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Artificial intelligence for the detection of pancreatic lesions

  • Review Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Pancreatic cancer is one of the most lethal neoplasms among common cancers worldwide, and PCLs are well-known precursors of this type of cancer. Artificial intelligence (AI) could help to improve and speed up the detection and classification of pancreatic lesions. The aim of this review is to summarize the articles addressing the diagnostic yield of artificial intelligence applied to medical imaging (computed tomography [CT] and/or magnetic resonance [MR]) for the detection of pancreatic cancer and pancreatic cystic lesions.

Methods

We performed a comprehensive literature search using PubMed, EMBASE, and Scopus (from January 2010 to April 2021) to identify full articles evaluating the diagnostic accuracy of AI-based methods processing CT or MR images to detect pancreatic ductal adenocarcinoma (PDAC) or pancreatic cystic lesions (PCLs).

Results

We found 20 studies meeting our inclusion criteria. Most of the AI-based systems used were convolutional neural networks. Ten studies addressed the use of AI to detect PDAC, eight studies aimed to detect and classify PCLs, and 4 aimed to predict the presence of high-grade dysplasia or cancer.

Conclusion

AI techniques have shown to be a promising tool which is expected to be helpful for most radiologists’ tasks. However, methodologic concerns must be addressed, and prospective clinical studies should be carried out before implementation in clinical practice.

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Funding

Financial support was received from Startup Capital (ACCIO/Generalitat de Catalunya-ACE015/20/000051).

Author information

Authors and Affiliations

  1. Scientific and Technical Department, Sycai Technologies S.L., Carrer Roc Boronat 117, MediaTIC Building, 08018, Barcelona, Spain

    Julia Arribas Anta, Iván Martínez-Ballestero, Daniel Eiroa, Javier García & Júlia Rodríguez-Comas

  2. Department of Gastroenterology, University Hospital, 12 Octubre. Av. de Córdoba, s/n, 28041, Madrid, Spain

    Julia Arribas Anta

  3. Department of Radiology, Institut de Diagnòstic per la Imatge (IDI), Hospital Universitari Vall d’Hebrón, Passeig de la Vall d’Hebron, 119-129, 08035, Barcelona, Spain

    Daniel Eiroa

Authors
  1. Julia Arribas Anta
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  2. Iván Martínez-Ballestero
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  3. Daniel Eiroa
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  4. Javier García
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  5. Júlia Rodríguez-Comas
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All authors have read and agreed to the published version of the manuscript.

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Correspondence to Júlia Rodríguez-Comas.

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J.R–C and JG. are full-time employees of Sycai Technologies. JAA I.M-B and D.E are partial-time employees of Sycai Technologies.

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Anta, J.A., Martínez-Ballestero, I., Eiroa, D. et al. Artificial intelligence for the detection of pancreatic lesions. Int J CARS 17, 1855–1865 (2022). https://doi.org/10.1007/s11548-022-02706-z

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