Role of artificial intelligence and novel visualization techniques in the early diagnosis of pancreatic cancer: a review

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Abstract

Pancreatic ductal adenocarcinoma is the most common pancreatic cancer. It is characterized by a progressive course or distant metastases in 80%–85% of cases. Despite advances in understanding of pancreatic ductal adenocarcinoma, the disease is consistently linked to poor prognosis due to late diagnosis and limited treatment options in advanced stages. Recently, image processing using artificial intelligence has been introduced for pancreatic ductal adenocarcinoma diagnosis and demonstrated promising results. This review summarizes current scientific data, evaluates the role of artificial intelligence in imaging and early detection of pancreatic ductal adenocarcinoma, and identifies issues that warrant further investigation. The search for publications was conducted using PubMed, Google Scholar, and eLibrary. The following Russian and English search keywords were used: ранняя диагностика рака поджелудочной железы (early diagnosis of pancreatic cancer), искусственный интеллект (artificial intelligence), протоковая аденокарцинома поджелудочной железы (pancreatic ductal adenocarcinoma), медицинская визуализация (medical visualization), наночастицы (nanoparticles), pancreatic cancer, artificial intelligence, early diagnosis pancreatic ductal adenocarcinoma, and pancreatic cancer imaging. Significant progress in early detection of pancreatic ductal adenocarcinoma using artificial intelligence technologies was observed. Current approaches include pre-imaging risk stratification and increased data volume by analyzing electronic medical records. Despite substantial achievements, the clinical implementation of artificial intelligence technologies remains challenging. The use of artificial intelligence along with biomarkers is a promising direction and may enhance theranostics of various malignancies, including pancreatic ductal adenocarcinoma.

About the authors

Ferida T. Musaeva

North Ossetian State Medical Academy

Email: feridamusaeva@yandex.ru
ORCID iD: 0009-0000-1407-7189
Russian Federation, Vladikavkaz

Elizaveta R. Sumenova

North Ossetian State Medical Academy

Email: lsumenova@bk.ru
ORCID iD: 0009-0001-8159-0860
Russian Federation, Vladikavkaz

Almaz Kh. Islamgulov

Bashkir State Medical University

Author for correspondence.
Email: aslmaz2000@rambler.ru
ORCID iD: 0000-0003-0567-7515
SPIN-code: 8701-3486
Russian Federation, Ufa

Zalina M. Kumykova

North Ossetian State Medical Academy

Email: kumykova_2001@mail.ru
ORCID iD: 0009-0007-5243-6796
Russian Federation, Vladikavkaz

Tamila S. Elipkhanova

Maikop State Technological University

Email: eltamila01@mail.ru
ORCID iD: 0009-0006-2901-5443
Russian Federation, Maikop

Alina I. Ushaeva

Russian University of Medicine

Email: ushaeva21@list.ru
ORCID iD: 0009-0007-3888-5683
Russian Federation, Moscow

Amina S. Khasieva

Maikop State Technological University

Email: Khasievaamina999@gmail.com
ORCID iD: 0009-0002-8153-4647
Russian Federation, Maikop

Ekaterina S. Ozerova

The Russian National Research Medical University named after N.I. Pirogov

Email: ozerovaekaterina201@gmail.com
ORCID iD: 0009-0004-8740-1313
Russian Federation, Moscow

Dina A. Khusnutdinova

Kazan Federal University

Email: dinakhusnutdinova02848@gmail.com
ORCID iD: 0009-0002-0562-8414
Russian Federation, Kazan

Alina A. Nabiullina

Kazan Federal University

Email: a.ayratovnaa@gmail.com
ORCID iD: 0009-0004-4365-444X
Russian Federation, Kazan

Yana Yu. Kulinskaya

Russian University of Medicine

Email: Yana.Kulinskaya00@mail.ru
ORCID iD: 0009-0000-7187-0044
Russian Federation, Moscow

Roksana R. Yakupova

Bashkir State Medical University

Email: roksana.yakupova.01@mail.ru
ORCID iD: 0000-0001-5869-607X
Russian Federation, Ufa

Arthur A. Mustafin

Bashkir State Medical University

Email: zacartim@mail.com
ORCID iD: 0009-0006-4747-6972
Russian Federation, Ufa

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