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
Abstract
BACKGROUND
Radiomics can assess prognostic factors in several types of tumors, but considering its prognostic ability in pancreatic cancer has been lacking.
AIM
To evaluate the performance of two different radiomics software in assessing survival outcomes in pancreatic cancer patients.
METHODS
We retrospectively reviewed pretreatment contrast-enhanced dual-energy computed tomography images from 48 patients with biopsy-confirmed pancreatic ductal adenocarcinoma who later underwent neoadjuvant chemoradiation and surgery. Tumors were segmented using TexRad software for 2-dimensional (2D) analysis and MIM software for 3D analysis, followed by radiomic feature extraction. Cox proportional hazard modeling correlated texture features with overall survival (OS) and progression-free survival (PFS). Cox regression was used to detect differences in OS related to pretreatment tumor size and residual tumor following treatment. The Wilcoxon test was used to show the relationship between tumor volume and the percent of residual tumor. Kaplan-Meier analysis was used to compare survival in patients with different tumor densities in Hounsfield units for both 2D and 3D analysis.
RESULTS
3D analysis showed that higher mean tumor density [hazard ratio (HR) = 0.971, P = 0.041)] and higher median tumor density (HR = 0.970, P = 0.037) correlated with better OS. 2D analysis showed that higher mean tumor density (HR = 0.963, P = 0.014) and higher mean positive pixels (HR = 0.962, P = 0.014) correlated with better OS; higher skewness (HR = 3.067, P = 0.008) and higher kurtosis (HR = 1.176, P = 0.029) correlated with worse OS. Higher entropy correlated with better PFS (HR = 0.056, P = 0.036). Models determined that patients with increased tumor size greater than 1.35 cm were likely to have a higher percentage of residual tumors of over 10%.
CONCLUSION
Several radiomics features can be used as prognostic tools for pancreatic cancer. However, results vary between 2D and 3D analyses. Mean tumor density was the only variable that could reliably predict OS, irrespective of the analysis used.
Core Tip: The use of radiomics to assess pancreatic cancer has been limited. This retrospective study evaluated the performance of 2-dimensional (2D) and 3D radiomic software in determining survival outcomes of pancreatic cancer patients. The mean tumor density was the only variable to reliably predict overall survival (OS) irrespective of the type of analysis. Mean tumor density may be able to differentiate survival and potentially may be help in treatment planning irrespective of the texture analysis software used. Higher skewness [hazard ratio (HR) = 3.067, P = 0.008] and higher kurtosis (HR = 1.176, P = 0.029) correlated with worse OS based on 2D analysis.
