Dynamic contrast‑enhanced MRI differentiates hepatocellular carcinoma from hepatic metastasis of rectal cancer by extracting pharmacokinetic parameters and radiomic features
- Jianzhi Li
- Feng Xue
- Xinghua Xu
- Qing Wang
- Xuexi Zhang
Affiliations: Department of Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong 250012, P.R. China, GE Healthcare, Shanghai 200000, P.R. China
- Published online on: August 7, 2020 https://doi.org/10.3892/etm.2020.9115
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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 (Ktrans), 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 Ktrans, 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.