Krishnan A. Imaging-pathology correlation in pancreatic cancer: Methodological considerations and future directions. World J Gastrointest Oncol 2025; 17(7): 103282 [DOI: 10.4251/wjgo.v17.i7.103282]
Corresponding Author of This Article
Arunkumar Krishnan, MD, Assistant Professor, Department of Supportive Oncology, Atrium Health Levine Cancer, 1021 Morehead Medical Drive, Suite 70100, Charlotte, NC 28204, United States. dr.arunkumar.krishnan@gmail.com
Research Domain of This Article
Oncology
Article-Type of This Article
Letter to the Editor
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastrointest Oncol. Jul 15, 2025; 17(7): 103282 Published online Jul 15, 2025. doi: 10.4251/wjgo.v17.i7.103282
Imaging-pathology correlation in pancreatic cancer: Methodological considerations and future directions
Arunkumar Krishnan
Arunkumar Krishnan, Department of Supportive Oncology, Atrium Health Levine Cancer, Charlotte, NC 28204, United States
Arunkumar Krishnan, Department of Internal Medicine, Wake Forest University School of Medicine, Winston-Salem, NC 27101, United States
Author contributions: Krishnan A conceptually developed the manuscript and conducted the assessment; Krishnan A was responsible for preparing the manuscript draft, which was subsequently reviewed and final approval.
Conflict-of-interest statement: The author declared no conflict of interest.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Arunkumar Krishnan, MD, Assistant Professor, Department of Supportive Oncology, Atrium Health Levine Cancer, 1021 Morehead Medical Drive, Suite 70100, Charlotte, NC 28204, United States. dr.arunkumar.krishnan@gmail.com
Received: November 14, 2024 Revised: February 27, 2025 Accepted: March 6, 2025 Published online: July 15, 2025 Processing time: 242 Days and 18.6 Hours
Abstract
A recent study by Luo et al examined the relationship between the pathological types of pancreatic cancer (PC) and their imaging characteristics. While this study presented an important step toward improving diagnostic accuracy for PC, we have several concerns regarding its generalizability, cohort selection, imaging variability, statistical methods, and potential confounding factors. We recommended that future research adopt multi-center, prospective designs to improve representation and minimize bias. Additionally, incorporating advanced imaging techniques such as radiomics and artificial intelligence and conducting more comprehensive statistical analyses would be valuable. By implementing these strategies, future studies can yield more reliable and externally validated findings that improve the clinical applicability of imaging-based differentiation of PC. Addressing these methodological issues could significantly advance the field of gastrointestinal oncology and improve patient management and outcomes.
Core Tip: A study by Luo et al examined the relationship between different pathological types of pancreatic cancer (PC) and their corresponding imaging features. This present study showed an advancement in improving the diagnostic accuracy for PC. However, to further improve the robustness and applicability of the findings, it is important to adopt a multi-center, prospective research design. Such an approach would provide better generalizability and representation among diverse patient populations. Additionally, integrating advanced imaging techniques, including radiomics and artificial intelligence-driven analyses, could significantly mitigate inconsistencies among different observers, thereby elevating the precision of diagnostics. While the findings are promising, future research would greatly benefit from using multivariable analyses and strategies to address missing data, which would help control for potential confounding factors, thus reinforcing the credibility of imaging-pathology correlations. Moreover, establishing external validation cohorts is important for verifying the predictive capabilities of these findings across various clinical settings and diverse patient demographics.
