific Data20163:160018.
54
Lambin PLeijenaar RTHDeist TM,et al.Radiomics:the bridge between medical imaging and personalized medicine[J].Nat Rev Clin Oncol201714(12):749-762.
55
Hosny AParmar CQuackenbush J,et al.Artificial intelligence in radiology[J].Nat Rev Cancer201818(8):500-510.
56
Manafi-Farid RKaramzade-Ziarati NVali R,et al.2-[18F]FDG PET/CT radiomics in lung cancer:An overview of the technical aspect and its emerging role in management of the disease[J].Methods2021188:84-97.
57
Zwanenburg A.Radiomics in nuclear medicine:robustness,reproducibility,standardization,and how to avoid data analysis traps and replication crisis[J].Eur J Nucl Med Mol Imaging201946(13):2638-2655.
58
Clark KVendt BSmith K,et al.The Cancer Imaging Archive(TCIA):maintaining and operating a public information repository[J].Journal of Digital Imaging201326(6):1045-1057.
59
Buckler A JBresolin LDunnick NR,et al.A collaborative enterprise for multi-stakeholder participation in the advancement of quantitative imaging[J].Radiology2011258(3):906-914.
60
Deist TMJochems Avan Soest J,et al.Infrastructure and distributed learning methodology for privacy-preserving multi-centric rapid learning health care:euroCAT[J].Clin Transl Radiat Oncol20174:24-31.
61
Pesapane FVolontéCCodari M,et al.Artificial intelligence as a medical device in radiology:ethical and regulatory issues in Europe and the United States[J].Insights Imaging20189(5):745-753.
62
Kaissis GAMakowski MRRückert D,et al.Secure,privacy-preserving and federated machine learning in medical imaging[J].Nature Machine Intelligence20202(6):305-311.
63
Rieke NHancox JLi W,et al.The future of digital health with federated learning[J].NPJ Digit Med20203:119.
[1] 燕速, 霍博文, 徐惠宁. 4K荧光腹腔镜扩大右半结肠CME+D3根治术及No.206、No.204组淋巴结清扫术[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 14-14.
[2] 姚宏伟, 魏鹏宇, 高加勒, 张忠涛. 不断提高腹腔镜右半结肠癌D3根治术的规范化[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 1-4.
[3] 杜晓辉, 崔建新. 腹腔镜右半结肠癌D3根治术淋巴结清扫范围与策略[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 5-8.
[4] 周岩冰, 刘晓东. 腹腔镜右半结肠癌D3根治术消化道吻合重建方式的选择[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 9-13.
[5] 唐旭, 韩冰, 刘威, 陈茹星. 结直肠癌根治术后隐匿性肝转移危险因素分析及预测模型构建[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 16-20.
[6] 张生军, 赵阿静, 李守博, 郝祥宏, 刘敏丽. 高糖通过HGF/c-met通路促进结直肠癌侵袭和迁移的实验研究[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 21-24.
[7] 张焱辉, 张蛟, 朱志贤. 留置肛管在中低位直肠癌新辅助放化疗后腹腔镜TME术中的临床研究[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 25-28.
[8] 李凤仪, 李若凡, 高旭, 张超凡. 目标导向液体干预对老年胃肠道肿瘤患者术后血流动力学、胃肠功能恢复的影响[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 29-32.
[9] 杨倩, 李翠芳, 张婉秋. 原发性肝癌自发性破裂出血急诊TACE术后的近远期预后及影响因素分析[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 33-36.
[10] 李建美, 邓静娟, 杨倩. 两种术式联合治疗肝癌合并肝硬化门静脉高压的安全性及随访评价[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 41-44.
[11] 栗艳松, 冯会敏, 刘明超, 刘泽鹏, 姜秋霞. STIP1在三阴性乳腺癌组织中的表达及临床意义研究[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 52-56.
[12] 钱龙, 陆晓峰, 王行舟, 杜峻峰, 沈晓菲, 管文贤. 神经系统调控胃肠道肿瘤免疫应答研究进展[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 86-89.
[13] 曹长青, 郭新艳, 高源, 张存, 唐海利, 樊东, 杨小军, 张松, 赵华栋. 肿瘤微环境参与介导HER2阳性乳腺癌曲妥珠单抗耐药的研究进展[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 90-95.
[14] 马伟强, 马斌林, 吴中语, 张莹. microRNA在三阴性乳腺癌进展中发挥的作用[J]. 中华普外科手术学杂志(电子版), 2024, 18(01): 111-114.
[15] 郭震天, 张宗明, 赵月, 刘立民, 张翀, 刘卓, 齐晖, 田坤. 机器学习算法预测老年急性胆囊炎术后住院时间探索[J]. 中华临床医师杂志(电子版), 2023, 17(9): 955-961.
阅读次数
全文


摘要