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Application and research progress of radiomics in evaluation of pancreatic cancer
LI Jingjing  LI Yuying  SHI Haifeng  HANG Junjie 

Cite this article as: Li JJ, Li YY, Shi HF, et al. Application and research progress of radiomics in evaluation of pancreatic cancer[J]. Chin J Magn Reson Imaging, 2022, 13(8): 150-153. DOI:10.12015/issn.1674-8034.2022.08.034.


[Abstract] The death rate of pancreatic cancer is increasing year by year. Early diagnosis and precise treatment are the key to improve the therapeutic effect. As a new technology, radiomics has been gradually applied to the diagnosis and treatment of pancreatic cancer due to its non-invasive analysis of tumor heterogeneity. Radiomics based on computed tomography (CT), MRI, positron emission tomography/computed tomography (PET/CT) can distinguish pancreatic cancer from other diseases that are easily misdiagnosed as pancreatic cancer, evaluate the treatment effect and predict survival, thus contributing to the individualized treatment of pancreatic cancer. The purpose of this article reviews the application and research progress of radiomics based on CT, MRI, PET/CT in differential diagnosis, curative effect evaluation and prognosis prediction of pancreatic cancer.
[Keywords] pancreatic cancer;radiomics;differential diagnosis;curative effect evaluation;prognosis prediction;magnetic resonance imaging

LI Jingjing1   LI Yuying1   SHI Haifeng2*   HANG Junjie3  

1 Graduate School of Dalian Medical University, Dalian 116044, China

2 Department of Medical Imaging, Changzhou No.2 People's Hospital, Changzhou 213003, China

3 Department of Oncology, Changzhou No. 2 People's Hospital, Changzhou 213003, China

Shi HF, E-mail: doctorstone771@163.com

Conflicts of interest   None.

Received  2022-04-22
Accepted  2022-08-05
DOI: 10.12015/issn.1674-8034.2022.08.034
Cite this article as: Li JJ, Li YY, Shi HF, et al. Application and research progress of radiomics in evaluation of pancreatic cancer[J]. Chin J Magn Reson Imaging, 2022, 13(8): 150-153.DOI:10.12015/issn.1674-8034.2022.08.034

[1]
Siegel R, Miller K, Sauer AG. Colorectal cancer statistics, 2020[J]. CA Cancer J Clin, 2020, 70(1): 7-30. DOI: 10.3322/caac.21590.
[2]
Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
[3]
Pandya G, Kirtonia A, Singh A, et al. A comprehensive review of the multifaceted role of the microbiota in human pancreatic carcinoma[J/OL]. Semin Cancer Biol, 2021 [2022-07-31]. https://www.sciencedirect.com/science/article/abs/pii/S1044579X21001577. DOI: 10.1016/j.semcancer.2021.05.027.
[4]
Ansari D, Tingstedt B, Andersson B, et al. Pancreatic cancer: yesterday, today and tomorrow[J]. Future Oncol, 2016, 12(16): 1929-1946. DOI: 10.2217/fon-2016-0010.
[5]
Abunahel BM, Pontre B, Kumar H, et al. Pancreas image mining: a systematic review of radiomics[J]. Eur Radiol, 2021, 31(5): 3447-3467. DOI: 10.1007/s00330-020-07376-6.
[6]
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.
[7]
van Timmeren JE, Cester D, Tanadini-Lang S, et al. Radiomics in medical imaging-"how-to" guide and critical reflection[J/OL]. Insights Imaging, 2020, 11(1) [2022-07-31]. https://insightsimaging.springeropen.com/articles/10.1186/s13244-020-00887-2. DOI: 10.1186/s13244-020-00887-2.
[8]
Mayerhoefer ME, Materka A, Langs G, et al. Introduction to radiomics[J]. J Nucl Med, 2020, 61(4): 488-495. DOI: 10.2967/jnumed.118.222893.
[9]
Lambin P, Leijenaar RTH, Deist TM, et al. Radiomics: the bridge between medical imaging and personalized medicine[J]. Nat Rev Clin Oncol, 2017, 14(12): 749-762. DOI: 10.1038/nrclinonc.2017.141.
[10]
Liang Y, Schott D, Zhang Y, et al. Auto-segmentation of pancreatic tumor in multi-parametric MRI using deep convolutional neural networks[J]. Radiother Oncol, 2020, 145: 193-200. DOI: 10.1016/j.radonc.2020.01.021.
[11]
Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis[J/OL]. Eur Radiol Exp, 2018, 2(1) [2022-07-31]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6234198. DOI: 10.1186/s41747-018-0068-z.
[12]
Avanzo M, Stancanello J, Pirrone G, et al. Radiomics and deep learning in lung cancer[J]. Strahlenther Onkol, 2020, 196(10): 879-887. DOI: 10.1007/s00066-020-01625-9.
[13]
Ma ZY, Gong YF, Zhuang HK, et al. Pancreatic neuroendocrine tumors: a review of serum biomarkers, staging, and management[J]. World J Gastroenterol, 2020, 26(19): 2305-2322. DOI: 10.3748/wjg.v26.i19.2305.
[14]
Ren S, Zhang J, Chen J, et al. Evaluation of Texture Analysis for the Differential Diagnosis of Mass-Forming Pancreatitis From Pancreatic Ductal Adenocarcinoma on Contrast-Enhanced CT Images[J/OL]. Front Oncol, 2019, 9 [2022-07-31]. https://www.frontiersin.org/articles/10.3389/fonc.2019.01171/full. DOI: 10.3389/fonc.2019.01171.
[15]
Li J, Liu F, Fang X, et al. CT radiomics features in differentiation of focal-type autoimmune pancreatitis from pancreatic ductal adenocarcinoma: a propensity score analysis[J]. Acad Radiol, 2022, 29(3): 358-366. DOI: 10.1016/j.acra.2021.04.014.
[16]
He M, Liu ZY, Lin YS, 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.
[17]
Ren S, Zhao R, Zhang JJ, 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.
[18]
Park S, Chu LC, Hruban RH, et al. Differentiating autoimmune pancreatitis from pancreatic ductal adenocarcinoma with CT radiomics features[J]. Diagn Interv Imaging, 2020, 101(9): 555-564. DOI: 10.1016/j.diii.2020.03.002.
[19]
Nakanishi R, Oki E, Hasuda H, et al. Radiomics texture analysis for the identification of colorectal liver metastases sensitive to first-line oxaliplatin-based chemotherapy[J]. Ann Surg Oncol, 2021, 28(6): 2975-2985. DOI: 10.1245/s10434-020-09581-5.
[20]
Cheng S, Jin Z, Xue H. Assessment of Response to Chemotherapy in Pancreatic Cancer with Liver Metastasis: CT Texture as a Predictive Biomarker[J/OL]. Diagnostics (Basel), 2021, 11(12) [2022-07-31]. https://www.mdpi.com/2075-4418/11/12/2252/htm. DOI: 10.3390/diagnostics11122252.
[21]
Nasief H, Zheng C, Schott D, et al. A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer[J/OL]. NPJ Precis Oncol, 2019, 3 [2022-07-31]. https://www.nature.com/articles/s41698-019-0096-z. DOI: 10.1038/s41698-019-0096-z.
[22]
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/OL]. Front Oncol, 2019, 9 [2022-07-31]. https://www.frontiersin.org/articles/10.3389/fonc.2019.01464. DOI: 10.3389/fonc.2019.01464.
[23]
Chen MY, Cao JS, Hu JH, et al. Clinical-radiomic analysis for pretreatment prediction of objective response to first transarterial chemoembolization in hepatocellular carcinoma[J]. Liver Cancer, 2021, 10(1): 38-51. DOI: 10.1159/000512028.
[24]
Cheng SH, Cheng YJ, Jin ZY, et al. Unresectable pancreatic ductal adenocarcinoma: role of CT quantitative imaging biomarkers for predicting outcomes of patients treated with chemotherapy[J]. Eur J Radiol, 2019, 113: 188-197. DOI: 10.1016/j.ejrad.2019.02.009.
[25]
Hang JJ, Xu KQ, Yin RH, et al. Role of CT texture features for predicting outcome of pancreatic cancer patients with liver metastases[J]. J Cancer, 2021, 12(8): 2351-2358. DOI: 10.7150/jca.49569.
[26]
Xie TS, Wang XY, Li ML, et al. Pancreatic ductal adenocarcinoma: a radiomics nomogram outperforms clinical model and TNM staging for survival estimation after curative resection[J]. Eur Radiol, 2020, 30(5): 2513-2524. DOI: 10.1007/s00330-019-06600-2.
[27]
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.
[28]
Liu J, Hu L, Zhou B, et al. Development and validation of a novel model incorporating MRI-based radiomics signature with clinical biomarkers for distinguishing pancreatic carcinoma from mass-forming chronic pancreatitis[J/OL]. Transl Oncol, 2022, 18 [2022-07-31]. https://www.sciencedirect.com/science/article/pii/S1936523322000195. DOI: 10.1016/j.tranon.2022.101357.
[29]
Deng Y, Ming B, Zhou T, et al. Radiomics Model Based on MR Images to Discriminate Pancreatic Ductal Adenocarcinoma and Mass-Forming Chronic Pancreatitis Lesions[J/OL]. Front Oncol, 2021, 11 [2022-07-31]. https://www.frontiersin.org/articles/10.3389/fonc.2021.620981. DOI: 10.3389/fonc.2021.620981.
[30]
Zhang H, Wang HX, Hao DP, et al. An MRI-based radiomic nomogram for discrimination between malignant and benign sinonasal tumors[J]. J Magn Reson Imaging, 2021, 53(1): 141-151. DOI: 10.1002/jmri.27298.
[31]
Simpson G, Spieler B, Dogan N, et al. Predictive value of 0.35 T magnetic resonance imaging radiomic features in stereotactic ablative body radiotherapy of pancreatic cancer: a pilot study[J]. Med Phys, 2020, 47(8): 3682-3690. DOI: 10.1002/mp.14200.
[32]
Liang L, Ding Y, Yu Y, et al. Whole-tumour evaluation with MRI and radiomics features to predict the efficacy of S-1 for adjuvant chemotherapy in postoperative pancreatic cancer patients: a pilot study[J/OL]. BMC Med Imaging, 2021, 21(1) [2022-07-31]. https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-021-00605-4. DOI: 10.1186/s12880-021-00605-4.
[33]
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.
[34]
Siegel R, Miller K, Fuchs HE, et al. Cancer statistics, 2022[J]. CA Cancer J Clin, 2022, 72(1): 7-33. DOI: 10.3322/caac.21708.
[35]
Tomaszewski MR, Dominguez-Viqueira W, Ortiz A, et al. Heterogeneity analysis of MRI T2 maps for measurement of early tumor response to radiotherapy[J/OL]. NMR Biomed, 2021, 34(3) [2022-07-31]. https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.4454. DOI: 10.1002/nbm.4454.
[36]
Tomaszewski MR, Latifi K, Boyer E, et al. Delta radiomics analysis of Magnetic Resonance guided radiotherapy imaging data can enable treatment response prediction in pancreatic cancer[J/OL]. Radiat Oncol, 2021, 16(1) [2022-07-31]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8672552. DOI: 10.1186/s13014-021-01957-5.
[37]
Noda Y, Tomita H, Ishihara T, et al. Prediction of overall survival in patients with pancreatic ductal adenocarcinoma: histogram analysis of ADC value and correlation with pathological intratumoral necrosis[J/OL]. BMC Med Imaging, 2022, 22(1) [2022-07-31]. https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-022-00751-3. DOI: 10.1186/s12880-022-00751-3.
[38]
Kaissis G, Ziegelmayer S, Lohofer F, et al. A machine learning model for the prediction of survival and tumor subtype in pancreatic ductal adenocarcinoma from preoperative diffusion-weighted imaging[J/OL]. Eur Radiol Exp, 2019, 3(1) [2022-07-31]. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6797674. DOI: 10.1186/s41747-019-0119-0.
[39]
Ziegelmayer S, Kaissis G, Harder F, et al. Deep Convolutional Neural Network-Assisted Feature Extraction for Diagnostic Discrimination and Feature Visualization in Pancreatic Ductal Adenocarcinoma (PDAC) versus Autoimmune Pancreatitis (AIP)[J/OL]. J Clin Med, 2020, 9(12) [2022-07-31]. https://www.mdpi.com/2077-0383/9/12/4013. DOI: 10.3390/jcm9124013.
[40]
Liu ZB, Li M, Zuo CJ, et al. Radiomics model of dual-time 2-[18F]FDG PET/CT imaging to distinguish between pancreatic ductal adenocarcinoma and autoimmune pancreatitis[J]. Eur Radiol, 2021, 31(9): 6983-6991. DOI: 10.1007/s00330-021-07778-0.
[41]
Zhang YQ, Cheng C, Liu ZB, et al. Radiomics analysis for the differentiation of autoimmune pancreatitis and pancreatic ductal adenocarcinoma in 18F-FDG PET/CT[J]. Med Phys, 2019, 46(10): 4520-4530. DOI: 10.1002/mp.13733.
[42]
Zhu AZ, Lee D, Shim H. Metabolic positron emission tomography imaging in cancer detection and therapy response[J]. Semin Oncol, 2011, 38(1): 55-69. DOI: 10.1053/j.seminoncol.2010.11.012.
[43]
Chicklore S, Goh V, Siddique M, et al. Quantifying tumour heterogeneity in 18F-FDG PET/CT imaging by texture analysis[J]. Eur J Nucl Med Mol Imaging, 2013, 40(1): 133-140. DOI: 10.1007/s00259-012-2247-0.
[44]
Yue Y, Osipov A, Fraass B, et al. Identifying prognostic intratumor heterogeneity using pre- and post-radiotherapy 18F-FDG PET images for pancreatic cancer patients[J]. J Gastrointest Oncol, 2017, 8(1): 127-138. DOI: 10.21037/jgo.2016.12.04.
[45]
Tuli R, Fraass B, Yang W, et al. Pretreatment 18 F-FDG-PET texture analysis to predict local response of pancreatic cancer to radiotherapy[J/OL]. J Clin Oncol2014, 32 [2022-07-31]. https://www.researchgate.net/publication/314032386. DOI: 10.1200/jco.2014.32.3_suppl.375.
[46]
Lee JW, Park SH, Ahn 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/OL]. Cancers (Basel), 2021, 13(14) [2022-07-31]. https://www.mdpi.com/2072-6694/13/14/3563. DOI: 10.3390/cancers13143563.
[47]
Cui Y, Song J, Pollom E, et al. Quantitative analysis of (18)F-fluorodeoxyglucose positron emission tomography identifies novel prognostic imaging biomarkers in locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy[J]. Int J Radiat Oncol Biol Phys, 2016, 96(1): 102-109. DOI: 10.1016/j.ijrobp.2016.04.034.
[48]
Mori M, Passoni P, Incerti 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 Oncol, 2020, 153: 258-264. DOI: 10.1016/j.radonc.2020.07.003.

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