Editorial for “Radiomic Analysis of Pharmacokinetic Heterogeneity Within Tumor Based on the Unsupervised Decomposition of DCE‐MRI for Predicting Histological Characteristics of Breast Cancer”

Radiomics characterization of tissues in an animal brain tumor model imaged using dynamic contrast enhanced (DCE) MRI

“…Physiological and mechanical properties of tumor have been shown to characterize heterogeneity in gene expression, and phenotype for Glioblastoma (GBM) 52 , 54 , 63 , 70 , 128 – 133 . Many studies 30 – 36 , 39 – 41 , 134 – 139 have developed different DCE-based radiomics model for characterization of tumor’s phenotype, vascular properties, and regional heterogeneity in the field of cancer research. However, none of these studies have explored the power of PK-based spatiotemporal radiomics information for characterization of tumor’s pathophysiological condition according to a DCE physiological-based model as well as its comparison with raw DCE MR information that includes the full spectrum of information content.…”

“…These studies also shown that compared to the entire tumor region, subregional PK-based radiomics information can enhance the predictive performance of the radiomic models. Many studies 30 – 41 , 134 – 137 , 139 , 140 have developed different DCE-based radiomics model for characterization of tumor’s phenotype, vascular properties, and regional heterogeneity in the field of cancer research. These studies mainly focused on the radiomics analysis of the parametric maps (such as K trans , v p , etc.)…”

“…Various radiomics analyses and adaptive models (AMs) have been developed to analyze multiparametric MR images of glioblastoma (GBM) to predict outcomes of clinical interest, such as recurrence and survival 7 – 16 , response to treatment 7 , 8 , 13 , 14 , 17 , molecular mutation status 18 – 22 , and subclinical peritumoral infiltration 22 – 28 . Radiomics has shown considerable success in the prediction of noninvasive biomarkers of outcome 5 , 29 , quantification and tissue characterization in the field of dynamic contrast enhanced (DCE) MRI 30 – 41 , and association of imaging biomarkers to biological mechanisms 5 , 42 , 43 . However, little progress has been made toward modeling and relating radiomics biomarkers to those fluxes, vasculature physiology, and flows that are known to be significant in steering tumor growth and invasiveness 44 – 54 .…”

Radiomics Characterization of Tissues in an Animal Brain Tumor Model Imaged Using Dynamic Contrast Enhanced (DCE) MRI

“…Physiological and mechanical properties of tumor have been shown to characterize heterogeneity in gene expression, and phenotype for Glioblastoma (GBM) 47,53,55,64,71,[125][126][127][128][129][130][131] . Many studies [31][32][33][34][35][36][37][38][39][40][41][42][132][133][134][135][136][137] Zhang et al 41,137 have recently shown the association between the subregional PK-based radiomics information of breast tumor and its histological characteristics. They have also shown that compared to the entire tumor region, subregional PK-based radiomics information can enhance the predictive performance of the radiomic models 41,137 .…”

“…Various radiomics analyses and adaptive models (AMs) have been developed to analyze multiparametric MR images of glioblastoma (GBM) to predict outcomes of clinical interest, such as recurrence and survival [7][8][9][10][11][12][13][14][15][16][17] , response to treatment 7,8,13,14,[17][18][19] , molecular mutation status 16,[20][21][22][23][24] , and subclinical peritumoral infiltration 18, [24][25][26][27][28][29] . Radiomics has shown considerable success in the prediction of noninvasive biomarkers of outcome 5,30 , quantification and tissue characterization in the field of dynamic contrast enhanced (DCE) MRI [31][32][33][34][35][36][37][38][39][40][41][42] , and association of imaging biomarkers to biological mechanisms 5,43,44 . However, little progress has been made toward modeling and ...…”