• Review •
Clinical research progress of radiomics in pancreatic cancer
Cite this article as: Chen YY, Huang XH, Liu N, et al. Clinical research progress of radiomics in pancreatic cancer[J]. Chin J Magn Reson Imaging, 2021, 12(8): 108-110. DOI:10.12015/issn.1674-8034.2021.08.025.
| [Abstract] Pancreatic cancer is a highly malignant digestive tract tumor with poor prognosis. Early diagnosis and treatment is the key to improve the prognosis of patients with pancreatic cancer. Radiomics is a new tool for high-throughput extraction and quantitative analysis of image features, which can effectively evaluate the heterogeneity of tumors and obtain more information than traditional imaging examinations. Radiomics has been gradually used in the diagnosis and differentiation of pancreatic cancer from other pancreatic diseases, biological behavior prediction, curative effect evaluation and prognosis evaluation of pancreatic cancer. The purpose of this article reviews the application and research progress of radiomics in pancreatic cancer. |
| [Keywords] radiomics；pancreatic cancer；biological behavior；prognosis evaluation |
CHEN Yuying1
HUANG Xiaohua1*
LIU Nian1
TANG Lingling1,
2
HU Yuntao1
1 Department of Radiology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
2 Department of Radiology, the Second Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, China
Huang XH, E-mail: 15082797553@163.com
Conflicts of interest None.
| ACKNOWLEDGMENTS This work was part of Science and Technology Plan Project of Sichuan Province (No. 2020088) and the Sichuan Provincial Health Research Project (No. 19PJ203). |
| Received 2021-03-23 |
| Accepted 2021-04-20 |
| DOI: 10.12015/issn.1674-8034.2021.08.025 |
| Cite this article as: Chen YY, Huang XH, Liu N, et al. Clinical research progress of radiomics in pancreatic cancer[J]. Chin J Magn Reson Imaging, 2021, 12(8): 108-110. DOI:10.12015/issn.1674-8034.2021.08.025. |
1
Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA: Cancer J Clin, 2018, 68(6): 394-424.
DOI:
10.3322/caac.21492.
2
Rahib L, Smith BD, Aizenberg R, et al. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States[J]. Cancer Res, 2014, 74(11): 2913-2921.
DOI:
10.1158/0008-5472.CAN-14-0155.
3
Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images are more than pictures, they are data[J]. Radiology, 2016, 278(2): 563-577.
DOI:
10.1148/radiol.2015151169.
4
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.
5
Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications[J]. CA: Cancer J Clin, 2019, 69(2): 127-157.
DOI:
10.3322/caac.21552.
6
He M, Liu Z, Lin Y, et al. Differentiation of atypical non-functional pancreatic neuroendocrine tumor and pancreatic ductal adenocarcinoma using CT based radiomics[J]. Eur J Radiol, 2019, 117: 102-111.
DOI:
10.1016/j.ejrad.2019.05.024.
7
Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis[J]. Eur Radiol Exp, 2018, 2(1): 36.
DOI:
10.1186/s41747-018-0068-z.
8
Rahib L, Smith BD, Aizenberg R, et al. Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the United States[J]. Cancer Res, 2014, 74(11): 2913-2921.
DOI:
10.1158/0008-5472.CAN-14-0155.
9
Gerlinger M, Rowan AJ, Horswell S, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing[J]. N Engl J Med, 2012, 366(10): 883-892.
DOI:
10.1056/NEJMoa1113205.
10
Chu LC, Park S, Kawamoto S, et al. Utility of CT radiomics features in differentiation of pancreatic ductal adenocarcinoma from normal pancreatic tissue[J]. AJR Am J Roentgenol, 2019, 213(2): 349-357.
DOI:
10.2214/AJR.18.20901.
11
He M, Liu Z, Lin Y, et al. Differentiation of atypical non-functional pancreatic neuroendocrine tumor and pancreatic ductal adenocarcinoma using CT based radiomics[J]. Eur J Radiol, 2019, 117: 102-111.
DOI:
10.1016/j.ejrad.2019.05.024.
12
Wang YW, Zhang XH, Wang BT, et al. Value of texture analysis of intravoxel incoherent motion parameters in differential diagnosis of pancreatic neuroendocrine tumor and pancreatic adenocarcinoma[J]. Chin Med Sci J, 2019, 34(1): 1-9.
DOI:
10.24920/003531.
13
Zhang Y, Cheng C, Liu Z, et al. Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in (18) F-FDG PET/CT[J]. Med Phys, 2019, 46(10): 4520-4530.
DOI:
10.1002/mp.13733.
14
Ren S, Zhao R, Zhang J, et al. Diagnostic accuracy of unenhanced CT texture analysis to differentiate mass-forming pancreatitis from pancreatic ductal adenocarcinoma[J]. Abdom Radiol (NY), 2020, 45(5): 1524-1533.
DOI:
10.1007/s00261-020-02506-6.
15
Jeon SK, Lee JM, Joo I, et al. Nonhypervascular pancreatic neuroendocrine tumors: Differential diagnosis from pancreatic ductal adenocarcinomas at MR imaging-retrospective cross-sectional study[J]. Radiology, 2017, 284(1): 77-87.
DOI:
10.1148/radiol.2016160586.
16
Wasif N, Ko CY, Farrell J, et al. Impact of tumor grade on prognosis in pancreatic cancer: should we include grade in AJCC staging?[J]. Ann Surg Oncol, 2010, 17(9): 2312-2320.
DOI:
10.1245/s10434-010-1071-7.
17
Chang N, Cui L, Luo Y, et al. Development and multicenter validation of a CT-based radiomics signature for discriminating histological grades of pancreatic ductal adenocarcinoma[J]. Quant Imaging Med Surg, 2020, 10(3): 692-702.
DOI:
10.21037/qims.2020.02.21.
18
Qiu W, Duan N, Chen X, et al. Pancreatic ductal adenocarcinoma: Machine learning-based quantitative computed tomography texture analysis for prediction of histopathological grade[J]. Cancer Manag Res, 2019, 11: 9253-9264.
DOI:
10.2147/CMAR.S218414.
19
Xing H, Hao Z, Zhu W, et al. Preoperative prediction of pathological grade in pancreatic ductal adenocarcinoma based on (18)F-FDG PET/CT radiomics[J]. Ejnmmi Res, 2021, 11(1): 19.
DOI:
10.1186/s13550-021-00760-3.
20
Cassinotto C, Chong J, Zogopoulos G, et al. Resectable pancreatic adenocarcinoma: Role of CT quantitative imaging biomarkers for predicting pathology and patient outcomes[J]. Eur J Radiol, 2017, 90: 152-158.
DOI:
10.1016/j.ejrad.2017.02.033.
21
Bian Y, Guo S, Jiang H, et al. Relationship between radiomics and risk of lymph node metastasis in pancreatic ductal adenocarcinoma[J]. Pancreas, 2019, 48(9): 1195-1203.
DOI:
10.1097/MPA.0000000000001404.
22
Liu P, Gu Q, Hu X, et al. Applying a radiomics-based strategy to preoperatively predict lymph node metastasis in the resectable pancreatic ductal adenocarcinoma[J]. J Xray Sci Technol, 2020, 28(6): 1113-1121.
DOI:
10.3233/XST-200730.
23
Poruk KE, Gay DZ, Brown K, et al. The clinical utility of CA 19-9 in pancreatic adenocarcinoma: diagnostic and prognostic updates[J]. Curr Mol Med, 2013, 13(3): 340-351.
DOI:
10.2174/1566524011313030003.
24
Nasief H, Hall W, Zheng C, et al. Improving treatment response prediction for chemoradiation therapy of pancreatic cancer using a combination of delta-radiomics and the clinical biomarker CA19-9[J]. Front Oncol, 2019, 9: 1464.
DOI:
10.3389/fonc.2019.01464.
25
Chen X, Oshima K, Schott D, et al. Assessment of treatment response during chemoradiation therapy for pancreatic cancer based on quantitative radiomic analysis of daily CTs: An exploratory study[J]. PLoS One, 2017, 12(6): e178961.
DOI:
10.1371/journal.pone.0178961.
26
Borhani AA, Dewan R, Furlan A, et al. Assessment of response to neoadjuvant therapy using CT texture analysis in patients with resectable and borderline resectable pancreatic ductal adenocarcinoma[J]. AJR Am J Roentgenol, 2020, 214(2): 362-369.
DOI:
10.2214/AJR.19.21152.
27
Zhang Y, Lobo-Mueller EM, Karanicolas P, et al. CNN-based survival model for pancreatic ductal adenocarcinoma in medical imaging[J]. BMC Med Imaging, 2020, 20(1): 11.
DOI:
10.1186/s12880-020-0418-1.
28
Ciaravino V, Cardobi N, DE Robertis R, et al. CT texture analysis of ductal adenocarcinoma downstaged after chemotherapy[J]. Anticancer Res, 2018, 38(8): 4889-4895.
DOI:
10.21873/anticanres.12803.
29
Yin P, Mao N, Liu X, et al. Can clinical radiomics nomogram based on 3D multiparametric MRI features and clinical characteristics estimate early recurrence of pelvic chondrosarcoma?[J]. J Magn Reson Imaging, 2020, 51(2): 435-445.
DOI:
10.1002/jmri.26834.
30
Tang TY, Li X, Zhang Q, et al. Development of a novel multiparametric MRI radiomic nomogram for preoperative evaluation of early recurrence in resectable pancreatic cancer[J]. J Magn Reson Imaging, 2020, 52(1): 231-245.
DOI:
10.1002/jmri.27024.
31
Sandrasegaran K, Lin Y, Asare-Sawiri M, et al. CT texture analysis of pancreatic cancer[J]. Eur Radiol, 2019, 29(3): 1067-1073.
DOI:
10.1007/s00330-018-5662-1.
32
Eilaghi A, Baig S, Zhang Y, et al. CT texture features are associated with overall survival in pancreatic ductal adenocarcinoma-a quantitative analysis[J]. BMC Med Imaging, 2017, 17(1): 38.
DOI:
10.1186/s12880-017-0209-5.
33
Eresen A, Yang J, Shangguan J, et al. MRI radiomics for early prediction of response to vaccine therapy in a transgenic mouse model of pancreatic ductal adenocarcinoma[J]. J Transl Med, 2020, 18(1): 61.
DOI:
10.1186/s12967-020-02246-7.
34
Eresen A, Yang J, Shangguan J, et al. Detection of immunotherapeutic response in a transgenic mouse model of pancreatic ductal adenocarcinoma using multiparametric MRI radiomics: A preliminary investigation[J]. Acad Radiol, 2021, 28(6): e147-e154.
DOI:
10.1016/j.acra.2020.04.026.
