94% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
GENERAL COMMENTARY article
Front. Oncol.
Sec. Cancer Imaging and Image-directed Interventions
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1527723
Commentary: An insight to PDAC tumor heterogeneity across pancreatic subregions using computed tomography images
Provisionally accepted- Shenyang Chest Hospital, Shenyang, China
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
I am writing to express my appreciation for the recent publication by Javed et al., titled"An insight to PDAC tumor heterogeneity across pancreatic subregions using computed tomography images" [1].This study offers a comprehensive exploration into the spatial heterogeneity of pancreatic ductal adenocarcinoma (PDAC) tumors using radiomics and machine learning techniques. The authors demonstrated that CT imaging can non-invasively differentiate tumors in the head from those in the body and tail regions, achieving promising classification accuracy. While the findings have significant implications for enhancing PDAC diagnosis and treatment strategies, I would like to offer some comments that could further enrich the discussion.The authors identified distinct radiomic features that differentiate PDAC tumors based on their pancreatic subregions. However, as acknowledged in the study, the integration of imaging features with genomic and molecular profiles was not explored. Existing research has shown that combining radiomic and molecular data can lead to more precise stratification of tumors and better prediction of treatment responses [2,3]. Currently, the molecular characteristics of non-small cell lung cancer (NSCLC) form the basis for clinical diagnosis and treatment of lesions (e.g., EGFR, KRAS), and the phenotypic associations between molecular subtypes and imaging semantic features have been widely studied. A recent study on NSCLC generated 10 meaningful gene sets through RNA sequencing analysis, including the epidermal growth factor (EGF) pathway [4]. An imaginggenomics map was created, with 32 statistically significant correlations between semantic imaging features and gene sets. For nodule attenuation and margins are associated with late cell-35 cycle genes, and the gene set the EGF pathway is significantly correlated the 36 presence of ground-glass opacity irregular nodules or nodules with margins. The
Keywords: Pancreatic ductal adenocarcinoma (PDAC), CT, pancreatic subregions, Radiomics, pancreas cancer
Received: 19 Nov 2024; Accepted: 10 Apr 2025.
Copyright: © 2025 Cao and Feng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yong Feng, Shenyang Chest Hospital, Shenyang, China
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.