Research Progress of Radiomics in Adrenal Tumors

Guixuan ZHANG; Xin SHI; Wei LI; Shuhua YE

doi:10.12259/j.issn.2095-610X.S20220324

Volume 43 Issue 3

Mar. 2022

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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

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

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Research Progress of Radiomics in Adrenal Tumors

doi: 10.12259/j.issn.2095-610X.S20220324

Guixuan ZHANG^{1, 2},
Xin SHI^{1, 3
,
,},
Wei LI^{1, 2
,
,},
Shuhua YE⁴

1.
Dept. of Urology，The Affiliated Hospital of Kunming University of Science and Technology，Kunming Yunnan 650032
2.
Dept. of Urology，The First People’s Hospital of Yunnan Province，Kunming Yunnan 650032
3.
Dept. of Urology，The 2nd Affiliated Hospital of Kunming Medicine University，Kunming Yunnan 650101
4.
Dept. of Asset Management，The 3rd Affiliated Hospital of Kunming Medical University，Kunming Yunnan 650118，China

Received Date: 2020-01-01
Available Online: 2022-02-18
Publish Date: 2022-03-22

Abstract

Abstract

With the development of medical diagnostic technology, the detection rate of adrenal tumor is increasing year by year. As a new technology, radiomics can mine, predict and analyze the deep-seated data which is difficult for human eyes to recognize. It has been widely concerned in the field of oncology. This article mainly introduces the research progress of radiomics in the diagnosis and treatment of adrenal tumors, including the adrenal tumor diagnosis and differential diagnosis, clinical decision-making and risk assessment, prognosis prediction and forecasting tumor biological behavior and so on. Finally, the problems existing in radiomics at the present stage are summarized and the future development direction is prospected, in order to provide reference for clinical diagnosis and treatment.
- Adrenal tumor,
- Radiomics,
- Research progress

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References(44)

References

[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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