人工智能在胰腺癌诊治中的应用现状
DOI: 10.12449/JCH241032
Current status of the application of artificial intelligence in the diagnosis and treatment of pancreatic cancer
-
摘要: 胰腺癌是消化系统常见的恶性肿瘤,早期诊断率低,手术病死率高,治愈率低,总体预后差。近年来,随着人工智能在医学领域的不断发展,机器学习、深度学习等人工智能技术被广泛应用于医学研究中。本文综述了近年来人工智能技术在胰腺癌筛查、诊断、治疗、并发症及预后预测等方面的应用,为人工智能在胰腺癌诊治中的应用提供依据和新思路。Abstract: Pancreatic cancer is a common malignant tumor of the digestive system, with a low early diagnosis rate, a high surgical mortality rate, a low cure rate, and a poor overall prognosis. In recent years, with the continuous development of artificial intelligence in the medical field, artificial intelligence techniques, such as machine learning and deep learning, have been widely used in medical research. This article reviews the application of artificial intelligence techniques in the screening, diagnosis, treatment, complications, and prognosis prediction of pancreatic cancer, so as to provide a basis and new ideas for the application of artificial intelligence in the diagnosis and treatment of pancreatic cancer.
-
Key words:
- Artificial Intelligence /
- Pancreatic Neoplasms /
- Machine Learning /
- Deep Learning
-
抗病毒治疗是慢性乙型肝炎(CHB)的关键措施,其中恩替卡韦(ETV) 是治疗HBV的一线核苷(酸)类似物(NAs) 之一。尽管其他研究曾报道了长期ETV治疗CHB的疗效和安全性,但其在中国慢性CHB(主要为基因型B和C)患者中的临床数据仍然有限。
马来酸ETV是正大天晴药业股份有限公司开发的ETV衍生物,多中心、随机、双盲双模拟、阳性药物对照临床研究显示其治疗48周时与原研ETV在治疗CHB时等效,基于上述结果,国家食品药品管理局已经批准其上市。作为此项研究的主要研究者,北京大学第一医院于岩岩教授对其长期疗效和安全性进行了进一步研究:此前已经报告144周的马来酸ETV治疗中国慢性CHB(主要为基因型B和C)患者有效而且安全。更长疗程的药物治疗效果和安全性如何,尚无知晓。
2022年7月6日于岩岩教授团队在线发表研究论文,旨在更新马来酸ETV治疗中国患者240周疗程的病毒学、血清学和生化结果。CHB受试者被随机分配接受0.5 mg/d ETV(A组)或0.5 mg/d马来酸ETV(B组)治疗48周,此后所有受试者从第49周开始接受0.5 mg/d马来酸ETV治疗。定期对患者进行随访,监测血清HBV标志物、肝生化等指标,记录不良事件(AE)。主要终点是治疗结束时每组HBV DNA的下降。次要终点包括治疗结束时HBV DNA不可测(<20 IU/mL) 的比率、HBeAg消失率、HBeAg血清转化率和血清ALT复常率。137例(A组71例) HBeAg阳性CHB患者和46例(A组21例)HBeAg阴性CHB患者完成了240周的治疗和随访。两组的基线特征可比。在HBeAg阳性CHB组,240周时两组的HBV DNA较基线下降平均值可比(A:6.67 log10 IU/mL vs B:6.74 log10 IU/mL;P>0.05),血清HBV DNA不可测率(A:91.55% vs B:87.88%;P>0.05)、HBeAg血清学转换率(A:26.98% vs B:20.97%;P>0.05)和ALT复常率(A:87.32% vs B:83.61%;P>0.05)均在组间可比。在HBeAg阴性CHB组,240周时两组的HBV DNA较基线下降平均值可比(A:6.05 log10 IU/mL vs B:6.10 log10 IU/mL;P>0.05),血清HBV DNA不可测比例(A:100% vs B:100%)和ALT复常率(A:90.91% vs B:95.45%)(P>0.05)也可比。在耐药方面,HBeAg阴性CHB组耐药率为0;HBeAg阳性CHB组144周时耐药率1.16%,此后直至240周新增1例ETV耐药。安全性方面,没有因为AE导致停药,无肝癌或死亡病例。
总之,作为国产抗HBV药物代表之一的马来酸ETV,长期治疗中国CHB(主要是基因型B或C)是安全有效的。
摘译自XU JH, FAN YN, YU YY, et al. 240-week entecavir maleate treatment in Chinese chronic hepatitis B predominantly genotype B or C[J]. J Viral Hepat, 2022, 29(10): 862-867. DOI: 10.1111/jvh.13724.
(北京大学第一医院感染疾病科 徐京杭 报道)
-
[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. 期刊类型引用(1)
1. 向文耀,李仕雄,吕日英. 恩替卡韦治疗后慢性乙型肝炎低病毒血症患者序贯联合艾米替诺福韦治疗的效果研究. 中国现代医学杂志. 2024(08): 15-20 . 百度学术
其他类型引用(1)
-