Predict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis

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...

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Autores principales:	I-Ting Liu, Chia-Sheng Yen, Wen-Lung Wang, Hung-Wen Tsai, Chang-Yao Chu, Ming-Yu Chang, Ya-Fu Hou, Chia-Jui Yen
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	liver fibrosis hepatocellular carcinoma recurrence SHG/TPEF microscopy artificial intelligence Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/09de4ccafef54ee6acefa4e32a6211a0
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spelling	oai:doaj.org-article:09de4ccafef54ee6acefa4e32a6211a02021-11-11T15:28:18ZPredict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis10.3390/cancers132153232072-6694https://doaj.org/article/09de4ccafef54ee6acefa4e32a6211a02021-10-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/21/5323https://doaj.org/toc/2072-6694Background: 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.I-Ting LiuChia-Sheng YenWen-Lung WangHung-Wen TsaiChang-Yao ChuMing-Yu ChangYa-Fu HouChia-Jui YenMDPI AGarticleliver fibrosishepatocellular carcinomarecurrenceSHG/TPEF microscopyartificial intelligenceNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5323, p 5323 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	liver fibrosis hepatocellular carcinoma recurrence SHG/TPEF microscopy artificial intelligence Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	liver fibrosis hepatocellular carcinoma recurrence SHG/TPEF microscopy artificial intelligence Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 I-Ting Liu Chia-Sheng Yen Wen-Lung Wang Hung-Wen Tsai Chang-Yao Chu Ming-Yu Chang Ya-Fu Hou Chia-Jui Yen Predict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis
description	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.
format	article
author	I-Ting Liu Chia-Sheng Yen Wen-Lung Wang Hung-Wen Tsai Chang-Yao Chu Ming-Yu Chang Ya-Fu Hou Chia-Jui Yen
author_facet	I-Ting Liu Chia-Sheng Yen Wen-Lung Wang Hung-Wen Tsai Chang-Yao Chu Ming-Yu Chang Ya-Fu Hou Chia-Jui Yen
author_sort	I-Ting Liu
title	Predict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis
title_short	Predict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis
title_full	Predict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis
title_fullStr	Predict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis
title_full_unstemmed	Predict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis
title_sort	predict early recurrence of resectable hepatocellular carcinoma using multi-dimensional artificial intelligence analysis of liver fibrosis
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/09de4ccafef54ee6acefa4e32a6211a0
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Predict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis

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