Journal of Surgery Concepts & Practice >

2025 , Vol. 30 >Issue 06: 479 - 482

DOI: https://doi.org/10.16139/j.1007-9610.2025.06.04

Experts forum

Application progress of artificial intelligence in preoperative diagnosis of pancreatic cancer

  • MAO Liang ,
  • QIU Yudong
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  • Department of Pancreatic and Metabolic Surgery, Nanjing Drum Tower Hospital, Affiliated Hospital of Nanjing University Medical School, Jiangsu Nanjing 210008, China

Received date: 2025-06-19

  Online published: 2026-01-26

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Abstract

Pancreatic cancer is the most challenging issue in the field of pancreatic surgery. Accurate preoperative diagnosis is the key to improving prognosis. Currently, there have been research reports on the application of artificial intelligence(AI) in the early diagnosis, differential diagnosis, and stratified diagnosis of pancreatic cancer. This article summarized the representative studies and focused on introducing their innovative thinking, modeling methods, research results, and clinical significance, demonstrating the application potential of AI in preoperative diagnosis of pancreatic cancer, with the aim of providing inspiration for subsequent research in this field.

Key words： Artificial intelligence (AI); Pancreatic cancer; Early diagnosis; Stratified diagnosis; Contrast-enhanced ultrasound; Ultrasonic endoscope (EUS); Rapid on-site evaluation (ROSE)

Cite this article

MAO Liang , QIU Yudong . Application progress of artificial intelligence in preoperative diagnosis of pancreatic cancer[J]. Journal of Surgery Concepts & Practice, 2025 , 30(06) : 479 -482 . DOI: 10.16139/j.1007-9610.2025.06.04

References

[1] HAN B, ZHENG R, ZENG H, et al. Cancer incidence and mortality in China, 2022[J]. J Natl Cancer Cent, 2024, 4(1):47-53.
[2] SIEGEL R L, KRATZER T B, GIAQUINTO A N, et al. Cancer statistics, 2025[J]. CA Cancer J Clin, 2025, 75(1):10-45.
[3] STOOP T F, JAVED A A, OBA A, et al. Pancreatic cancer[J]. Lancet, 2025, 405(10485):1182-1202.
[4] WOOD L D, CANTO M I, JAFFEE E M, et al. Pancreatic cancer: pathogenesis, screening, diagnosis, and treatment[J]. Gastroenterology, 2022, 163(2):386-402.
[5] MUKHERJEE S, PATRA A, KHASAWNEH H, et al. Radiomics-based machine-learning models can detect pancreatic cancer on prediagnostic computed tomography scans at a substantial lead time before clinical diagnosis[J]. Gastroenterology, 2022, 163(5):1435-1446.
[6] SIJITHRA P C, SANTHI N, RAMASAMY N. A review study on early detection of pancreatic ductal adenocarcinoma using artificial intelligence assisted diagnostic methods[J]. Eur J Radiol, 2023, 166:110972.
[7] CAO K, XIA Y, YAO J, et al. Large-scale pancreatic cancer detection via non-contrast CT and deep learning[J]. Nat Med, 2023, 29(12):3033-3043.
[8] TONG T, GU J, XU D, et al. Deep learning radiomics based on contrast-enhanced ultrasound images for assisted diagnosis of pancreatic ductal adenocarcinoma and chronic pancreatitis[J]. BMC Med, 2022, 20(1):74.
[9] LI X Z, SONG J, SUN Z X, et al. Diagnostic performance of contrast-enhanced ultrasound for pancreatic neoplasms: a systematic review and meta-analysis[J]. Dig Liver Dis, 2018, 50(2):132-138.
[10] KUWAHARA T, HARA K, MIZUNO N, et al. Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses[J]. Endoscopy, 2023, 55(2):140-149.
[11] ZHANG S, ZHOU Y, TANG D, et al. A deep learning-based segmentation system for rapid onsite cytologic pathology evaluation of pancreatic masses: a retrospective, multicenter, diagnostic study[J]. EBioMedicine, 2022, 80:104022.
[12] CHU L C, FISHMAN E K. Pancreatic ductal adenocarcinoma staging: a narrative review of radiologic techniques and advances[J]. Int J Surg, 2024, 110(10):6052-6063.
[13] YAO J, CAO K, HOU Y, et al. Deep learning for fully automated prediction of overall survival in patients undergoing resection for pancreatic cancer: a retrospective multicenter study[J]. Ann Surg, 2023, 278(1):e68-e79.
[14] MIZRAHI J D, SURANA R, VALLE J W, et al. Pancreatic cancer[J]. Lancet, 2020, 395(10242):2008-2020.
[15] PRYCE B R, WANG D J, ZIMMERS T A, et al. Cancer cachexia: involvement of an expanding macroenvironment[J]. Cancer Cell, 2023, 41(3):581-584.
[16] LáINEZ RAMOS-BOSSINI A J, GáMEZ MARTíNEZ A, LUENGO GóMEZ D, et al. Computed tomography-based sarcopenia and pancreatic cancer survival-a comprehensive meta-analysis exploring the influence of definition criteria, prevalence, and treatment intention[J]. Cancers (Basel), 2025, 17(4):607.
[17] BEDRIKOVETSKI S, SEOW W, KROON H M, et al. Artificial intelligence for body composition and sarcopenia evaluation on computed tomography: a systematic review and meta-analysis[J]. Eur J Radiol, 2022, 149:110218.
[18] SGUANCI M, PALOMARES S M, CANGELOSI G, et al. Artificial intelligence in the management of malnutrition in cancer patients: a systematic review[J]. Adv Nutr, 2025, 16(7):100438.
[19] PORCIELLO G, DI LAURO T, LUONGO A, et al. Optimizing nutritional care with machine learning: identifying sarcopenia risk through body composition parameters in cancer patients-insights from the nutritional and sarcopenia risk screening project (NUTRISCREEN)[J]. Nutrients, 2025, 17(8):1376.
[20] MUKUND A, AFRIDI M A, KAROLAK A, et al. Pancreatic ductal adenocarcinoma (PDAC): a review of recent advancements enabled by artificial intelligence[J]. Cancers (Basel), 2024, 16(12):2240.
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