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A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients
BACKGROUND AND AIM: The presence of microvascular invasion (MVI) will impair the surgical outcome of hepatocellular carcinoma (HCC). Adipose and muscle tissues have been confirmed to be associated with the prognosis of HCC. We aimed to develop and validate a nomogram based on adipose and muscle rela...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10548702/ https://www.ncbi.nlm.nih.gov/pubmed/37794517 http://dx.doi.org/10.1186/s40364-023-00527-z |
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author | Mao, Xin-Cheng Shi, Shuo Yan, Lun-Jie Wang, Han-Chao Ding, Zi-Niu Liu, Hui Pan, Guo-Qiang Zhang, Xiao Han, Cheng-Long Tian, Bao-Wen Wang, Dong-Xu Tan, Si-Yu Dong, Zhao-Ru Yan, Yu-Chuan Li, Tao |
author_facet | Mao, Xin-Cheng Shi, Shuo Yan, Lun-Jie Wang, Han-Chao Ding, Zi-Niu Liu, Hui Pan, Guo-Qiang Zhang, Xiao Han, Cheng-Long Tian, Bao-Wen Wang, Dong-Xu Tan, Si-Yu Dong, Zhao-Ru Yan, Yu-Chuan Li, Tao |
author_sort | Mao, Xin-Cheng |
collection | PubMed |
description | BACKGROUND AND AIM: The presence of microvascular invasion (MVI) will impair the surgical outcome of hepatocellular carcinoma (HCC). Adipose and muscle tissues have been confirmed to be associated with the prognosis of HCC. We aimed to develop and validate a nomogram based on adipose and muscle related-variables for preoperative prediction of MVI in HCC. METHODS: One hundred fifty-eight HCC patients from institution A (training cohort) and 53 HCC patients from institution B (validation cohort) were included, all of whom underwent preoperative CT scan and curative resection with confirmed pathological diagnoses. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to data dimensionality reduction and screening. Nomogram was constructed based on the independent variables, and evaluated by external validation, calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA). RESULTS: Histopathologically identified MVI was found in 101 of 211 patients (47.9%). The preoperative imaging and clinical variables associated with MVI were visceral adipose tissue (VAT) density, intramuscular adipose tissue index (IMATI), skeletal muscle (SM) area, age, tumor size and cirrhosis. Incorporating these 6 factors, the nomogram achieved good concordance index of 0.79 (95%CI: 0.72–0.86) and 0.75 (95%CI: 0.62–0.89) in training and validation cohorts, respectively. In addition, calibration curve exhibited good consistency between predicted and actual MVI probabilities. ROC curve and DCA of the nomogram showed superior performance than that of models only depended on clinical or imaging variables. Based on the nomogram score, patients were divided into high (> 273.8) and low (< = 273.8) risk of MVI presence groups. For patients with high MVI risk, wide-margin resection or anatomical resection could significantly improve the 2-year recurrence free survival. CONCLUSION: By combining 6 preoperative independently predictive factors of MVI, a nomogram was constructed. This model provides an optimal preoperative estimation of MVI risk in HCC patients, and may help to stratify high-risk individuals and optimize clinical decision making. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40364-023-00527-z. |
format | Online Article Text |
id | pubmed-10548702 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105487022023-10-05 A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients Mao, Xin-Cheng Shi, Shuo Yan, Lun-Jie Wang, Han-Chao Ding, Zi-Niu Liu, Hui Pan, Guo-Qiang Zhang, Xiao Han, Cheng-Long Tian, Bao-Wen Wang, Dong-Xu Tan, Si-Yu Dong, Zhao-Ru Yan, Yu-Chuan Li, Tao Biomark Res Research BACKGROUND AND AIM: The presence of microvascular invasion (MVI) will impair the surgical outcome of hepatocellular carcinoma (HCC). Adipose and muscle tissues have been confirmed to be associated with the prognosis of HCC. We aimed to develop and validate a nomogram based on adipose and muscle related-variables for preoperative prediction of MVI in HCC. METHODS: One hundred fifty-eight HCC patients from institution A (training cohort) and 53 HCC patients from institution B (validation cohort) were included, all of whom underwent preoperative CT scan and curative resection with confirmed pathological diagnoses. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied to data dimensionality reduction and screening. Nomogram was constructed based on the independent variables, and evaluated by external validation, calibration curve, receiver operating characteristic (ROC) curve and decision curve analysis (DCA). RESULTS: Histopathologically identified MVI was found in 101 of 211 patients (47.9%). The preoperative imaging and clinical variables associated with MVI were visceral adipose tissue (VAT) density, intramuscular adipose tissue index (IMATI), skeletal muscle (SM) area, age, tumor size and cirrhosis. Incorporating these 6 factors, the nomogram achieved good concordance index of 0.79 (95%CI: 0.72–0.86) and 0.75 (95%CI: 0.62–0.89) in training and validation cohorts, respectively. In addition, calibration curve exhibited good consistency between predicted and actual MVI probabilities. ROC curve and DCA of the nomogram showed superior performance than that of models only depended on clinical or imaging variables. Based on the nomogram score, patients were divided into high (> 273.8) and low (< = 273.8) risk of MVI presence groups. For patients with high MVI risk, wide-margin resection or anatomical resection could significantly improve the 2-year recurrence free survival. CONCLUSION: By combining 6 preoperative independently predictive factors of MVI, a nomogram was constructed. This model provides an optimal preoperative estimation of MVI risk in HCC patients, and may help to stratify high-risk individuals and optimize clinical decision making. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s40364-023-00527-z. BioMed Central 2023-10-04 /pmc/articles/PMC10548702/ /pubmed/37794517 http://dx.doi.org/10.1186/s40364-023-00527-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Mao, Xin-Cheng Shi, Shuo Yan, Lun-Jie Wang, Han-Chao Ding, Zi-Niu Liu, Hui Pan, Guo-Qiang Zhang, Xiao Han, Cheng-Long Tian, Bao-Wen Wang, Dong-Xu Tan, Si-Yu Dong, Zhao-Ru Yan, Yu-Chuan Li, Tao A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients |
title | A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients |
title_full | A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients |
title_fullStr | A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients |
title_full_unstemmed | A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients |
title_short | A model based on adipose and muscle-related indicators evaluated by CT images for predicting microvascular invasion in HCC patients |
title_sort | model based on adipose and muscle-related indicators evaluated by ct images for predicting microvascular invasion in hcc patients |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10548702/ https://www.ncbi.nlm.nih.gov/pubmed/37794517 http://dx.doi.org/10.1186/s40364-023-00527-z |
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