Abstract 543: Characterization of extravascular extracellular space of rat brain tumors using wavelet-based radiomics analysis of dynamic contrast enhanced MRI

Abstract

Introduction: Research studies have already shown that tumor aggressiveness and response to chemical and radiation therapies are influenced by the extravascular extracellular space (VEES) of the tumor microenvironment. Assessment of VEES has been reported to be fundamental to understanding tumor response to treatment and probability of recurrence. Purpose: This pilot study investigates the association between wavelet-based radiomic features extracted from dynamic contrast-enhanced magnetic resonance images (DCE-MRI) of rat brain tumors against VEES estimated by pharmacokinetic modeling. Methods: Eight immune-compromised-RNU/RNU rats were implanted with human U251n cancer cells to form an orthotopic glioma (IACUC #1509). For each rat, two DCE-MRI studies (multi slice/echo GE, 3 slice(2mm),128x64, FOV:32x32mm2, TR/(TE1-TE2)=24ms/(2ms-4ms), flip angle=18º, 400 acquisitions, 1.55 sec interval, Magnevist was injected at acquisition no. 15) were performed (24h apart) using a 7T Varian (Agilent, 20cm bore) scanner. A single 20Gy stereotactic radiation exposure was performed before the second study. The post treatment MRIs were taken a range of 1-6.5 hrs post radiation. The time trace of relaxivity change (ΔR1) in all the voxels of the animal’s brain for all studies were calculated. Wavelet decomposition analysis was performed on the ΔR1 for each voxel and frequency-based localized approximations with 4 degrees of regularities were estimated. The VEES map was estimated from ΔR1 by the pharmacokinetic (modified Toft’s) model and a nested model selection technique. Finally, the Pearson correlation coefficients between the VEES map and corresponding wavelet coefficient maps in the tumor region were calculated. Results: The average voxel-wise Pearson correlation coefficients between the VEES maps (averaged for all animals) and their corresponding wavelet-based, radiomics coefficient maps were: r= -0.680, r= -0.802, r= -0.813, and r= -0.791 with p<0.0001 for the 4 wavelet coefficients (from higher to lower frequencies), respectively. Discussion & Conclusion: This pilot study suggests that wavelet based radiomic analysis has potential to provide information pertinent to the tumor microenvironment, which correlates well with pharmacokinetic modeling. As such, this work represents an important first step toward potentially connecting radiomics with underlying biological mechanisms. Citation Format: Hassan Bagher-Ebadian, Stephen L. Brown, Olivia Valadie, Julian A. Rey, Ning Wen, Malisa Sarntinoranont, James R. Ewing, Indrin J. Chetty. Characterization of extravascular extracellular space of rat brain tumors using wavelet-based radiomics analysis of dynamic contrast enhanced MRI [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 543.

Three-dimensional wavelet decomposition-based radiomics analysis for tumor characterization in patients with oropharyngeal squamous cell carcinoma

“…29,30,36,48,67 We have recently demonstrated that radiomic features extracted from primary tumor gross tumor volumes (GTVs) delineated on contrast-enhanced (CE) CT images of oropharyngeal squamous cell carcinoma (OPSCC) patients can be used to construct a highly sensitive and specific classifier for characterization and prediction of human papilloma virus (HPV). 53,68,69 In a related study, we have also shown 36,[50][51][52] that frequency analysis of these images can provide valuable information to enhance the predictive power of the radiomics-based model for classification of HPV+/-in OPSCC patients.…”

“…Our research group [50][51][52][53] among others 29,30,34,35,[54][55][56][57][58][59][60][61][62][63][64][65][66] have shown that the frequency and wavelet analysis of multispectral features in the frequency domain can reveal more structured and relevant information details pertinent to the outcome of interest compared to the spatial domain. 29,30,36,48,67 We have recently demonstrated that radiomic features extracted from primary tumor gross tumor volumes (GTVs) delineated on contrast-enhanced (CE) CT images of oropharyngeal squamous cell carcinoma (OPSCC) patients can be used to construct a highly sensitive and specific classifier for characterization and prediction of human papilloma virus (HPV).…”