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Prediction of Histological Grades and Ki-67 Expression of Hepatocellular Carcinoma Based on Sonazoid Contrast Enhanced Ultrasound Radiomics Signatures
Objectives: Histopathological tumor grade and Ki-67 expression level are key aspects concerning the prognosis of patients with hepatocellular carcinoma (HCC) lesions. The aim of this study was to investigate whether the radiomics model derived from Sonazoid contrast-enhanced (S-CEUS) images could pr...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497787/ https://www.ncbi.nlm.nih.gov/pubmed/36140576 http://dx.doi.org/10.3390/diagnostics12092175 |
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author | Dong, Yi Zuo, Dan Qiu, Yi-Jie Cao, Jia-Ying Wang, Han-Zhang Wang, Wen-Ping |
author_facet | Dong, Yi Zuo, Dan Qiu, Yi-Jie Cao, Jia-Ying Wang, Han-Zhang Wang, Wen-Ping |
author_sort | Dong, Yi |
collection | PubMed |
description | Objectives: Histopathological tumor grade and Ki-67 expression level are key aspects concerning the prognosis of patients with hepatocellular carcinoma (HCC) lesions. The aim of this study was to investigate whether the radiomics model derived from Sonazoid contrast-enhanced (S-CEUS) images could predict histological grades and Ki-67 expression of HCC lesions. Methods: This prospective study included 101 (training cohort: n = 71; validation cohort: n = 30) patients with surgical resection and histopathologically confirmed HCC lesions. Radiomics features were extracted from the B mode and Kupffer phase of S-CEUS images. Maximum relevance minimum redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) were used for feature selection, and a stepwise multivariate logit regression model was trained for prediction. Model accuracy, sensitivity, and specificity in both training and testing datasets were used to evaluate performance. Results: The prediction model derived from Kupffer phase images (CE-model) displayed a significantly better performance in the prediction of stage III HCC patients, with an area under the receiver operating characteristic curve (AUROC) of 0.908 in the training dataset and 0.792 in the testing set. The CE-model demonstrated generalizability in identifying HCC patients with elevated Ki-67 expression (>10%) with a training AUROC of 0.873 and testing AUROC of 0.768, with noticeably higher specificity of 92.3% and 80.0% in training and testing datasets, respectively. Conclusions: The radiomics model constructed from the Kupffer phase of S-CEUS images has the potential for predicting Ki-67 expression and histological stages in patients with HCC. |
format | Online Article Text |
id | pubmed-9497787 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94977872022-09-23 Prediction of Histological Grades and Ki-67 Expression of Hepatocellular Carcinoma Based on Sonazoid Contrast Enhanced Ultrasound Radiomics Signatures Dong, Yi Zuo, Dan Qiu, Yi-Jie Cao, Jia-Ying Wang, Han-Zhang Wang, Wen-Ping Diagnostics (Basel) Article Objectives: Histopathological tumor grade and Ki-67 expression level are key aspects concerning the prognosis of patients with hepatocellular carcinoma (HCC) lesions. The aim of this study was to investigate whether the radiomics model derived from Sonazoid contrast-enhanced (S-CEUS) images could predict histological grades and Ki-67 expression of HCC lesions. Methods: This prospective study included 101 (training cohort: n = 71; validation cohort: n = 30) patients with surgical resection and histopathologically confirmed HCC lesions. Radiomics features were extracted from the B mode and Kupffer phase of S-CEUS images. Maximum relevance minimum redundancy (MRMR) and least absolute shrinkage and selection operator (LASSO) were used for feature selection, and a stepwise multivariate logit regression model was trained for prediction. Model accuracy, sensitivity, and specificity in both training and testing datasets were used to evaluate performance. Results: The prediction model derived from Kupffer phase images (CE-model) displayed a significantly better performance in the prediction of stage III HCC patients, with an area under the receiver operating characteristic curve (AUROC) of 0.908 in the training dataset and 0.792 in the testing set. The CE-model demonstrated generalizability in identifying HCC patients with elevated Ki-67 expression (>10%) with a training AUROC of 0.873 and testing AUROC of 0.768, with noticeably higher specificity of 92.3% and 80.0% in training and testing datasets, respectively. Conclusions: The radiomics model constructed from the Kupffer phase of S-CEUS images has the potential for predicting Ki-67 expression and histological stages in patients with HCC. MDPI 2022-09-08 /pmc/articles/PMC9497787/ /pubmed/36140576 http://dx.doi.org/10.3390/diagnostics12092175 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Dong, Yi Zuo, Dan Qiu, Yi-Jie Cao, Jia-Ying Wang, Han-Zhang Wang, Wen-Ping Prediction of Histological Grades and Ki-67 Expression of Hepatocellular Carcinoma Based on Sonazoid Contrast Enhanced Ultrasound Radiomics Signatures |
title | Prediction of Histological Grades and Ki-67 Expression of Hepatocellular Carcinoma Based on Sonazoid Contrast Enhanced Ultrasound Radiomics Signatures |
title_full | Prediction of Histological Grades and Ki-67 Expression of Hepatocellular Carcinoma Based on Sonazoid Contrast Enhanced Ultrasound Radiomics Signatures |
title_fullStr | Prediction of Histological Grades and Ki-67 Expression of Hepatocellular Carcinoma Based on Sonazoid Contrast Enhanced Ultrasound Radiomics Signatures |
title_full_unstemmed | Prediction of Histological Grades and Ki-67 Expression of Hepatocellular Carcinoma Based on Sonazoid Contrast Enhanced Ultrasound Radiomics Signatures |
title_short | Prediction of Histological Grades and Ki-67 Expression of Hepatocellular Carcinoma Based on Sonazoid Contrast Enhanced Ultrasound Radiomics Signatures |
title_sort | prediction of histological grades and ki-67 expression of hepatocellular carcinoma based on sonazoid contrast enhanced ultrasound radiomics signatures |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497787/ https://www.ncbi.nlm.nih.gov/pubmed/36140576 http://dx.doi.org/10.3390/diagnostics12092175 |
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