外科理论与实践 ›› 2025, Vol. 30 ›› Issue (06): 479-482.doi: 10.16139/j.1007-9610.2025.06.04
收稿日期:2025-06-19
出版日期:2025-11-25
发布日期:2026-01-26
通讯作者:
仇毓东,E-mail: yudongqiu510@163.comReceived:2025-06-19
Online:2025-11-25
Published:2026-01-26
摘要:
胰腺癌是胰腺外科领域最严峻的挑战,准确的术前诊断是改善预后的关键。目前,人工智能(AI)在胰腺癌早期诊断、鉴别诊断和分层诊断中的应用均有研究报道。本文概述其中的代表性研究,并重点介绍其创新思维、建模方法、研究结果和临床意义,展示AI在胰腺癌术前诊断中的应用潜力,以期对该领域的后续研究有所启示。
中图分类号:
毛谅, 仇毓东. 人工智能在胰腺癌术前诊断中的应用进展[J]. 外科理论与实践, 2025, 30(06): 479-482.
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.
| [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.
doi: 10.1016/S0140-6736(25)00261-2 pmid: 40187844 |
| [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.
doi: 10.1053/j.gastro.2022.03.056 URL |
| [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.
doi: 10.1053/j.gastro.2022.06.066 URL |
| [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.
doi: 10.1016/j.ejrad.2023.110972 URL |
| [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.
doi: 10.1038/s41591-023-02640-w |
| [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.
doi: 10.1186/s12916-022-02258-8 |
| [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.
doi: 10.1016/j.dld.2017.10.012 URL |
| [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.
doi: 10.1055/a-1873-7920 URL |
| [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.
doi: 10.1016/j.ebiom.2022.104022 URL |
| [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.
doi: 10.1097/JS9.0000000000000899 URL |
| [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.
doi: 10.1097/SLA.0000000000005465 URL |
| [14] |
MIZRAHI J D, SURANA R, VALLE J W, et al. Pancreatic cancer[J]. Lancet, 2020, 395(10242):2008-2020.
doi: S0140-6736(20)30974-0 pmid: 32593337 |
| [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.
doi: 10.1016/j.ccell.2023.02.007 pmid: 36868225 |
| [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.
doi: 10.3390/cancers17040607 URL |
| [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.
doi: 10.1016/j.ejrad.2022.110218 URL |
| [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.
doi: 10.1016/j.advnut.2025.100438 URL |
| [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.
doi: 10.3390/nu17081376 URL |
| [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.
doi: 10.3390/cancers16122240 URL |
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