1. Predict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis
- Author
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Chia Jui Yen, I-Ting Liu, Ming-Yu Chang, Hung Wen Tsai, Ya-Fu Hou, Chia-Sheng Yen, Chang-Yao Chu, and Wen-Lung Wang
- Subjects
Cancer Research ,recurrence ,business.industry ,Proportional hazards model ,medicine.medical_treatment ,Liver fibrosis ,Area under the curve ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,hepatocellular carcinoma ,medicine.disease ,artificial intelligence ,Article ,SHG/TPEF microscopy ,Text mining ,Oncology ,Resectable Hepatocellular Carcinoma ,Hepatocellular carcinoma ,medicine ,Artificial intelligence ,Hepatectomy ,business ,Alpha-fetoprotein ,RC254-282 ,liver fibrosis - Abstract
Simple Summary Hepatocellular carcinoma (HCC) is the third most commonly diagnosed cancer in the world, and surgical resection is the commonly used curative management of early-stage disease. However, the recurrence rate is high after resection, and liver fibrosis has been thought to increase the risk of recurrence. Conventional histological staging of fibrosis is highly subjective to observer variations. To overcome this limitation, we used a fully quantitative fibrosis assessment tool, qFibrosis (utilizing second harmonic generation and two-photon excitation fluorescence microscopy), with multi-dimensional artificial intelligence analysis to establish a fully-quantitative, accurate fibrotic score called a “combined index”, which can predict early recurrence of HCC after curative intent resection. Therefore, we can pay more attention on the patients with high risk of early recurrence. 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.
- Published
- 2021