Potential of using qFibrosis analysis to predict recurrent and survival outcome of patients with hepatocellular carcinoma after hepatic resection.

ABSTRACT

BACKGROUND: There remains a lack of studies addressing the stromal background and fibrosis features and its prognostic value in liver cancer. qFibrosis can identify, quantify and visualize the fibrosis features from biopsy samples. In this study, we aim to demonstrate the prognostic value of histological features by using qFibrosis analysis in liver cancer patients. METHODS: Liver specimen from 201 patients with hepatocellular carcinoma underwent curative resection were imaged and assessed using qFibrosis system, and generated a total of 33 and 156 collagen parameters from tumor part and non-tumor liver tissue, respectively. We used these collagen parameters on patients to build two combined indexes, RFS-index and OS-index, in order to differentiate patients with early recurrence and early death, respectively. The models were validated using leave-one-out method. RESULTS: Both combined indexes had significant prediction value of patients' outcome. The RFS-index of 0.52 well differentiates patients with early recurrence (p < 0.001), and the OS-index of 0.73 well differentiates patients with early death during follow-up (p = 0.02). CONCLUSIONS: Combined index calculated with qFibrosis from digital readout of fibrotic status of peri-tumor liver specimen in patients with HCC have prediction values for their disease and survival outcomes. These results demonstrated the potentials to transform histopathological features into quantifiable data that could be used to correlate with clinical outcome.