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

doi:10.4329/wjr.v15.i11.304

Radiomics analysis with three-dimensional and two-dimensional segmentation to predict survival outcomes in pancreatic cancer

World J Radiol. 2023 Nov 28;15(11):304-314. doi: 10.4329/wjr.v15.i11.304.

Authors

Mohammed Saleh¹, Mayur Virarkar², Hagar S Mahmoud¹, Vincenzo K Wong¹, Carlos Ignacio Gonzalez Baerga³, Miti Parikh⁴, Sherif B Elsherif², Priya R Bhosale¹

Affiliations

¹ Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States.
² Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States.
³ Department of Diagnostic Radiology, The University of Florida College of Medicine, Jacksonville, FL 32209, United States. carlos.gonzalezbaerga@jax.ufl.edu.
⁴ Keck School of Medicine, University of South California, Los Angeles, CA 90033, United States.

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.

Keywords: Cancer; Pancreas; Radiomics; Segmentation.