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Preoperative clinical-radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using [Formula: see text] F-FDG PET/CT

PURPOSE: To develop a clinical-radiomics nomogram by incorporating radiomics score and clinical predictors for preoperative prediction of microvascular invasion in hepatocellular carcinoma. METHODS: A total of 97 HCC patients were retrospectively enrolled from Shanghai Universal Medical Imaging Diag...

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Autores principales: Wang, Yutao, Luo, Shuying, Jin, Gehui, Fu, Randi, Yu, Zhongfei, Zhang, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013080/
https://www.ncbi.nlm.nih.gov/pubmed/35428272
http://dx.doi.org/10.1186/s12880-022-00796-4
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author Wang, Yutao
Luo, Shuying
Jin, Gehui
Fu, Randi
Yu, Zhongfei
Zhang, Jian
author_facet Wang, Yutao
Luo, Shuying
Jin, Gehui
Fu, Randi
Yu, Zhongfei
Zhang, Jian
author_sort Wang, Yutao
collection PubMed
description PURPOSE: To develop a clinical-radiomics nomogram by incorporating radiomics score and clinical predictors for preoperative prediction of microvascular invasion in hepatocellular carcinoma. METHODS: A total of 97 HCC patients were retrospectively enrolled from Shanghai Universal Medical Imaging Diagnostic Center and Changhai Hospital Affiliated to the Second Military Medical University. 909 CT and 909 PET slicers from 97 HCC patients were divided into a training cohort (N = 637) and a validation cohort (N = 272). Radiomics features were extracted from each CT or PET slicer, and features selection was performed with least absolute shrinkage and selection operator regression and radiomics score was also generated. The clinical-radiomics nomogram was established by integrating radiomics score and clinical predictors, and the performance of the models were evaluated from its discrimination ability, calibration ability, and clinical usefulness. RESULTS: The radiomics score consisted of 45 selected features, and age, the ratio of maximum to minimum tumor diameter, and [Formula: see text] F-FDG uptake status were independent predictors of microvascular invasion. The clinical-radiomics nomogram showed better performance for MVI detection (0.890 [0.854, 0.927]) than the clinical nomogram (0.849 [0.804, 0.893]) ([Formula: see text] ). Both nomograms showed good calibration and the clinical-radiomics nomogram’s clinical practicability outperformed the clinical nomogram. CONCLUSIONS: With the combination of radiomics score and clinical predictors, the clinical-radiomics nomogram can significantly improve the predictive efficacy of microvascular invasion in hepatocellular carcinoma ([Formula: see text] ) compared with clinical nomogram.
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spelling pubmed-90130802022-04-17 Preoperative clinical-radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using [Formula: see text] F-FDG PET/CT Wang, Yutao Luo, Shuying Jin, Gehui Fu, Randi Yu, Zhongfei Zhang, Jian BMC Med Imaging Research PURPOSE: To develop a clinical-radiomics nomogram by incorporating radiomics score and clinical predictors for preoperative prediction of microvascular invasion in hepatocellular carcinoma. METHODS: A total of 97 HCC patients were retrospectively enrolled from Shanghai Universal Medical Imaging Diagnostic Center and Changhai Hospital Affiliated to the Second Military Medical University. 909 CT and 909 PET slicers from 97 HCC patients were divided into a training cohort (N = 637) and a validation cohort (N = 272). Radiomics features were extracted from each CT or PET slicer, and features selection was performed with least absolute shrinkage and selection operator regression and radiomics score was also generated. The clinical-radiomics nomogram was established by integrating radiomics score and clinical predictors, and the performance of the models were evaluated from its discrimination ability, calibration ability, and clinical usefulness. RESULTS: The radiomics score consisted of 45 selected features, and age, the ratio of maximum to minimum tumor diameter, and [Formula: see text] F-FDG uptake status were independent predictors of microvascular invasion. The clinical-radiomics nomogram showed better performance for MVI detection (0.890 [0.854, 0.927]) than the clinical nomogram (0.849 [0.804, 0.893]) ([Formula: see text] ). Both nomograms showed good calibration and the clinical-radiomics nomogram’s clinical practicability outperformed the clinical nomogram. CONCLUSIONS: With the combination of radiomics score and clinical predictors, the clinical-radiomics nomogram can significantly improve the predictive efficacy of microvascular invasion in hepatocellular carcinoma ([Formula: see text] ) compared with clinical nomogram. BioMed Central 2022-04-15 /pmc/articles/PMC9013080/ /pubmed/35428272 http://dx.doi.org/10.1186/s12880-022-00796-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Wang, Yutao
Luo, Shuying
Jin, Gehui
Fu, Randi
Yu, Zhongfei
Zhang, Jian
Preoperative clinical-radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using [Formula: see text] F-FDG PET/CT
title Preoperative clinical-radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using [Formula: see text] F-FDG PET/CT
title_full Preoperative clinical-radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using [Formula: see text] F-FDG PET/CT
title_fullStr Preoperative clinical-radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using [Formula: see text] F-FDG PET/CT
title_full_unstemmed Preoperative clinical-radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using [Formula: see text] F-FDG PET/CT
title_short Preoperative clinical-radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using [Formula: see text] F-FDG PET/CT
title_sort preoperative clinical-radiomics nomogram for microvascular invasion prediction in hepatocellular carcinoma using [formula: see text] f-fdg pet/ct
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9013080/
https://www.ncbi.nlm.nih.gov/pubmed/35428272
http://dx.doi.org/10.1186/s12880-022-00796-4
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