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REVIEW
Progress in the application of radiomics in head and neck diseases
WANG Anran  LI Quanjiang  HUANG Zhongxin  GU Jinming  Peng Juan  LUO Tianyou  LÜ Fajin 

Cite this article as: Wang AR, Li QJ, Huang ZX, et al. Progress in the application of radiomics in head and neck diseases[J]. Chin J Magn Reson Imaging, 2021, 12(1): 100-102. DOI:10.12015/issn.1674-8034.2021.01.023.


[Abstract] Radiomics refers to the high-throughput extraction of large amounts of image features from radiographic images, which extracts more information from image-based features for evidence-based clinical diagnosis and better clinical decision making. In this series, the progress in the application of radiomics in head and neck diseases was reviewed in recent years, including disease diagnosis, efficacy evaluation, prognosis prediction, tumor genotypes, molecular markers, and deep learning application. The demerits of radiomics at this stage and its future development direction in medical imaging diagnosis were also summarized at the end of article.
[Keywords] radiomics;medical imaging diagnosis of head and neck;precision medicine

WANG Anran   LI Quanjiang   HUANG Zhongxin   GU Jinming   Peng Juan*   LUO Tianyou   LÜ Fajin  

Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing 400016, China

*Corresponding author: Peng J,E-mail: pengjuan1209@126.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  Supported by the Science and Technology Bureau of Yuzhong District, Chongqing, China No. 20190111
Received  2020-06-01
Accepted  2020-09-28
DOI: 10.12015/issn.1674-8034.2021.01.023
Cite this article as: Wang AR, Li QJ, Huang ZX, et al. Progress in the application of radiomics in head and neck diseases[J]. Chin J Magn Reson Imaging, 2021, 12(1): 100-102. DOI:10.12015/issn.1674-8034.2021.01.023.

1
Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer, 2012, 48(4): 441-446. DOI: 10.1016/j.ejca.2011.11.036
2
Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology, 2016, 278(2): 563-577. DOI: 10.1148/radiol.2015151169
3
Angermueller C, Parnamaa T, Parts L, et al. Deep learning for computational biology. Mol Syst Biol, 2016, 12(7): 878-878. DOI: 10.15252/msb.20156651
4
Wu W, Ye J, Wang Q, et al. CT-based radiomics signature for the preoperative discrimination between head and neck squamous cell carcinoma grades. Front Oncol, 2019, 9: 821. DOI: 10.3389/fonc.2019.00821
5
van Den Burg EL, Van Hoof M, Postma AA, et al. An exploratory study to detect Meniere's disease in conventional MRI scans using radiomics. Front Neurol, 2016, 7: 190. DOI: 10.3389/fneur.2016.00190
6
Seidler M, Forghani B, Reinhold C, et al. Dual-energy CT texture analysis with machine learning for the evaluation and characterization of cervical lymphadenopathy. Comput Struct Biotechnol, 2019, 17: 1009-1015. DOI: 10.1016/j.csbj.2019.07.004
7
Brown AM, Nagala S, Mclean MA, et al. Multi-institutional validation of a novel textural analysis tool for preoperative stratification of suspected thyroid tumors on diffusion-weighted MRI. Magn Reson Med, 2016, 75(4): 1708-1716. DOI: 10.1002/mrm.25743
8
Fruehwald-Pallamar J, Czerny C, Holzer-Fruehwald L, et al. Texture-based and diffusion-weighted discrimination of parotid gland lesions on MR images at 3.0 Tesla. NMR Biomed, 2013, 26(11): 1372-1379. DOI: 10.1002/nbm.2962
9
Park M, Kim J, Choi YS, et al. Application of dynamic contrast-enhanced MRI parameters for differentiating squamous cell carcinoma and malignant lymphoma of the oropharynx. Am J Roentgenol, 2016, 206(2): 401-407. DOI: 10.2214/AJR.15.14550
10
Zhang B, Ouyang F, Gu D, et al. Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics. Oncotarget, 2017, 8(42): 72457-72465. DOI: 10.18632/oncotarget.19799
11
Zhai TT, Langendijk JA, van Dijk LV, et al. Pre-treatment radiomic features predict individual lymph node failure for head and neck cancer patients. Radiother Oncol, 2020, 146: 58-65. DOI: 10.1016/j.radonc.2020.02.005
12
Bologna M, Calareso G, Resteghini C, et al. Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer. NMR Biomed, 2020: e4265. DOI: 10.1002/nbm.4265
13
Sheikh K, Lee SH, Cheng Z, et al. Predicting acute radiation induced xerostomia in head and neck cancer using MR and CT radiomics of parotid and submandibular glands. Radiat Oncol, 2019, 14(1): 131. DOI: 10.1186/s13014-019-1339-4
14
Bogowicz M, Riesterer O, Ikenberg K, et al. Computed tomography radiomics predicts HPV status and local tumor control after definitive radiochemotherapy in head and neck squamous cell carcinoma. Int J Radiat Oncol Biol Phys, 2017, 99(4): 921-928. DOI: 10.1016/j.ijrobp.2017.06.002
15
Bogowicz M, Riesterer O, Stark LS, et al. Comparison of PET and CT radiomics for prediction of local tumor control in head and neck squamous cell carcinoma. Acta Oncol, 2017, 56(11): 1531-1536. DOI: 10.1080/0284186X.2017.1346382
16
Mo X, Wu X, Dong D, et al. Prognostic value of the radiomics-based model in progression-free survival of hypopharyngeal cancer treated with chemoradiation. Eur Radiol, 2020, 30(2): 833-843. DOI: 10.1007/s00330-019-06452-w
17
Aerts HJ, Velazquez ER, Leijenaar RT, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun, 2014, 5: 4006. DOI: 10.1038/ncomms5006
18
Grossmann P, Gutman DA, Dunn WD, et al. Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in glioblastoma. BMC Cancer, 2016, 16:611. DOI: 10.1186/s12885-016-2659-5
19
Yu J, Shi Z, Lian Y, et al. Noninvasive IDH1 mutation estimation based on a quantitative radiomics approach for grade II glioma. Eur Radiol, 2017, 27(8): 3509-3522. DOI: 10.1007/s00330-016-4653-3
20
Dang M, Lysack JT, Wu T, et al. MRI texture analysis predicts p53 status in head and neck squamous cell carcinoma. Am J Neuroradiol, 2015, 36(1): 166-170. DOI: 10.3174/ajnr.A4110
21
Pereira S, Pinto A, Alves V, et al. Brain tumor segmentation using convolutional neural networks in MRI images. IEEE Trans Med Imaging, 2016, 35(5): 1240-1251. DOI: 10.1109/TMI.2016.2538465
22
Diamant A, Chatterjee A, Vallières M, et al. Deep learning in head & neck cancer outcome prediction. Sci Rep, 2019, 9(1): 2764. DOI: 10.1038/s41598-019-39206-1
23
Lu D, Popuri K, Ding GW, et al. Multimodal and multiscale deep neural networks for the early diagnosis of Alzheimer's disease using structural MR and FDG-PET images. Sci Rep, 2018, 8(1): 5697. DOI: 10.1038/s41598-018-22871-z
24
Hazlett HC, Gu H, Munsell BC, et al. Early brain development in infants at high risk for autism spectrum disorder. Nature, 2017, 542(7641): 348-351. DOI: 10.1038/nature21369
25
Hosseini-Asl E, Ghazal M, Mahmoud A, et al. Alzheimer's disease diagnostics by a 3D deeply supervised adaptable convolutional network. Front Biosci (Landmark Ed), 2018, 23: 584-596. DOI: 10.2741/4606

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