Vol. 28 No. 3 (2025) Cover Image
Vol. 28 No. 3 (2025)

Published: September 30, 2025

Pages: 451-460

Articles

Advancements in Cancer Detection: An Artificial Intelligence-Based Approach Using PET/CT Datasets

Faten Imad Ali 1 Hadeel K. AlJobouri 1 Ali M. Hasan 2
1 Dept. of Biomedical Engineering, Al-Nahrain University, College of Engineering, Baghdad, Iraq.
2 Physiology and Medical Physics Department, Al-Nahrain University, College of Medicine, Baghdad, Iraq.
History
  • Received: October 23, 2024
  • Revised: February 1, 2025
  • Accepted: March 10, 2025
  • Available Online: September 29, 2025
DOI

https://doi.org/10.29194/NJES.28030451

Abstract

Artificial intelligence (AI) is rapidly advancing as a valuable tool in oncology for enhancing detection and management of cancer. The integration of AI with PET/CT imaging presents significant scenarios for improving efficiency and accuracy of cancer diagnosis. This study examines the current applications of AI with PET/CT imaging, highlighting its role in diagnosing, differentiating, delineating, staging, assessing therapy response, determining prognosis, and enhancing image quality. A comprehensive literature search was conducted in six data-bases to get the most recent works, use Springer, Scopus, PubMed, Web of Science, IEEE, and Google Scholar in the last five years (2019-2024), identifying 80 studies that met the criteria for inclusion that focused on AI-driven models applied to PET/CT data in various cancers, with lung cancer being the most studied. Other cancers examined include head and neck, breast, lymph nodes, whole body, and others. All studies involved human subjects. The findings indicate that AI holds promise in improving cancer detection, identifying benign from malignant tumors, aiding in segmentation, response evaluation, staging, and determining the prognosis. However, the application of AI-powered models and PET/CT-derived radiomics in clinical practice is limited because of issues of data normalization, reproducibility, and the requirement of large multi-center data sets for improving model generalizability. All these limitations have to be solved to guarantee the dependable and ethical use of AI in day-to-day clinical activities.

Keywords

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