[1] |
KENNER B, CHARI ST, KELSEN D, et al. Artificial intelligence and early detection of pancreatic cancer: 2020 summative review[J]. Pancreas, 2021, 50( 3): 251- 279. DOI: 10.1097/MPA.0000000000001762.
|
[2] |
KAUL V, ENSLIN S, GROSS SA. History of artificial intelligence in medicine[J]. Gastrointest Endosc, 2020, 92( 4): 807- 812. DOI: 10.1016/j.gie.2020.06.040.
|
[3] |
CAI J, CHEN HD, LU M, et al. Advances in the epidemiology of pancreatic cancer: Trends, risk factors, screening, and prognosis[J]. Cancer Lett, 2021, 520: 1- 11. DOI: 10.1016/j.canlet.2021.06.027.
|
[4] |
HUANG JJ, LOK V, NGAI CH, et al. Worldwide burden of, risk factors for, and trends in pancreatic cancer[J]. Gastroenterology, 2021, 160( 3): 744- 754. DOI: 10.1053/j.gastro.2020.10.007.
|
[5] |
GRANATA V, FUSCO R, SETOLA SV, et al. Risk assessment and pancreatic cancer: Diagnostic management and artificial intelligence[J]. Cancers, 2023, 15( 2): 351. DOI: 10.3390/cancers15020351.
|
[6] |
YANG JS, XU RY, WANG CC, et al. Early screening and diagnosis strategies of pancreatic cancer: A comprehensive review[J]. Cancer Commun, 2021, 41( 12): 1257- 1274. DOI: 10.1002/cac2.12204.
|
[7] |
PEREIRA SP, OLDFIELD L, NEY A, et al. Early detection of pancreatic cancer[J]. Lancet Gastroenterol Hepatol, 2020, 5( 7): 698- 710. DOI: 10.1016/S2468-1253(19)30416-9.
|
[8] |
STOFFEL EM, BRAND RE, GOGGINS M. Pancreatic cancer: Changing epidemiology and new approaches to risk assessment, early detection, and prevention[J]. Gastroenterology, 2023, 164( 5): 752- 765. DOI: 10.1053/j.gastro.2023.02.012.
|
[9] |
BOURSI B, FINKELMAN B, GIANTONIO BJ, et al. A clinical prediction model to assess risk for pancreatic cancer among patients with new-onset diabetes[J]. Gastroenterology, 2017, 152( 4): 840- 850. DOI: 10.1053/j.gastro.2016.11.046.
|
[10] |
PLACIDO D, YUAN B, HJALTELIN JX, et al. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories[J]. Nat Med, 2023, 29( 5): 1113- 1122. DOI: 10.1038/s41591-023-02332-5.
|
[11] |
BLYUSS O, ZAIKIN A, CHEREPANOVA V, et al. Development of PancRISK, a urine biomarker-based risk score for stratified screening of pancreatic cancer patients[J]. Br J Cancer, 2020, 122( 5): 692- 696. DOI: 10.1038/s41416-019-0694-0.
|
[12] |
CAO K, XIA YD, YAO JW, 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.
|
[13] |
Chinese Pancreatic Surgery Association, Chinese Society of Surgery, Chinese Medical Association. Guidelines for the diagnosis and treatment of pancreatic cancer in China(2021)[J]. Chin J Dig Surg, 2021, 20( 7): 713- 729. DOI: 10.3760/cma.j.cn115610-20210618-00289.
中华医学会外科学分会胰腺外科学组. 中国胰腺癌诊治指南(2021)[J]. 中华消化外科杂志, 2021, 20( 7): 713- 729. DOI: 10.3760/cma.j.cn115610-20210618-00289.
|
[14] |
MIZRAHI JD, SURANA R, VALLE JW, et al. Pancreatic cancer[J]. Lancet, 2020, 395( 10242): 2008- 2020. DOI: 10.1016/S0140-6736(20)30974-0.
|
[15] |
CHEN PT, WU TH, WANG PC, et al. Pancreatic cancer detection on CT scans with deep learning: A nationwide population-based study[J]. Radiology, 2023, 306( 1): 172- 182. DOI: 10.1148/radiol.220152.
|
[16] |
MA H, LIU ZX, ZHANG JJ, et al. Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis[J]. World J Gastroenterol, 2020, 26( 34): 5156- 5168. DOI: 10.3748/wjg.v26.i34.5156.
|
[17] |
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.
|
[18] |
MARYA NB, POWERS PD, CHARI ST, et al. Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis[J]. Gut, 2021, 70( 7): 1335- 1344. DOI: 10.1136/gutjnl-2020-322821.
|
[19] |
TONOZUKA R, ITOI T, NAGATA N, et al. Deep learning analysis for the detection of pancreatic cancer on endosonographic images: A pilot study[J]. J Hepatobiliary Pancreat Sci, 2021, 28( 1): 95- 104. DOI: 10.1002/jhbp.825.
|
[20] |
HUANG BW, HUANG HR, ZHANG ST, et al. Artificial intelligence in pancreatic cancer[J]. Theranostics, 2022, 12( 16): 6931- 6954. DOI: 10.7150/thno.77949.
|
[21] |
MAHMOUDI T, KOUZAHKANAN ZM, RADMARD AR, et al. Segmentation of pancreatic ductal adenocarcinoma(PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors[J]. Sci Rep, 2022, 12( 1): 3092. DOI: 10.1038/s41598-022-07111-9.
|
[22] |
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.
|
[23] |
WITKIEWICZ AK, MCMILLAN EA, BALAJI U, et al. Whole-exome sequencing of pancreatic cancer defines genetic diversity and therapeutic targets[J]. Nat Commun, 2015, 6: 6744. DOI: 10.1038/ncomms7744.
|
[24] |
BAGANTE F, SPOLVERATO G, RUZZENENTE A, et al. Artificial neural networks for multi-omics classifications of hepato-pancreato-biliary cancers: Towards the clinical application of genetic data[J]. Eur J Cancer, 2021, 148: 348- 358. DOI: 10.1016/j.ejca.2021.01.049.
|
[25] |
WEI Q, RAMSEY SA. Predicting chemotherapy response using a variational autoencoder approach[J]. BMC Bioinformatics, 2021, 22( 1): 453. DOI: 10.1186/s12859-021-04339-6.
|
[26] |
CHEN DS, MELLMAN I. Elements of cancer immunity and the cancer-immune set point[J]. Nature, 2017, 541( 7637): 321- 330. DOI: 10.1038/nature21349.
|
[27] |
BIAN Y, LIU YF, LI J, et al. Machine learning for computed tomography radiomics: Prediction of tumor-infiltrating lymphocytes in patients with pancreatic ductal adenocarcinoma[J]. Pancreas, 2022, 51( 5): 549- 558. DOI: 10.1097/MPA.0000000000002069.
|
[28] |
WATSON MD, BAIMAS-GEORGE MR, MURPHY KJ, et al. Pure and hybrid deep learning models can predict pathologic tumor response to neoadjuvant therapy in pancreatic adenocarcinoma: A pilot study[J]. Am Surg, 2021, 87( 12): 1901- 1909. DOI: 10.1177/0003134820982557.
|
[29] |
Study Group of Pancreatic Surgery in Chinese Society of Surgery of Chinese Medical Association; Pancreatic Disease Committee of Chinese Research Hospital Association; Editorial Board of Chinese Journal of Surgery. A consensus statement on the diagnosis, treatment, and prevention of common complications after pancreatic surgery(2017)[J]. Chin J Surg, 2017, 55( 5): 328- 334. DOI: 10.3760/cma.j.issn.0529-5815.2017.05.003.
中华医学会外科学分会胰腺外科学组, 中国研究型医院学会胰腺病专业委员会, 中华外科杂志编辑部. 胰腺术后外科常见并发症诊治及预防的专家共识(2017)[J]. 中华外科杂志, 2017, 55( 5): 328- 334. DOI: 10.3760/cma.j.issn.0529-5815.2017.05.003.
|
[30] |
CALLERY MP, PRATT WB, KENT TS, et al. A prospectively validated clinical risk score accurately predicts pancreatic fistula after pancreatoduodenectomy[J]. J Am Coll Surg, 2013, 216( 1): 1- 14. DOI: 10.1016/j.jamcollsurg.2012.09.002.
|
[31] |
SHEN ZY, CHEN HD, WANG WS, et al. Machine learning algorithms as early diagnostic tools for pancreatic fistula following pancreaticoduodenectomy and guide drain removal: A retrospective cohort study[J]. Int J Surg, 2022, 102: 106638. DOI: 10.1016/j.ijsu.2022.106638.
|
[32] |
YOO J, YOON SH, LEE DH, et al. Body composition analysis using convolutional neural network in predicting postoperative pancreatic fistula and survival after pancreatoduodenectomy for pancreatic cancer[J]. Eur J Radiol, 2023, 169: 111182. DOI: 10.1016/j.ejrad.2023.111182.
|
[33] |
KAMBAKAMBA P, MANNIL M, HERRERA PE, et al. The potential of machine learning to predict postoperative pancreatic fistula based on preoperative, non-contrast-enhanced CT: A proof-of-principle study[J]. Surgery, 2020, 167( 2): 448- 454. DOI: 10.1016/j.surg.2019.09.019.
|
[34] |
HAN IW, CHO K, RYU Y, et al. Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence[J]. World J Gastroenterol, 2020, 26( 30): 4453- 4464. DOI: 10.3748/wjg.v26.i30.4453.
|
[35] |
WALCZAK S, VELANOVICH V. An evaluation of artificial neural networks in predicting pancreatic cancer survival[J]. J Gastrointest Surg, 2017, 21( 10): 1606- 1612. DOI: 10.1007/s11605-017-3518-7.
|
[36] |
LIN JX, YIN MY, LIU L, et al. The development of a prediction model based on random survival forest for the postoperative prognosis of pancreatic cancer: A SEER-based study[J]. Cancers, 2022, 14( 19): 4667. DOI: 10.3390/cancers14194667.
|
[37] |
HE M, CHEN XY, WELS M, et al. Computed tomography-based radiomics evaluation of postoperative local recurrence of pancreatic ductal adenocarcinoma[J]. Acad Radiol, 2023, 30( 4): 680- 688. DOI: 10.1016/j.acra.2022.05.019.
|
[38] |
YOKOYAMA S, HAMADA T, HIGASHI M, et al. Predicted prognosis of patients with pancreatic cancer by machine learning[J]. Clin Cancer Res, 2020, 26( 10): 2411- 2421. DOI: 10.1158/1078-0432.CCR-19-1247.
|
[39] |
LEE W, PARK HJ, LEE HJ, et al. Preoperative data-based deep learning model for predicting postoperative survival in pancreatic cancer patients[J]. Int J Surg, 2022, 105: 106851. DOI: 10.1016/j.ijsu.2022.106851.
|
[40] |
KUMAR V, GU YH, BASU S, et al. Radiomics: The process and the challenges[J]. Magn Reson Imaging, 2012, 30( 9): 1234- 1248. DOI: 10.1016/j.mri.2012.06.010.
|
[41] |
VARGHESE BA, CEN SY, HWANG DH, et al. Texture analysis of imaging: What radiologists need to know[J]. AJR Am J Roentgenol, 2019, 212( 3): 520- 528. DOI: 10.2214/AJR.18.20624.
|
[42] |
KATTA MR, KALLURU PKR, BAVISHI DA, et al. Artificial intelligence in pancreatic cancer: Diagnosis, limitations, and the future prospects-a narrative review[J]. J Cancer Res Clin Oncol, 2023, 149( 9): 6743- 6751. DOI: 10.1007/s00432-023-04625-1.
|
[43] |
LIANG ZX, YE LS, YANG Y. Application of artificial intelligence in liver transplantation[J]. J Clin Hepatol, 2022, 38( 1): 30- 34. DOI: 10.3969/j.issn.1001-5256.2022.01.005.
梁智星, 叶林森, 杨扬. 人工智能在肝移植中的应用[J]. 临床肝胆病杂志, 2022, 38( 1): 30- 34. DOI: 10.3969/j.issn.1001-5256.2022.01.005.
|