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影像组学在肾上腺肿瘤中的研究进展

张桂煊 石鑫 李伟 叶淑华

张桂煊, 石鑫, 李伟, 叶淑华. 影像组学在肾上腺肿瘤中的研究进展[J]. 昆明医科大学学报, 2022, 43(3): 142-147. doi: 10.12259/j.issn.2095-610X.S20220324
引用本文: 张桂煊, 石鑫, 李伟, 叶淑华. 影像组学在肾上腺肿瘤中的研究进展[J]. 昆明医科大学学报, 2022, 43(3): 142-147. doi: 10.12259/j.issn.2095-610X.S20220324
Guixuan ZHANG, Xin SHI, Wei LI, Shuhua YE. Research Progress of Radiomics in Adrenal Tumors[J]. Journal of Kunming Medical University, 2022, 43(3): 142-147. doi: 10.12259/j.issn.2095-610X.S20220324
Citation: Guixuan ZHANG, Xin SHI, Wei LI, Shuhua YE. Research Progress of Radiomics in Adrenal Tumors[J]. Journal of Kunming Medical University, 2022, 43(3): 142-147. doi: 10.12259/j.issn.2095-610X.S20220324

影像组学在肾上腺肿瘤中的研究进展

doi: 10.12259/j.issn.2095-610X.S20220324
基金项目: 国家自然科学基金资助项目(7176030331);云南省科技厅应用基础研究基金资助项目(202101AS070041);云南省教育厅科学研究基金资助项目(2020J0184);云南省科技厅-昆明医科大学应用基础研究联合专项基金资助项目(202101AY070001-138)
详细信息
    作者简介:

    张桂煊(1996~),男,山东枣庄人,在读硕士研究生,主要从事泌尿系疾病临床研究工作

    通讯作者:

    石鑫, E-mail:shixin14678@163.com

    李伟,E-mail:lw13907801155@163.com

  • 中图分类号: R699

Research Progress of Radiomics in Adrenal Tumors

  • 摘要: 随着医学诊断技术的发展,肾上腺肿瘤的检出率逐年增加。影像组学作为一种新兴的技术,能够对人眼难以识别的深层次数据进行挖掘、预测和分析,在肿瘤领域已经得到了广泛关注。主要介绍影像组学在肾上腺肿瘤诊疗中的研究进展,包括肾上腺肿瘤诊断及鉴别诊断、临床决策与风险评估、预后预测以及预测肿瘤生物学行为等,最后总结现阶段影像组学存在的问题并对未来的发展方向进行展望,以期为临床诊疗提供借鉴。
  • [1] Low G,Sahi K. Clinical and imaging overview of functional adrenal neoplasms[J]. Int J Urol,2012,19(8):697-708. doi: 10.1111/j.1442-2042.2012.03014.x
    [2] Lam A K. Update on adrenal tumours in 2017 World Health Organization (WHO) of endocrine tumours[J]. Endocr Pathol,2017,28(3):213-227. doi: 10.1007/s12022-017-9484-5
    [3] Zheng X,Yao Z,Huang Y,et al. Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer[J]. Nat Commun,2020,11(1):1-9. doi: 10.1038/s41467-019-13993-7
    [4] Gillies R J,Schabath M B. Radiomics improves cancer screening and early detection[J]. Cancer Epidemiol Biomarkers Prev,2020,29(12):2556-2567. doi: 10.1158/1055-9965.EPI-20-0075
    [5] Ji G W,Zhu F P,Xu Q,et al. Radiomic features at contrast-enhanced CT predict recurrence in early stage hepatocellular carcinoma:A multi-institutional study[J]. Radiology,2020,294(3):568-579. doi: 10.1148/radiol.2020191470
    [6] Huang Y,Liu Z,He L,et al. Radiomics signature:A potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer[J]. Radiology,2016,281(3):947-957. doi: 10.1148/radiol.2016152234
    [7] Hectors S J,Cherny M,Yadav K K,et al. Radiomics features measured with multiparametric magnetic resonance imaging predict prostate cancer aggressiveness[J]. J Urol,2019,202(3):498-505. doi: 10.1097/JU.0000000000000272
    [8] Lambin P,Rios-Velazquez E,Leijenaar R,et al. Radiomics:Extracting more information from medical images using advanced feature analysis[J]. Eur J Cancer,2012,48(4):441-446. doi: 10.1016/j.ejca.2011.11.036
    [9] Kumar V,Gu Y,Basu S,et al. Radiomics:The process and the challenges[J]. Magn Reson Imaging,2012,30(9):1234-1248. doi: 10.1016/j.mri.2012.06.010
    [10] Mackin D,Fave X,Zhang L,et al. Measuring computed tomography scanner variability of radiomics features[J]. Invest Radiol,2015,50(11):757-765. doi: 10.1097/RLI.0000000000000180
    [11] Chaddad A,Kucharczyk M J,Daniel P,et al. Radiomics in glioblastoma:Current status and challenges facing clinical implementation[J]. Frontiers in Oncology,2019,9(5):1-9.
    [12] Wu Y P,Lin Y S,Wu W G,et al. Semiautomatic segmentation of glioma on mobile devices[J]. Journal of Healthcare Engineering,2017,2017(6):1-10.
    [13] Gillies R J,Kinahan P E,Hricak H. Radiomics:images are more than pictures,they are data[J]. Radiology,2016,278(2):563-577. doi: 10.1148/radiol.2015151169
    [14] Cardenas C E,Yang J,Anderson B M,et al. Advances in auto-segmentation[J]. Semin Radiat Oncol,2019,29(3):185-197. doi: 10.1016/j.semradonc.2019.02.001
    [15] Spuhler K D,Ding J,Liu C,et al. Task-based assessment of a convolutional neural network for segmenting breast lesions for radiomic analysis[J]. Magn Reson Med,2019,82(2):786-795. doi: 10.1002/mrm.27758
    [16] Liu Z,Wang S,Dong D,et al. The applications of radiomics in precision diagnosis and treatment of oncology:opportunities and challenges[J]. Theranostics,2019,9(5):1303-1322. doi: 10.7150/thno.30309
    [17] Lin Y C,Lin C H,Lu H Y,et al. Deep learning for fully automated tumor segmentation and extraction of magnetic resonance radiomics features in cervical cancer[J]. Eur Radiol,2020,30(3):1297-1305. doi: 10.1007/s00330-019-06467-3
    [18] Traverso A,Wee L,Dekker A,et al. Repeatability and reproducibility of radiomic features:a systematic review[J]. Int J Radiat Oncol Biol Phys,2018,102(4):1143-1158. doi: 10.1016/j.ijrobp.2018.05.053
    [19] Braman N M,Etesami M,Prasanna P,et al. Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI[J]. Breast Cancer Res,2017,19(1):1-14. doi: 10.1186/s13058-016-0797-y
    [20] Avanzo M,Wei L,Stancanello J,et al. Machine and deep learning methods for radiomics[J]. Med Phys,2020,47(5):185-202.
    [21] Park J E,Park S Y,Kim H J,et al. Reproducibility and generalizability in radiomics modeling:possible strategies in radiologic and statistical perspectives[J]. Korean J Radiol,2019,20(7):1124-1137. doi: 10.3348/kjr.2018.0070
    [22] Parmar C,Grossmann P,Bussink J,et al. Machine learning methods for quantitative radiomic biomarkers[J]. Sci Rep,2015,5(1):1-11.
    [23] Coroller T P,Agrawal V,Narayan V,et al. Radiomic phenotype features predict pathological response in non-small cell lung cancer[J]. Radiother Oncol,2016,119(3):480-486. doi: 10.1016/j.radonc.2016.04.004
    [24] Gnep K,Fargeas A,Gutiérrez-Carvajal R E,et al. Haralick textural features on T(2) -weighted MRI are associated with biochemical recurrence following radiotherapy for peripheral zone prostate cancer[J]. J Magn Reson Imaging,2017,45(1):103-117. doi: 10.1002/jmri.25335
    [25] Fassnacht M,Arlt W,Bancos I,et al. Management of adrenal incidentalomas:European society of endocrinology clinical practice guideline in collaboration with the European network for the study of adrenal tumors[J]. Eur J Endocrinol,2016,175(2):G1-G34. doi: 10.1530/EJE-16-0467
    [26] Torresan F,Crimì F,Ceccato F,et al. Radiomics:a new tool to differentiate adrenocortical adenoma from carcinoma[J]. BJS Open,2021,5(1):1-7.
    [27] Seo J M,Park B K,Park S Y,et al. Characterization of lipid-poor adrenal adenoma:chemical-shift MRI and washout CT[J]. AJR Am J Roentgenol,2014,202(5):1043-1050. doi: 10.2214/AJR.13.11389
    [28] Umanodan T,Fukukura Y,Kumagae Y,et al. ADC histogram analysis for adrenal tumor histogram analysis of apparent diffusion coefficient in differentiating adrenal adenoma from pheochromocytoma[J]. J Magn Reson Imaging,2017,45(4):1195-1203. doi: 10.1002/jmri.25452
    [29] Gaujoux S,Mihai R. European Society of Endocrine Surgeons (ESES) and European Network for the Study of Adrenal Tumours (ENSAT) recommendations for the surgical management of adrenocortical carcinoma[J]. Br J Surg,2017,104(4):358-376. doi: 10.1002/bjs.10414
    [30] Yi X,Guan X,Zhang Y,et al. Radiomics improves efficiency for differentiating subclinical pheochromocytoma from lipid-poor adenoma:a predictive,preventive and personalized medical approach in adrenal incidentalomas[J]. Epma Journal,2018,9(4):421-429. doi: 10.1007/s13167-018-0149-3
    [31] Moawad A W,Ahmed A,Fuentes D T,et al. Machine learning-based texture analysis for differentiation of radiologically indeterminate small adrenal tumors on adrenal protocol CT scans[J]. Abdominal Radiology,2021,46(10):4853-4863. doi: 10.1007/s00261-021-03136-2
    [32] Young W F,Stanson A W,Thompson G B,et al. Role for adrenal venous sampling in primary aldosteronism[J]. Surgery,2004,136(6):1227-1235. doi: 10.1016/j.surg.2004.06.051
    [33] He K,Zhang ZT,Wang ZH,et al. A clinical-radiomic nomogram based on unenhanced computed tomography for predicting the risk of aldosterone-producing adenoma[J]. Frontiers in Oncology,2021,11(7):1-9.
    [34] Hasegawa T,Yamakado K,Nakatsuka A,et al. Unresectable adrenal metastases:clinical outcomes of radiofrequency ablation[J]. Radiology,2015,277(2):584-593. doi: 10.1148/radiol.2015142029
    [35] Lam K Y,Lo C Y. Metastatic tumours of the adrenal glands:a 30-year experience in a teaching hospital[J]. Clin Endocrinol (Oxf),2002,56(1):95-101. doi: 10.1046/j.0300-0664.2001.01435.x
    [36] Abrams H L,Spiro R,Goldstein N. Metastases in carcinoma; analysis of 1000 autopsied cases[J]. Cancer,1950,3(1):74-85. doi: 10.1002/1097-0142(1950)3:1<74::AID-CNCR2820030111>3.0.CO;2-7
    [37] Huang J,Xie X,Lin J,et al. Percutaneous radiofrequency ablation of adrenal metastases from hepatocellular carcinoma:a single-center experience[J]. Cancer Imaging,2019,19(1):1-8. doi: 10.1186/s40644-018-0187-z
    [38] Welch B T,Callstrom M R,Carpenter P C,et al. A single-institution experience in image-guided thermal ablation of adrenal gland metastases[J]. J Vasc Interv Radiol,2014,25(4):593-598. doi: 10.1016/j.jvir.2013.12.013
    [39] Wolf F J,Dupuy D E,Machan J T,et al. Adrenal neoplasms:effectiveness and safety of CT-guided ablation of 23 tumors in 22 patients[J]. Eur J Radiol,2012,81(8):1717-1723. doi: 10.1016/j.ejrad.2011.04.054
    [40] Welch B T,Atwell T D,Nichols D A,et al. Percutaneous image-guided adrenal cryoablation:procedural considerations and technical success[J]. Radiology,2011,258(1):301-307. doi: 10.1148/radiol.10100631
    [41] Daye D,Staziaki P V,Furtado V F,et al. CT texture analysis and machine learning improve post-ablation prognostication in patients with adrenal metastases:a proof of concept[J]. Cardiovascular and Interventional Radiology,2019,42(12):1771-1776. doi: 10.1007/s00270-019-02336-0
    [42] De Cubas A A,Leandro-García L J,Schiavi F,et al. Integrative analysis of miRNA and mRNA expression profiles in pheochromocytoma and paraganglioma identifies genotype-specific markers and potentially regulated pathways[J]. Endocr Relat Cancer,2013,20(4):477-493. doi: 10.1530/ERC-12-0183
    [43] Ansquer C,Drui D,Mirallie E,et al. Usefulness of FDG-PET/CT-based radiomics for the characterization and genetic orientation of pheochromocytomas before surgery[J]. Cancers,2020,12(9):1-15.
    [44] Plouin P F,Amar L,Dekkers O M,et al. European Society of Endocrinology Clinical Practice Guideline for long-term follow-up of patients operated on for a phaeochromocytoma or a paraganglioma[J]. European Journal of Endocrinology,2016,174(5):G1-G10. doi: 10.1530/EJE-16-0033
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  • 收稿日期:  2020-01-01
  • 网络出版日期:  2022-02-18
  • 刊出日期:  2022-03-22

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