Predict early recurrence of resectable hepatocellular carcinoma using multi-dimensional artificial intelligence analysis of liver fibrosis

I. Ting Liu, Chia Sheng Yen, Wen Lung Wang, Hung Wen Tsai, Chang Yao Chu, Ming Yu Chang, Ya Fu Hou, Chia Jui Yen

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Background: Liver fibrosis is thought to be associated with early recurrence of hepatocellular carcinoma (HCC) after resection. To recognize HCC patients with higher risk of early recurrence, we used a second harmonic generation and two-photon excitation fluorescence (SHG/TPEF) microscopy to create a fully quantitative fibrosis score which is able to predict early recurrence. Methods: The study included 81 HCC patients receiving curative intent hepatectomy. Detailed fibrotic features of resected hepatic tissues were obtained by SHG/TPEF microscopy, and we used multi-dimensional artificial intelligence analysis to create a recurrence prediction model “combined index” according to the morphological collagen features of each patient’s non-tumor hepatic tissues. Results: Our results showed that the “combined index” can better predict early recurrence (area under the curve = 0.917, sensitivity = 81.8%, specificity = 90.5%), compared to alpha fetoprotein level (area under the curve = 0.595, sensitivity = 68.2%, specificity = 47.6%). Using a Cox proportional hazards analysis, a higher “combined index” is also a poor prognostic factor of disease-free survival and overall survival. Conclusions: By integrating multi-dimensional artificial intelligence and SHG/TPEF microscopy, we may locate patients with a higher risk of recurrence, follow these patients more carefully, and conduct further management if needed.

Original languageEnglish
Article number5323
JournalCancers
Volume13
Issue number21
DOIs
Publication statusPublished - 2021 Nov 1

All Science Journal Classification (ASJC) codes

  • Oncology
  • Cancer Research

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