Cargando…
Revolutionising hepatocellular carcinoma surveillance: Harnessing contrast-enhanced ultrasound and serological indicators for postoperative early recurrence prediction
This study aimed to develop a noninvasive predictive model for identifying early postoperative recurrence of hepatocellular carcinoma (within 2 years after surgery) based on contrast-enhanced ultrasound and serum biomarkers. Additionally, the model’s validity was assessedthrough internal and externa...
Autores principales: | , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476781/ https://www.ncbi.nlm.nih.gov/pubmed/37657058 http://dx.doi.org/10.1097/MD.0000000000034937 |
_version_ | 1785101004961742848 |
---|---|
author | Tu, Haibin Feng, Siyi Chen, Lihong Huang, Yujie Zhang, Juzhen Wu, Xiaoxiong |
author_facet | Tu, Haibin Feng, Siyi Chen, Lihong Huang, Yujie Zhang, Juzhen Wu, Xiaoxiong |
author_sort | Tu, Haibin |
collection | PubMed |
description | This study aimed to develop a noninvasive predictive model for identifying early postoperative recurrence of hepatocellular carcinoma (within 2 years after surgery) based on contrast-enhanced ultrasound and serum biomarkers. Additionally, the model’s validity was assessedthrough internal and external validation. Clinical data were collected from patients who underwent liver resection at the First Hospital of Quanzhou and Mengchao Hepatobiliary Hospital. The data included general information, contrast-enhanced ultrasound parameters, Liver Imaging Reporting and Data System (LI-RADS) classification, and serum biomarkers. The data from Mengchao Hospital were divided into 2 groups, with a ratio of 6:4, to form the modeling and internal validation sets, respectively. On the other hand, the data from the First Hospital of Quanzhou served as the external validation group. The developed model was named the Hepatocellular Carcinoma Early Recurrence (HCC-ER) prediction model. The predictive efficiency of the HCC-ER model was compared with other established models. The baseline characteristics were found to be well-balanced across the modeling, internal validation, and external validation groups. Among the independent risk factors identified for early recurrence, LI-RADS classification, alpha-fetoprotein, and tumor maximum diameter exhibited hazard ratios of 1.352, 1.337, and 1.135 respectively. Regarding predictive accuracy, the HCC-ER, Tumour-Node-Metastasis, Barcelona Clinic Liver Cancer, and China Liver Cancer models demonstrated prediction errors of 0.196, 0.204, 0.201, and 0.200 in the modeling group; 0.215, 0.215, 0.218, and 0.212 in the internal validation group; 0.210, 0.215, 0.216, and 0.221 in the external validation group. Using the HCC-ER model, risk scores were calculated for all patients, and a cutoff value of 50 was selected. This cutoff effectively distinguished the high-risk recurrence group from the low-risk recurrence group in the modeling, internal validation, and external validation groups. However, the calibration curve of the predictive model slightly overestimated the risk of recurrence. The HCC-ER model developed in this study demonstrated high accuracy in predicting early recurrence within 2 years after hepatectomy. It provides valuable information for developing precise treatment strategies in clinical practice and holds considerable promise for further clinical implementation. |
format | Online Article Text |
id | pubmed-10476781 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-104767812023-09-05 Revolutionising hepatocellular carcinoma surveillance: Harnessing contrast-enhanced ultrasound and serological indicators for postoperative early recurrence prediction Tu, Haibin Feng, Siyi Chen, Lihong Huang, Yujie Zhang, Juzhen Wu, Xiaoxiong Medicine (Baltimore) 5700 This study aimed to develop a noninvasive predictive model for identifying early postoperative recurrence of hepatocellular carcinoma (within 2 years after surgery) based on contrast-enhanced ultrasound and serum biomarkers. Additionally, the model’s validity was assessedthrough internal and external validation. Clinical data were collected from patients who underwent liver resection at the First Hospital of Quanzhou and Mengchao Hepatobiliary Hospital. The data included general information, contrast-enhanced ultrasound parameters, Liver Imaging Reporting and Data System (LI-RADS) classification, and serum biomarkers. The data from Mengchao Hospital were divided into 2 groups, with a ratio of 6:4, to form the modeling and internal validation sets, respectively. On the other hand, the data from the First Hospital of Quanzhou served as the external validation group. The developed model was named the Hepatocellular Carcinoma Early Recurrence (HCC-ER) prediction model. The predictive efficiency of the HCC-ER model was compared with other established models. The baseline characteristics were found to be well-balanced across the modeling, internal validation, and external validation groups. Among the independent risk factors identified for early recurrence, LI-RADS classification, alpha-fetoprotein, and tumor maximum diameter exhibited hazard ratios of 1.352, 1.337, and 1.135 respectively. Regarding predictive accuracy, the HCC-ER, Tumour-Node-Metastasis, Barcelona Clinic Liver Cancer, and China Liver Cancer models demonstrated prediction errors of 0.196, 0.204, 0.201, and 0.200 in the modeling group; 0.215, 0.215, 0.218, and 0.212 in the internal validation group; 0.210, 0.215, 0.216, and 0.221 in the external validation group. Using the HCC-ER model, risk scores were calculated for all patients, and a cutoff value of 50 was selected. This cutoff effectively distinguished the high-risk recurrence group from the low-risk recurrence group in the modeling, internal validation, and external validation groups. However, the calibration curve of the predictive model slightly overestimated the risk of recurrence. The HCC-ER model developed in this study demonstrated high accuracy in predicting early recurrence within 2 years after hepatectomy. It provides valuable information for developing precise treatment strategies in clinical practice and holds considerable promise for further clinical implementation. Lippincott Williams & Wilkins 2023-09-01 /pmc/articles/PMC10476781/ /pubmed/37657058 http://dx.doi.org/10.1097/MD.0000000000034937 Text en Copyright © 2023 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC) (https://creativecommons.org/licenses/by-nc/4.0/) , where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. |
spellingShingle | 5700 Tu, Haibin Feng, Siyi Chen, Lihong Huang, Yujie Zhang, Juzhen Wu, Xiaoxiong Revolutionising hepatocellular carcinoma surveillance: Harnessing contrast-enhanced ultrasound and serological indicators for postoperative early recurrence prediction |
title | Revolutionising hepatocellular carcinoma surveillance: Harnessing contrast-enhanced ultrasound and serological indicators for postoperative early recurrence prediction |
title_full | Revolutionising hepatocellular carcinoma surveillance: Harnessing contrast-enhanced ultrasound and serological indicators for postoperative early recurrence prediction |
title_fullStr | Revolutionising hepatocellular carcinoma surveillance: Harnessing contrast-enhanced ultrasound and serological indicators for postoperative early recurrence prediction |
title_full_unstemmed | Revolutionising hepatocellular carcinoma surveillance: Harnessing contrast-enhanced ultrasound and serological indicators for postoperative early recurrence prediction |
title_short | Revolutionising hepatocellular carcinoma surveillance: Harnessing contrast-enhanced ultrasound and serological indicators for postoperative early recurrence prediction |
title_sort | revolutionising hepatocellular carcinoma surveillance: harnessing contrast-enhanced ultrasound and serological indicators for postoperative early recurrence prediction |
topic | 5700 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10476781/ https://www.ncbi.nlm.nih.gov/pubmed/37657058 http://dx.doi.org/10.1097/MD.0000000000034937 |
work_keys_str_mv | AT tuhaibin revolutionisinghepatocellularcarcinomasurveillanceharnessingcontrastenhancedultrasoundandserologicalindicatorsforpostoperativeearlyrecurrenceprediction AT fengsiyi revolutionisinghepatocellularcarcinomasurveillanceharnessingcontrastenhancedultrasoundandserologicalindicatorsforpostoperativeearlyrecurrenceprediction AT chenlihong revolutionisinghepatocellularcarcinomasurveillanceharnessingcontrastenhancedultrasoundandserologicalindicatorsforpostoperativeearlyrecurrenceprediction AT huangyujie revolutionisinghepatocellularcarcinomasurveillanceharnessingcontrastenhancedultrasoundandserologicalindicatorsforpostoperativeearlyrecurrenceprediction AT zhangjuzhen revolutionisinghepatocellularcarcinomasurveillanceharnessingcontrastenhancedultrasoundandserologicalindicatorsforpostoperativeearlyrecurrenceprediction AT wuxiaoxiong revolutionisinghepatocellularcarcinomasurveillanceharnessingcontrastenhancedultrasoundandserologicalindicatorsforpostoperativeearlyrecurrenceprediction |