Potential of using qFibrosis analysis to predict recurrent and survival outcome of patients with hepatocellular carcinoma after hepatic resection.
Journal
Oncology
ISSN
1423-0232
Date Issued
2024-03-25
Author(s)
Abstract
Background: There remains a lack of studies addressing the stromal background and fibrosis features and their prognostic value in liver cancer. qFibrosis can identify, quantify, and visualize the fibrosis features in 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 specimens from 201 patients with hepatocellular carcinoma (HCC) who 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 the leave-one-out method.
Results: Both combined indexes had significant prediction value for 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 a digital readout of the fibrotic status of peri-tumor liver specimen in patients with HCC has prediction values for their disease and survival outcomes. These results demonstrated the potential to transform histopathological features into quantifiable data that could be used to correlate with clinical outcome.
Subjects
Artificial intelligence
Digital pathology
Hepatocellular carcinoma
Outcome
qFibrosis
SDGs
Publisher
S. Karger AG
Type
journal article