Intelligent Detection for Pancreatic Cancer Diagnosis: Future Directions
DOI:
https://doi.org/10.12775/JEHS.2023.14.01.013Keywords
oncology, pancreatic cancer, artificial intelligenceAbstract
Introduction: Artificial intelligence is one of the most modern information systems that bases its operation on analogous functioning to the human mind. Particular hopes are placed in the treatment of diseases considered incurable. One such disease is pancreatic cancer, which has an extremely poor prognosis. Thanks to the use of machine learning and deep learning, artificial intelligence analyzes the available images taken during computed tomography and compares them with the introduced changes characteristic of pancreatic cancer. The advantage of the machines is the ability to analyze data, scans and images in a fraction of a second.
Material and methods: The authors reviewed the peer-reviewed international literature describing the application of artificial intelligence in oncology. Databases such as PubMed, SCOPUS and Web of were searched with the keywords "artificial intelligence", "oncology" and "pancreatic cancer". The review included articles from 2004-2023.
Discussion: Pancreatic cancer is an important topic of research due to unsatisfactory treatment results and the lack of screening tests allowing for quick detection of people at risk. Artificial intelligence is becoming a promising solution for asymptomatic patients, who can be classified into risk groups much faster than traditional methods. Unlike humans, intelligent machines can work faster and continuously, delivering content and results much faster.
Conclusions: Artificial intelligence is a promising tool in the diagnosis of pancreatic cancer. Research results indicate that it is the most widely used in the field of oncological radiology, thanks to which imaging of early stages of cancer is becoming easier to detect. There is a need to verify machine methods and avoid bias when privatizing smart machines. The good of the patient should always come first, especially in a disease with an unfavorable prognosis, such as pancreatic cancer.
References
Kelly CJ, Karthikesalingam A, Suleyman, M, Corrado G, King D. Key Challenges for Delivering Clinical Impact with Artificial Intelligence. BMC Med. 2019;17:195.
Russel S, Norvig P. Artificial Intelligence: a Modern Approach. 3rd edition. Upper Saddle River. New Jersey: Prentice Hall; 2009.
Maddox TM, Rumsfeld JS, Payne PR. Questions for artificial intelligence in health care. JAMA. 2019;32:31–2.
https://www.nfz.gov.pl/aktualnosci/aktualnosci-centrali/coraz-wiecej-pieniedzy-przeznaczamy-na-leczenie-nowotworow-i-leki-onkologiczne,7590.html. Dostęp z dnia 29.07.2023r.
https://www.gov.pl/web/zdrowie/narodowa-strategia-onkologiczna. Dostęp z dnia 29.07.2023r.
https://www.gov.pl/web/zdrowie/narodowa-strategia-onkologiczna-nso. Dostęp z dnia 29.07.2023r.
Kamisawa T, Wood LD, Itoi T, Takaori K. Pancreatic cancer. Lancet. 2016;388:73–85.
Howlader N, Noone AM, Krapcho M, Miller D, Brest A, Yu M, Ruhl J, Tatalovich Z, Mariotto A, Lewis DR, Chen HS, Feuer EJ, Cronin KA. SEER cancer statistics review, 1975–2016. National cancer institute. Bethesda. 2018 SEER.
Khorana AA, Mangu PB, Berlin J, Engebretson A, Hong TS, Maitra A, Mohile SG, Mumber M, Schulick R, Shapiro M, Urba S, Zeh HJ, Katz MHG. Potentially curable pancreatic cancer: American society of clinical oncology clinical practice guideline update. J. Clin. Oncol. 2017;35:2324–2328.
Rahib L, Smith BD, AizenbergR, Rosenzweig AB, Fleshman JM, Matrisian LM. Projecting cancer incidence and deaths to 2030: The unexpected burden of thyroid, liver, and pancreas cancers in the United States. Cancer Res. 2014;74:2913–2921.
Siegel RL, Miller KD, Jemal A. Cancer statistics 2020. CA Cancer J. Clin. 2020;70:7–30.
Balaban EP, Mangu PB, Khorana AA, Shah MA, Mukherjee S, Crane CH, Javle MM, Eads JR, Allen P, Ko AH, Engebretson A, Herman JM, Strickler JH, Benson AB 3rd, Urba S, Yee NS. Locally Advanced, Unresectable Pancreatic Cancer: American Society of Clinical Oncology Clinical Practice Guideline. J. Clin. Oncol. 2016;34(22):2654-68.
Kim CA, Lelond S, Daeninck PJ, Rabbani R, Lix L, McClement S, Chochinov HM, Goldenberg BA. The impact of early palliative care on the quality of life of patients with advanced pancreatic cancer: The IMPERATIVE case-crossover study. Support Care Cancer. 2023;31(4):250.
Takhar AS, Palaniappan P, Dhingsa R, Lobo DN. Recent developments in diagnosis of pancreatic cancer. BMJ. 2004; 329:668–673.
Tanaka M, Fernandez-Del Castillo C, Kamisawa T, Jang JY, Levy P, Ohtsuka T, Salvia R, Shimizu Y, Tada M, Wolfgang CL. Revisions of international consensus Fukuoka guidelines for the management of IPMN of the pancreas. Pancreatology. 2017;17:738–753.
Jiang J, Chao WL, Culp S, Krishna SG. Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma. Cancers (Basel). 2023;15(9):2410.
Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115-118.
McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, Back T, Chesus M, Corrado GS, Darzi A, Etemadi M, Garcia-Vicente F, Gilbert FJ, Halling-Brown M, Hassabis D, Jansen S, Karthikesalingam A, Kelly CJ, King D, Ledsam JR, Melnick D, Mostofi H, Peng L, Reicher JJ, Romera-Paredes B, Sidebottom R, Suleyman M, Tse D, Young KC, De Fauw J, Shetty S. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89-94.
Zhu Z, Xia Y, Xie L, Fishman EK, Yuille AL. Multi-scale coarse-to-fine segmentation for screening pancreatic ductal adenocarcinoma. In Lecture Notes in Computer Science. Springer. 2019;11769:3–12.
Ma H, Liu ZX, Zhang JJ, Wu FT, Xu CF, Shen Z, Yu CH, Li YM. Construction of a convolutional neural network classifier developed by computed tomography images for pancreatic cancer diagnosis. World J. Gastroenterol. 2020;26(34):5156-5168.
Liu KL, Wu T, Chen PT, Tsai YM, Roth H, Wu MS, Liao WC, Wang W. Deep learning to distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross-racial external validation. Lancet Digit Health. 2020;2(6):303-e313.
Baldota S, Sharma S, Malathy C. Deep transfer learning for pancreatic cancer detection. 12th International Conference on Computing Communication and Networking Technologies (ICCCNT). India. 2021:1–7.
Debernardi S, O'Brien H, Algahmdi AS, Malats N, Stewart GD, Plješa-Ercegovac M, Costello E, Greenhalf W, Saad A, Roberts R, Ney A, Pereira SP, Kocher HM, Duffy S, Blyuss O, Crnogorac-Jurcevic T. A combination of urinary biomarker panel and PancRISK score for earlier detection of pancreatic cancer: A case-control study. PLoS Med. 2020;17(12):1003489.
Gupta A, Koul A, Kumar Y. Pancreatic Cancer detection using machine and deep learning techniques. 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM). India. 2022:151–155.
Li J, Zhang H, Zhu H, Dai Z. 25-hydroxyvitamin D concentration is positively associated with overall survival in advanced pancreatic cancer: A systematic review and meta-analysis. Nutr. Res. 2023;117:73-82.
Wang L, Domchek SM, Kochman ML, Katona BW. Reaching beyond family history as inclusion criteria for pancreatic cancer surveillance in high-risk populations. Genes Cancer. 2022;13:49–51.
Ali H, Pamarthy R, Vallabhaneni M, Sarfraz S, Ali H, Rafique H. Pancreatic cancer incidence trends in the United States from 2000–2017: Analysis of Surveillance, Epidemiology and End Results (SEER) database. F1000Res. 2021;10:529.
Canto MI, Hruban RH, Fishman EK, Kamel IR, Schulick R, Zhang Z, Topazian M, Takahashi N, Fletcher J, Petersen G, Klein AP, Axilbund J, Griffin C, Syngal S, Saltzman JR, Mortele KJ, Lee J, Tamm E, Vikram R, Bhosale P, Margolis D, Farrell J, Goggins M. American Cancer of the Pancreas Screening (CAPS) Consortium. Frequent detection of pancreatic lesions in asymptomatic high-risk individuals. Gastroenterology. 2012;142(4):796-804.
Li P, Hu Y, Scelo G, Myrskyla M, Martikainen P. Pre-existing psychological disorders, diabetes, and pancreatic cancer: A population-based study of 38,952 Finns. Cancer Epidemiol. 2022;82:102307.
Sah RP, Nagpal SJ, Mukhopadhyay D, Chari ST. New insights into pancreatic cancer-induced paraneoplastic diabetes. Nat. Rev. Gastroenterol. Hepatol. 2013;10:423–433.
Sharma A, Kandlakunta H, Nagpal SJS, Feng Z, Hoos W, Petersen GM, Chari ST. Model to Determine Risk of Pancreatic Cancer in Patients With New-Onset Diabetes. Gastroenterology 2018;155:730–739.
Chen W, Butler RK, Lustigova E, Chari ST, Maitra A, Rinaudo JA, Wu BU. Risk Prediction of Pancreatic Cancer in Patients With Recent-onset Hyperglycemia: A Machine-learning Approach. J. Clin. Gastroenterol. 2023;57:103–110.
Klein AP, Lindström S, Mendelsohn JB, Steplowski E, Arslan AA, Bueno-de-Mesquita HB, Fuchs CS, Gallinger S, Gross M, Helzlsouer K, Holly EA, Jacobs EJ, Lacroix A, Li D, Mandelson MT, Olson SH, Petersen GM, Risch HA, Stolzenberg-Solomon RZ, Zheng W, Amundadottir L, Albanes D, Allen NE, Bamlet WR, Boutron-Ruault MC, Buring JE, Bracci PM, Canzian F, Clipp S, Cotterchio M, Duell EJ, Elena J, Gaziano JM, Giovannucci EL, Goggins M, Hallmans G, Hassan M, Hutchinson A, Hunter DJ, Kooperberg C, Kurtz RC, Liu S, Overvad K, Palli D, Patel AV, Rabe KG, Shu XO, Slimani N, Tobias GS, Trichopoulos D, Van Den Eeden SK, Vineis P, Virtamo J, Wactawski-Wende J, Wolpin BM, Yu H, Yu K, Zeleniuch-Jacquotte A, Chanock SJ, Hoover RN, Hartge P, Kraft P. An absolute risk model to identify individuals at elevated risk for pancreatic cancer in the general population. PLoS One. 2013;8(9):72311.
Chen W, Zhou Y, Asadpour V, Parker RA, Puttock EJ, Lustigova E, Wu BU. Quantitative Radiomic Features from Computed Tomography Can Predict Pancreatic Cancer up to 36 Months before Diagnosis. Clin. Transl. Gastroenterol. 2014;00548.
Qureshi TA, Gaddam S, Wachsman AM, Wang L, Azab L, Asadpour V, Chen W, Xie Y, Wu B, Pandol SJ, Li D. Predicting pancreatic ductal adenocarcinoma using artificial intelligence analysis of pre-diagnostic computed tomography images. Cancer Biomark. 2022;33(2):211-217.
Chakraborty J, Midya A, Gazit L, Attiyeh M, Langdon-Embry L, Allen PJ, Do RKG, Simpson AL. CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas. Med. Phys. 2018;45:5019–5029.
Chu LC, Park S, Soleimani S, Fouladi DF, Shayesteh S, He J, Javed AA, Wolfgang CL, Vogelstein B, Kinzler KW, Hruban RH, Afghani E, Lennon AM, Fishman EK, Kawamoto S. Classification of pancreatic cystic neoplasms using radiomic feature analysis is equivalent to an experienced academic radiologist: a step toward computer-augmented diagnostics for radiologists. Abdom Radiol (NY). 2022;47(12):4139-4150.
Matsuyama T, Ohno Y, Yamamoto K, Ikedo M, Yui M, Furuta M, Fujisawa R, Hanamatsu S, Nagata H, Ueda T, Ikeda H, Takeda S, Iwase A, Fukuba T, Akamatsu H, Hanaoka R, Kato R, Murayama K, Toyama H. Comparison of utility of deep learning reconstruction on 3D MRCPs obtained with three different k-space data acquisitions in patients with IPMN. Eur Radiol. 2022;32(10):6658-6667.
Yamashita R, Bird K, Cheung PY, Decker JH, Flory MN, Goff D, Morimoto LN, Shon A, Wentland AL, Rubin DL, Desser TS. Automated Identification and Measurement Extraction of Pancreatic Cystic Lesions from Free-Text Radiology Reports Using Natural Language Processing. Radiol Artif Intell. 2021;42(2):210092.
Fang YT, Lan Q, Xie T, Liu YF, Mei SY, Zhu BF. New Opportunities and Challenges for Forensic Medicine in the Era of Artificial Intelligence Technology. Fa Yi Xue Za Zhi. 2020;36(1):77-85.
Chu LC, Ahmed T, Blanco A, Javed A, Weisberg EM, Kawamoto S, Hruban RH, Kinzler KW, Vogelstein B, Fishman EK. Radiologists' Expectations of Artificial Intelligence in Pancreatic Cancer Imaging: How Good Is Good Enough? J. Comput. Assist Tomogr. 2023.
Ramaekers M, Viviers CGA, Janssen BV, Hellström TAE, Ewals L, van der Wulp K, Nederend J, Jacobs I, Pluyter JR, Mavroeidis D, van der Sommen F, Besselink MG, Luyer MDP. E/MTIC Oncology Collaborative Group. Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions. J. Clin. Med. 2023;12(13):4209.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Klaudia Kister, Jakub Laskowski, Magdalena Mazur, Monika Zach-Źródlak, Aleksandra Małolepsza, Natalia Rektor, Julia Czechowska, Lidia Rosa, Paulina Bronst, Anna Szabrańska
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The periodical offers access to content in the Open Access system under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0
Stats
Number of views and downloads: 269
Number of citations: 0