Saleh M, Virarkar M, Mahmoud HS, Wong VK, Gonzalez Baerga CI, Parikh M, Elsherif SB, Bhosale PR. Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer. World J Radiol 2023; 15(11): 304-314 [PMID: 38058604 DOI: 10.4329/wjr.v15.i11.304]
Corresponding Author of This Article
Carlos Ignacio Gonzalez Baerga, MD, Research Assistant, Department of Diagnostic Radiology, The University of Florida College of Medicine, 655 8th Street West, Jacksonville, FL 32209, United States. carlos.gonzalezbaerga@jax.ufl.edu
Research Domain of This Article
Medicine, Research & Experimental
Article-Type of This Article
Retrospective Study
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 Radiol. Nov 28, 2023; 15(11): 304-314 Published online Nov 28, 2023. doi: 10.4329/wjr.v15.i11.304
Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer
Mohammed Saleh, Mayur Virarkar, Hagar S Mahmoud, Vincenzo K Wong, Carlos Ignacio Gonzalez Baerga, Miti Parikh, Sherif B Elsherif, Priya R Bhosale
Mohammed Saleh, Hagar S Mahmoud, Vincenzo K Wong, Priya R Bhosale, Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
Mayur Virarkar, Carlos Ignacio Gonzalez Baerga, Sherif B Elsherif, Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States
Miti Parikh, Keck School of Medicine, University of South California, Los Angeles, CA 90033, United States
Author contributions: Saleh M, Virarkar M, Mahmoud HS, Wong VK, Gonzalez Baerga CI, Parikh M, Elsherif SB, and Bhosale PR have equally contributed to this paper with conception and design of the study, literature review and analysis, drafting and critical revision and editing, and final approval of the final version.
Institutional review board statement: The study was reviewed and approved by the University of Texas MD Anderson Cancer Center Institutional Review Board (approval No. 4 IRB00005015).
Informed consent statement: The informed consent was waived by the University of Texas MD Anderson Cancer Center Institutional Review Board.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: No additional data are available.
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: Carlos Ignacio Gonzalez Baerga, MD, Research Assistant, Department of Diagnostic Radiology, The University of Florida College of Medicine, 655 8th Street West, Jacksonville, FL 32209, United States. carlos.gonzalezbaerga@jax.ufl.edu
Received: August 28, 2023 Peer-review started: August 28, 2023 First decision: September 19, 2023 Revised: September 20, 2023 Accepted: October 23, 2023 Article in press: October 23, 2023 Published online: November 28, 2023 Processing time: 88 Days and 2.6 Hours
ARTICLE HIGHLIGHTS
Research background
Radiomics can determine prognostic factors of several types of tumors.
Research motivation
Lack of evidence supporting radiomic studies on pancreatic cancer.
Research objectives
Compare two different radiomic softwares in assessing survival outcomes in pancreatic cancer patients.
Research methods
Retrospective review of pretreatment dual energy computed tomography (CT) images of 48 patients with biopsy confirmed lesions. Tumors were segmented using TexRad [2-dimensional (2D)] analysis software and MIM (3D) analysis software and radiomic features were extracted to compare with overall surgical (OS) and progression free survival (PFS).
Research results
3D analysis demonstrates that higher mean tumor density and median tumor density correlated with better OS, while 2D analysis showed that higher mean tumor density and mean positive pixels correlated with better OS. 2D analysis also showed higher skewness and kurtosis correlated with worse OS. Higher entropy correlated with better PFS. Patients with increased tumor size greater than 1.35 cm were likely to have a higher percentage of residual tumor above 10%.
Research conclusions
Radiomic features can serve as prognosis tools for pancreatic cancer and determine OS.
Research perspectives
This study serves as a guide for future research that can be verified through a prospective approach, while also contributing to possible alternatives to determine prognosis in patients using radiomic features.
