Computer-Aided Detection for Pancreatic Cancer Diagnosis: Radiological Challenges and Future Directions

• Published in: Journal of clinical medicine Vol. 12; no. 13; p. 4209
• Main Authors: Ramaekers, Mark, Viviers, Christiaan G. A., Janssen, Boris V., Hellström, Terese A. E., Ewals, Lotte, van der Wulp, Kasper, Nederend, Joost, Jacobs, Igor, Pluyter, Jon R., Mavroeidis, Dimitrios, van der Sommen, Fons, Besselink, Marc G., Luyer, Misha D. P.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 22.06.2023; MDPI
• Subjects: Algorithms; Artificial intelligence; Cancer therapies; Clinical medicine; Computer-aided medical diagnosis; Deep learning; Diagnosis; Diagnosis, Radioscopic; Disease; Hospitals; Machine learning; Magnetic resonance imaging; Medical imaging; Medical prognosis; Neural networks; Pancreatic cancer; Radiomics; Review; Surgery; Technology application; Tomography; Tumors; Netherlands; radiological imaging; clinical implementation; diagnostics; computer-aided detection; artificial intelligence; pancreatic ductal adenocarcinoma
• ISSN: 2077-0383; 2077-0383
• DOI: 10.3390/jcm12134209

Radiological imaging plays a crucial role in the detection and treatment of pancreatic ductal adenocarcinoma (PDAC). However, there are several challenges associated with the use of these techniques in daily clinical practice. Determination of the presence or absence of cancer using radiological imaging is difficult and requires specific expertise, especially after neoadjuvant therapy. Early detection and characterization of tumors would potentially increase the number of patients who are eligible for curative treatment. Over the last decades, artificial intelligence (AI)-based computer-aided detection (CAD) has rapidly evolved as a means for improving the radiological detection of cancer and the assessment of the extent of disease. Although the results of AI applications seem promising, widespread adoption in clinical practice has not taken place. This narrative review provides an overview of current radiological CAD systems in pancreatic cancer, highlights challenges that are pertinent to clinical practice, and discusses potential solutions for these challenges.