Background: Pancreatic cancer is the fourth leading cause of cancer deaths in the United States, and early detection remains a significant challenge. Screening the general population is not feasible, but the rise of artificial intelligence (AI) has introduced new possibilities for improving early diagnosis and patient outcomes. Methods: A systematic literature search was conducted using PubMed, Google Scholar, and MEDLINE using MeSH terms “Artificial intelligence or AI”, and “diagnosis” and “pancreatic carcinoma or pancreatic adenocarcinoma”. Prisma guidelines were adhered to, and a total of 47 studies resulted, 10 articles were duplicates, 11 articles were excluded as they did not align with the topic, 7 articles could not be retrieved, 13 articles were excluded as they did not fit the criteria, 6 retrospective studies are included in this study. The inclusion criteria for this study are AI being used in the diagnosis, only in pancreatic cancer, within the last 5 years, only in English, and only retrospective studies were included. Results: One study used Digital Imaging Processing (DIP) for analyzing Endoscopic ultrasound (EUS) images from 153 pancreatic cancer patients, yielding a sensitivity of 97.98% and a specificity of 94.32%. Two studies explored Computer Aided Diagnosis (CAD) models applied to PET/CT and EUS images, achieving a sensitivity of 95.23% and specificity of 97.51% in PET/CT scans and 83.3% and 93.3% in EUS images, respectively. Another study used a Faster R-CNN model to analyze CT images from 338 pancreatic cancer patients showed high diagnostic accuracy in much less time. Additionally, two studies utilized Natural Language Processing (NLP) for identifying family histories of pancreatic cancer and detecting pancreatic cysts, with the latter achieving sensitivity and specificity rates of 99.9% and 98.8%. Conclusions: The current strategies for early diagnosis of pancreatic cancer focus on serum biomarkers and EUS-guided Fine Needle Aspiration (EUS-FNA). Sensitivity varies and depends on the physician’s expertise. AI is showing promise in improving pancreatic cancer diagnosis by enhancing early detection and accuracy. Techniques like deep learning, NLP-based models, Faster R-CNN, and CAD systems analyze medical data and images more effectively than manual methods. AI holds the potential to shape the future of pancreatic cancer diagnosis and improve patient outcomes.
References
[1]
Siegel, R.L., Miller, K.D. and Jemal, A. (2018) Cancer Statistics, 2018. CA: ACancerJournalforClinicians, 68, 7-30. https://doi.org/10.3322/caac.21442
[2]
Permuth-Wey, J. and Egan, K.M. (2008) Family History Is a Significant Risk Factor for Pancreatic Cancer: Results from a Systematic Review and Meta-Analysis. FamilialCancer, 8, 109-117. https://doi.org/10.1007/s10689-008-9214-8
[3]
Siegel, R.L., Miller, K.D., Fuchs, H.E. and Jemal, A. (2021) Cancer Statistics, 2021. CA: ACancerJournalforClinicians, 71, 7-33. https://doi.org/10.3322/caac.21654
[4]
Wang, X. (2014) Utility of PET/CT in Diagnosis, Staging, Assessment of Resectability and Metabolic Response of Pancreatic Cancer. WorldJournalofGastroenterology, 20, Article No. 15580. https://doi.org/10.3748/wjg.v20.i42.15580
[5]
DeWitt, J., Devereaux, B., Chriswell, M., McGreevy, K., Howard, T., Imperiale, T.F., et al. (2004) Comparison of Endoscopic Ultrasonography and Multidetector Computed Tomography for Detecting and Staging Pancreatic Cancer. AnnalsofInternalMedicine, 141, 753-763. https://doi.org/10.7326/0003-4819-141-10-200411160-00006
[6]
Brand, B., Pfaff, T. and Binmoelle, K.F. (2000) Endoscopic Ultrasound for Differential Diagnosis of Focal Pancreatic Lesions, Confirmed by Surgery. ScandinavianJournalofGastroenterology, 35, 1221-1228. https://doi.org/10.1080/003655200750056736
[7]
Mishra, G., Zhao, Y., Sweeney, J., Pineau, B.C., Case, D., Ho, C., et al. (2006) Determination of Qualitative Telomerase Activity as an Adjunct to the Diagnosis of Pancreatic Adenocarcinoma by Eus-Guided Fine-Needle Aspiration. GastrointestinalEndoscopy, 63, 648-654. https://doi.org/10.1016/j.gie.2005.11.056
[8]
Eloubeidi, M.A., Chen, V.K., Eltoum, I.A., Jhala, D., Chhieng, D.C., Jhala, N., et al. (2003) Endoscopic Ultrasound-Guided Fine Needle Aspiration Biopsy of Patients with Suspected Pancreatic Cancer: Diagnostic Accuracy and Acute and 30-Day Complications. AmericanJournalofGastroenterology, 98, 2663-2668. https://doi.org/10.1111/j.1572-0241.2003.08666.x
[9]
Horwhat, J.D., Paulson, E.K., McGrath, K., Stanley Branch, M., Baillie, J., Tyler, D., et al. (2006) A Randomized Comparison of Eus-Guided FNA versus CT or Us-Guided FNA for the Evaluation of Pancreatic Mass Lesions. GastrointestinalEndoscopy, 63, 966-975. https://doi.org/10.1016/j.gie.2005.09.028
[10]
McGuigan, A., Kelly, P., Turkington, R.C., Jones, C., Coleman, H.G. and McCain, R.S. (2018) Pancreatic Cancer: A Review of Clinical Diagnosis, Epidemiology, Treatment and Outcomes. WorldJournalofGastroenterology, 24, 4846-4861. https://doi.org/10.3748/wjg.v24.i43.4846
[11]
Azencott, C.-A. (2022) Introduction au Machine Learning. 2nd Edition, Dunod, 272 p.
[12]
Charniak, E. (2021) Introduction au Deep Learning. Dunod, 176 p.
[13]
Sy, I., Bousso, M., Correa, A.I. and Dieng, M. (2023) State of the Art of Artificial Intelligence Applications in Oncology. OpenJournalofAppliedSciences, 13, 2245-2262. https://doi.org/10.4236/ojapps.2023.1312175
[14]
Zhang, M., Yang, H., Jin, Z., Yu, J., Cai, Z. and Li, Z. (2010) Differential Diagnosis of Pancreatic Cancer from Normal Tissue with Digital Imaging Processing and Pattern Recognition Based on a Support Vector Machine of EUS Images. GastrointestinalEndoscopy, 72, 978-985. https://doi.org/10.1016/j.gie.2010.06.042
[15]
Harewood, G.C. and Wiersema, M.J. (2002) Endosonography-Guided Fine Needle Aspiration Biopsy in the Evaluation of Pancreatic Masses. TheAmericanJournalofGastroenterology, 97, 1386-1391. https://doi.org/10.1111/j.1572-0241.2002.05777.x
[16]
Agarwal, B., Abu-Hamda, E., Molke, K.L., Correa, A.M. and Ho, L. (2004) Endoscopic Ultrasound-Guided Fine Needle Aspiration and Multidetector Spiral CT in the Diagnosis of Pancreatic Cancer. TheAmericanJournalofGastroenterology, 99, 844-850. https://doi.org/10.1111/j.1572-0241.2004.04177.x
[17]
Ardengh, J.C., Lopes, C.V., Campos, A.D., et al. (2007) Endoscopic Ultrasound and Fine Needle Aspiration in Chronic Pancreatitis: Differential Diagnosis between Pseudotumoral Masses and Pancreatic Cancer. JOP, 8, 413-421.
[18]
Du, T., Bill, K.A., Ford, J., Barawi, M., Hayward, R.D., Alame, A., et al. (2018) The Diagnosis and Staging of Pancreatic Cancer: A Comparison of Endoscopic Ultrasound and Computed Tomography with Pancreas Protocol. TheAmericanJournalofSurgery, 215, 472-475. https://doi.org/10.1016/j.amjsurg.2017.11.021
[19]
Walczak, S. and Velanovich, V. (2017) An Evaluation of Artificial Neural Networks in Predicting Pancreatic Cancer Survival. JournalofGastrointestinalSurgery, 21, 1606-1612. https://doi.org/10.1007/s11605-017-3518-7
[20]
Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M., et al. (2017) Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nature, 542, 115-118. https://doi.org/10.1038/nature21056
[21]
Nakamura, K., Yoshida, H., Engelmann, R., MacMahon, H., Katsuragawa, S., Ishida, T., et al. (2000) Computerized Analysis of the Likelihood of Malignancy in Solitary Pulmonary Nodules with Use of Artificial Neural Networks. Radiology, 214, 823-830. https://doi.org/10.1148/radiology.214.3.r00mr22823
[22]
Chen, C., Chou, Y., Han, K., Hung, G., Tiu, C., Chiou, H., et al. (2003) Breast Lesions on Sonograms: Computer-Aided Diagnosis with Nearly Setting-Independent Features and Artificial Neural Networks. Radiology, 226, 504-514. https://doi.org/10.1148/radiol.2262011843
[23]
Hou, Q., Bing, Z., Hu, C., Li, M., Yang, K., Mo, Z., et al. (2018) Rankprod Combined with Genetic Algorithm Optimized Artificial Neural Network Establishes a Diagnostic and Prognostic Prediction Model That Revealed C1QTNF3 as a Biomarker for Prostate Cancer. EBioMedicine, 32, 234-244. https://doi.org/10.1016/j.ebiom.2018.05.010
[24]
Horie, Y., Yoshio, T., Aoyama, K., Yoshimizu, S., Horiuchi, Y., Ishiyama, A., et al. (2019) Diagnostic Outcomes of Esophageal Cancer by Artificial Intelligence Using Convolutional Neural Networks. GastrointestinalEndoscopy, 89, 25-32. https://doi.org/10.1016/j.gie.2018.07.037
[25]
Lu, Y., Yu, Q., Gao, Y., Zhou, Y., Liu, G., Dong, Q., et al. (2018) Identification of Metastatic Lymph Nodes in MR Imaging with Faster Region-Based Convolutional Neural Networks. CancerResearch, 78, 5135-5143. https://doi.org/10.1158/0008-5472.can-18-0494
[26]
Roch, A.M., Mehrabi, S., Krishnan, A., Schmidt, H.E., Kesterson, J., Beesley, C., et al. (2015) Automated Pancreatic Cyst Screening Using Natural Language Processing: A New Tool in the Early Detection of Pancreatic Cancer. HPB, 17, 447-453. https://doi.org/10.1111/hpb.12375
[27]
Mehrabi, S., Krishnan, A., Roch, A.M., et al. (2015) Identification of Patients with Family History of Pancreatic Cancer—Investigation of an NLP System Portability. Studies in Health Technology and Informatics, Vol. 216, IOS Press, 604-608. https://doi.org/10.3233/978-1-61499-564-7-604
[28]
Kurt, M., Ozkan, M., Cakiroglu, M., Kocaman, O., Yilmaz, B., Can, G., et al. (2016) Age-Based Computer-Aided Diagnosis Approach for Pancreatic Cancer on Endoscopic Ultrasound Images. EndoscopicUltrasound, 5, 101-107. https://doi.org/10.4103/2303-9027.180473
[29]
Li, S., Jiang, H., Wang, Z., Zhang, G. and Yao, Y. (2018) An Effective Computer Aided Diagnosis Model for Pancreas Cancer on PET/CT Images. ComputerMethodsandProgramsinBiomedicine, 165, 205-214. https://doi.org/10.1016/j.cmpb.2018.09.001
[30]
Liu, S., Li, S., Guo, Y., Zhou, Y., Zhang, Z., Li, S., et al. (2019) Establishment and Application of an Artificial Intelligence Diagnosis System for Pancreatic Cancer with a Faster Region-Based Convolutional Neural Network. ChineseMedicalJournal, 132, 2795-2803. https://doi.org/10.1097/cm9.0000000000000544
