Differentiating Liver Metastases from Primary Liver Cancer: A Retrospective Study of Imaging and Pathological...

Background and Objectives: This study aimed to identify and analyze imaging and pathological features that differentiate liver metastases from primary liver cancer in patients with histopathological confirmation, and to evaluate the diagnostic accuracy of imaging modalities. Materials and Methods: This retrospective study included 137 patients who underwent liver biopsy or resection between 2016 and 2024, comprising 126 patients with liver metastases and 11 patients with primary liver cancer (hepatocellular carcinoma). Imaging features on contrast-enhanced MRI were evaluated, including lesion number, size, margins, enhancement patterns, presence of capsule, T1/T2 signal characteristics, diffusion-weighted imaging (DWI) signal, and portal vein thrombosis. Laboratory data such as liver function tests and alpha-fetoprotein (AFP) levels were collected. Pathological features recorded included tumor differentiation, vascular invasion, necrosis, and fibrosis. Statistical analyses were performed using chi-squared tests, t-tests, and logistic regression, with a significance level of p < 0.05. The diagnostic accuracy of imaging features was assessed using receiver operating characteristic (ROC) curve analysis. Results: Liver metastases were more likely to present as multiple lesions (82.5% vs. 27.3%, p < 0.001), had irregular margins (78.6% vs. 36.4%, p = 0.002), rim enhancement (74.6% vs. 18.2%, p < 0.001), and were hypointense on T1-weighted images (85.7% vs. 45.5%, p = 0.004). Primary liver cancers were more likely to be solitary (72.7% vs. 17.5%, p < 0.001), have smooth margins (63.6% vs. 21.4%, p = 0.002), exhibit arterial phase hyperenhancement (81.8% vs. 23.8%, p < 0.001), and portal venous washout (72.7% vs. 19.0%, p < 0.001). Vascular invasion was more common in primary liver cancer (45.5% vs. 11.1%, p = 0.01). AFP levels > 400 ng/mL were significantly associated with primary liver cancer (63.6% vs. 4.8%, p < 0.001). ROC curve analysis showed that a combination of imaging features had an area under the curve (AUC) of 0.91 for differentiating the two entities. Conclusions: Imaging features such as lesion number, margin characteristics, enhancement patterns, T1/T2 signal characteristics, and portal venous washout, along with pathological features like vascular invasion and AFP levels, can effectively differentiate liver metastases from primary liver cancer. The diagnostic accuracy of imaging is high when multiple features are combined.