Share：
Share this content in WeChat
X
[Chinese][PDF]8854
• Special Focus •
Application and value of magnetic resonance imaging techniques in the diagnosis and treatment of pancreatic cancer driven by precision medicine
CHEN Hebing  XUE Huadan 

Cite this article as: CHEN H B, XUE H D. Application and value of magnetic resonance imaging techniques in the diagnosis and treatment of pancreatic cancer driven by precision medicine[J]. Chin J Magn Reson Imaging, 2025, 16(5): 1-7. DOI:10.12015/issn.1674-8034.2025.05.001.


[Abstract] Pancreatic cancer is a highly malignant tumor with rapid progression and extremely poor prognosis, posing a severe threat to patient survival. Magnetic resonance imaging (MRI), has several advantages, such as being radiation-free, having high soft tissue resolution, and providing multiparametric, multisequence, and multiplanar imaging. Additionally, radiomics and deep learning, which are artificial intelligence technologies used for mining higher-dimensional information, have further expanded the application value of MRI in the diagnosis and treatment of pancreatic cancer. Therefore, this article innovatively reviews various MRI techniques, including conventional MRI, functional MRI, MR metabolic imaging, and the applications of radiomics and deep learning in the differential diagnosis, therapeutic efficacy assessment, and survival prognosis prediction of pancreatic cancer. It also discusses the advantages and current limitations of these techniques, thereby providing references for further research improvements and aiming to promote the translational application of different MRI techniques in the precision diagnosis and treatment of pancreatic cancer, ultimately improving patients quality of life and extending survival.
[Keywords] pancreatic cancer；magnetic resonance imaging；functional magnetic resonance imaging；metabolic imaging；radiomics；machine learning；deep learning

CHEN Hebing   XUE Huadan*  

Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100730, China

Corresponding author: XUE H D, E-mail: bjdanna95@163.com

Conflicts of interest   None.

Received  2025-02-27
Accepted  2025-04-22
DOI: 10.12015/issn.1674-8034.2025.05.001
Cite this article as: CHEN H B, XUE H D. Application and value of magnetic resonance imaging techniques in the diagnosis and treatment of pancreatic cancer driven by precision medicine[J]. Chin J Magn Reson Imaging, 2025, 16(5): 1-7. DOI:10.12015/issn.1674-8034.2025.05.001.

[1]
ZENG H M, ZHENG R S, SUN K X, et al. Cancer survival statistics in China 2019-2021: a multicenter, population-based study[J]. J Natl Cancer Cent, 2024, 4(3): 203-213. DOI: 10.1016/j.jncc.2024.06.005.
[2]
JAJODIA A, WANG A, ALABOUSI M, et al. MRI vs. CT for pancreatic adenocarcinoma vascular invasion: comparative diagnostic test accuracy systematic review and meta-analysis[J]. Eur Radiol, 2023, 33(10): 6883-6891. DOI: 10.1007/s00330-023-09659-0.
[3]
CHEN F M, NI J M, ZHANG Z Y, et al. Presurgical evaluation of pancreatic cancer: a comprehensive imaging comparison of CT versus MRI[J]. AJR Am J Roentgenol, 2016, 206(3): 526-535. DOI: 10.2214/AJR.15.15236.
[4]
GE Y J, DU K, YU Y, et al. Correlation between the quantitative parameters of DCE-MRI and the pathological features of advanced pancreatic cancer and the value of predicting the early efficacy of chemotherapy[J]. Chin J CT MRI, 2023, 21(12): 119-121. DOI: 10.3969/j.issn.1672-5131.2023.12.036.
[5]
WANG N, GADDAM S, XIE Y, et al. Multitasking dynamic contrast enhanced magnetic resonance imaging can accurately differentiate chronic pancreatitis from pancreatic ductal adenocarcinoma[J/OL]. Front Oncol, 2022, 12: 1007134 [2025-01-25]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9853434/. DOI: 10.3389/fonc.2022.1007134.
[6]
KIM H, MORGAN D E, SCHEXNAILDER P, et al. Accurate therapeutic response assessment of pancreatic ductal adenocarcinoma using quantitative dynamic contrast-enhanced magnetic resonance imaging with a point-of-care perfusion phantom: A pilot study[J]. Invest Radiol, 2019, 54(1): 16-22. DOI: 10.1097/RLI.0000000000000505.
[7]
DO R K, REYNGOLD M, PAUDYAL R, et al. Diffusion-weighted and dynamic contrast-enhanced MRI derived imaging metrics for stereotactic body radiotherapy of pancreatic ductal adenocarcinoma: preliminary findings[J]. Tomography, 2020, 6(2): 261-271. DOI: 10.18383/j.tom.2020.00015.
[8]
KANG J H, LEE S S, KIM J H, et al. Multiparametric MRI for prediction of treatment response to neoadjuvant FOLFIRINOX therapy in borderline resectable or locally advanced pancreatic cancer[J]. Eur Radiol, 2021, 31(2): 864-874. DOI: 10.1007/s00330-020-07134-8.
[9]
HUSSIEN N, HUSSIEN R S, SAAD D H A, et al. The role of MRI pancreatic protocol in assessing response to neoadjuvant therapy for patients with borderline resectable pancreatic cancer[J/OL]. Front Oncol, 2021, 11: 796317 [2025-01-25]. https://pmc.ncbi.nlm.nih.gov/articles/PMC8792857/. DOI: 10.3389/fonc.2021.796317.
[10]
OKADA K I, KAWAI M, HIRONO S, et al. Diffusion-weighted MRI predicts the histologic response for neoadjuvant therapy in patients with pancreatic cancer: a prospective study (DIFFERENT trial)[J]. Langenbecks Arch Surg, 2020, 405(1): 23-33. DOI: 10.1007/s00423-020-01857-4.
[11]
DALAH E, ERICKSON B, OSHIMA K, et al. Correlation of ADC with pathological treatment response for radiation therapy of pancreatic cancer[J]. Transl Oncol, 2018, 11(2): 391-398. DOI: 10.1016/j.tranon.2018.01.018.
[12]
QU C, ZENG P E, HU W Y, et al. Multiparametric quantitative diffusion weighted magnetic resonance imaging can effectively predict the response to neoadjuvant therapy in borderline resectable pancreatic ductal adenocarcinoma[J/OL]. Eur J Radiol, 2025, 183: 111893 [2025-04-18]. https://www.ejradiology.com/article/S0720-048X(24)00609-0/fulltext. DOI: 10.1016/j.ejrad.2024.111893.
[13]
RIVIERE D M, VAN GEENEN E M, VAN DER KOLK B M, et al. Improving preoperative detection of synchronous liver metastases in pancreatic cancer with combined contrast-enhanced and diffusion-weighted MRI[J]. Abdom Radiol, 2019, 44(5): 1756-1765. DOI: 10.1007/s00261-018-1867-7.
[14]
MARION-AUDIBERT A M, VULLIERME M P, RONOT M, et al. Routine MRI with DWI sequences to detect liver metastases in patients with potentially resectable pancreatic ductal carcinoma and normal liver CT: a prospective multicenter study[J/OL]. AJR Am J Roentgenol, 2018, 211(5): W217-W225 [2025-01-31]. https://www.ajronline.org/doi/10.2214/AJR.18.19640. DOI: 10.2214/AJR.18.19640.
[15]
DONG L, LI K, PENG T. Diagnostic value of diffusion-weighted imaging/magnetic resonance imaging for peritoneal metastasis from malignant tumor: a systematic review and meta-analysis[J/OL]. Medicine (Baltimore), 2021, 100(5): e24251 [2025-04-04]. https://pmc.ncbi.nlm.nih.gov/articles/PMC7870229/. DOI: 10.1097/md.0000000000024251.
[16]
ZHANG Z H, ZHANG Y Q, HU F X, et al. Value of diffusion kurtosis MR imaging and conventional diffusion weighed imaging for evaluating response to first-line chemotherapy in unresectable pancreatic cancer[J/OL]. Cancer Imaging, 2024, 24(1): 29 [2025-01-18]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10898033/. DOI: 10.1186/s40644-024-00674-y.
[17]
LI J L, LIANG L L, YU H, et al. Whole-tumor histogram analysis of non-Gaussian distribution DWI parameters to differentiation of pancreatic neuroendocrine tumors from pancreatic ductal adenocarcinomas[J]. Magn Reson Imaging, 2019, 55: 52-59. DOI: 10.1016/j.mri.2018.09.017.
[18]
RONG D L, MAO Y Z, HU W M, et al. Intravoxel incoherent motion magnetic resonance imaging for differentiating metastatic and non-metastatic lymph nodes in pancreatic ductal adenocarcinoma[J]. Eur Radiol, 2018, 28(7): 2781-2789. DOI: 10.1007/s00330-017-5259-0.
[19]
MA C, LI Y J, WANG L, et al. Intravoxel incoherent motion DWI of the pancreatic adenocarcinomas: monoexponential and biexponential apparent diffusion parameters and histopathological correlations[J/OL]. Cancer Imaging, 2017, 17(1): 12 [2025-01-20]. https://pmc.ncbi.nlm.nih.gov/articles/PMC5410078/. DOI: 10.1186/s40644-017-0114-8.
[20]
ZHU M L, ZHANG C D, YAN J X, et al. Accuracy of quantitative diffusion-weighted imaging for differentiating benign and malignant pancreatic lesions: a systematic review and meta-analysis[J]. Eur Radiol, 2021, 31(10): 7746-7759. DOI: 10.1007/s00330-021-07880-3.
[21]
KLAASSEN R, GURNEY-CHAMPION O J, ENGELBRECHT M R W, et al. Evaluation of six diffusion-weighted MRI models for assessing effects of neoadjuvant chemoradiation in pancreatic cancer patients[J]. Int J Radiat Oncol Biol Phys, 2018, 102(4): 1052-1062. DOI: 10.1016/j.ijrobp.2018.04.064.
[22]
WASSENAAR N P M, VAN SCHELT A S, SCHRAUBEN E M, et al. MR elastography of the pancreas: bowel preparation and repeatability assessment in pancreatic cancer patients and healthy controls[J]. J Magn Reson Imaging, 2024, 59(5): 1582-1592. DOI: 10.1002/jmri.28918.
[23]
LIU D X, CHEN J J, ZHANG Y F, et al. Magnetic resonance elastography-derived stiffness: potential imaging biomarker for differentiation of benign and malignant pancreatic masses[J]. Abdom Radiol, 2023, 48(8): 2604-2614. DOI: 10.1007/s00261-023-03956-4.
[24]
SEELEN L W F, VAN DEN WILDENBERG L, GURSAN A, et al. 31P MR spectroscopy in the pancreas: repeatability, comparison with liver, and pilot pancreatic cancer data[J]. J Magn Reson Imaging, 2024, 60(6): 2657-2666. DOI: 10.1002/jmri.29326.
[25]
WANG A S, LODI A, RIVERA L B, et al. HR-MAS MRS of the pancreas reveals reduced lipid and elevated lactate and taurine associated with early pancreatic cancer[J]. NMR Biomed, 2014, 27(11): 1361-1370. DOI: 10.1002/nbm.3198.
[26]
CHOWDHURY R, MUELLER C A, SMITH L, et al. Quantification of prostate cancer metabolism using 3D multiecho bSSFP and hyperpolarized [1-13C] pyruvate: metabolism differs between tumors of the same gleason grade[J]. J Magn Reson Imaging, 2023, 57(6): 1865-1875. DOI: 10.1002/jmri.28467.
[27]
LOFT M, CLEMMENSEN A, CHRISTENSEN E N, et al. First-in-human: simultaneous hyperpolarized 1- 13 C-pyruvate magnetic resonance spectroscopy and 18 F-FDG PET (hyperPET) imaging of a patient with lymphoma[J]. Clin Nucl Med, 2025, 50(2): 186-187. DOI: 10.1097/RLU.0000000000005534.
[28]
LOW J C M, CAO J B, HESSE F, et al. Deuterium metabolic imaging differentiates glioblastoma metabolic subtypes and detects early response to chemoradiotherapy[J]. Cancer Res, 2024, 84(12): 1996-2008. DOI: 10.1158/0008-5472.CAN-23-2552.
[29]
MARKOVIC S, ROUSSEL T, AGEMY L, et al. Deuterium MRSI characterizations of glucose metabolism in orthotopic pancreatic cancer mouse models[J/OL]. NMR Biomed, 2021, 34(9): e4569 [2025-02-10]. https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.4569. DOI: 10.1002/nbm.4569.
[30]
MARTINHO R P, BAO Q, MARKOVIC S, et al. Identification of variable stages in murine pancreatic tumors by a multiparametric approach employing hyperpolarized C MRSI, 1 diffusivityH and 1 H T1 MRI[J/OL]. NMR Biomed, 2021, 34(2): e4446 [2025-02-07]. https://analyticalsciencejournals.onlinelibrary.wiley.com/doi/10.1002/nbm.4446. DOI: 10.1002/nbm.4446.
[31]
WANG L X, ZHOU Z W, GADDAM S, et al. Chemical exchange saturation transfer for pancreatic ductal adenocarcinoma evaluation[J]. Pancreas, 2022, 51(5): 463-468. DOI: 10.1097/MPA.0000000000002059.
[32]
ZHANG Z, ZHOU N, GUO X, et al. Pretherapeutic assessment of pancreatic cancer: comparison of FDG PET/CT plus delayed PET/MR and contrast-enhanced CT/MR[J/OL]. Front Oncol, 2021, 11: 790462 [2025-02-24]. https://pmc.ncbi.nlm.nih.gov/articles/PMC8794800/. DOI: 10.3389/fonc.2021.790462.
[33]
COHEN D, KESLER M, KURASH M M, et al. A lesson in humility: the added values of PET-MRI over PET-CT in detecting malignant hepatic lesions[J]. Eur J Nucl Med Mol Imaging, 2023, 50(5): 1423-1433. DOI: 10.1007/s00259-022-06099-8.
[34]
PANDA A, GARG I, TRUTY M J, et al. Borderline resectable and locally advanced pancreatic cancer: FDG PET/MRI and CT tumor metrics for assessment of pathologic response to neoadjuvant therapy and prediction of survival[J]. AJR Am J Roentgenol, 2021, 217(3): 730-740. DOI: 10.2214/AJR.20.24567.
[35]
YEH R, DERCLE L, GARG I, et al. The role of 18F-FDG PET/CT and PET/MRI in pancreatic ductal adenocarcinoma[J]. Abdom Radiol, 2018, 43(2): 415-434. DOI: 10.1007/s00261-017-1374-2.
[36]
WANG T, PENG Y, LI R, et al. Preliminary study on SPECT/CT imaging of pancreatic cancer xenografts by targeting integrin α5 in pancreatic stellate cells[J]. J Cancer, 2021, 12(6): 1729-1733. DOI: 10.7150/jca.51190.
[37]
TATEKAWA S, OFUSA K, CHIJIMATSU R, et al. Methylosystem for cancer sieging strategy[J/OL]. Cancers (Basel), 2021, 13(20): 5088 [2025-01-27]. https://pmc.ncbi.nlm.nih.gov/articles/PMC8534198/. DOI: 10.3390/cancers13205088.
[38]
ZHANG Z Y, GUO S W, CHENG C, et al. Integrated 68 Ga-FAPI-04 PET/MR in pancreatic cancer: prediction of tumor response and tumor resectability after neoadjuvant therapy[J]. Clin Nucl Med, 2024, 49(8): 715-721. DOI: 10.1097/RLU.0000000000005300.
[39]
ZHANG Z Y, GUO S W, SU W W, et al. Preoperative assessment of pancreatic cancer with [68Ga] Ga-DOTA-FAPI-04 PET/MR versus [18F]-FDG PET/CT plus contrast-enhanced CT: a prospective preliminary study[J]. Eur J Nucl Med Mol Imaging, 2025, 52(3): 1017-1027. DOI: 10.1007/s00259-024-06943-z.
[40]
TOMASZEWSKI M R, GILLIES R J. The biological meaning of radiomic features[J]. Radiology, 2021, 298(3): 505-516. DOI: 10.1148/radiol.2021202553.
[41]
KEVIN ZHOU S, GREENSPAN H, DAVATZIKOS C, et al. A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises[J]. Proc IEEE Inst Electr Electron Eng, 2021, 109(5): 820-838. DOI: 10.1109/JPROC.2021.3054390.
[42]
DE ROBERTIS R, TOMAIUOLO L, PASQUAZZO F, et al. Correlation between ADC histogram-derived metrics and the time to metastases in resectable pancreatic adenocarcinoma[J/OL]. Cancers (Basel), 2022, 14(24): 6050 [2025-02-25]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9775993/. DOI: 10.3390/cancers14246050.
[43]
SHI Z S, MA C L, HUANG X M, et al. Magnetic resonance imaging radiomics-based nomogram from primary tumor for pretreatment prediction of peripancreatic lymph node metastasis in pancreatic ductal adenocarcinoma: a multicenter study[J]. J Magn Reson Imaging, 2022, 55(3): 823-839. DOI: 10.1002/jmri.28048.
[44]
SHI L, WANG L, WU C, et al. Preoperative prediction of lymph node metastasis of pancreatic ductal adenocarcinoma based on a radiomics nomogram of dual-parametric MRI imaging[J/OL]. Front Oncol, 2022, 12: 927077 [2025-02-17]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9298539/. DOI: 10.3389/fonc.2022.927077.
[45]
HUANG Y, ZHOU S, LUO Y, et al. Development and validation of a radiomics model of magnetic resonance for predicting liver metastasis in resectable pancreatic ductal adenocarcinoma patients[J/OL]. Radiat Oncol, 2023, 18(1): 79 [2025-01-30]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10170860/. DOI: 10.1186/s13014-023-02273-w.
[46]
YUAN Z Y, SHU Z Y, PENG J X, et al. Prediction of postoperative liver metastasis in pancreatic ductal adenocarcinoma based on multiparametric magnetic resonance radiomics combined with serological markers: a cohort study of machine learning[J]. Abdom Radiol, 2024, 49(1): 117-130. DOI: 10.1007/s00261-023-04047-0.
[47]
VAISH U, JAIN T, ARE A C, et al. Cancer-associated fibroblasts in pancreatic ductal adenocarcinoma: an update on heterogeneity and therapeutic targeting[J/OL]. Int J Mol Sci, 2021, 22(24): 13408 [2025-02-03]. https://pmc.ncbi.nlm.nih.gov/articles/PMC8706283/. DOI: 10.3390/ijms222413408.
[48]
DENG D, PATEL R, CHIANG C Y, et al. Role of the tumor microenvironment in regulating pancreatic cancer therapy resistance[J/OL]. Cells, 2022, 11(19): 2952 [2025-01-28]. https://pmc.ncbi.nlm.nih.gov/articles/PMC9563251/. DOI: 10.3390/cells11192952.
[49]
SHERMAN M H, BEATTY G L. Tumor microenvironment in pancreatic cancer pathogenesis and therapeutic resistance[J]. Annu Rev Pathol, 2023, 18: 123-148. DOI: 10.1146/annurev-pathmechdis-031621-024600.
[50]
MENG Y H, ZHANG H, LI Q, et al. Magnetic resonance radiomics and machine-learning models: an approach for evaluating tumor-stroma ratio in patients with pancreatic ductal adenocarcinoma[J]. Acad Radiol, 2022, 29(4): 523-535. DOI: 10.1016/j.acra.2021.08.013.
[51]
MENG Y H, ZHANG H, LI Q, et al. Noncontrast magnetic resonance radiomics and multilayer perceptron network classifier: an approach for predicting fibroblast activation protein expression in patients with pancreatic ductal adenocarcinoma[J]. J Magn Reson Imaging, 2021, 54(5): 1432-1443. DOI: 10.1002/jmri.27648.
[52]
DE GRANDIS M C, ASCENTI V, LANZA C, et al. Locoregional therapies and remodeling of tumor microenvironment in pancreatic cancer[J/OL]. Int J Mol Sci, 2023, 24(16): 12681 [2025-01-28]. https://pmc.ncbi.nlm.nih.gov/articles/PMC10454061/. DOI: 10.3390/ijms241612681.
[53]
ZHANG S L, YOU H Y, FAN H R, et al. Transcytosis-triggering nanoparticles for overcoming stromal barriers and reversing immunosuppression in pancreatic cancer combinatorial therapy[J]. Nano Lett, 2025, 25(7): 2949-2959. DOI: 10.1021/acs.nanolett.4c06372.
[54]
NIU N N, SHEN X Q, WANG Z, et al. Tumor cell-intrinsic epigenetic dysregulation shapes cancer-associated fibroblasts heterogeneity to metabolically support pancreatic cancer[J]. Cancer Cell, 2024, 42(5): 869-884. DOI: 10.1016/j.ccell.2024.03.005.
[55]
DONG S, LI X, CHEN Z, et al. MMP28 recruits M2-type tumor-associated macrophages through MAPK/JNK signaling pathway-dependent cytokine secretion to promote the malignant progression of pancreatic cancer[J/OL]. J Exp Clin Cancer Res, 2025, 44(1): 60 [2025-02-25]. https://pmc.ncbi.nlm.nih.gov/articles/PMC11837641/. DOI: 10.1186/s13046-025-03321-x.
[56]
ZHENG H, YANG X, HUANG N, et al. Chimeric antigen receptor macrophages targeting c-MET(CAR-M-c-MET) inhibit pancreatic cancer progression and improve cytotoxic chemotherapeutic efficacy[J/OL]. Mol Cancer, 2024, 23(1): 270 [2025-02-16]. https://pmc.ncbi.nlm.nih.gov/articles/PMC11622543/. DOI: 10.1186/s12943-024-02184-8.
[57]
BIAN Y, LIU C, LI Q, et al. Preoperative radiomics approach to evaluating tumor-infiltrating CD8+ T cells in patients with pancreatic ductal adenocarcinoma using noncontrast magnetic resonance imaging[J]. J Magn Reson Imaging, 2022, 55(3): 803-814. DOI: 10.1002/jmri.27871.
[58]
LI Q, YU J, ZHANG H, et al. Prediction of tumor-infiltrating CD20+ B-cells in patients with pancreatic ductal adenocarcinoma using a multilayer perceptron network classifier based on non-contrast MRI[J/OL]. Acad Radiol, 2022, 29(9): e167-e177 [2025-01-24]. https://www.academicradiology.org/article/S1076-6332(21)00541-9/fulltext. DOI: 10.1016/j.acra.2021.11.013.
[59]
LU W X, WU G Y, MIAO X Y, et al. The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy[J]. Cancer Immunol Immunother, 2024, 73(5): 87. DOI: 10.1007/s00262-024-03644-2.
[60]
TANG T Y, 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.
[61]
OTTENS T, BARBIERI S, ORTON M R, et al. Deep learning DCE-MRI parameter estimation: application in pancreatic cancer[J/OL]. Med Image Anal, 2022, 80: 102512 [2024-12-28]. https://www.sciencedirect.com/science/article/pii/S1361841522001591?via%3Dihub. DOI: 10.1016/j.media.2022.102512.

PREV Research progress of PCASL imaging technology artifact and its clinic
NEXT Diagnostic value of non-contrast MRI in evaluating benign and malignant mural nodules of intraductal papillary mucinous neoplasms of the pancreas
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn