Application of artificial intelligence in risk assessment and diagnosis of pancreatic cancer_CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY

Authors：

CHEN Kehan ¹ , ZHAO Huabin ¹ ,  LU Huimin ²

1. Division of Pancreatic Surgery, Department of General Surgery, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu 610041, P. R. China;
2. Pancreatitis Center, Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

LU Huimin, Email: hm.lu@scu.edu.cn

Keywords：

artificial intelligence; pancreatic cancer; risk assessment; diagnosis

DOI：

10.7507/1007-9424.202306043

Video：

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Objective To explore the application of artificial intelligence in the risk assessment and diagnosis of pancreatic cancer, and to point out its limitations and future suggestions, so as to promote the further application of artificial intelligence in the future. Method The related literatures on the application of artificial intelligence in the risk assessment and diagnosis of pancreatic cancer at home and abroad in recent years were reviewed. Results The usage of artificial intelligence models to assess high-risk patients was beneficial to the diagnosis of pancreatic cancer, although more data were needed to support its role in pancreatic cancer screening. In terms of early diagnosis, artificial intelligence technology could rapidly locate high-risk groups through medical imaging, pathological examination, biomarkers, and so on, and then detected pancreatic cancer at an early stage. Conclusion Despite some limitations, artificial intelligence will play an important role in the early diagnosis and risk prediction of pancreatic cancer in the future due to its powerful computational power.

Citation： CHEN Kehan, ZHAO Huabin, LU Huimin. Application of artificial intelligence in risk assessment and diagnosis of pancreatic cancer. CHINESE JOURNAL OF BASES AND CLINICS IN GENERAL SURGERY, 2023, 30(8): 995-1000. doi: 10.7507/1007-9424.202306043 Copy

1.	Rawla P, Sunkara T, Gaduputi V. Epidemiology of pancreatic cancer: global trends, etiology and risk factors. World J Oncol, 2019, 10(1): 10-27.
2.	Hu C, Li M. In advanced pancreatic cancer: the value and significance of interventional therapy. J Interv Med, 2020, 3(3): 118-121.
3.	Kenner B, Chari ST, Kelsen D, et al. Artificial intelligence and early detection of pancreatic cancer: 2020 summative review. Pancreas, 2021, 50(3): 251-279.
4.	Granata V, Grassi R, Fusco R, et al. Assessment of ablation therapy in pancreatic cancer: the radiologist’s challenge. Front Oncol, 2020, 10: 560952. doi: 10.3389/fonc.2020.560952.
5.	Granata V, Fusco R, Salati S, et al. A systematic review about imaging and histopathological findings for detecting and evaluating electroporation based treatments response. Int J Environ Res Public Health, 2021, 18(11): 5592. doi: 10.3390/ijerph18115592.
6.	Qureshi TA, Gaddam S, Wachsman AM, et al. Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images. Cancer Biomark, 2022, 33(2): 211-217.
7.	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. Sci Rep, 2022, 12(1): 3092. doi: 10.1038/s41598-022-07111-9.
8.	Singhi AD, Koay EJ, Chari ST, et al. Early detection of pancreatic cancer: opportunities and challenges. Gastroenterology, 2019, 156(7): 2024-2040.
9.	柯能文, 刘续宝. 胰腺癌外科治疗华西10年经验. 中国普外基础与临床杂志, 2021, 28(1): 4-7.
10.	Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. Int J Environ Res Public Health, 2021, 18(1): 271. doi: 10.3390/ijerph18010271.
11.	Jones CM, Buchlak QD, Oakden-Rayner L, et al. Chest radiographs and machine learning - past, present and future. J Med Imaging Radiat Oncol, 2021, 65(5): 538-544.
12.	Luchini C, Pea A, Scarpa A. Artificial intelligence in oncology: current applications and future perspectives. Br J Cancer, 2022, 126(1): 4-9.
13.	Induja SN, Raji CG. Computational methods for predicting chronic disease in healthcare communities. Bangalore, India: 2019 International Conference on Data Science and Communication, 2019.
14.	Kumar U. Applications of machine learning in disease prescreening//Research anthology on artificial intelligence applications in security. IGI Global, 2021: 1052-1084.
15.	Liu SL, Li S, Guo YT, et al. Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network. Chin Med J (Engl), 2019, 132(23): 2795-2803.
16.	Zhu W, Xie L, Han J, et al. the application of deep learning in cancer prognosis prediction. Cancers (Basel), 2020, 12(3): 603. doi: 10.3390/cancers12030603.
17.	Vial A, Stirling D, Field M, et al. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Translational Cancer Research, 2018, 7(3): 803-816.
18.	Zhao Y, Kosorok MR, Zeng D. Reinforcement learning design for cancer clinical trials. Stat Med, 2009, 28(26): 3294-3315.
19.	Hu JX, Zhao CF, Chen WB, et al. Pancreatic cancer: a review of epidemiology, trend, and risk factors. World J Gastroenterol, 2021, 27(27): 4298-4321.
20.	Cai J, Chen H, Lu M, et al. Advances in the epidemiology of pancreatic cancer: trends, risk factors, screening, and prognosis. Cancer Lett, 2021, 520: 1-11.
21.	De Re V, Caggiari L, De Zorzi M, et al. Genetic diversity of the KIR/HLA system and susceptibility to hepatitis C virus-related diseases. PLoS One, 2015, 10(2): e0117420. doi: 10.1371/journal.pone.0117420.
22.	Capurso G, Paiella S, Falconi M. Screening for pancreatic cancer-a compelling challenge. Hepatobiliary Surg Nutr, 2021, 10(2): 264-266.
23.	吴万龙, 彭兵. 胰腺癌流行病学及危险因素. 中国普外基础与临床杂志, 2019, 26(12): 1500-1504.
24.	杨卿菁, 李懋, 李振录, 等. 饮食因素与胰腺癌发病风险相关性研究的进展. 中国普外基础与临床杂志, 2022, 29(8): 1101-1108.
25.	Muhammad W, Hart GR, Nartowt B, et al. Pancreatic cancer prediction through an artificial neural network. Front Artif Intell, 2019, 2: 2. doi: 10.3389/frai.2019.00002.
26.	Lee HA, Chen KW, Hsu CY. Prediction model for pancreatic cancer-a population-based study from NHIRD. Cancers (Basel), 2022, 14(4): 882. doi: 10.3390/cancers14040882.
27.	Mendoza Ladd A, Diehl DL. Artificial intelligence for early detection of pancreatic adenocarcinoma: the future is promising. World J Gastroenterol, 2021, 27(13): 1283-1295.
28.	Faur AC, Lazar DC, Ghenciu LA. Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis. World J Gastroenterol, 2023, 29(12): 1811-1823.
29.	Goyal H, Mann R, Gandhi Z, et al. Application of artificial intelligence in pancreaticobiliary diseases. Ther Adv Gastrointest Endosc, 2021, 14: 2631774521993059.
30.	Kuwahara T, Hara K, Mizuno N, et al. Usefulness of deep learning analysis for the diagnosis of malignancy in intraductal papillary mucinous neoplasms of the pancreas. Clin Transl Gastroenterol, 2019, 10(5): 1-8.
31.	Chen X, Chen Y, Ma C, et al. Classification of pancreatic tumors based on MRI images using 3D convolutional neural networks. the 2nd International Symposium on Image Computing and Digital Medicine, Chengdu: 2018.
32.	Hussein S, Kandel P, Bolan CW, et al. Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches. IEEE Trans Med Imaging, 2019, 38(8): 1777-1787.
33.	Chakraborty J, Midya A, Gazit L, et al. CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas. Med Phys, 2018, 45(11): 5019-5029.
34.	Alves N, Schuurmans M, Litjens G, et al. Fully automatic deep learning framework for pancreatic ductal adenocarcinoma detection on computed tomography. Cancers (Basel), 2022, 14(2): 376. doi: 10.3390/cancers14020376.
35.	Huang B, Huang H, Zhang S, et al. Artificial intelligence in pancreatic cancer. Theranostics, 2022, 12(16): 6931-6954.
36.	Ko J, Bhagwat N, Yee SS, et al. Combining machine learning and nanofluidic technology to diagnose pancreatic cancer using exosomes. ACS Nano, 2017, 11(11): 11182-11193.
37.	Serrao EM, Kettunen MI, Rodrigues TB, et al. MRI with hyperpolarised [1-¹³C]pyruvate detects advanced pancreatic preneoplasia prior to invasive disease in a mouse model. Gut, 2016, 65(3): 465-475.
38.	Qiao Z, Ge J, He W, et al. Artificial intelligence algorithm-based computerized tomography image features combined with serum tumor markers for diagnosis of pancreatic cancer. Comput Math Methods Med, 2022, 2022: 8979404.
39.	Naito Y, Tsuneki M, Fukushima N, et al. A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy. Sci Rep, 2021, 11(1): 8454. doi: 10.1038/s41598-021-87748-0.
40.	Si K, Xue Y, Yu X, et al. Fully end-to-end deep-learning-based diagnosis of pancreatic tumors. Theranostics, 2021, 11(4): 1982-1990.
41.	Hameed BS, Krishnan UM. Artificial intelligence-driven diagnosis of pancreatic cancer. Cancers (Basel), 2022, 14(21): 5382. doi: 10.3390/cancers14215382.
42.	Winkler JK, Fink C, Toberer F, et al. Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA Dermatol, 2019, 155(10): 1135-1141.
43.	Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med, 2019, 25(1): 44-56.
44.	Hassan C, Badalamenti M, Maselli R, et al. Computer-aided detection-assisted colonoscopy: classification and relevance of false positives. Gastrointest Endosc, 2020, 92(4): 900-904. e4.
45.	Chassagnon G, Dohan A. Artificial intelligence: from challenges to clinical implementation. Diagn Interv Imaging, 2020, 101(12): 763-764.
46.	Carter SM, Rogers W, Win KT, et al. The ethical, legal and social implications of using artificial intelligence systems in breast cancer care. Breast, 2020, 49: 25-32.
47.	Beg S, Ragunath K, Wyman A, et al. Quality standards in upper gastrointestinal endoscopy: a position statement of the British Society of Gastroenterology (BSG) and Association of Upper Gastrointestinal Surgeons of Great Britain and Ireland (AUGIS). Gut, 2017, 66(11): 1886-1899.
48.	Hoerter N, Gross SA, Liang PS. Artificial intelligence and polyp detection. Curr Treat Options Gastroenterol, 2020. doi: 10.1007/s11938-020-00274-2. Online ahead of print.
49.	Vandewinckele L, Claessens M, Dinkla A, et al. Overview of artificial intelligence-based applications in radiotherapy: recommendations for implementation and quality assurance. Radiother Oncol, 2020, 153: 55-66.
50.	Wang P, Xiao X, Glissen Brown JR, et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng, 2018, 2(10): 741-748.
51.	Zhang WH, Zhang SY, Hou QQ, et al. The significance of the CLDN18-ARHGAP fusion gene in gastric cancer: a systematic review and meta-analysis. Front Oncol, 2020, 10: 1214. doi: 10.3389/fonc.2020.01214.
52.	Gong D, Wu L, Zhang J, et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol, 2020, 5(4): 352-361.

1. Rawla P, Sunkara T, Gaduputi V. Epidemiology of pancreatic cancer: global trends, etiology and risk factors. World J Oncol, 2019, 10(1): 10-27.
2. Hu C, Li M. In advanced pancreatic cancer: the value and significance of interventional therapy. J Interv Med, 2020, 3(3): 118-121.
3. Kenner B, Chari ST, Kelsen D, et al. Artificial intelligence and early detection of pancreatic cancer: 2020 summative review. Pancreas, 2021, 50(3): 251-279.
4. Granata V, Grassi R, Fusco R, et al. Assessment of ablation therapy in pancreatic cancer: the radiologist’s challenge. Front Oncol, 2020, 10: 560952. doi: 10.3389/fonc.2020.560952.
5. Granata V, Fusco R, Salati S, et al. A systematic review about imaging and histopathological findings for detecting and evaluating electroporation based treatments response. Int J Environ Res Public Health, 2021, 18(11): 5592. doi: 10.3390/ijerph18115592.
6. Qureshi TA, Gaddam S, Wachsman AM, et al. Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images. Cancer Biomark, 2022, 33(2): 211-217.
7. 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. Sci Rep, 2022, 12(1): 3092. doi: 10.1038/s41598-022-07111-9.
8. Singhi AD, Koay EJ, Chari ST, et al. Early detection of pancreatic cancer: opportunities and challenges. Gastroenterology, 2019, 156(7): 2024-2040.
9. 柯能文, 刘续宝. 胰腺癌外科治疗华西10年经验. 中国普外基础与临床杂志, 2021, 28(1): 4-7.
10. Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: opportunities and challenges. Int J Environ Res Public Health, 2021, 18(1): 271. doi: 10.3390/ijerph18010271.
11. Jones CM, Buchlak QD, Oakden-Rayner L, et al. Chest radiographs and machine learning - past, present and future. J Med Imaging Radiat Oncol, 2021, 65(5): 538-544.
12. Luchini C, Pea A, Scarpa A. Artificial intelligence in oncology: current applications and future perspectives. Br J Cancer, 2022, 126(1): 4-9.
13. Induja SN, Raji CG. Computational methods for predicting chronic disease in healthcare communities. Bangalore, India: 2019 International Conference on Data Science and Communication, 2019.
14. Kumar U. Applications of machine learning in disease prescreening//Research anthology on artificial intelligence applications in security. IGI Global, 2021: 1052-1084.
15. Liu SL, Li S, Guo YT, et al. Establishment and application of an artificial intelligence diagnosis system for pancreatic cancer with a faster region-based convolutional neural network. Chin Med J (Engl), 2019, 132(23): 2795-2803.
16. Zhu W, Xie L, Han J, et al. the application of deep learning in cancer prognosis prediction. Cancers (Basel), 2020, 12(3): 603. doi: 10.3390/cancers12030603.
17. Vial A, Stirling D, Field M, et al. The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review. Translational Cancer Research, 2018, 7(3): 803-816.
18. Zhao Y, Kosorok MR, Zeng D. Reinforcement learning design for cancer clinical trials. Stat Med, 2009, 28(26): 3294-3315.
19. Hu JX, Zhao CF, Chen WB, et al. Pancreatic cancer: a review of epidemiology, trend, and risk factors. World J Gastroenterol, 2021, 27(27): 4298-4321.
20. Cai J, Chen H, Lu M, et al. Advances in the epidemiology of pancreatic cancer: trends, risk factors, screening, and prognosis. Cancer Lett, 2021, 520: 1-11.
21. De Re V, Caggiari L, De Zorzi M, et al. Genetic diversity of the KIR/HLA system and susceptibility to hepatitis C virus-related diseases. PLoS One, 2015, 10(2): e0117420. doi: 10.1371/journal.pone.0117420.
22. Capurso G, Paiella S, Falconi M. Screening for pancreatic cancer-a compelling challenge. Hepatobiliary Surg Nutr, 2021, 10(2): 264-266.
23. 吴万龙, 彭兵. 胰腺癌流行病学及危险因素. 中国普外基础与临床杂志, 2019, 26(12): 1500-1504.
24. 杨卿菁, 李懋, 李振录, 等. 饮食因素与胰腺癌发病风险相关性研究的进展. 中国普外基础与临床杂志, 2022, 29(8): 1101-1108.
25. Muhammad W, Hart GR, Nartowt B, et al. Pancreatic cancer prediction through an artificial neural network. Front Artif Intell, 2019, 2: 2. doi: 10.3389/frai.2019.00002.
26. Lee HA, Chen KW, Hsu CY. Prediction model for pancreatic cancer-a population-based study from NHIRD. Cancers (Basel), 2022, 14(4): 882. doi: 10.3390/cancers14040882.
27. Mendoza Ladd A, Diehl DL. Artificial intelligence for early detection of pancreatic adenocarcinoma: the future is promising. World J Gastroenterol, 2021, 27(13): 1283-1295.
28. Faur AC, Lazar DC, Ghenciu LA. Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis. World J Gastroenterol, 2023, 29(12): 1811-1823.
29. Goyal H, Mann R, Gandhi Z, et al. Application of artificial intelligence in pancreaticobiliary diseases. Ther Adv Gastrointest Endosc, 2021, 14: 2631774521993059.
30. Kuwahara T, Hara K, Mizuno N, et al. Usefulness of deep learning analysis for the diagnosis of malignancy in intraductal papillary mucinous neoplasms of the pancreas. Clin Transl Gastroenterol, 2019, 10(5): 1-8.
31. Chen X, Chen Y, Ma C, et al. Classification of pancreatic tumors based on MRI images using 3D convolutional neural networks. the 2nd International Symposium on Image Computing and Digital Medicine, Chengdu: 2018.
32. Hussein S, Kandel P, Bolan CW, et al. Lung and pancreatic tumor characterization in the deep learning era: novel supervised and unsupervised learning approaches. IEEE Trans Med Imaging, 2019, 38(8): 1777-1787.
33. Chakraborty J, Midya A, Gazit L, et al. CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas. Med Phys, 2018, 45(11): 5019-5029.
34. Alves N, Schuurmans M, Litjens G, et al. Fully automatic deep learning framework for pancreatic ductal adenocarcinoma detection on computed tomography. Cancers (Basel), 2022, 14(2): 376. doi: 10.3390/cancers14020376.
35. Huang B, Huang H, Zhang S, et al. Artificial intelligence in pancreatic cancer. Theranostics, 2022, 12(16): 6931-6954.
36. Ko J, Bhagwat N, Yee SS, et al. Combining machine learning and nanofluidic technology to diagnose pancreatic cancer using exosomes. ACS Nano, 2017, 11(11): 11182-11193.
37. Serrao EM, Kettunen MI, Rodrigues TB, et al. MRI with hyperpolarised [1-¹³C]pyruvate detects advanced pancreatic preneoplasia prior to invasive disease in a mouse model. Gut, 2016, 65(3): 465-475.
38. Qiao Z, Ge J, He W, et al. Artificial intelligence algorithm-based computerized tomography image features combined with serum tumor markers for diagnosis of pancreatic cancer. Comput Math Methods Med, 2022, 2022: 8979404.
39. Naito Y, Tsuneki M, Fukushima N, et al. A deep learning model to detect pancreatic ductal adenocarcinoma on endoscopic ultrasound-guided fine-needle biopsy. Sci Rep, 2021, 11(1): 8454. doi: 10.1038/s41598-021-87748-0.
40. Si K, Xue Y, Yu X, et al. Fully end-to-end deep-learning-based diagnosis of pancreatic tumors. Theranostics, 2021, 11(4): 1982-1990.
41. Hameed BS, Krishnan UM. Artificial intelligence-driven diagnosis of pancreatic cancer. Cancers (Basel), 2022, 14(21): 5382. doi: 10.3390/cancers14215382.
42. Winkler JK, Fink C, Toberer F, et al. Association between surgical skin markings in dermoscopic images and diagnostic performance of a deep learning convolutional neural network for melanoma recognition. JAMA Dermatol, 2019, 155(10): 1135-1141.
43. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med, 2019, 25(1): 44-56.
44. Hassan C, Badalamenti M, Maselli R, et al. Computer-aided detection-assisted colonoscopy: classification and relevance of false positives. Gastrointest Endosc, 2020, 92(4): 900-904. e4.
45. Chassagnon G, Dohan A. Artificial intelligence: from challenges to clinical implementation. Diagn Interv Imaging, 2020, 101(12): 763-764.
46. Carter SM, Rogers W, Win KT, et al. The ethical, legal and social implications of using artificial intelligence systems in breast cancer care. Breast, 2020, 49: 25-32.
47. Beg S, Ragunath K, Wyman A, et al. Quality standards in upper gastrointestinal endoscopy: a position statement of the British Society of Gastroenterology (BSG) and Association of Upper Gastrointestinal Surgeons of Great Britain and Ireland (AUGIS). Gut, 2017, 66(11): 1886-1899.
48. Hoerter N, Gross SA, Liang PS. Artificial intelligence and polyp detection. Curr Treat Options Gastroenterol, 2020. doi: 10.1007/s11938-020-00274-2. Online ahead of print.
49. Vandewinckele L, Claessens M, Dinkla A, et al. Overview of artificial intelligence-based applications in radiotherapy: recommendations for implementation and quality assurance. Radiother Oncol, 2020, 153: 55-66.
50. Wang P, Xiao X, Glissen Brown JR, et al. Development and validation of a deep-learning algorithm for the detection of polyps during colonoscopy. Nat Biomed Eng, 2018, 2(10): 741-748.
51. Zhang WH, Zhang SY, Hou QQ, et al. The significance of the CLDN18-ARHGAP fusion gene in gastric cancer: a systematic review and meta-analysis. Front Oncol, 2020, 10: 1214. doi: 10.3389/fonc.2020.01214.
52. Gong D, Wu L, Zhang J, et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol, 2020, 5(4): 352-361.

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