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• Review •
Research progress of artificial intelligence and radiomics in preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma
LI Ruitong  ZHAO Haifeng  WANG Xiaodong  CHEN Tiantian  WANG Xiaohuan  ZHANG Xuan  ZHANG Hao 

Cite this article as: LI R T, ZHAO H F, WANG X D, et al. Research progress of artificial intelligence and radiomics in preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma[J]. Chin J Magn Reson Imaging, 2025, 16(3): 211-215. DOI:10.12015/issn.1674-8034.2025.03.036.


[Abstract] Pancreatic ductal adenocainoma (PDAC) is extremely malignant and has a very poor prognosis. Lymph node metastasis is a very important indicator of its advanced and poor prognosis. Preoperative prediction of lymph node metastasis of PDAC can help clinicians determine the best surgical method and lymph node dissection range, and improve the postoperative survival rate of patients. It is difficult for traditional imaging examination to accurately predict it. Artificial intelligence (AI) and imaging omics are gradually widely used because they can find imaging features that are difficult to be observed by the naked eye and extract quantitative information from images. This review summarizes the researches of AI in preoperative evaluation of PDAC lymph node metastasis in recent years, aiming to provide reference for the future application and research direction of AI and imaging omics in preoperative prediction of PDAC lymph node metastasis, so as to assist the clinic to provide patients with more accurate and effective treatment plans.
[Keywords] pancreatic cancer；pancreatic ductal adenocarcinoma；lymph node metastasis；computed tomography；magnetic resonance imaging；imaging omics；artificial intelligence；early diagnosis

LI Ruitong1, 2   ZHAO Haifeng1, 2   WANG Xiaodong1, 2   CHEN Tiantian1, 2   WANG Xiaohuan1, 2   ZHANG Xuan1, 2   ZHANG Hao2*  

1 The First Clinical Medical College of Lanzhou University, Lanzhou 730000, China

2 Intelligent Imaging Medical Engineering Research, Department of Radiology, the First Hospital of Lanzhou University, Lanzhou 730000, China

Corresponding author: ZHANG H, E-mail: zhanghao@lzu.edu.cn

Conflicts of interest   None.

Received  2024-12-02
Accepted  2025-03-07
DOI: 10.12015/issn.1674-8034.2025.03.036
Cite this article as: LI R T, ZHAO H F, WANG X D, et al. Research progress of artificial intelligence and radiomics in preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma[J]. Chin J Magn Reson Imaging, 2025, 16(3): 211-215. DOI:10.12015/issn.1674-8034.2025.03.036.

[1]
ERDEM S, BOLLI M, MÜLLER S A, et al. Role of lymphadenectomy in resectable pancreatic cancer[J]. Langenbecks Arch Surg, 2020, 405(7): 889-902. DOI: 10.1007/s00423-020-01980-2.
[2]
FAUR A C, LAZAR D C, GHENCIU L A. Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis[J]. World J Gastroenterol, 2023, 29(12): 1811-1823. DOI: 10.3748/wjg.v29.i12.1811.
[3]
ZHENG Z J, WANG M J, TAN C L, et al. Prognostic impact of lymph node status in patients after total pancreatectomy for pancreatic ductal adenocarcinoma: a strobe-compliant study[J/OL]. Medicine, 2020, 99(8): e19327 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/32080152/. DOI: 10.1097/MD.0000000000019327.
[4]
HIRASHITA T, IKENAGA N, NAKATA K, et al. Prognostic significance of lymph node metastasis in pancreatic tail cancer: a multicenter retrospective study[J]. Ann Gastroenterol Surg, 2024, 8(4): 681-690. DOI: 10.1002/ags3.12771.
[5]
YAMADA M, SUGIURA T, OKAMURA Y, et al. Clinical implication of node-negative resectable pancreatic cancer[J]. Ann Surg Oncol, 2021, 28(4): 2257-2264. DOI: 10.1245/s10434-020-09543-x.
[6]
WANG J, LYU S C, ZHU J Q, et al. Extended lymphadenectomy benefits patients with borderline resectable pancreatic head cancer-a single-center retrospective study[J]. Gland Surg, 2021, 10(10): 2910-2924. DOI: 10.21037/gs-21-201.
[7]
KOTB A, HAJIBANDEH S, HAJIBANDEH S, et al. Meta-analysis and trial sequential analysis of randomised controlled trials comparing standard versus extended lymphadenectomy in pancreatoduodenectomy for adenocarcinoma of the head of pancreas[J]. Langenbecks Arch Surg, 2021, 406(3): 547-561. DOI: 10.1007/s00423-020-01999-5.
[8]
CHEN R F, ZHOU Q B. Value of enlarged lymph node dissection in pancreatic cancer[J]. Chin J Bases Clin Gen Surg, 2018, 25(6): 653-656. DOI: 10.7507/1007-9424.201805044.
[9]
LYU S C, WANG H X, LIU Z P, et al. Clinical value of extended lymphadenectomy in radical surgery for pancreatic head carcinoma at different T stages[J]. World J Gastrointest Surg, 2022, 14(11): 1204-1218. DOI: 10.4240/wjgs.v14.i11.1204.
[10]
PERLMUTTER B C, HOSSAIN M S, NAPLES R, et al. Survival impact based on hepatic artery lymph node status in pancreatic adenocarcinoma: a study of patients receiving modern chemotherapy[J]. J Surg Oncol, 2021, 123(2): 399-406. DOI: 10.1002/jso.26281.
[11]
JIA Z S, WANG L, LIU Z Q. The mechanism of tumor microenvironment in gemcitabine resistance of pancreatic cancer and targeting strategies[J]. Chin J Biochem Mol Biol, 2024, 40(9): 1215-1221. DOI: 10.13865/j.cnki.cjbmb.2024.07.1083.
[12]
TSENG D S J, VAN SANTVOORT H C, FEGRACHI S, et al. Diagnostic accuracy of CT in assessing extra-regional lymphadenopathy in pancreatic and peri-ampullary cancer: a systematic review and meta-analysis[J]. Surg Oncol, 2014, 23(4): 229-235. DOI: 10.1016/j.suronc.2014.10.005.
[13]
CAI F J, WANG X D, YANG Y S. Disputes and reflections of lymph node dissection in pancreaticoduodenectomy for pancreatic cancer[J]. J Hepatopancreatobiliary Surg, 2023, 35(8): 449-453, 458. DOI: 10.11952/j.issn.1007-1954.2023.08.001.
[14]
LI Q, ZHOU Z H, CHEN Y K, et al. Fully automated magnetic resonance imaging-based radiomics analysis for differentiating pancreatic adenosquamous carcinoma from pancreatic ductal adenocarcinoma[J]. Abdom Radiol, 2023, 48(6): 2074-2084. DOI: 10.1007/s00261-023-03801-8.
[15]
KO S W, JO I H, YOON S B. Feasibility and clinical utility of endoscopic ultrasound-guided tissue acquisition for comprehensive genomic profiling in pancreatic cancer: a systematic review and meta-analysis[J]. Pancreatology, 2025, 25(1): 89-97. DOI: 10.1016/j.pan.2024.12.012.
[16]
TAMM E P, BALACHANDRAN A, BHOSALE P R, et al. Imaging of pancreatic adenocarcinoma: update on staging/resectability[J]. Radiol Clin North Am, 2012, 50(3): 407-428. DOI: 10.1016/j.rcl.2012.03.008.
[17]
SIJITHRA P C, SANTHI N, RAMASAMY N. A review study on early detection of pancreatic ductal adenocarcinoma using artificial intelligence assisted diagnostic methods[J/OL]. Eur J Radiol, 2023, 166: 110972 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/37454557/. DOI: 10.1016/j.ejrad.2023.110972.
[18]
AHMAD QURESHI T, JAVED S, SARMADI T, et al. Artificial intelligence and imaging for risk prediction of pancreatic cancer: a narrative review[J/OL]. Chin Clin Oncol, 2022, 11(1): 1 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/37454557/. DOI: 10.21037/cco-21-117.
[19]
BIAN Y, ZHENG Z L, FANG X, et al. Artificial intelligence to predict lymph node metastasis at CT in pancreatic ductal adenocarcinoma[J]. Radiology, 2023, 306(1): 160-169. DOI: 10.1148/radiol.220329.
[20]
WEN Y J, SONG Z H, LI Q, et al. A nomogram based on dual-layer detector spectral computed tomography quantitative parameters and morphological quantitative indicator for distinguishing metastatic and nonmetastatic regional lymph nodes in pancreatic ductal adenocarcinoma[J]. Quant Imaging Med Surg, 2024, 14(7): 4376-4387. DOI: 10.21037/qims-23-1624.
[21]
ADHAM S, FERRI M, LEE S Y, et al. Pancreatic ductal adenocarcinoma (PDAC) regional nodal disease at standard lymphadenectomy: is MRI accurate for identifying node-positive patients?[J]. Eur Radiol, 2023, 33(9): 5976-5983. DOI: 10.1007/s00330-023-09597-x.
[22]
GHIDINI M, VUOZZO M, GALASSI B, et al. The role of positron emission tomography/computed tomography (PET/CT) for staging and disease response assessment in localized and locally advanced pancreatic cancer[J/OL]. Cancers, 2021, 13(16): 4155 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/34439307/. DOI: 10.3390/cancers13164155.
[23]
MIRZA-AGHAZADEH-ATTARI M, MADANI S P, SHAHBAZIAN H, et al. Predictive role of radiomics features extracted from preoperative cross-sectional imaging of pancreatic ductal adenocarcinoma in detecting lymph node metastasis: a systemic review and meta-analysis[J]. Abdom Radiol, 2023, 48(8): 2570-2584. DOI: 10.1007/s00261-023-03940-y.
[24]
CASTELLANA R, FANNI S C, RONCELLA C, et al. Radiomics and deep learning models for CT pre-operative lymph node staging in pancreatic ductal adenocarcinoma: A systematic review and meta-analysis[J/OL]. Eur J Radiol, 2024, 176: 111510 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/38781919/. DOI: 10.1016/j.ejrad.2024.111510.
[25]
JUNG H K, KIM K, PARK J E, et al. Image-based generative artificial intelligence in radiology: comprehensive updates[J]. Korean J Radiol, 2024, 25(11): 959-981. DOI: 10.3348/kjr.2024.0392.
[26]
PODINĂ N, GHEORGHE E C, CONSTANTIN A, et al. Artificial intelligence in pancreatic imaging: a systematic review[J]. United European Gastroenterol J, 2025, 13(1): 55-77. DOI: 10.1002/ueg2.12723.
[27]
MIRANDA J, HORVAT N, ARAUJO-FILHO J A B, et al. The role of radiomics in rectal cancer[J]. J Gastrointest Cancer, 2023, 54(4): 1158-1180. DOI: 10.1007/s12029-022-00909-w.
[28]
RUSSO L, CHARLES-DAVIES D, BOTTAZZI S, et al. Radiomics for clinical decision support in radiation oncology[J/OL]. Clin Oncol, 2024, 36(8): e269-e281 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/38548581/. DOI: 10.1016/j.clon.2024.03.003.
[29]
LIU L H, ZHOU Z. Advances in radiomics in accurate diagnosis, treatment and prognosis evaluation of hepatocellular carcinoma[J]. Chin J Magn Reson Imag, 2025, 16(1): 216-221, 227. DOI: 10.12015/issn.1674-8034.2025.01.035.
[30]
HUANG B W, HUANG H R, ZHANG S T, et al. Artificial intelligence in pancreatic cancer[J]. Theranostics, 2022, 12(16): 6931-6954. DOI: 10.7150/thno.77949.
[31]
WEN D Y, CHEN J M, TANG Z P, et al. Noninvasive prediction of lymph node metastasis in pancreatic cancer using an ultrasound-based clinicoradiomics machine learning model[J/OL]. Biomed Eng Online, 2024, 23(1): 56 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/38890695/. DOI: 10.1186/s12938-024-01259-3.
[32]
BIAN Y, GUO S W, 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.
[33]
BIAN Y, GUO S W, JIANG H, et al. Radiomics nomogram for the preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma[J/OL]. Cancer Imaging, 2022, 22(1): 4 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/34991733/. DOI: 10.1186/s40644-021-00443-1.
[34]
LI K, YAO Q D, XIAO J J, et al. Contrast-enhanced CT radiomics for predicting lymph node metastasis in pancreatic ductal adenocarcinoma: a pilot study[J/OL]. Cancer Imaging, 2020, 20(1): 12 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/32000852/. DOI: 10.1186/s40644-020-0288-3.
[35]
LIU P, GU Q B, HU X L, 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.
[36]
FU N Z, FU W L, CHEN H D, et al. A deep-learning radiomics-based lymph node metastasis predictive model for pancreatic cancer: a diagnostic study[J]. Int J Surg, 2023, 109(8): 2196-2203. DOI: 10.1097/JS9.0000000000000469.
[37]
AN C, LI D Y, LI S, et al. Deep learning radiomics of dual-energy computed tomography for predicting lymph node metastases of pancreatic ductal adenocarcinoma[J]. Eur J Nucl Med Mol Imaging, 2022, 49(4): 1187-1199. DOI: 10.1007/s00259-021-05573-z.
[38]
LI Q, SONG Z H, ZHANG D, et al. Feasibility of a CT-based lymph node radiomics nomogram in detecting lymph node metastasis in PDAC patients[J/OL]. Front Oncol, 2022, 12: 992906 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/36276058/. DOI: 10.3389/fonc.2022.992906.
[39]
RUPERT J E, NARASIMHAN A, JENGELLEY D H A, et al. Tumor-derived IL-6 and trans-signaling among tumor, fat, and muscle mediate pancreatic cancer Cachexia[J/OL]. J Exp Med, 2021, 218(6): e20190450 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/33851955/. DOI: 10.1084/jem.20190450.
[40]
GU Q B, HE M Q, HE Y Q, et al. CT-measured body composition radiomics predict lymph node metastasis in localized pancreatic ductal adenocarcinoma[J/OL]. Discov Oncol, 2023, 14(1): 16 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/36735166/. DOI: 10.1007/s12672-023-00624-3.
[41]
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.
[42]
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.
[43]
DENG X J, LIU M L, SUN J Q, et al. Feasibility of MRI-based radiomics features for predicting lymph node metastases and VEGF expression in cervical cancer[J/OL]. Eur J Radiol, 2021, 134: 109429 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/33290975/. DOI: 10.1016/j.ejrad.2020.109429.
[44]
SHI L, WANG L, WU C Y, 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 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/35875061/. DOI: 10.3389/fonc.2022.927077.
[45]
LIU Z Y. Value of enhanced magnetic resonance imaging in predicting lymph node metastasis of pancreatic ductal adenocarcinoma[D]. Nanchong: North Sichuan Medical College, 2023. DOI: 10.27755/d.cnki.gcbyx.2023.000484.
[46]
LIU Z Q, SUN W G. Histogram analysis based on MRI for predicting lymph node metastasis in pancreatic cancer: a preliminary study[J]. Chin Comput Med Imag, 2022, 28(1): 63-67. DOI: 10.19627/j.cnki.cn31-1700/th.2022.01.017.
[47]
ZENG P E, QU C, CUI J J, et al. The value of multi-parametric MRI radiomics model in predicting lymph node metastasis of pancreatic ductal adenocarcinoma[J]. Chin J Magn Reson Imag, 2024, 15(3): 114-121. DOI: 10.12015/issn.1674-8034.2024.03.019.
[48]
XIE N, FAN X H, CHEN D S, et al. Peritumoral and intratumoral texture features based on multiparametric MRI and multiple machine learning methods to preoperatively evaluate the pathological outcomes of pancreatic cancer[J]. J Magn Reson Imaging, 2023, 58(2): 379-391. DOI: 10.1002/jmri.28538.
[49]
ZENG P E, QU C, LIU J F, et al. Comparison of MRI and CT-based radiomics for preoperative prediction of lymph node metastasis in pancreatic ductal adenocarcinoma[J]. Acta Radiol, 2023, 64(7): 2221-2228. DOI: 10.1177/02841851221142552.
[50]
JAMSHIDI N, SENTHILVELAN J, DAWSON D W, et al. Construction of a radiogenomic association map of pancreatic ductal adenocarcinoma[J/OL]. BMC Cancer, 2023, 23(1): 189 [2025-02-25]. https://pubmed.ncbi.nlm.nih.gov/36843111/. DOI: 10.1186/s12885-023-10658-z.

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