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A radiomics nomogram for predicting cytokeratin 19–positive hepatocellular carcinoma: a two-center study
OBJECTIVES: We aimed to construct and validate a radiomics-based nomogram model derived from gadoxetic acid–enhanced magnetic resonance (MR) images to predict cytokeratin (CK) 19–positive (+) hepatocellular carcinoma (HCC) and patients’ prognosis. METHODS: A two-center and time-independent cohort of...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10174303/ https://www.ncbi.nlm.nih.gov/pubmed/37182122 http://dx.doi.org/10.3389/fonc.2023.1174069 |
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author | Zhang, Liqing Zhou, Heshan Zhang, Xiaoqian Ding, Zhongxiang Xu, Jianfeng |
author_facet | Zhang, Liqing Zhou, Heshan Zhang, Xiaoqian Ding, Zhongxiang Xu, Jianfeng |
author_sort | Zhang, Liqing |
collection | PubMed |
description | OBJECTIVES: We aimed to construct and validate a radiomics-based nomogram model derived from gadoxetic acid–enhanced magnetic resonance (MR) images to predict cytokeratin (CK) 19–positive (+) hepatocellular carcinoma (HCC) and patients’ prognosis. METHODS: A two-center and time-independent cohort of 311 patients were retrospectively enrolled (training cohort, n = 168; internal validation cohort, n = 72; external validation cohort, n = 71). A total of 2286 radiomic features were extracted from multisequence MR images with the uAI Research Portal (uRP), and a radiomic feature model was established. A combined model was established by incorporating the clinic-radiological features and the fusion radiomics signature using logistic regression analysis. Receiver operating characteristic curve (ROC) was used to evaluate the predictive efficacy of these models. Kaplan–Meier survival analysis was used to assess 1-year and 2-year progression-free survival (PFS) and overall survival (OS) in the cohort. RESULTS: By combining radiomic features extracted in DWI phase, arterial phase, venous and delay phase, the fusion radiomics signature achieved AUCs of 0.865, 0.824, and 0.781 in the training, internal, and external validation cohorts. The final combined clinic-radiological model showed higher AUC values in the three datasets compared with the fusion radiomics model. The nomogram based on the combined model showed satisfactory prediction performance in the training (C-index, 0.914), internal (C-index, 0.855), and external validation (C-index, 0.795) cohort. The 1-year and 2-year PFS and OS of the patients in the CK19+ group were 76% and 73%, and 78% and 68%, respectively. The 1-year and 2-year PFS and OS of the patients in the CK19-negative (−) group were 81% and 77%, and 80% and 74%, respectively. Kaplan–Meier survival analysis showed no significant differences in 1-year PFS and OS between the groups (P = 0.273 and 0.290), but it did show differences in 2-year PFS and OS between the groups (P = 0.032 and 0.040). Both PFS and OS were lower in CK19+ patients. CONCLUSION: The combined model based on clinic-radiological radiomics features can be used for predicting CK19+ HCC noninvasively to assist in the development of personalized treatment. |
format | Online Article Text |
id | pubmed-10174303 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101743032023-05-12 A radiomics nomogram for predicting cytokeratin 19–positive hepatocellular carcinoma: a two-center study Zhang, Liqing Zhou, Heshan Zhang, Xiaoqian Ding, Zhongxiang Xu, Jianfeng Front Oncol Oncology OBJECTIVES: We aimed to construct and validate a radiomics-based nomogram model derived from gadoxetic acid–enhanced magnetic resonance (MR) images to predict cytokeratin (CK) 19–positive (+) hepatocellular carcinoma (HCC) and patients’ prognosis. METHODS: A two-center and time-independent cohort of 311 patients were retrospectively enrolled (training cohort, n = 168; internal validation cohort, n = 72; external validation cohort, n = 71). A total of 2286 radiomic features were extracted from multisequence MR images with the uAI Research Portal (uRP), and a radiomic feature model was established. A combined model was established by incorporating the clinic-radiological features and the fusion radiomics signature using logistic regression analysis. Receiver operating characteristic curve (ROC) was used to evaluate the predictive efficacy of these models. Kaplan–Meier survival analysis was used to assess 1-year and 2-year progression-free survival (PFS) and overall survival (OS) in the cohort. RESULTS: By combining radiomic features extracted in DWI phase, arterial phase, venous and delay phase, the fusion radiomics signature achieved AUCs of 0.865, 0.824, and 0.781 in the training, internal, and external validation cohorts. The final combined clinic-radiological model showed higher AUC values in the three datasets compared with the fusion radiomics model. The nomogram based on the combined model showed satisfactory prediction performance in the training (C-index, 0.914), internal (C-index, 0.855), and external validation (C-index, 0.795) cohort. The 1-year and 2-year PFS and OS of the patients in the CK19+ group were 76% and 73%, and 78% and 68%, respectively. The 1-year and 2-year PFS and OS of the patients in the CK19-negative (−) group were 81% and 77%, and 80% and 74%, respectively. Kaplan–Meier survival analysis showed no significant differences in 1-year PFS and OS between the groups (P = 0.273 and 0.290), but it did show differences in 2-year PFS and OS between the groups (P = 0.032 and 0.040). Both PFS and OS were lower in CK19+ patients. CONCLUSION: The combined model based on clinic-radiological radiomics features can be used for predicting CK19+ HCC noninvasively to assist in the development of personalized treatment. Frontiers Media S.A. 2023-04-27 /pmc/articles/PMC10174303/ /pubmed/37182122 http://dx.doi.org/10.3389/fonc.2023.1174069 Text en Copyright © 2023 Zhang, Zhou, Zhang, Ding and Xu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Zhang, Liqing Zhou, Heshan Zhang, Xiaoqian Ding, Zhongxiang Xu, Jianfeng A radiomics nomogram for predicting cytokeratin 19–positive hepatocellular carcinoma: a two-center study |
title | A radiomics nomogram for predicting cytokeratin 19–positive hepatocellular carcinoma: a two-center study |
title_full | A radiomics nomogram for predicting cytokeratin 19–positive hepatocellular carcinoma: a two-center study |
title_fullStr | A radiomics nomogram for predicting cytokeratin 19–positive hepatocellular carcinoma: a two-center study |
title_full_unstemmed | A radiomics nomogram for predicting cytokeratin 19–positive hepatocellular carcinoma: a two-center study |
title_short | A radiomics nomogram for predicting cytokeratin 19–positive hepatocellular carcinoma: a two-center study |
title_sort | radiomics nomogram for predicting cytokeratin 19–positive hepatocellular carcinoma: a two-center study |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10174303/ https://www.ncbi.nlm.nih.gov/pubmed/37182122 http://dx.doi.org/10.3389/fonc.2023.1174069 |
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