Dynamic contrast‑enhanced MRI differentiates hepatocellular carcinoma from hepatic metastasis of rectal cancer by extracting pharmacokinetic parameters and radiomic features

Abstract

The aim of the present study was to explore how dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may differentiate hepatocellular carcinoma (HCC) from hepatic metastasis of rectal cancer (HMRC) by extracting pharmacokinetic parameters and radiomic features. A total of 75 patients, including 41 cases with HCC and 34 cases with HMRC, underwent DCE-MRI examination. Dual-input two-compartment extended Tofts tracer kinetic model attached to a specialized image post-processing software package from OmniKinetics; GE Healthcare was used to calculate the values of the pharmacokinetic parameters and radiomic features, which were extracted from the lesions at the same region of interest. These values were evaluated using Student's t-test and receiver operating characteristic curves, and discriminant models were built to differentiate between HCC and HRMC. The results identified statistically significant differences in the values of the pharmacokinetic parameters hepatic perfusion index (HPI), endothelial transfer constant (K trans), initial area under the gadolinium concentration curve during the first 60 sec (IAUC) between the HCC and HRMC groups. In addition, statistically significant differences in 17 radiomic features were observed between the two groups (P<0.05). The areas under the receiver operating characteristic (ROC) curves of the pharmacokinetic parameters K trans , IAUC and HPI were 0.73, 0.77 and 0.67, respectively. The range of the areas under the ROC curves of the 17 radiomic features with statistical differences was 0.63-0.79. In addition, when pharmacokinetic parameters and radiomic features were incorporated, the area under the ROC curve was 0.86. The accuracy of Fisher's discriminant analysis model based on radiomic features was 89.3%, and the leave-one-out cross-validation accuracy was 80.0%. In conclusion, DCE-MRI was demonstrated to be useful in the differential diagnosis of HCC and HMRC by extracting pharmacokinetic parameters and radiomic features, and incorporation of the two paths improved the diagnostic efficacy. A discriminant model based on radiomic features further enhanced the identification of HCC and HMRC.

Metastatic Clear Cell Renal Cell Carcinoma to Pancreas and Distant Organs 24 Years After Radical Nephrectomy: A Case Report and Literature Review

“…The preoperative misdiagnosis rate reaches about 70% ( 14 ). The examination measures commonly used for the metastatic tumors of the pancreas or liver include enhanced CT, enhanced MRI, PET/CT, PET/MRI, and endoscopic ultrasound-guided biopsy ( 3 , 15 , 16 ).…”

Early Diagnosis Value of DCE-MRI Hemodynamic Parameters in Hepatocellular Carcinoma

“…DCE-MRI is a noninvasive imaging technique, the principle of which is that tissue T1 is shortened after intravascular injection of paramagnetic contrast agent. If repeated imaging is performed, the change of signal intensity in tissues can be measured, and the diffusion of contrast agent to surrounding tissues can be monitored over time [23]. DCE-MRI technology, different from traditional MRI, has developed from simple imaging anatomical morphological examination to micromolecular examination, with higher objectivity and accuracy [6].…”

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 ...…”